Author response:

The following is the authors’ response to the previous reviews

eLife Assessment

This work presents a valuable self-supervised method for the segmentation of 3D cells in microscopy images, alongside an implementation as a Napari plugin and an annotated dataset. While the Napari plugin is readily applicable and promises to eliminate time consuming data labeling to speed up quantitative analysis, there is incomplete evidence to support the claim that the segmentation method generalizes to other light-sheet microscopy image datasets beyond the two specific ones used here.

Technical Note: We showed the utility of CellSeg3D in the first submission and in our revision on 5 distinct datasets; 4 of which we showed F1-Score performance on. We do not know which “two datasets” are referenced. We also already showed this is not limited to LSM, but was used on confocal images; we already limited our scope and changed the title in the last rebuttal, but just so it’s clear, we also benchmark on two non-LSM datasets.

In this revision, we have now additionally extended our benchmarking of Cellpose and StarDrist on all 4 benchmark datasets, where our Wet3D (our novel contribution of a self-supervised model) outperforms or matches these supervised baselines. Moreover, we perform rigorous testing of our model’s generalization by training on one dataset and testing generalization to the other 3; we believe this is on par (or beyond) what most cell segmentation papers do, thus we hope that “incomplete” can now be updated.

Public Reviews:

Reviewer #1 (Public review):

This work presents a self-supervised method for the segmentation of 3D cells in microscopy images, an annotated dataset, as well as a napari plugin. While the napari plugin is potentially useful, there is insufficient evidence in the manuscript to support the claim that the proposed method is able to segment cells in other light-sheet microscopy image datasets than the two specific ones used here.

Thank you again for your time. We benchmarked already on four datasets the performance of WNet3Dd (our 3D SSL contribution) - thus, we do not know which two you refer to. Moreover, we now additionally benchmarked Cellpose and StarDist on all four so readers can see that on all datasets, WNet3D outperforms or matches these supervised methods.

I acknowledge that the revision is now more upfront about the scope of this work. However, my main point still stands: even with the slight modifications to the title, this paper suggests to present a general method for self-supervised 3D cell segmentation in light-sheet microscopy data. This claim is simply not backed up.

We respectfully disagree; we benchmark on four 3D datasets: three curated by others and used in learning ML conference proceedings, and one that we provide that is a new ground truth 3D dataset - the first of its kind - on mesoSPIM-acquired brain data. We believe benchmarking on four datasets is on par (or beyond) with current best practices in the field. For example, Cellpose curated one dataset and tested on held-out test data on this one dataset (https://www.nature.com/articles/s41592-020-01018-x) and benchmarked against StarDist and Mask R-CNN (two models). StarDist (Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy) benchmarked on two datasets and against two models, IFT-Watershed and 3D U-Net. Thus, we feel our benchmarking on more models and more datasets is sufficient to claim our model and associated code is of interest to readers and supports our claims (for comparison, Cellpose’s title is “Cellpose: a generalist algorithm for cellular segmentation”, which is much broader than our claim).

I still think the authors should spell out the assumptions that underlie their method early on (cells need to be well separated and clearly distinguishable from background). A subordinate clause like "often in cleared neural tissue" does not serve this purpose. First, it implies that the method is also suitable for non-cleared tissue (which would have to be shown). Second, this statement does not convey the crucial assumptions of well separated cells and clear foreground/background differences that the method is presumably relying on.

We expanded the manuscript now quite significantly. To be clear, we did show our method works on non-cleared tissue; the Mouse Skull, 3D platynereis-Nuclei, and 3D platynereis-ISH-Nuclei is not cleared tissue, and not all with LSM, but rather with confocal microscopy. We attempted to make that more clear in the main text.

Additionally, we do not believe it needs to be well separated and have a perfectly clean background. While we removed statements like "often in cleared neural tissue", expanded the benchmarking, and added a new demo figure for the readers to judge. As in the last rebuttal, we provide video-evidence (https://www.youtube.com/watch?v=U2a9IbiO7nE) of the WNet3D working on the densely packed and hard to segment by a human, Mouse Skull dataset and linked this directly in the figure caption.

We have re-written the main manuscript in an attempt to clarify the limitations, including a dedicated “limitations” section. Thank you for the suggestion.

It does appear that the proposed method works very well on the two investigated datasets, compared to other pre-trained or fine-tuned models. However, it still remains unclear whether this is because of the proposed method or the properties of those specific datasets (namely: well isolated cells that are easily distinguished from the background). I disagree with the authors that a comparison to non-learning methods "is unnecessary and beyond the scope of this work". In my opinion, this is exactly what is needed to proof that CellSeg3D's performance can not be matched with simple image processing.

We want to again stress we benchmarked WNet3D on four datasets, not two. But now additionally added benchmarking with Cellpose, StarDist and a non-deep learning method as requested (see new Figures 1 and 3).

As I mentioned in the original review, it appears that thresholding followed by connected component analysis already produces competitive segmentations. I am confused about the authors' reply stating that "[this] is not the case, as all the other leading methods we fairly benchmark cannot solve the task without deep learning". The methods against which CellSeg3D is compared are CellPose and StarDist, both are deep-learning based methods.

That those methods do not perform well on this dataset does not imply that a simpler method (like thresholding) would not lead to competitive results. Again, I strongly suggest the authors include a simple, non-learning based baseline method in their analysis, e.g.: * comparison to thresholding (with the same post-processing as the proposed method) * comparison to a normalized cut segmentation (with the same post-processing as the proposed method)

We added a non-deep learning based approach, namely, comparing directly to thresholding with the same post hoc approach we use to go from semantic to instance segmentation. WNet3D (and other deep learning approaches) perform favorably (see Figure 2 and 3).

Regarding my feedback about the napari plugin, I apologize if I was not clear. The plugin "works" as far as I tested it (i.e., it can be installed and used without errors). However, I was not able to recreate a segmentation on the provided dataset using the plugin alone (see my comments in the original review). I used the current master as available at the time of the original review and default settings in the plugin.

We updated the plugin and code for the revision at your request to make this possible directly in the napari GUI in addition to our scripts and Jupyter Notebooks (please see main and/or `pip install --upgrade napari-cellseg3d`’ the current is version 0.2.1). Of course this means the original submission code (May 2024) will not have this in the GUI so it would require you to update to test this. Alternatively, you can see the demo video we now provide for ease: https://www.youtube.com/watch?v=U2a9IbiO7nE (we understand testing code takes a lot of time and commitment).

We greatly thank the review for their time, and we hope our clarifications, new benchmarking, and re-write of the paper now makes them able to change their assessment from incomplete to a more favorable and reflective eLife adjective.

Reviewer #2 (Public review):

Summary:

The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.

Strengths:

-  The idea behind the self-supervised learning loss is interesting.

-  It provides a new annotated dataset for an important segmentation problem.

-  The paper addresses an important challenge. Data annotation is very time-consuming for 3d microscopy data, so a self-supervised method that yields similar results to supervised segmentation would provide massive benefits.

-  The comparison to other methods on the provided dataset is extensive and experiments are reproducible via public notebooks.

Weaknesses:

The experiments presented by the authors support the core claims made in the paper. However, they do not convincingly prove that the method is applicable to segmentation problems with more complex morphologies or more crowded cells/nuclei.

Major weaknesses:

(1) The method only provides functionality for semantic segmentation outputs and instance segmentation is obtained by morphological post-processing. This approach is well known to be of limited use for segmentation of crowded objects with complex morphology. This is the main reason for prediction of additional channels such as in StarDist or CellPose. The experiments do not convincingly show that this limitation can be overcome as model comparisons are only done on a single dataset with well separated nuclei with simple morphology. Note that the method and dataset are still a valuable contribution with this limitation, which is somewhat addressed in the conclusion. However, I find that the presentation is still too favorable in terms of the presentation of practical applications of the method, see next points for details.

Thank you for noting the methods strengths and core features. Regarding weaknesses, we have revised the manuscript again and added direct benchmarking now on four datasets and a fifth “worked example” (https://www.youtube.com/watch?v=3UOvvpKxEAo&t=4s) in a new Figure 4.

We also re-wrote the paper to more thoroughly present the work (previously we adhered to the “Brief Communication” eLife format), and added an explicit note in the results about model assumptions.

(2) The experimental set-up for the additional datasets seems to be unrealistic as hyperparameters for instance segmentation are derived from a grid search and it is unclear how a new user could find good parameters in the plugin without having access to already annotated ground-truth data or an extensive knowledge of the underlying implementations.

We agree that of course with any self-supervised method the user will need a sense of what a good outcome looks like; that is why we provide Google Colab Notebooks

(https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks) and the napari-plugin GUI for extensive visualization and even the ability to manually correct small subsets of the data and refine the WNet3D model.

We attempted to make this more clear with a new Figure 2 and additional functionality directly into the plugin (such as the grid search). But, we believe this “trade-off” for SSL approaches over very labor intensive 3D labeling is often worth it; annotators are also biased so extensive checking of any GT data is equally required.

We also added the “grid search” functionality in the GUI (please `pip install --upgrade napari-cellseg3d`; the latest v0.2.1) to supplement the previously shared Notebook (https://github.com/C-Achard/cellseg3d-figures/blob/main/thresholds_opti/find_best_threshold s.ipynb) and added a new YouTube video: https://www.youtube.com/watch?v=xYbYqL1KDYE.

(3) Obtaining segmentation results of similar quality as reported in the experiments within the napari plugin was not possible for me. I tried this on the "MouseSkull" dataset that was also used for the additional results in the paper.

Again we are sorry this did not work for you, but we added new functionality in the GUI and made a demo video (https://www.youtube.com/watch?v=U2a9IbiO7nE) where you either update your CellSeg3D code or watch the video to see how we obtained these results.

Here, I could not find settings in the "Utilities->Convert to instance labels" widget that yielded good segmentation quality and it is unclear to me how a new user could find good parameter settings. In more detail, I cannot use the "Voronoi-Otsu" method due to installation issues that are prohibitive for a non expert user and the "Watershed" segmentation method yields a strong oversegmentation.

Sorry to hear of the installation issue with Voronoi-Otsu; we updated the documentation and the GUI to hopefully make this easier to install. While we do not claim this code is for beginners, we do aim to be a welcoming community, thus we provide support on GitHub, extensive docs, videos, the GUI, and Google Colab Notebooks to help users get started.

Comments on revised version

Many of my comments were addressed well:

-  It is now clear that the results are reproducible as they are well documented in the provided notebooks, which are now much more prominently referenced in the text.

Thanks!

-  My concerns about an unfair evaluation compared to CellPose and StarDist were addressed. It is now clear that the experiments on the mesoSPIM dataset are extensive and give an adequate comparison of the methods.

Thank you; to note we additionally added benchmarking of Cellpose and StarDist on the three additional datasets (for R1), but hopefully this serves to also increase your confidence in our approach.

-  Several other minor points like reporting of the evaluation metric are addressed.

I have changed my assessment of the experimental evidence to incomplete/solid and updated the review accordingly. Note that some of my main concerns with the usability of the method for segmentation tasks with more complex morphology / more crowded cells and with the napari plugin still persist. The main points are (also mentioned in Weaknesses, but here with reference to the rebuttal letter):

- Method comparison on datasets with more complex morphology etc. are missing. I disagree that it is enough to do this on one dataset for a good method comparison.

We benchmarked WNet3D (our contribution) on four datasets, and to aid the readers we additionally now added Cellpose and StarDist benchmarking on all four. WNet3D performs favorably, even on the crowded and complex Mouse Skull data. See the new Figure 3 as well as the associated video: https://www.youtube.com/watch?v=U2a9IbiO7nE&t=1s.

-  The current presentation still implies that CellSeg3d **and the napari plugin** work well for a dataset with complex nucleus morphology like the Mouse Skull dataset. But I could not get this to work with the napari plugin, see next points.

- First, deriving hyperparameters via grid search may lead to over-optimistic evaluation results. How would a user find these parameters without having access to ground-truth? Did you do any experiments on the robustness of the parameters?

-  In my own experiments I could not do this with the plugin. I tried this again, but ran into the same problems as last time: pyClesperanto does not work for me. The solution you link requires updating openCL drivers and the accepted solution in the forum post is "switch to a different workstation".

We apologize for the confusion here; the accepted solution (not accepted by us) was user specific as they switched work stations and it worked, so that was their solution. Other comments actually solved the issue as well. For ease this package can be installed on Google Colab (here is the link from our repo for ease: https://colab.research.google.com/github/AdaptiveMotorControlLab/CellSeg3d/blob/main/not ebooks/Colab_inference_demo.ipynb) where pyClesperanto can be installed via: !pip install pyclesperanto-prototype without issue on Google Colab.

This a) goes beyond the time I can invest for a review and b) is unrealistic to expect computationally inexperienced users to manage. Then I tried with the "watershed" segmentation, but this yields a strong oversegmentation no matter what I try, which is consistent with the predictions that look like a slightly denoised version of the input images and not like a proper foreground-background segmentation. With respect to the video you provide: I would like to see how a user can do this in the plugin without having a prior knowledge on good parameters or just pasting code, which is again not what you would expect a computationally unexperienced user to do.

We agree with the reviewer that the user needs domain knowledge, but we never claim our method was for inexperienced users. Our main goal was to show a new computer vision method with self-supervised learning (WNet3D) that works on LSM and confocal data for cell nuclei. To this end, we made you a demo video to show how a user can visually perform a thresholding check https://www.youtube.com/watch?v=xYbYqL1KDYE&t=5s, and we added all of these new utilities to the GUI, thanks for the suggestion. Otherwise, the threshold can also be done in a Notebook (as previously noted).

I acknowledge that some of these points are addressed in the limitations, but the text still implies that it is possible to get good segmentation results for such segmentation problems: "we believe that our self-supervised semantic segmentation model could be applied to more challenging data as long as the above limitations are taken into account." From my point of view the evidence for this is still lacking and would need to be provided by addressing the points raised above for me to further raise the Incomplete/solid rating, especially showing how this can be done wit the napari plugin. As an alternative, I would also consider raising it if the claims are further reduced and acknowledge that the current version of the method is only a good method for well separated nuclei.

We hope our new benchmarking and clear demo on four datasets helps improve your confidence in our evidence in our approach. We also refined our over text and hope our contributions, the limitations and the advantages are now more clear.

I understand that this may be frustrating, but please put yourself in the role of a new reader of this work: the impression that is made is that this is a method that can solve 3D segmentation tasks in light-sheet microscopy with unsupervised learning. This would be a really big achievement! The wording in the limitation section sounds like strategic disclaimers that imply that it is still possible to do this, just that it wasn't tested enough.

But, to the best of my assessment, the current version of the method only enables the more narrow case of well separated nuclei with a simple morphology. This is still a quite meaningful achievement, but more limited than the initial impression. So either the experimental evidence needs to be improved, including a demonstration how to achieve this in practice, including without deriving parameters via grid-search and in the plugin, or the claim needs to be meaningfully toned down.

Thanks for raising this point; we do think that WNet3D and the associated CellSeg3D package - aimed to continue to integrate state of the art models, is a non-trivial step forward. Have we completely solved the problem, certainly not, but given the limited 3D cell segmentation tools that exist, we hope this, coupled with our novel 3D dataset, pushes the field forward. We don’t show it works on the narrow well-separated use case, but rather show this works even better than supervised models on the very challenging benchmark Mouse Skull. Given we now show evidence that we outperform or match supervised algorithms with an unsupervised approach, we respectfully do think this is a noteworthy achievement. Thank you for your time in assessing our work.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

__* SUMMARY

This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

MAJOR AND MINOR COMMENTS *__

Overall Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.

This information is now provided in the figure legends (numbers of cells analyzed and/or numbers of embryos) except for data in Figure 5, which are presented in a new Supplementary Table

Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

We have used SD throughout the study. This information has now been added in figure legends.

Results 2

____A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.

We now cite a recent review on spinal cord development (Saade and E. Marti, Nature Reviews Neuroscience, 2025) to illustrate this point

The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.

We have now reformulated this paragraph as follows: "At E3, the transcript was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside (Saade and Marti, 2025)). One day later, at E4, this salt and pepper expression was still detected in the ventricular zone, while it markedly increased in the region of the mantle zone that is immediately adjacent to the ventricular zone. This region is enriched in nascent neurons on their way to differentiation that are still HuC/D negative. In contrast, the transcript was completely excluded from the more basal region of the mantle zone, where mature HuC/D positive neurons accumulate.

It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.

Thank you for the suggestion. We have now added a dotted line that separates the ventricular zone from the mantle zone at E3 and E4 in Figure 2A

Reference should be included for pRb expression dynamics.

This section has been rewritten in response to comments from Reviewer #3, and now contains several references regarding pRb expression dynamics. See detailed response to Reviewer #3 for the new version

Could the Myc tag insertion approach disrupt protein function or turnover? ____Why was the insertion target site at the C terminus chosen?

The first reason was practical: at the time when we decided to generate a KI in Cdkn1c, we had already generated several successful KIs at C-termini of other genes, in particular using the P2A-Gal4 approach (see Petit-Vargas et al, 2024), and had not yet experimented with N-terminal Gal4-P2A. We therefore decided to use the same approach for Cdkn1c.

We also chose to target the C-terminus to avoid affecting the active CKI domain which is located at the N-terminus.

Nevertheless, the C-terminal targeting may have an impact on the turnover: it has been described that CDK2 phosphorylation of a Threonin close to the C-terminus of Cdkn1c leads to its targeting for degradation by the proteasome from late G1 (Kamura et al, PNAS, 2003; doi: 10.1073/pnas.1831009100). We can therefore not rule out that the addition of the Myc tags close to this phosphorylation site modulates the dynamics of Cdkn1c degradation. We note, however, that we observed little overlap between the Cdkn1c-Myc and pRb signals in cycling progenitors, suggesting that Cdkn1c is effectively degraded from late G1.

OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?

Although it could be done, we have not attempted to do this for CDKN1c because our current experience of endogenous tagging of several genes with a similar expression level (based on our scRNAseq data) and nuclear localization (Hes5, Pax7) with a fluorescent reporter shows that the fluorescent signal is extremely low or undetectable in live conditions; Therefore we favored the multi-Myc tagging approach, and indeed we find that the Myc signal in progenitors is also very low even though it is amplified by the immunohistology method; this suggests that most likely, the only signal that would be detected -if any- with a fluorescent approach would be the peak of expression in newborn neurons.

In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?

The reviewer refers to the control gRNA condition in panel C, that shows that two small patches of GFP-positive cells are visible in the whole spinal cord of this particular embryo.

Technically, the origin of these "background" cells could be multiple. A spontaneous legitimate insertion at the CDKN1c locus by homologous recombination is possible, although we tend to think it is unlikely, given the extremely short length of the arms of homology; illegitimate insertions of the Myc-P2A-Gal4 cassette at off-target sites of the control gRNA is a possibility. Alternatively, a low-level leakage of Gal4 expression from the donor vector could lead to a detectable nls-GFP expression in a few cells via Gal4-UAS amplification.

In any case, these cells are observed at a very low frequency (1 or 2 patches of cells/embryo) relative to the signal obtained in presence of the CDKN1c gRNA#1 (probably several thousand positive cells per embryo). This suggests that if similar "background" cells are also present in presence of the CDKN1c gRNA, they would not significantly contribute to the signal, and would not impact the interpretation.

In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

It is indeed possible that the CDKN1c protein is more stable than the transcript in newborn neurons and remains detectable in the mantle zone after the mRNA disappears. In Gui et al, 2006, where they use an anti-CDKN1c antibody to label the protein in mouse spinal cord transverse sections at E11.5 (Figure 1B), a few positive cells are also visible basally. They could correspond to neurons that have not yet degraded CDKN1c, although it is unclear in the picture whether these cells are really in the mantle zone or in the adjacent dorsal root ganglion; we note that a similar differential expression dynamics between mRNA and protein has been described for Tis21/Btg2 in the developing mouse cortex, where the protein, but not the mRNA, is detected in some differentiated bIII-tubulin-positive neurons (Iacopetti et al, 1999).

However, related to our response above to a previous comment from the same reviewer, we cannot rule out the possibility that the Myc tags modulate the turnover of CDKN1c protein and slow down the dynamics of its degradation in differentiating neurons.

We have added a sentence to indicate the presence of these cells: "In addition, a few Myc-positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript."

Results

It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).

We did not quantify the level of mRNA reduction, it was just evaluated by eye. The reason for choosing shRNA1 for the whole study was dictated by 1) the fact that we more consistently saw (by eye) a reduction in the signal on the electroporated side with this construct than with the other shRNAs, and 2) that the effect on neurogenesis was also more consistent.

We will perform additional experiments to provide some quantitation of the shRNA effect, as this is also requested by Reviewer #3.

As our Cdkn1c KI approach offers a direct read-out of the protein levels in the ventricular and mantle zones, and since our shRNA strategy of "partial knock-down" is based on the idea that the shRNA effect should be more complete in progenitors expressing Cdkn1c at low levels than in newborn progenitors that express the protein at a higher level, we propose to validate the shRNA in the Cdkn1c-Myc knock-in background, by comparing the Myc signal intensity between control and Cdkn1c shRNA conditions

Figure panels are not currently cited in order. Citation or figure order could be changed.

We have now added a common citation of the panels referring to analyses at 24 and 48 hours after electroporation (now Figure 3A-F), allowing us to display the experimental data on the figure according to the timing post electroporation, while the text details the phenotype at the later time point first.

The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.

We have added images in a revised version of the Figure 3, as requested

A supplementary figure showing the Caspase3 experiment should be added.

We have added data showing Caspase3 experiments in Supplementary Figure 3D

OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

We agree with the reviewer that direct tracking is the most direct method for the identification of pairs of sister cells. However, it remains technically challenging, and the added value compared to the retrospective identification would be limited, while requiring a great workload, especially considering the many different experimental conditions that we have explored in this study.

Results 4

How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.

We have not done these quantifications in the original version of the study. We will add a quantification of the signal intensity in the ventricular and mantle zones for the revised version of the manuscript, as also requested by reviewer #3.

In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.

All images in the figure are single Z confocal images. Images in Column 2 (showing both electroporated sides of the same tube) were acquired with a 20x objective, whereas the insets shown in Columns 1 and 3 are 100x confocal images. 100x images on both sides were acquired with the same acquisition parameters, and the display parameters are the same for both images in the figure. The signal intensity can therefore be compared directly between columns 1 and 3.

We have modified the legend of the Figure to indicate these points: "The insets shown in Columns 1 and 3 are 100x confocal images acquired in the same section and are presented with the same display parameters".

In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

Our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

We have modified the manuscript to elaborate on our interpretation of this result: "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3H)."

Results 5 ____The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.

Thank you for pointing this out. We have modified the sentence in the main text.

"We found that the proportion of pRb positive progenitors having entered S phase (EdU positive cells) was significantly higher at all time points examined more than 4h30 after FT injection in the Cdkn1c knock-down condition compared to the control population (Figure 5D)"

OPTIONAL Could CyclinD1 activity be directly assessed?

This is an interesting suggestion. For example, using the fluorescent CDK4/6 sensor developed by Yang et al (eLife, 2020; https://doi.org/10.7554/eLife.44571) in a CDKN1c shRNA condition would represent an elegant experimental alternative to complement our rescue experiments with the double CDKN1c/CyclinD1 shRNA. However, we fear that setting up and calibrating such a tool for in vivo usage in the chick embryo represents too much of a challenge for incorporation in this study.

General ____Scale bars missing fig s1c s4d.

Thanks for pointing this out. Scale bars have been added in the figures and corresponding legends

OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.

OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?

We agree that it will be interesting and important that our findings are replicated in other species, experimental systems, and even tissues, or by alternative experimental approaches. Nevertheless, it is probably beyond the scope of this study.

A model cartoon to summarise outcomes would be useful.

We thank the reviewer for the suggestion. We will propose a summary cartoon for the revised version of the manuscript.

Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

Positivity or negativity was decided by eye. However, for each experiment, we ensured that all images of perturbed conditions and the relevant controls were analyzed with the same display parameters and by the same experimenter to guarantee that the criteria to determine positivity or negativity were constant.

Reviewer #1 (Significance (Required)):

SIGNIFICANCE

Strengths: This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

• Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
• A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
• Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
• Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
• Mechanistic insight by examining downstream target CyclinD1.
• Clearly presented with useful illustrations throughout.
• Logic is clear and examination thorough.
• Conclusions are warranted on the basis of their findings. ____Limitations ____T____his study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.

Some aspects of quantification require explanation in order for the experiments to be replicated.

It is imperative that precise sample sizes are included for all experiments presented.

Advance: ____First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.

Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.

Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

Audience:

Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

Please define your field of expertise with a few keywords to help the authors contextualize your point

Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

Major comments

I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed?

As already stated in our response to a similar question from reviewer #1, our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN).

Regarding the results of Pax7 overexpression presented in figure 4D (now Figure 4E in the revised version), we had made the choice to merge PN and NN values in the main text to focus on the neurogenic transition from PP to PN/NN collectively. We agree with this reviewer, as well as with reviewer #1, that it should be more detailed and better discussed. We therefore propose to modify the paragraph as follows (and as already indicated above in the response to reviewer #1):

"We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that Cdkn1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of Cdkn1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal Cdkn1c shRNA approach (see Figure 3F, now 3H)."

Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c?

It is likely that a proportion of cells that would normally have done a NN division are pushed to a direct differentiation that bypasses their last division in the Pax7-CDKN1c condition, and that they contribute to the general increase in neuron production observed in our quantification 48hae (Figure 3F -previously 3C). However, these cases would not contribute to the increase in the NN quantification in pairs of sister cells 6 hours after division at 24hae (Figure 4E - previously 4D), because by design they would not incorporate FlashTag. The rise in NN is therefore the result of a PN to NN conversion.

Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification

« We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 6A)."

* * Minor comments

Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

Some studies suggest that HuC/D can, under certain conditions, be observed in the nucleus of neurons. However, HuC/D is a RNA binding protein whose localization is mainly expected to be cytoplasmic. In our experience (Tozer et al, 2017), and in other publications using the antibody in the chick spinal cord (see, for example, le Dreau et al, 2014), it is observed in the cell body of differentiated neurons, as in the current manuscript.

Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

This has now been modified in the figures.

Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis? Or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

We will modify the introduction and discussion in several instances, in order to address the above suggestions and we will:

• add references to its role in other contexts and/or species.

• expand the discussion on the cross talk between neurogenic factors and CDK inhibitors in other cellular contexts.

• add a dedicated paragraph in the discussion to answer reviewer#2's questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages?

Reviewer #2 (Significance (Required)):

The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

____I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

__*

Reviewer #3 (Evidence, reproducibility and clarity (Required)): *__

Summary: In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely.

By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

Major comments:

1.-The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions.

However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified.

Throughout their comments on the manuscript, this reviewer raises several points regarding the characterization of pRb expression in our model and of our use of this marker in our study. We take these comments into account and propose to expand on pRb characteristics in the first occurrence of pRb as a marker of cycling cells in the manuscript. The modifications rely on:

• the quotation of several studies showing that phosphorylation of Rb is regulated during the cell cycle, and that "it is not detectable during a period of variable length in early G1 in several cell types (Moser et al, 2018;Spencer et al, 2013; Gookin et al, 2017), including neural progenitors in the developing chick spinal cord (Molina et al, 2022). Apart from this absence in early G1, pRb is detected throughout the rest of the cell cycle until mitosis".

• a more detailed description of our own characterization of pRb dynamics in a synchronous cohort of cycling cells, which reveals a similar heterogeneity in the timing of the onset of Rb phosphorylation after mitosis. This description was initially shown in supplementary figure 3 and will be transferred to a new supplementary figure 2 to account for the fact that it will now be cited earlier in the manuscript.

Regarding the specific question the "suitability (of pRb) as a neurogenic division marker": we do not directly "use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors", but we use Rb phosphorylation to discriminate between progenitors (pRb+) and neurons (pRb-) identity in pairs of sister cells to retrospectively identify the mode of division of their mother.

Given that Rb is unphosphorylated during a period of variable length after mitosis (see references above), pRb is not a reliable marker of ALL cycling progenitors. We developed an assay to identify the timepoint (the maximal length of this "pRb-negative" phase) after which Rb is phosphorylated in all cycling progenitors (new Supplementary Figure 2). This assay relies on a time course of pRb detection in cohorts of FlashTag-positive pairs of sister cells born at E3. This time course experiment allowed us to identify a plateau after which the proportion of pRb-positive cells in the cohort remains constant. From this timepoint, this proportion corresponds to the proportion of cycling cells in the cohort. Rb phosphorylation therefore becomes a discriminating factor between cycling progenitors (pRb+) and non-cycling neurons (pRb-).

We are confident that this provides a solid foundation for the determination of the identity of pairs of sister cells in all our Flash-Tag based assays, which retrospectively identify the mode of division of a progenitor on the basis of the phosphorylation status of its daughter cells 6 hours after division.

We propose to modify the main text to describe the strategy and protocol more explicitly, by introducing the sentence highlighted in yellow in the following paragraph where the paired-cell analysis is first introduced (in the section on CDKN1c knock-down):

"This approach allows to retrospectively deduce the mode of division used by the mother progenitor cell. We injected the cell permeant dye "FlashTag" (FT) at E3 to specifically label a cohort of progenitors that undergoes mitosis synchronously (Baek et al., 2018; Telley et al., 2016 and see Methods), and let them develop for 6 hours before analyzing the fate of their progeny using pRb immunoreactivity (Figure 3D). Our characterization of pRb immunoreactivity in the tissue had established beforehand that 6 hours after mitosis, all progenitors can reliably be detected with this marker (Supplementary Figure 2, Methods). Therefore, at this timepoint after FT injection, two-cell clones selected on the basis of FT incorporation can be categorized as PP, PN, or NN based on pRb positivity (P) or not (N) (see Methods, new Figure 3G and new Supplementary Figures 2 and 4)."

We also modified accordingly the legend to Supplementary Figure 2 (previously Supplementary Figure 3, which describes the identification of the plateau of pRb.

Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c.

In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components.

We agree with the reviewer that Rb phosphorylation may be a direct or indirect target of Cdkn1c activity, and exploring the molecular aspects of the cellular and developmental phenomena that we describe in our manuscript would represent an interesting follow up study.

____A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

To complement our analyses of the modes of division, we propose to use a positive marker to assess neural identity in parallel to the absence of pRb within pairs of cells. This approach may be the most meaningful in the gain of function context (Pax7 driven expression of Cdkn1c) because in this context, the time-point to reach the plateau of Rb phosphorylation used in our FT-based assay may indeed be delayed. On the opposite, in the context of loss of functions, the plateau may be reached earlier, which would have no effect on this assay.

2.-Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division.

This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.

The reviewer probably mistyped and meant 6-hour post injection, which is the duration that we use for paired cell tracking. We would like to emphasize that in addition to the FlashTag label, we benefit from the electroporation reporter to assess clonality. Altogether, we combine 5 criteria to define a clonal relationship :

• 2 cells are positive for Flash Tag
• The Flash Tag intensity is similar between the 2 cells
• The 2 cells are positive for the electroporation reporter
• The electroporation reporter intensity is similar between the two cells
• the position of the two cells is consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995;__; __Loulier et al, 2014): they are found on a shared line along the apico-basal axis, and share the same Dorso-Ventral and Antero-Posterior position . This combination is already described in the Methods section. We propose to modify the paragraph to include the sentence highlighted in yellow in the text below;

"Cell identity of transfected GFP positive cells was determined as follows: cells positive for pRb and FT were classified as progenitors and cells positive for FT and negative for pRb as neurons. In addition, a similar intensity of both the GFP and FT signals within pairs of cells, and a relative position of the two cells consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995; Loulier et al, 2014) were used as criteria to further ascertain sisterhood. This combination restricts the density of events fulfilling all these independent criteria, and can confidently be used to ensure a robust identification of pairs of sister cells."

3.- The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.

- "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation.

We have entirely remodeled this section, which describes the expression of Myc-tagged Cdkn1c relative to pRb and now provide several references that describe the generally accepted view that pRb is specific of cycling cells, regulated during the cell cycle, and in particular absent in early G1. We also remove the mention of the "Restriction point" in the main text to avoid any confusion on the timing of phosphorylation, as the notion of restriction point is not useful in our study. The section now reads as follows:

"To ascertain that Cdkn1c is translated in neural progenitors, we used an anti-pRb antibody, recognizing a phosphorylated form of the Retinoblastoma (Rb) protein that is specifically detected in cycling cells (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) , including neural progenitors of the developing chick spinal cord (Molina et al., 2022). In the ventricular zone of transverse sections at E4 (48hae), we detected triple Cdkn1c-Myc/GFP/pRb positive cells (arrowheads in Figure 2B), providing direct evidence for the Cdkn1c protein in cycling progenitors. We also observed many double GFP/pRb positive cells that were Myc negative (arrowheads in Figure 2B). The observation of UAS-driven GFP in these pRb-positive cells is evidence for the translation of Gal4 and therefore provides a complementary demonstration that the Cdkn1c *transcript is translated in progenitors. The absence of Myc detection in these double GFP/pRb positive cells also suggests that Cdkn1c/Cdkn1c-Myc stability is regulated during the cell cycle. *

Finally, we observed double Myc/GFP-positive cells that were pRb-negative (Figure 2B; asterisks). One characteristic of Rb phosphorylation as a marker of cycling cells is a period in early G1 during which it is not detectable, as described in several cell types (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) including chick spinal cord neural progenitors (Molina et al., 2022). Using a method that specifically labels a synchronous cohort of dividing cells in the neural tube, we similarly observed a period in early G1 during which pRb is not detectable in some progenitors at E3 (See Supplementary Figure 2 and Methods). Hence, the double Myc/GFP positive and pRb negative cells may correspond to progenitors in early G1. Alternatively, they may be nascent neurons whose cell body has not yet translocated basally (see Figure 2C). Finally, we observed a pool of GFP positive/pRb negative nuclei with a strong Myc signal in the region of the mantle zone that is in direct contact with the ventricular zone (VZ), corresponding to the region where the transcript is most strongly detected (see Figure 2A). This pool of cells with a high Cdkn1c expression likely corresponds to immature neurons exiting the cell cycle and on their way to differentiation (Figure 2B; double asterisks). In addition, a few Myc positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript.

In summary, our dual Myc and Gal4 knock-in strategy which reveals the history of Cdkn1c transcription and translation confirms that Cdkn1c is expressed at low level in a subset of progenitors in the chick spinal neural tube, as previously suggested (Gui et al., 2007; Mairet-Coello et al., 2012). In addition, the restricted overlap of Cdkn1c-Myc detection with Rb phosphorylation suggests that in progenitors, Cdkn1c is degraded during or after G1 completion. "

This section will again be remodeled in a future revised version of the manuscript, in which we will add quantifications of Myc levels, as requested by Reviewer 1 above, and also by Reviewer #3 below.

Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).

In the original version of the manuscript, the section describing the dynamics of CDKN1c-Myc in the KI experiments presented in Figure 2 relied on the idea that the dynamics of pRb in chick spinal progenitors is similar to what I described in other tissues and cell types, without providing any references to substantiate this fact. Actually, Molina et al provide a characterization of pRb in combination with their cell cycle reporter and conclude that pRb negative progenitors are in G1 ("We also verified that phospho-Rb- and HuC/D-negative cells were in G1 by using our FUCCI G1 and PCNA reporters"). We will now cite this reference to support our claim. In addition, our characterization of Rb progressive phosphorylation in the synchronic Flash-Tag cohort of newborn sister cells provides a complementary demonstration that a fraction of the progenitors are pRb-negative when they exit mitosis (i.e. in early G1). This analysis was initially only introduced in the supplementary Figure 3, as support for the section that presents the Paired-cell assay used in Figure 3. We propose to introduce the data from Supplementary Figure 3 earlier in the manuscript (now Supplementary Figure 2), in order to better introduce the reader with the dynamics of pRb in cycling cells in our model. This will better support our description of the Cdkn1c-Myc dynamics in relation with pRb. We therefore propose to reformulate this whole section as follows.

- It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.

- It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).

These are two interesting suggestions. To complement our data with guide #1, we have performed Myc-immunostaining experiments on transverse sections in the context of guide #3, showing exactly the same pattern of Myc signal, with low expression in the VZ, and a peak of signal in the part of the mantle zone that is immediately touching the VZ. This confirms the specificity of the spatial distribution of the Cdkn1c-Myc signal. These data have been added in a revised version of Supplementary Figure 1.

We will perform the suggested quantifications using guides #1 and #3, which both show a good KI efficiency. We do not think it is useful to do these experiments with guide #2, whose efficiency is much lower, and which would lead to a very sparse signal.

- The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).

We will perform these experiments to validate guide cutting efficiency using the Tide method (Brinkman et al, 2014)

- In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.

We will perform genomic PCR experiments to confirm in-frame insertion of the Myc tags at the Cdkn1c locus

4.- In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c.

Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression.

All the predicted isoforms in the chick genome contain the sequence targeted by shRNA1, which is located in the CKI domain, the region of the protein that is most conserved between species. Besides, all the isoforms annotated in the mouse and human genomes also contain the region targeted by shRNA1. We are therefore confident that shRNA1 should target all chick isoforms.

A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2).

This approach (qRT-PCR on sorted cells) would enable us to focus solely on electroporated cells, but it would result in an averaged quantification of Cdkn1c depletion. In order to obtain additional information on the shRNA-dependent decrease in Cdkn1C in the different neural cell populations (progenitor versus differentiating neuron), we propose an alternative approach consisting in monitoring the level of Cdkn1c protein, assessed through Cdkn1c-Myc signal in knock-in cells, in the presence versus absence of Cdkn1c shRNA.

- As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons.

We have addressed the case of pRb dynamics in progenitors above and added a reference documented pRb expression during the cell cycle of chick neural progenitors (Molina et al, 2022).

Regarding Sox2 persistence: we consistently detect a small fraction of double positive Sox2+/HuC/D+ cells in chick spinal cord transverse sections. We have shown that this marker of differentiating neurons (HuC/D) only becomes detectable more than 8 hours after mitosis in newborn neurons at E3 (Baek et al, 2018), indicating that Sox2 protein can persist for up to at least 8 hours in newborn neurons.

We now cite a paper showing that a similar persistence of Sox2 protein is reported in differentiating neurons of the human neocortex, where double Sox2/NeuN positive cells are frequently observed in cerebral organoids (Coquand et al, Nature Cell Biology 2024__)__

- The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption?

We do not think that these numbers are equal for both conditions, and we did not formulate this assumption. We only indicate (in the methods section) that this undefined/undetermined population (based on negativity for both markers) is a mix of two possible cell types. However, we do not offer any interpretation of the CDKN1c phenotypes based on the changes in this population. Indeed, our interpretation of the knock-down phenotype is solely based on the increase in pRb-positive and decrease in HuC/D-positive cells, which both suggest a delay in neurogenesis. We understand from the reviewer's comment that depicting an "undefined" population on the graph may cause some confusion. We therefore propose to present the data on pRb and HuC/D in different graphs, rather than on a combined plot, and to remove the reference to undefined cells in Figure 3, as well as in Figures 4 and 5 depicting the gain of function and double knock-down experiments. We have implemented these changes in updated versions of the figures.

- In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe?

We have now performed experiments with an anti phospho Histone 3 antibody, which labels mitotic cells, at 24 and 48 hours post electroporation. We do not see any ectopic mitoses upon Cdkn1c knock-down with this marker, and we have produced a Supplementary Figure with these data. This is consistent with the fact that we also do not see ectopic pRb or Sox2 positive cells in the mantle zone in the knock-down experiments. These data (pH3 and Sox2) have been added in the new Supplementary Figure 3E and F.

We have now modified the main text to include these data:

"In the context of a full knock-out of Cdkn1c in the mouse spinal cord, a reduction in neurogenesis was also observed, which was attributed to a failure of prospective neurons to exit the cell cycle, resulting in the observation of ectopic mitoses in the mantle zone (Gui et al, 2007). In contrast with this phenotype, using an anti phospho-Histone3 antibody, we did not observe any ectopic mitoses 24 or 48 hours after electroporation in our knock-down condition (Supplementary Figure 3E-F). This is consistent with the fact that we also do not observe ectopic cycling cells with pRb (Figure 3A and D) and Sox2 (Supplementary Figure 3E-F) antibodies. We therefore postulated that the reduced neurogenesis that we observe upon a partial Cdkn1c knock-down may result from a delayed transition of progenitors from the proliferative to neurogenic modes of division."

- The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye.

- Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells?

The key criterion for cells that are not directly touching each other is that their relative position corresponds to the classical "radial" organization of clones in this tissue (Leber and Sanes, 1995__; __Loulier et al, Neuron, 2014). In other words, we make sure that they are located on a same apico-basal axis, as is the case for the NN clone presented on the figure. As stated above in our response to major comment #2, we have modified the Methods section accordingly.

Can they provide further image examples of different types of clones?

We now provide additional examples in a new Supplementary Figure 4

- Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity?

The plateau for Rb phosphorylation in progenitors is reached before 6 hours post mitosis at E3. At the same age, we have previously shown (Baek et al, PLoS Biology 2018) in a similar time course experiment in pairs of FT+ cells that the HuC/D signal is not detected in newborn neurons 8 hours after mitosis. HuC/D only starts to appear between 8 and 12 hours, and still increases between 8 and 16 hours. The plateau would therefore be very delayed for HuC/D compared to pRb. This long delay in the appearance of this « positive » marker of neural differentiation is the main reason why we chose to use Rb phosphorylation status for the analysis of synchronous cohorts of pairs of sister cells, because pRb becomes a discriminating factor much earlier than HuC/D after mitosis.

- In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?

We have carried out similar experiments at E2, showing a plateau of 95% of pRb-positive cells in the FT-positive population (see graph on the right). This provides a retrospective estimate of the mode of division of the mother cells at this stage (roughly 90% of PP and 10% of PN) which is consistent with the vast majority of PP divisions described by Saade et al (2013, see Figure S1) at this stage.

5.- In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is:

- Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024.

We have now performed Pax7 immunostainings on transverse sections at 24 and 48 hours post electroporation, both with the Pax7-CDKN1c-Gal4 and with the Pax7-Gal4 control constructs. We present these data in the new supplementary figure 7. In both conditions, we find that the Pax7 protein is still present in KI-positive cells. We observe a modest increase in Pax7 signal intensity in these cells, suggesting either that the insertion of exogenous sequences stabilizes the Pax7 transcript, or that the C-terminal modification of Pax7 protein with the P2A tag increases its stability. This does not affect the interpretation of the CDKN1c overexpression phenotype, because we used the Pax7-Gal4 construct that shows the same modification of Pax7 stability as a control for this experiment. We have introduced this comment in the legend of Supplementary Figure 7.

- Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level?

Cdkn1c transcription is regulated by multiple transcription factors and non-coding RNAs (see for example Creff and Besson, 2020, or Rossi et al, 2018 for a review). To our knowledge, these studies focus more on the regulation of Cdkn1c global expression than on the regulation of its levels during cell cycle progression. Although it is very likely that transcriptional regulation contributes, post-translational regulation, and in particular degradation by the proteasome, is also a key factor in the cell cycle regulation of Cdkn1c activity

If so, how does this differ from the promoter activity of Pax7?

The transcriptional regulation of Pax7 and Cdkn1c is probably controlled by different regulators, since their expression profiles are very different. Regardless of the mechanisms that control their expression, the rationale for choosing Pax7 as a driver for Cdkn1c expression was that Pax7 expression precedes that of Cdkn1c in the progenitor population, and that it disappears in newborn neurons, when that of Cdkn1c peaks. This provided us with a way to advance the timing of Cdkn1c expression onset in proliferative progenitors.

- It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B.

We will carry out experiments similar to those shown in Figure 2B in order to characterise the dynamics of Cdkn1c in a context of overexpression, in relation to pRb.

In addition, we will include a more precise quantification of the "misexpressed" compared to "endogenous" Cdkn1c -Myc levels, as already mentioned in the answer to a request by reviewer1.

6.-In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons.

- In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845).

The Nowakowski non linear regression method has been used often in the literature in the same tissue, and is generally used to calculate fixed values for Tc, Ts, etc... This method is based on several selective criteria, and in particular the assumption that "all of the cells have the same cycle times". Yet, many studies have documented that cell cycle parameters change during the transition from proliferative to neurogenic modes of division during which our analysis is performed; live imaging data in the chick spinal cord have illustrated very different cell cycle durations at a given time point (see Molina et al). We therefore think that the proposed formulas do not reflect the heterogenous reality of neural progenitors of the embryonic spinal cord. However, the cumulative approach described by Nowakowski is useful to show qualitative differences between populations (e.g. a global decrease of the cycle length, like in our comparison between control and shRNA conditions). For these reasons, we prefer to display only the raw measurements rather than the regression curves.

- Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why?

Le Dreau et al count the EdU+ proportion of cells in the total population of electroporated cells located in the VZ (which includes progenitors, but also future neurons that have been labelled during the previous cycles -at least for the time points after 2hours- and have not yet translocated to the mantle zone), whereas we only consider pRb+ progenitors in the analysis. In addition, the experiments are not performed at the same developmental stage. Altogether, this may account for the different curves obtained in our study.

- It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

We will perform cumulative EDU incorporation experiments similar to that shown in Figure 5D to measure G1 length for the cdkn1c-sh - ccnd1-sh knock down double conditions, as well as in the Ccnd1 knock down condition alone.

Minor comments

__*Introduction:

• The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. ____Nature Communications 2020).*__

We will modify the introduction in several instances, in order to address suggestions by Reviewers #2 (see above) and #3, in particular to expand the description of the role of Cdkn1c during cortical development

1) Transcriptional signature of the neurogenic transition (Figure 1).

- In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).

We have now listed the genes used to determine the progenitor and neuron score in the main text of the result section

- Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).

We have now added the detail of what 'filtering' means in the diagram

- In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.

We have reworded this sentence, adding a reference to the expression profile of Tis 21. The paragraph now reads as follows:

« However, Cdkn1c expression is maintained longer and transiently peaks at high levels after Tis21 expression is switched off. Given that Tis21 is no more expressed in neurons (Iacopetti et al, 1999), this suggests that Cdkn1c expression is transiently upregulated in nascent neurons before fading off in more mature cells. »

- "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.

We have now added references linking the function and/or expression profile of these genes to the neurogenic transition: Dll1 (Henrique et al., 1995), the bHLH transcription factors Hes6 (Fior and Henrique, 2005), NeuroG1 and NeuroG2 (Lacomme et al., 2012; Sommer et al., 1996) and the coactivator Gadd45g (Kawaue et al., 2014).

- There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

- Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper.

We have corrected the colour code errors in Figure 1c and Supp Figure 3B (now changed to Supplementary Figure 5 in the modified revision)

____It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors.

We have so far refrained from performing the suggested combined analysis based on cell cycle and cell type scores, as the "neurogenic progenitor population" (based on neurogenic progenitor score values) in which Cdkn1c expression is initiated represents a small number of cells in our scRNAseq, and felt that the significance of such an analysis is uncertain. We will perform this analysis in the revised version

2) Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2).

- Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2).

The scale bar is actually valid for the whole panel A. The E2 section in the original figure appeared as "large" as the E3 section along the DV axis probably because the cutting angle was not perfectly transverse at E2, artificially lengthening the section. In a new version of the figure, we have replaced the E2 images with another section from the same experiment. The scale bar remains valid for the whole panel.

- Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A.

We have now added a diagram for the knock-in strategy in Figure 2B, and modified the legend of the figure accordingly.

- Indicate hours post-electroporation. Indicate which guide is used in the main text.

We have now added the post-electroporation timing and guide used in the main text.

3) Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3).

- In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed.

- Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region?

__We have modified this sentence as follows: "__Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal two thirds of the neural tube, except for the Pax7-Cdkn1c misexpression analysis, which was performed in the more dorsal Pax7 domain."

This is valid both for the whole population and clonal analyses

- Figure 3. Would have a better flow if 3C preceded 3A and 3B.

We have modified the Figure accordingly.

- Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification.

We have modified the Figure accordingly

- In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B.

This explanation corresponds indeed to Figure 5A. We have corrected this mistake in the new version of the manuscript.

4) Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).

- "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain.

We have added references to the expression profile of Pax7 in the dorsal neural tube (Jostes et al, 1990). In addition, the new Supplementary Figure 7 shows anti-Pax7 staining that confirm this expression pattern at E3 and E4

- "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells.

As stated in the response to Major Point 5 above, we will perform a quantification based on Myc immunofluorescence to compare endogenous Cdkn1c expression versus Cdkn1c expression upon overexpression.

- "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe.

We have added in the main text that the quantification was performed 48hae.

- Legend of figure 4D should indicate that the quantification has been done 24hpe.

We have added the timing of quantification in the legend of Figure 4D.

- "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment.

These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification:

« We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)."

- "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections.

This whole section has been modified in response to a question from reviewer 1. The new version does not contain percentages in the main text, and reads as follows:

« Using the FlashTag cohort labeling approach described above, we traced the fate of daughter cells born 24 hae. We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3F). Overall, these data show that inducing a premature low-level expression of Cdkn1c in cycling progenitors is sufficient to accelerate the transition towards neurogenic modes of division. »

- Figure sup 4C includes references to 3 gRNAs even when only one is used in the study.

The three guides listed in the original Supplementary Figure 4C correspond to the guides that we tested in Petit-Vargas et al. 2024. In this study, we only used the most efficient of these three guides. We have modified Figure 4C by quoting only this guide.

5) The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5)

- "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing

We have included references related to the activity of the CyclinD1/CDK4-6 complex in the developing CNS, and the antagonistic activities of CyclinD1 and Cdkn1c in this model

- "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation in the developing CNS (Lobjois et al, 2004, 2008, Lange 2009, Gui et al 2007), and is inhibited by Cdkn1c (Gui et al, 2007)."

- It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G.

We have added the experimental set-up information in Figure 5.

- Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

The analyses were carried out on two thirds of the neural tube (dorsal 2/3), excluding the ventral zone, as specified above (and in the Methods section)

- It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G.

- For Figure 4C and D, it would be valuable to add images to illustrate the quantification.

We have added images:

• in Supplementary Figure 7C to illustrate what is quantified in Figures 4C (now 4C and 4D);
• In Figure 5E to illustrate what is quantified in Figure 5D
• In Supplementary Figure 8B to illustrate what is quantified in Figure 5G (now Figure 5H and 5I) Regarding the requested images for Figures 4D and 5F, they correspond to the same types of images already shown in Figure 3E. Since we have now added several additional examples of representative pairs of each type of mode of division in the new Supplementary Figure 4, we do not think that adding more of these images in figures 4 and 5 would strengthen the result of the quantifications.

Discussion:

- "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).

We have now included the references suggested by the reviewer.

- "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

We propose to reformulate this paragraph in the discussion as follows to take this remark into account

"This allows us to re-interpret the role of Cdkn1c during spinal neurogenesis: while previously mostly considered as a binary regulator of cell cycle exit in newborn neurons, we demonstrate that Cdkn1c is also an intrinsic regulator of the transition from the proliferative to neurogenic status in cycling progenitors. This occurs through a change in their mode of division, and our double knock-down experiments suggest that the onset of Cdkn1c expression may promote this change by counteracting a CyclinD1/CDK6 complex dependent mechanism."

Other comments:

- To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.

We have modified the figures to systematically show the electroporated side of the neural tube on the same side of the image for single electroporations.

____- Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.

This information is now provided in the figure legends (numbers of cells analysed and/or numbers of embryos), except for data in Figure 5, which are presented in a new Supplementary Table 1.

All experiments were performed on vibratome sections, except for in situ hybridization experiments, which were performed on cryostat sections. This last information was already indicated in the relevant figure legends

- Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.

- There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.

We have now homogenized the nomenclature in the figures.

- "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

In the original version of the manuscript, the anti-Sox2 antibody was not used; we have now added experiments using this antibody in the modified version of the manuscript; this sentence in the Methods thus remains unchanged.

Reviewer #3 (Significance (Required)):

__*Significance:

In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. *__

The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

The strengths of the study include:

The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

- The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.

- The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

The aspects to improve:

- The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.

The sequencing dataset has been deposited in NCBI's Gene Expression Omnibus database. It is currently under embargo, but will be made available upon acceptance and publication of the peer reviewed manuscript. Access is nonetheless available to the reviewers via a token that can be retrieved from the Review Commons website.

The following information will be added in the final manuscript.

Data availability

Single cell RNA sequencing data have been deposited in NCBI's Gene Expression Omnibus (GEO) repository under the accession number GSE273710, and are available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273710."

- The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed. - Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

- Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.

- The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.

- The presentation of the existing literature could be more up to date.

- The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

Field of expertise of the reviewer: neural development, cell biology, embryology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely. By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

Majors comments:

1. The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions. However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified. Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
2. Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division. This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.
3. The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.
   • "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation. Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).
   • The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).
   • In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.
   • It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.
4. It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).
5. In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c. Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression. A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2). - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons. - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption? - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe? - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye. - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells? Can they provide further image examples of different types of clones? - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity? - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?. 5. In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is: - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024. - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level? If so, how does this differ from the promoter activity of Pax7? - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B. 6. In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons. - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845). - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why? - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

Minor comments

Introduction:

• The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. Nature Communications 2020).

• Transcriptional signature of the neurogenic transition (Figure 1).

  • In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).
  • Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).
  • In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.
  • "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.
  • There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors. 2. Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2). - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2). - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A. - Indicate hours post-electroporation. Indicate which guide is used in the main text. 3. Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3). - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed. - Figure 3. Would have a better flow if 3C preceded 3A and 3B. - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification. - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region? - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper. - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B. 4. Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).<br /> - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain. - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells. - For Figure 4C and D, it would be valuable to add images to illustrate the quantification. - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe. - Legend of figure 4D should indicate that the quantification has been done 24hpe. - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment. - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections. - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study. 5. The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5) - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G. - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G. - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

Discussion:

• "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).
• "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

Other comments:

• There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.
• To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.
• Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.
• Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.
• "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

Significance

In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

The strengths of the study include:

The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

• The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.
• The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

The aspects to improve:

• The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.
• The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed.
• Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
• Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.
• The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.
• The presentation of the existing literature could be more up to date.
• The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

Field of expertise of the reviewer: neural development, cell biology, embryology.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study addresses the question of how task-relevant sensory information affects activity in the motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to the motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

Strengths:

The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity.

The task is also well designed to suit the questions being asked and well controlled.

We appreciate these kind comments.

It is commendable that the authors compare single units to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics. However, the subtypes (PD shift, gain, and addition) are not sufficiently justified. The authors also do not address that single units exhibit mixed modulation, but RNN units are not treated as such.

We’re sorry that we didn’t provide sufficient grounds to introduce the subtypes. We have updated this in the revised manuscript, in Lines 102-104 as:

“We determined these modulations on the basis of the classical cosine tuning model (Georgopoulos et al., 1982) and several previous studies (Bremner and Andersen, 2012; Pesaran et al., 2010; Sergio et al., 2005).”

In our study, we applied the subtype analysis as a criterion to identify the modulation in neuron populations, rather than sorting neurons into exclusively different cell types.

Weaknesses:

The main weaknesses of the study lie in the categorization of the single units into PD shift, gain, and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single-unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

In our study, the mixed selectivity or specifically the target-motion modulation on reach- direction tuning is a significant feature of the single neurons. We categorized the neurons into three subclasses, not intending to claim their absolute cell types, but meaning to distinguish target-motion modulation patterns. To further characterize these three patterns, we also investigated their interaction by perturbing connection weights in RNN.

Yes, it’s important to consider the role of rotating tuning curves in neural dynamics during interception. In our case, we observed population neural state with sliding windows, and we focused on the period around movement onset (MO) due to the unexpected ring-like structure and the highest decoding accuracy of transferred decoders (Figure S7C). Then, the single-unit analyses were implemented.

This paper shows sensory information can affect motor cortical activity whilst not affecting motor output. However, it is not the first to do so and fails to cite other papers that have investigated sensory modulation of the motor cortex (Stavinksy et al. 2017 Neuron, Pruszynski et al. 2011 Nature, Omrani et al. 2016 eLife). These studies should be mentioned in the Introduction to capture better the context around the present study. It would also be beneficial to add a discussion of how the results compare to the findings from these other works.

Thanks for the reminder. We’ve introduced these relevant researches in the updated manuscript in Lines 422-426 as:

“To further clarify, the discussing target-motion effect is different from the sensory modulation in action selection (Cisek and Kalaska, 2005), motor planning (Pesaran et al., 2006), visual replay and somatosensory feedback (Pruszynski et al., 2011; Stavisky et al., 2017; Suway and Schwartz, 2019; Tkach et al., 2007), because it occurred around movement onset and in predictive control trial-by-trial.”

This study also uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

I have clarified some of the areas that would benefit from further analysis below:

(1) Task:

The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys.

A great suggestion; however, it is hardly feasible as the Utah arrays have already been removed.

(2) Single unit analyses:

In some analyses, the effects of target speed look more driven by target movement direction (e.g. Figures 1D and E). To confirm target speed is the main modulator, it would be good to compare how much more variance is explained by models including speed rather than just direction. More target speeds may have been helpful here too.

A nice suggestion. The fitting goodness of the simple model (only movement direction) is much worse than the complex models (including target speed). We’ve updated the results in the revised manuscript in Lines 119-122, as “We found that the adjusted R2 of a full model (0.55 ± 0.24, mean ± sd.) can be higher than that of the PD shift (0.47 ± 0.24), gain (0.46 ± 0.22), additive (0.41 ± 0.26), and simple models (only reach direction, 0.34 ± 0.25) for three monkeys (1162 neurons, ranksum test, one-tailed, p<0.01, Figure S5).”

The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

A good point. It is a pity that we haven’t found an appropriate unsupervised method.

The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results.

Thanks for the suggestion and close reading. Because the movement onset (MO) is the key time point of this study, we colored this time period in Figure 1 to highlight the perimovement neuronal activity.

(3) Decoder:

One feature of the task is that the reach endpoints tile the entire perimeter of the target circle (Figure 1B). However, this feature is not exploited for much of the single-unit analyses. This is most notable in Figure 2, where the use of a SVM limits the decoding to discrete values (the endpoints are divided into 8 categories). Using continuous decoding of hand kinematics would be more appropriate for this task.

This is a very reasonable suggestion. In the revised manuscript, we’ve updated the continuous decoding results with support vector regression (SVR) in Figure S7A and in Lines 170-173 as:

“These results were stable on the data of the other two monkeys and the pseudopopulation of all three monkeys (Figure S6) and reconfirmed by the continuous decoding results with support vector regressions (Figure S7A), suggesting that target motion information existed in M1 throughout almost the entire trial.”

(4) RNN:

Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. Furthermore, it would be informative to compare the neural data to the RNN activity using canonical correlation or Procrustes analyses. These would help validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. There is also an absence of alternate models to compare the perturbation model results to.

Thank you for these helpful suggestions. We have performed decoding analysis on RNN units and updated in Figure S12A and Lines 333-334 as: “First, from the decoding result, target motion information existed in nodes’ population dynamics shortly after TO (Figure S12A).”

We also have included the results of canonical correlation analysis and Procrustes analysis in Table S2 and Lines 340-342 as: “We then performed canonical component analysis (CCA) and Procrustes analysis (Table S2; see Methods), the results also indicated the similarity between network dynamics and neural dynamics.”

Reviewer #2 (Public Review):

Summary:

In this manuscript, Zhang et al. examine neural activity in the motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that (1) the reach direction has consistent positioning around the ring, and (2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

Strengths:

I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target task to better characterize the breadth of how the motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

Thank you for your recognition of our work.

Weaknesses:

While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a singleneuron representational lens. This would be fine as an initial analysis since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how the motor cortex or its neural geometry might be contributing to the execution of this novel task.

This paper shows the sensory modulation on motor tuning in single units and neural population during motor execution period. It’s a pity that the findings were constrained in certain time windows. We are still working on this task, please look forward to our following work.

Main Comments:

My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

(1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

It’s a great idea! We are on the way, and it seems promising.

(2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space (this is actually fairly easy to see in the reach direction components of the dPCA plot in the supplement--the rings will be highly aligned in this space). Presumably, then, the null space should contain information about the target movement. dPCA shows that there's not a single dimension that clearly delineates target speed, but the ring tilt is likely evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")-this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

Thank you for this nice suggestion. While it was feasible to identify potent subspaces encoding reach direction and null spaces for target-velocity modulation, as suggested by the reviewer, the challenge remained that unsupervised methods were insufficient to isolate a pure target-velocity subspace from numerous possible candidates due to the small variance of target-velocity information. Although dPCA components can be used to construct orthogonal subspaces for individual task variables, we found that the targetvelocity information remained highly entangled with reach-direction representation. More details can be found in Figure S8C and its caption as below:

“We used dPCA components with different features to construct three subspaces (same data in A, reach-direction space #3, #4, #5; target-velocity space #10, #15, #17; interaction space #6, #11, #12), and we projected trial-averaged data into these orthogonal subspaces using different colormaps. This approach allowed us to obtain a “potent subspace” coding reach direction and a “null space” for target velocity. The results showed that the reach-direction subspace effectively represented the reach direction. However, while the target-velocity subspace encoded the target velocity information, it still contained reach-direction clusters within each target-velocity condition, corroborating the results of the addition model in the main text (Figure 4). The interaction subspace revealed that multiple reach-direction rings were nested within each other, similar to the findings from the gain model (Figure 3 & 4). The interaction subspace also captured more variance than target-velocity subspace, consistent with our PCA results, suggesting the target-velocity modulation primarily coexists with reach-direction coding. Furthermore, we explored alternative methods to verify whether orthogonal subspaces could effectively separate the reach direction and target velocity. We could easily identify the reach-direction subspace, but its orthogonal subspace was relatively large, and the target-velocity information exhibited only small variance, making it difficult to isolate a subspace that purely encodes target velocity.”

(3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons (especially considering that 43% of nodes were unclassifiable). It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

We are sorry that we did not clarify the definition of “none” type, which can be misleading. The 43% unclassifiable nodes include those inactive ones; when only activate (taskrelated) nodes included, the ratio of unclassifiable nodes would be much lower. We recomputed the ratios with only activated units and have updated Table 1. By perturbing the connectivity, we intended to explore the interaction between different modulations.

Thank you for the great advice. We considered moving neural states from one ring to another without changing the directional cluster. However, we found that this perturbation design might not be fully developed: since the top two PCs are highly correlated with movement direction, such a move—similar to exchanging two states within the same cluster but under different target-motion conditions—would presumably not affect the behavior.

Reviewer #3 (Public Review):

Summary:

This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

This paradigm allows the authors to investigate how, given a fixed reach endpoint (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

At the level of single neurons, the authors found that target motion modulates the activity in three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to the target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain-modulated neurons.

Finally, the authors studied the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units were found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the “ neural population” resembled that observed in the monkeys.

Strengths:

- The experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.

- The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.

- The paper gives a complete picture of the effect of the target motion on neural activity, by including analyses at the single neuron level as well as at the population level. Additionally, the authors link those two levels of representation by highlighting how gain modulation contributes to shaping the population representation.

Thank you for your recognition.

Weaknesses:

- One of the main premises of the paper is the fact that the motor output for a given reach point is preserved across different target motions. However, as the authors briefly mention in the conclusion, they did not record muscle activity during the task, but only hand velocity, making it impossible to directly verify how preserved muscle patterns were across movements. While the authors highlight that they did not see any difference in their results when resampling the data to control for similar hand velocities across conditions, this seems like an important potential caveat of the paper whose implications should be discussed further or highlighted earlier in the paper.

Thanks for the suggestion. We’ve highlighted the resampling results as an important control in the revised manuscript in Figure S11 and Lines 257-260 as:

“To eliminate hand-speed effect, we resampled trials to construct a new dataset with similar distributions of hand speed in each target-motion condition and found similar orbital neural geometry. Moreover, the target-motion gain model provided a better explanation compared to the hand-speed gain model (Figure S11).”

- The main takeaway of the RNN analysis is not fully clear. The authors find that an RNN trained given a sensory input representing a moving target displays modulation to target motion that resembles what is seen in real data. This is interesting, but the authors do not dissect why this representation arises, and how robust it is to various task design choices. For instance, it appears that the network should be able to solve the task using only the motion intention input, which contains the reach endpoint information. If the target motion input is not used for the task, it is not obvious why the RNN units would be modulated by this input (especially as this modulation must lie in the nullspace of the movement hand velocity if the velocity depends only on the reach endpoint). It would thus be important to see alternative models compared to true neural activity, in addition to the model currently included in the paper. Besides, for the model in the paper, it would therefore be interesting to study further how the details of the network setup (eg initial spectral radius of the connectivity, weight regularization, or using only the target position input) affect the modulation by the motion input, as well as the trained population geometry and the relative ratios of modulated cells after training.

Great suggestions. In the revised manuscript, we’ve added the results of three alternative modes in Table S4 and Lines 355-365 as below:

“We also tested three alternative network models: (1) only receives motor intention and a GO-signal; (2) only receives target location and a GO-signal; (3) initialized with sparse connection (sparsity=0.1); the unmentioned settings and training strategies were as the same as those for original models (Table S4; see Methods). The results showed that the three modulations could emerge in these models as well, but with obviously distinctive distributions. In (1), the ring-like structure became overlapped rings parallel to the PC1PC2 plane or barrel-like structure instead; in (2), the target-motion related tilting tendency of the neural states remained, but the projection of the neural states on the PC1-PC2 plane was distorted and the reach-direction clusters dispersed. These implies that both motor intention and target location seem to be needed for the proposed ring-like structure. The initialization of connection weights of the hidden layer can influence the network’s performance and neural state structure, even so, the ring-like structure”

- Additionally, it is unclear what insights are gained from the perturbations to the network connectivity the authors perform, as it is generally expected that modulating the connectivity will degrade task performance and the geometry of the responses. If the authors wish the make claims about the role of the subpopulations, it could be interesting to test whether similar connectivity patterns develop in networks that are not initialized with an all-to-all random connectivity or to use ablation experiments to investigate whether the presence of multiple types of modulations confers any sort of robustness to the network.

Thank you for these great suggestions. By perturbations, we intended to explore the contribution of interaction between certain subpopulations. We’ve included the ablation experiments in the updated manuscript in Table S3 and Lines 344-346 as below: “The ablation experiments showed that losing any kind of modulation nodes would largely deteriorate the performance, and those nodes merely with PD-shift modulation could mostly impact the neural state structure (Table S3).”

- The results suggest that the observed changes in motor cortical activity with target velocity result from M1 activity receiving an input that encodes the velocity information. This also appears to be the assumption in the RNN model. However, even though the input shown to the animal during preparation is indeed a continuously moving target, it appears that the only relevant quantity to the actual movement is the final endpoint of the reach. While this would have to be a function of the target velocity, one could imagine that the computation of where the monkeys should reach might be performed upstream of the motor cortex, in which case the actual target velocity would become irrelevant to the final motor output. This makes the results of the paper very interesting, but it would be nice if the authors could discuss further when one might expect to see modulation by sensory information that does not directly affect motor output in M1, and where those inputs may come from. It may also be interesting to discuss how the findings relate to previous work that has found behaviourally irrelevant information is being filtered out from M1 (for instance, Russo et al, Neuron 2020 found that in monkeys performing a cycling task, context can be decoded from SMA but not from M1, and Wang et al, Nature Communications 2019 found that perceptual information could not be decoded from PMd)?

How and where sensory information modulating M1 are very interesting and open questions. In the revised manuscript, we discuss these in Lines 435-446, as below: “It would be interesting to explore whether other motor areas also allow sensory modulation during flexible interception. The functional differences between M1 and other areas lead to uncertain speculations. Although M1 has pre-movement activity, it is more related to task variables and motor outputs. Recently, a cycling task sets a good example that the supplementary motor area (SMA) encodes context information and the entire movement (Russo et al., 2020), while M1 preferably relates to cycling velocity (Saxena et al., 2022). The dorsal premotor area (PMd) has been reported to capture potential action selection and task probability, while M1 not (Cisek and Kalaska, 2005; Glaser et al., 2018; Wang et al., 2019). If the neural dynamics of other frontal motor areas are revealed, we might be able to tell whether the orbital neural geometry of mixed selectivity is unique in M1, or it is just inherited from upstream areas like PMd. Either outcome would provide us some insights into understanding the interaction between M1 and other frontal motor areas in motor planning.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

At times the writing was a little hard to parse. It could benefit from being fleshed out a bit to link sentences together better.

There are a few grammatical errors, such as:

"These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact each other, so the PD-shift nodes should not be neglected."

should be

"These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact WITH each other, so the PDshift nodes should not be neglected."

The discussion could also be more extensive to benefit non-experts in the field.

Thank you. We have proofread and polished the updated manuscript.

Reviewer #2 (Recommendations For The Authors):

Other comments:

- The authors mention mixed selectivity a few times, but Table 1 doesn't have a column for mixed selective neurons--this seems like an important oversight. Likewise, it would be good to see an example of a "mixed" neuron.

- The structure of the writing in the results section often talked about the supplementary results before the main results - this seems backwards. If the supplementary results are important enough to come before the main figures, then they should not be supplementary. Otherwise, if the results are truly supplementary, they should come after the main results are discussed.

- Line 305: Authors say "most" RNN units could be classified, and this is technically true, but only barely, according to Table 1. It might be good to put the actual percentage here in the text.

- Figure 5a: typo ("Motion intention" rather than "Motor")

- I couldn't find any mention of code or data availability in the manuscript.

- There were a number of lines that didn't make much sense to me and should probably be rewritten or expanded on:

- Lines 167-168: "These results qualitatively imply the interaction as that target speeds..." - Lines 178-179: "However, these neural trajectories were not yet the ideal description, because they were shaped mostly by time."

- Lines 187-188: "...suggesting that target motion affects M1 neural dynamics via a topologically invariant transformation."

- Lines 224-226: "Note that here we performed an linear transformation on all resulting neural state points to make the ellipse of the static condition orthogonal to the z-axis for better visualization." Does this mean that the z-axis is not PC 3 anymore?

- Lines 272-274: "These simulations suggest that the existence of PD-shift and additive modulation would not disrupt the neural geometry that is primarily driven by gain modulation; rather it is possible that these three modulations support each other in a mixed population."

Thank you for these detailed suggestions. By “mixed selectivity”, we mean the joint tuning of both target-motion and movement. In this case, the target-motion modulated neurons (regardless of the modulation type) are of mixed selectivity. The term “motor intention” refers to Mazzoni et al., 1996, Journal of Neurophysiology. We also revised the manuscript for better readership.

We have updated the data and code availability in Data availability as below:

“The example experimental datasets and relevant analysis code have been deposited in Mendeley Data at https://data.mendeley.com/datasets/8gngr6tphf. The RNN relevant code and example model datasets are available at https://github.com/yunchenyc/RNN_ringlike_structure.“

Reviewer #3 (Recommendations For The Authors):

Minor typos:

Line 153: “there were”

Line 301: “network was trained to generate”

Line 318: “interact with each other”

Suggested reformulations :

Line 310 : “tilting angles followed a pattern similar to that seen in the data” Line 187 : the claim of a “topologically invariant transformation” seems strong as the analysis is quite qualitative.

Suggested changes to the paper (aside from those mentioned in the main review): It could be nice to show behaviour in a main figure panel early on in the paper. This could help with the task description (as it would directly show how the trials are separated based on endpoint) and could allow for discussing the potential caveats of the assumption that behaviour is preserved.

Thank you. We have corrected these typos and writing problems. As the similar task design has been reported, we finally decided not to provide extra figures or videos. Still, we thank this nice suggestion.

Welcome back. In this video, I want to cover the Health Check feature within Route 53. Health checks support many of the advanced architectures of Route 53, and so it's essential that you understand how they work as an architect, developer, or engineer. So let's jump in and get started.

First, let's quickly step through some high-level concepts of Health checks. Health checks are separate from but are used by records inside Route 53. You don't create the checks within records. Health checks exist separately. You configure them separately. They evaluate something's health, and they can be used by records within Route 53.

Health checks are performed by a fleet of health checkers, which are distributed globally. This means that if you're checking the health of systems which are hosted on the public internet, then you need to allow these checks to occur from the health checkers. If you think they're bots or exploit attempts and block them, then it will cause false alarms. Health checks, as I just indicated, are not just limited to AWS targets. You can check anything which is accessible over the public internet. It just needs an IP address.

The checks occur every 30 seconds by default, or this can be increased to every 10 seconds at an additional cost. The checks can be TCP checks, where Route 53 tries to establish a TCP connection with the endpoint, and this needs to be successful within 10 seconds. You can have HTTP checks, where Route 53 must be able to establish a TCP connection with the endpoint within 4 seconds, and in addition, the endpoint must respond with an HTTP status code in the 200 range or 300 range within 2 seconds after connecting. And this is more accurate for web applications than a simple TCP check.

And finally, with HTTP and HTTPS checks, you can also perform string matching. Route 53 must be able to establish a TCP connection with the endpoint within 4 seconds, and the endpoint must respond with an HTTP status code in the 200 or 300 range within 2 seconds, and Route 53 health checker, when it receives the status code, it must also receive the response body from the endpoint within the next 2 seconds. Route 53 searches the response body for the string that you specify. The string must appear entirely in the first 5,120 bytes of the response body, or the endpoint fails the health check. This is the most accurate because not only do you check that the application is responding using HTTP or HTTPS, but you can also check the content of that response versus what the application should do in normal circumstances.

Based on these health checks, an endpoint is either healthy or unhealthy. It moves between those states based on its health, based on the checks conducted. Now lastly, the checks themselves can be one of 3 types. You can have endpoint checks, and these are checks which assess the health of an actual endpoint that you specify. You can use CloudWatch alarm checks, which react to CloudWatch alarms that can be configured separately and can involve some detailed in OS or in-app tests if you use the CloudWatch agent, which we cover elsewhere in the course. Finally, checks can be what's known as calculated checks, so checks of other checks. So you can create health checks which check application-wide health with lots of individual components.

Now, you're going to get the opportunity to actually implement a health check in a demo lesson, which is coming up very shortly in this section of the course. But what I want to do before that is to just give you an overview of exactly how the console looks when you're creating a health check. So let's move across to the console.

Okay, so we're at the AWS console, logged in to the general account in the Northern Virginia region. So to create a health check, we need to move to the Route 53 console, so I'm going to go ahead and do that. Remember how earlier in the theory component of this lesson, I mentioned how health checks are created externally from records? So rather than going into a hosted zone, selecting a record, and configuring a health check there, to create a health check, we go to the menu on the left and click on health checks. Then we'll click on create health check, and this is where we enter the information required to create the health check.

First, we need to give it a name, so let's just say that we use the example of test health check. I mentioned that there are three different types of health checks. We've got an endpoint health check, and this checks the health of the particular endpoint. We can use the status of other health checks, so this is a calculated health check, and as I mentioned, this allows you to create a health check which monitors the application as a whole and involves the health status of individual application components, and then finally we can use the status of a CloudWatch alarm to form the basis of this health check.

If we select endpoint for now, then you're able to pick either IP address or domain name. So you can specify the domain name of an application endpoint or you can use an IP address. If you pick domain name, then what this configures is that all of the Route 53 health checkers will resolve this domain name first and then perform a health check on the resulting IP address.

Now, in either case, you've got the option of either picking TCP, which does a simple TCP check, in which case you need to specify either the IPv4 or IPv6 address together with a port number. If you choose to use the more extensive HTTP or HTTPS health check, then you're asked to specify the same IP address and port number, so that will be used to establish the TCP connection. You can also specify the host name, and if you specify that, it will pass this value to the endpoint as a host header. So if you've got lots of different virtual hosts configured, then this is how you can specify a particular host that the website should deliver.

You're also able, because this is HTTP, you can specify a path to use for this health check. You can either specify the route path or you can specify a particular path to check. If you change this to HTTPS, then all of this information is the same, only this time it will use secure HTTP rather than normal HTTP.

Now, if we scroll down and expand advanced configuration, it's here where you can select the request interval, so the default is every 30 seconds, or you can specify fast and have the checks occur every 10 seconds. Now, this is a check every 10 seconds from every health checker involved within this health check, so the actual frequency of the health checks occurring on the endpoint will be much more frequent. This is one check every 10 seconds from every health checker.

You can specify the failure threshold, so this is the number of consecutive health checks that an endpoint must pass or fail for Route 53 to change the current status. So if you want to allocate a buffer and allow for the opportunity of the odd fail check not to influence the health state, then you can specify a suitable value in this box. It's here where you can specify a simple check, so HTTP or HTTPS, or you can elect to use string matching to do more rich checks of application health. So if you know that your application should deliver a certain string in the request body, then you can specify that here.

Now, you can also configure a number of advanced options. One of them is the latency graph, so you can show the latency of the checks against this endpoint. You can invert the health check status, so if the health check of an application is unhealthy, you can invert it to healthy and vice versa. So this is a fairly situational option that I haven't found much use for.

You also have the option of disabling the health check. This might be useful if you're performing temporary maintenance on an application, and if you check this box, then even if the application endpoint reports as unhealthy, it's considered healthy. You also get the option of specifying the health checker regions. You can use the recommended suggestion, and the health checkers will come from these locations, or you can select customize and pick the particular regions that you want to use. In most cases, you would use the recommended options.

Now, if we just go ahead and enter some sample values here, so I'm going to use 1.1.1.1. I'm going to leave the host name blank, I'm going to set the port number to 80, and then I'll scroll down and just enter a search string. Again, we're not going to create this or just enter a placeholder, click on next, and it's here where you can configure what happens when the health check fails.

Now, this is completely optional. We can use health checks within resource records only; we don't have to configure any notification, but if we do want to configure a notification, then we can create an alarm, and we can send this to either an existing or new SNS topic, and this is a method of how we can integrate this with other systems so we can have other AWS services configured to respond to notifications on this topic or we could integrate external systems so that when a health check fails, external action is taken. But this is what I wanted to show you. I just wanted to give you an overview of how it looks creating a health check within the console UI.

Now, don't worry, you're actually going to be doing this in a demo lesson, which is coming up elsewhere in this section, but I wanted to give you that initial exposure to how the console looks when creating a health check. At this point, let's go ahead and finish up the theory component of this lesson by returning to the architecture. Now you've seen how a health check is created architecturally, health checks look something like this: let's assume that somewhere near the UK we have an application Catergram, and we point a Route 53 record at this application, so let's assume that this is Catergram.io. What we can do is to associate a health check with this resource record, and doing so means that our application will be health checked by a globally distributed set of health checkers. So each of these health checkers performs a periodic check of our application, and based on this check, they report the resource as healthy or unhealthy.

If more than 18 percent of the health checkers report as healthy, then the health check overall is healthy, otherwise it's reported as unhealthy, and in most cases, records which are unhealthy are not returned in response to queries. Now, you're going to see throughout this section of the course and the wider course itself how health checks can be used to influence how DNS responds to queries and how applications can react to component failure. So Route 53 is an essential design and operational tool that you can use to influence how resolution requests occur and how they're routed through to your various different application components, and so understanding health checks is essential to be able to design Route 53 infrastructure, integrate this with your applications, and then manage it day to day as an operational engineer. So it's really important that you understand this topic end to end, no matter which stream of the AWS certifications that you're currently studying for.

Now, that's everything that I wanted to cover in this video. Go ahead and complete the video, and when you're ready, I'll look forward to you joining me in the next.

synthèse analyse les principaux thèmes et idées clés issus des sources fournies, qui abordent divers aspects de la situation des jeunes en France (15-25 ans), notamment en matière de sport, de santé mentale, d'addictions, de citoyenneté, d'impôts et de justice pénale.

1. Activité physique et sportive

Manque de données et de ciblage: Il n'existe pas d'étude nationale ou locale sur l'occupation effective et les profils des utilisateurs des installations sportives extérieures, en particulier pour les 15-25 ans.

Ce manque de données limite la capacité des acteurs publics à évaluer la performance de leurs dépenses et à cibler efficacement les jeunes.

Difficulté d'association des jeunes: Les communes rencontrent des difficultés pour impliquer les jeunes en amont des projets sportifs.

Les besoins sont souvent définis par les associations, qui ne représentent qu'une partie de cette tranche d'âge.

Inaptitudes en EPS: Le volume des inaptitudes en éducation physique et sportive pourrait révéler une dégradation de l'état de santé global des élèves ou traduire des freins socio-culturels ou religieux. Il n'existe pas de suivi académique ou national des absences en EPS.

Un arrêté de 1989 prévoit l'adaptation de la pratique en cas d'inaptitude partielle, soulignant la nécessité d'un suivi statistique et pédagogique, ainsi qu'une sensibilisation des médecins.

Hétérogénéité des politiques sportives: Malgré des efforts récents, les politiques sportives peinent à cibler efficacement des publics hétérogènes.

Structure du tissu associatif sportif: Une majorité des clubs sportifs sont de proximité (< 100 licenciés) et représentent une part significative des licenciés (66%), mais leur poids économique est plus faible (31%) comparé aux clubs intermédiaires et élite.

2. Santé mentale et Maisons des Adolescents (MDA)

Rôle essentiel des MDA: Les MDA jouent un rôle d'écoute, d'évaluation et d'orientation pour les jeunes en difficulté. L'accompagnement individuel permet de les diriger vers les solutions les plus adaptées.

Des témoignages soulignent l'impact positif des MDA : "Depuis la première fois que je suis venue ici, tout a changé et en bien, les personnes qui m’ont suivie m’ont beaucoup aidée et montré les démarches à suivre pour mon cas." (une femme de 18 ans).

Pluridisciplinarité et orientation sanitaire: Les MDA regroupent des professionnels de différentes disciplines (médecins, psychologues, infirmiers, éducateurs spécialisés), ce qui leur donne une orientation principalement sanitaire, complétée par une dimension socio-éducative.

Principaux sujets évoqués par les jeunes en MDA: Le mal-être, l'estime de soi, l'anxiété sont les sujets les plus fréquemment abordés (72%), suivis des relations familiales (21%) et de la scolarité (10%).

Manque de connaissance des MDA par les jeunes: Une part importante des jeunes ne sait pas ce qu'est une MDA : "À la question « savez-vous ce qu’est une MDA ? », 37 % « non pas du tout »."

Accessibilité géographique inégale: L'implantation des MDA dans les grandes villes garantit l'accès à de nombreux jeunes, mais elles sont moins accessibles en zones rurales.

Des initiatives "d'aller-vers" et des équipes mobiles se développent pour réduire les zones non couvertes.

"Il faudrait qu’il y ait des MDA dans plus de villes car pas facile de faire 30 minutes de route quand on habite en campagne et qu’il n’y a pas de bus. Ou un bus itinérant" (parent d’une fille de 13 ans).

Coordination avec d'autres structures: Une meilleure articulation et complémentarité entre les MDA et les

Points d'Accueil et d'Écoute Jeunes (PAEJ) sont nécessaires pour une meilleure lisibilité pour le public et les partenaires.

Financements en hausse mais hétérogènes: Les MDA sont principalement financées par les Agences Régionales de Santé (ARS), les départements et, marginalement, le bloc communal et les régions.

Malgré une augmentation des financements suite aux Assises de la santé mentale et de la psychiatrie en 2021, l'hétérogénéité des modalités de pilotage et la souplesse du cahier des charges ne garantissent pas une harmonisation des ressources ni une offre proportionnée aux besoins des territoires.

Nécessité d'évaluer l'impact des MDA: L'ANMDA est favorable à une évaluation scientifique de l'impact des MDA pour valoriser leurs résultats et identifier des axes d'amélioration.

Augmentation des troubles psychiques: La pandémie et des facteurs à plus long terme (anxiété liée aux écrans, écoanxiété, violences) ont entraîné une augmentation des troubles psychiques chez les jeunes, avec une hausse notable des prescriptions de psychotropes, surtout chez les jeunes filles.

Renforcement du dispositif "Mon soutien psy":

L'assurance maladie contribue au renforcement du dispositif "Mon soutien psy" en augmentant le nombre de séances prises en charge et en facilitant l'accès direct aux psychologues.

3. Addictions chez les jeunes

Consommation en baisse chez les mineurs, préoccupante chez les majeurs: Une baisse de la consommation d'alcool et de cannabis est observée chez les mineurs depuis 2010, mais la consommation d'alcool et de drogues illicites chez les 15-24 ans en France reste supérieure à la moyenne de l'Union européenne.

Estimation du nombre de jeunes souffrant d'addictions: Plusieurs méthodes d'estimation donnent des chiffres variables, soulignant la complexité de cette évaluation.

L'OFDT estime qu'un pourcentage significatif des jeunes de 17 ans présente un risque élevé d'usage problématique ou de dépendance au cannabis.

Risques liés à la consommation de cannabis: L'usage précoce et régulier de cannabis est un facteur de risque de troubles psychiques et socio-comportementaux.

Sous-dimensionnement des dispositifs spécifiques:

Les Consultations Jeunes Consommateurs (CJC), dispositif spécifique aux jeunes, semblent sous-dimensionnées malgré leur utilité et pertinence reconnues par les ARS. "Enfin le seul dispositif spécifique aux jeunes - celui des « consultations jeunes consommateurs » (CJC), aujourd’hui au nombre de 260 (réparties en 540 points d’accueil) - paraît sous-dimensionné." Une évaluation nationale des CJC est nécessaire pour envisager leur développement.

Manque de coordination et de données financières: Le financement des activités hospitalières liées aux addictions chez les jeunes manque de lisibilité, et il est difficile de calculer les coûts d'hospitalisation spécifiques.

Il manque également un état des lieux national agrégé de l'offre médico-sociale destinée aux jeunes en état de dépendance.

Stratégie interministérielle sans objectifs chiffrés clairs:

La stratégie interministérielle de mobilisation contre les conduites addictives manque d'objectifs de santé publique clairs, notamment en termes de diminution de la consommation d'alcool et de drogues chez les jeunes.

Importance de la prévention et de l'orientation: Les structures existantes devraient davantage jouer leur rôle de prévention et d'orientation, avec le soutien des ARS.

Exemples internationaux de prévention: Les exemples du Danemark (programme MOVE) montrent l'efficacité d'une mobilisation de tous les acteurs autour d'un programme de prévention ambitieux.

Débats autour de la légalisation du cannabis: La légalisation ou la dépénalisation du cannabis dans d'autres pays a souvent entraîné une hausse de sa consommation, avec des conséquences potentielles pour la lutte contre les addictions.

4. Journée Défense et Citoyenneté (JDC)

Évolution du contexte et des enjeux: Le contexte international actuel et la montée en puissance du Service National Universel (SNU) renouvellent les enjeux de la JDC, qui mérite d'être redéfinie. "Or, les contextes sociaux, nationaux et internationaux ont évolué depuis l'instauration de cette journée... ont renouvelé les enjeux de la JDC, qui mérite donc d'être redéfinie."

Objectifs multiples et tensions: La JDC est à la fois un temps fort obligatoire du parcours citoyen, un outil de maintien du lien entre l'armée et la jeunesse, un moyen de rappeler le devoir de défense, et potentiellement un outil de recrutement pour les armées.

La volonté du ministère des armées de "remilitariser" la JDC en l'orientant davantage vers le recrutement et le recensement des compétences s'écarte potentiellement de l'équilibre initial entre Défense et Citoyenneté.

Recrutement : objectif de plus en plus assumé: Bien que le recrutement ne soit pas un objectif explicite du code du service national pour la JDC, les armées le considèrent indispensable pour atteindre leurs objectifs d'engagement. Une étude a établi une corrélation entre la participation à la JDC et le recrutement dans l'armée de terre.

Adaptation et expérimentation de la JDC: Des évolutions sont en cours, avec une "JDC adaptée" et un projet de "JDC Nouvelle Génération" qui pourraient transformer profondément le contenu et les objectifs du dispositif.

JDC en ligne pour les Français de l'étranger: La mise en place d'une organisation et la mobilisation des moyens nécessaires au déploiement de la JDC en ligne pour les jeunes Français résidant à l'étranger est une recommandation.

Bilan mitigé du test d'illettrisme: Le test d'illettrisme réalisé lors de la JDC ne répond pas pleinement aux objectifs du ministère des armées et empiète sur le temps disponible pour les autres contenus.

Sa suppression pourrait être envisagée sous réserve de modifications législatives.

5. Les jeunes et l'impôt

Entrée progressive dans l'impôt: L'entrée des jeunes dans l'impôt est marquée par des disparités de revenus et dépend de plusieurs facteurs (études, charge de famille, âge).

Le rattachement au foyer fiscal des parents ou l'imposition distincte constituent une spécificité française.

Dispositifs atténuant l'impôt: Plusieurs dispositifs (exonérations, déductions, crédits d'impôt) atténuent l'impôt dû par les jeunes et leurs familles, notamment en lien avec les études, l'apprentissage, les stages et certaines formes de volontariat.

Accès aux informations fiscales: La Direction Générale des Finances Publiques (DGFiP) facilite l'accès des jeunes à leur espace particulier sur impots.gouv.fr, notamment par l'envoi d'un courrier dès l'âge de 20 ans.

Imposition des jeunes mariés: Des règles spécifiques s'appliquent à l'imposition des jeunes mariés ou pacsés qui peuvent être rattachés au foyer fiscal de l'un ou l'autre de leurs parents sous forme d'abattement.

6. Les jeunes et la justice pénale

Rupture de la réponse pénale à la majorité: La réponse pénale face aux jeunes de 15 à 25 ans n'est pas homogène, marquée par une rupture lors du passage à la majorité. La justice des mineurs privilégie l'éducatif et l'individualisation, tandis que les jeunes majeurs relèvent du droit commun avec des peines de prison plus fréquentes. "Face aux jeunes de 15 à 25 ans, la réponse pénale n’est pas homogène... la réponse marque une rupture, les « jeunes majeurs » basculant dans le droit commun des procédures et des conditions d’exécution des peines."

Évolution de la délinquance des mineurs: Si le nombre de mineurs condamnés pour crimes a diminué, la direction de la protection judiciaire de la jeunesse fait état d'une tendance à la hausse du nombre et des durées d'incarcération pour des faits d'une extrême violence.

Atténuation de la responsabilité pénale des mineurs: Le code de la justice pénale des mineurs prévoit l'atténuation de la responsabilité des mineurs en fonction de leur âge et de leur discernement.

Alternatives aux poursuites pour les mineurs: La réponse pénale privilégie davantage les alternatives aux poursuites pour les mineurs que pour les jeunes majeurs.

Stabilité du taux de récidive: Le taux de jeunes de 15 à 25 ans condamnés en état de récidive ou de réitération légales reste stable autour de 45%, soulignant que la politique à l'égard des jeunes délinquants n'atteint pas pleinement ses objectifs de prévention de la récidive.

Difficultés d'insertion des jeunes sortant de prison: Des études mettent en lumière les difficultés de santé mentale et d'insertion rencontrées par les jeunes sortant de prison.

Nécessité de renforcer l'accompagnement et la coordination: L'action des services du ministère de la justice doit être articulée avec celle des autres acteurs (éducation, collectivités territoriales, aide sociale à l'enfance) et l'accompagnement des parents doit être renforcé. "Le principe de responsabilité parentale est inscrit dans le droit positif... C’est d’ailleurs en vertu de ce principe que l’aide sociale à l’enfance et la protection judiciaire de la jeunesse interviennent dans le milieu familial pour conforter, en premier lieu, le rôle des parents."

Conseil des droits et devoirs des familles (CDDF): Ce dispositif de prévention de la délinquance, visant à impliquer les parents, a vu son instauration obligatoire dans les grandes communes supprimée en 2019.

7. Éducation Artistique et Culturelle (EAC)

Importance de l'accès à l'EAC: L'éducation artistique et culturelle est reconnue comme essentielle pour le développement des jeunes.

Périmètre de l'EAC: Le périmètre de l'EAC s'étend au-delà des arts et lettres pour inclure la culture scientifique, le numérique et les médias.

Organisation territoriale complexe: La gouvernance territoriale de l'EAC, basée sur des comités de pilotage régionaux et locaux, est mise en œuvre de manière diverse selon les territoires.

8. Préoccupations des jeunes

Inégalités sociales et environnement: Les préoccupations majeures des jeunes portent sur les inégalités sociales et les questions environnementales.

Enjeux de sécurité: Les enjeux de sécurité, de liberté, de propriété et de résistance à l'oppression restent importants.

En conclusion, ces sources mettent en évidence la complexité des enjeux liés à la jeunesse en France.

Des efforts sont déployés dans de nombreux domaines, mais des défis persistent en termes de connaissance des publics, de ciblage des politiques, de coordination des acteurs, d'évaluation des dispositifs et d'adaptation aux évolutions sociétales.

Une approche globale et concertée est nécessaire pour répondre efficacement aux besoins et aux aspirations des jeunes.

Briefing Document : Analyse des enjeux et dispositifs relatifs à la jeunesse en France

Date : 2024-11-07

Source : Excerpts from "https://www.ccomptes.fr/sites/default/files/2025-03/20250319-RPA2025-volume1_1.pdf" (Cour des Comptes, Rapport sur la politique en faveur de la jeunesse, mars 2025, Volume 1)

Objet : Synthèse des principaux thèmes, idées et faits marquants concernant la politique en faveur de la jeunesse en France, tels qu'analysés par la Cour des Comptes.

Public Cible : Toute personne intéressée par les politiques publiques relatives à la jeunesse (décideurs politiques, administrations, acteurs du secteur, chercheurs, etc.).

Principaux Thèmes et Idées Clés :

Le rapport de la Cour des Comptes met en lumière plusieurs aspects cruciaux de la politique en faveur de la jeunesse, allant du financement des dispositifs à l'accès aux droits, en passant par l'emploi, la formation, le logement et la mobilité.

1. Complexité et Manque de Coordination des Dispositifs :

Le paysage des dispositifs dédiés à la jeunesse est caractérisé par sa complexité et un manque de coordination.

De nombreuses aides financières et dispositifs existent, souvent liés à l'âge et parfois au statut (étudiant, apprenti, demandeur d'emploi).

Leur origine est diverse (État, régions, départements, communes, organismes divers), et leurs conditions d'obtention varient (âge, ressources, statut, domiciliation).

Citation : "Il est difficile de dresser un tableau complet de ces aides, qui peuvent être présentées selon leur objet (logement, transports, sport, culture, prêt, aides, bourses, etc.), selon l’origine de l’aide (...), ou leur matérialité (...). Ces différentes catégories peuvent s’exclure mutuellement ou être cumulables, être soumises à des conditions (...) et varier sur les limites d’âge."

Le rapport souligne un manque de coordination et de paramétrage des moyens déployés, ainsi qu'un pilotage davantage axé sur l'offre (cibles en volume) que sur les besoins et les résultats. La clarté et le partage des critères d'orientation des jeunes entre les acteurs sont également insuffisants.

Citation : "Au-delà d’une refonte de la stratégie d’ensemble, les moyens déployés doivent être mieux coordonnés et paramétrés. La détermination des cibles nationales assignées à chaque dispositif ne repose pas sur une évaluation rigoureuse des besoins. Les règles de répartition territoriale des moyens ne garantissent pas une allocation optimale des ressources. Le pilotage par l’offre, fondé sur des cibles en volume pour chaque dispositif, doit céder la place à un pilotage par les besoins et les résultats."

2. Définition Juridique de la Jeunesse et Droits :

Le droit ne reconnaît pas une notion unique de "jeunesse", établissant une distinction radicale entre mineurs et majeurs (18 ans).

Cependant, il existe une progressivité des compétences juridiques et sociales pour les mineurs dès 12 ans, créant une forme de "pré-majorité" avec des droits acquis selon l'âge, l'accord des parents ou les circonstances (soins médicaux, contraception, porter plainte, etc.).

Citation : "Un premier constat s’impose : le droit ne connaît pas la notion de jeunesse. Il établit un partage radical entre mineurs et majeurs et concourt ainsi à définir deux grandes catégories juridiques de « jeunes » selon qu’ils ont atteint, ou non, 18 ans."

3. Financement des Politiques Jeunesse :

Les crédits budgétaires spécifiquement dédiés à la jeunesse restent marginaux et peu évolutifs, incitant les universités à se tourner vers des financements ponctuels via des appels à projets (Plan d'Investissement d'Avenir, France 2030).

La pérennisation du financement des projets à moyen terme est une préoccupation.

Le Document de Politique Transversale (DPT) "Politique en faveur de la jeunesse" présente les axes stratégiques et les crédits de l'État, mais il n'existe pas d'équivalent au niveau des collectivités territoriales et de la sécurité sociale, ce qui limite la vision globale des financements.

Les dépenses de l'État en faveur des 15-25 ans concernent majoritairement l'éducation (enseignement scolaire et supérieur) et l'accompagnement des mutations industrielles (apprentissage). Des crédits importants sont également alloués via la mission Cohésion des territoires (aides personnelles au logement).

Citation : "Cependant, les crédits budgétaires en question restent marginaux et peu évolutifs, ce qui a incité les universités à s’orienter vers les appels à projets du plan d’investissement d’avenir et du plan France 2030. Ceux-ci ont été déterminants pour mettre en place sur le terrain des projets structurants.

Cependant, ces abondements extra-budgétaires ne sont attribués que pour une période donnée et les universités doivent désormais anticiper la façon dont elles financeront à moyen terme les projets en cours ou ceux qui restent à lancer."

Citation : "Les crédits de l'État destinés aux 15-25 ans concernent massivement l’éducation, du lycée au supérieur."

4. Inégalités Territoriales :

La situation de la jeunesse varie considérablement d'un territoire à l'autre. L'indice de jeunesse révèle des "îlots de jeunesse" principalement autour des grandes métropoles.

Les territoires ruraux sont confrontés à des défis spécifiques en matière d'accès à l'enseignement supérieur et à l'emploi.

Citation : "Ces tendances nationales sont variables d’un territoire à l’autre. À l’échelle départementale, en 2023, les 15-29 ans représentaient moins de 15 % de la population dans 30 départements, et plus de 20 % pour dix d’entre eux."

5. L'Obligation de Formation pour les 16-18 Ans :

Le rapport analyse la mise en œuvre de l'obligation de formation pour les jeunes de 16 à 18 ans.

Si des solutions sont proposées majoritairement via les dispositifs d'insertion professionnelle de droit commun (Pacea, CEJ), des écarts importants existent dans le repérage des jeunes concernés.

La coordination avec les départements, qui ont un rôle dans les politiques sociales, n'est pas toujours systématique.

Citation : "Contrairement à ce que suggère son intitulé, celle-ci consiste au moins autant à accompagner qu’à former les jeunes concernés."

6. Réussite dans l'Enseignement Supérieur :

Malgré les dispositifs d'accompagnement (plan "Réussir en licence", loi ORE), le taux de diplomation en licence en trois ans reste inférieur à 50%.

La performance des formations en termes de réussite étudiante est progressivement intégrée dans l'allocation des ressources aux universités via les contrats d'objectifs, de moyens et de performance (COMP).

Le rapport plaide pour une meilleure visibilité des dispositifs de prévention de l'échec et un suivi statistique consolidé.

Citation : "Globalement, depuis la mise en place du plan « Réussir en licence », en 2007, et l’entrée en vigueur de la loi du 8 mars 2018 relative à l’orientation et à la réussite (ORE), la part des étudiants ayant obtenu un diplôme de licence en trois ans a progressé de 5,8 points entre la cohorte 2013 et la cohorte 2019."

7. Accès des Jeunes Ruraux à l'Enseignement Supérieur :

L'accès est limité par une offre de formation de proximité restreinte et des freins socio-économiques (revenus plus faibles, éloignement géographique).

Les dispositifs d'aide aux étudiants ne prennent que faiblement en compte le critère d'éloignement.

Le rapport recommande de simplifier la gestion des aides et de revoir les modalités d'attribution pour mieux intégrer l'éloignement.

Citation : "L’accès des jeunes issus des territoires ruraux à l’enseignement supérieur est un enjeu majeur de cohésion sociale et territoriale.

Or, cette problématique est aujourd’hui faiblement prise en compte par les politiques publiques."

8. Insertion Professionnelle et Dispositifs d'Accompagnement :

Le rapport analyse l'impact des différents dispositifs d'insertion (Pacea, CEJ, AIJ).

Il souligne les biais potentiels liés aux cibles en volume fixées nationalement, qui peuvent inciter les opérateurs à orienter les jeunes vers certains dispositifs pour atteindre leurs objectifs de financement, plutôt qu'en fonction des besoins réels.

Le pilotage par l'offre est critiqué au profit d'un pilotage par les besoins et les résultats.

Citation : "La montée en charge du CEJ s’est par exemple opérée au détriment de l’AIJ (France Travail) et du Pacea (missions locales), dispositifs préexistants et de moindre intensité : il est possible qu’une partie des jeunes orientés vers le CEJ l’aient été moins parce qu’ils avaient réellement besoin d’un accompagnement intensif que parce que les prescripteurs étaient soucieux d’atteindre leurs objectifs."

9. Logement des Jeunes :

La politique du logement étudiant s'est longtemps concentrée sur les résidences universitaires.

La garantie Visale est un dispositif important pour faciliter l'accès au logement des jeunes, mais elle rencontre parfois des réticences de la part des bailleurs.

Des dispositifs expérimentaux visent à accompagner globalement les jeunes précaires, où le logement est un outil de stabilisation. Une meilleure coordination locale et la mobilisation du foncier universitaire sont encouragées.

Citation : "Depuis le début des années 2010, le logement étudiant à vocation sociale fait l’objet d’objectifs de production à travers des plans gouvernementaux..."

10. Mobilité des Jeunes et Transports Collectifs :

Le droit à la mobilité, bien que général, a des implications pour les jeunes. Les politiques tarifaires des autorités organisatrices de mobilité (AOM) pourraient être mieux ciblées.

Des initiatives temporaires comme les Pass Jeunes et le Pass Rail ont montré leur succès.

Le développement de l'offre de transports collectifs vers les zones périurbaines et rurales est un enjeu majeur pour l'égalité d'accès.

Citation : "Le droit aux transports pour tous est inscrit dans la loi depuis 1982.

Il a été transformé en un droit à la mobilité en 2019 qui ne concerne pas spécifiquement les jeunes, sauf pour les transports scolaires."

11. Jeunes Majeurs Sortant de l'Aide Sociale à l'Enfance (ASE) :

La loi du 7 février 2022 a renforcé le droit à l'accompagnement pour les jeunes majeurs sortant de l'ASE, mais des disparités persistent dans les niveaux de prise en charge selon les départements.

La notion de "contrat jeune majeur" est variable.

L'accès au droit commun (insertion, logement) pour ces jeunes doit être renforcé.

Le pilotage des dispositifs et le suivi de l'atteinte des objectifs sont souvent insuffisants.

Citation : "Les jeunes majeurs issus de l’aide sociale à l’enfance (ASE), et plus largement tout jeune de 18 à 21 ans sans « ressources ou soutien familial suffisants », sont pris en charge à leur demande par les départements en application de l’article L. 222-5 du code de l’action sociale et des familles."

Conclusion :

Le rapport de la Cour des Comptes dresse un tableau complexe de la politique en faveur de la jeunesse en France.

Il met en évidence la nécessité d'une meilleure coordination et d'un pilotage axé sur les besoins et les résultats, d'une prise en compte accrue des inégalités territoriales, et d'un renforcement de l'accès aux droits pour tous les jeunes, en particulier ceux en situation de vulnérabilité.

La simplification et la clarification des dispositifs, ainsi qu'une vision globale du financement, apparaissent comme des leviers essentiels pour améliorer l'efficacité et l'équité des politiques publiques dédiées à la jeunesse.

Le Comité d'éducation à la santé, à la citoyenneté et à l'environnement (CESCE) est l'instance qui permet de mettre en œuvre la politique de l'établissement en matière d'éducation et de prévention dans ces trois domaines.

En tant qu'instance de réflexion, d'observation et de veille, le CESCE conçoit, met en œuvre et évalue les actions de prévention et d'éducation à la citoyenneté, à la santé et aux questions d'environnement en lien avec le projet d'établissement.

Cette démarche globale et fédératrice favorise la cohérence et la lisibilité de la politique éducative et encourage les partenariats.

Le CESCE est réglementairement présidé par le chef d'établissement, qui peut déléguer cette présidence.

Il est réuni régulièrement et ses membres évaluent les projets et le bilan annuel des actions, produisant un compte-rendu intégré au bilan et rapport annuel de l'EPLE.

Pour nourrir les quatre parcours éducatifs, le CESCE peut exploiter ses prérogatives et ses domaines d'intervention de la manière suivante :

• Parcours Avenir : Ce parcours vise à permettre à chaque élève de la 6e à la terminale d'acquérir les premières clés de compréhension du monde professionnel pour construire son projet d'orientation scolaire et professionnelle, notamment en facilitant les échanges entre l’École et le monde économique.

Le CESCE peut contribuer à ce parcours en organisant des actions de sensibilisation aux métiers liés à la santé et à l'environnement.

Il peut initier des partenariats avec des professionnels de ces secteurs (par exemple, des professionnels de santé locaux, des entreprises engagées dans le développement durable) pour des interventions en milieu scolaire ou des visites d'entreprises.

Les actions de prévention des conduites à risque (addictions, etc.) abordées par le CESCE peuvent également éclairer les élèves sur les conséquences de certains choix sur leur avenir professionnel.

• Parcours d'éducation artistique et culturelle (PEAC) :

Le PEAC met en cohérence la formation des élèves en s'appuyant sur les enseignements artistiques, les rencontres avec les artistes et les œuvres, et les pratiques artistiques.

Bien que le CESCE ne soit pas directement centré sur l'art, il peut intégrer une dimension artistique et culturelle dans ses actions de sensibilisation à la santé et à l'environnement.

Par exemple, des projets de création artistique (affiches, vidéos, pièces de théâtre) peuvent être réalisés par les élèves sur des thèmes de santé ou d'environnement.

Le CESCE peut également collaborer avec des acteurs culturels locaux pour des interventions ou des événements liant la santé, la citoyenneté ou l'environnement à l'expression artistique.

• Parcours éducatif de santé : Ce parcours regroupe la protection de la santé des élèves, la prévention des conduites à risque et l'éducation à la santé.

C'est le domaine d'action le plusDirect du CESCE, comme son nom l'indique.

Le CESCE est l'instance de coordination de ce parcours au sein de l'établissement. Il conçoit, met en œuvre et évalue les actions de prévention concernant les conduites addictives, l'alimentation, l'activité physique, l'éducation à la sexualité, la prévention des violences, etc..

Le CESCE peut s'appuyer sur des textes officiels comme le Code de la santé publique et mobiliser des partenaires tels que l'agence régionale de santé (ARS) pour mener à bien ses actions.

L'utilisation du Logiciel infirmier de l'Éducation Nationale (LIEN) pour le suivi de la santé mentale des élèves peut également alimenter les réflexions et les actions du CESCE.

• Parcours citoyen de l'élève : Ce parcours vise à former des citoyens conscients de leurs droits, devoirs et responsabilités, en s'appuyant sur l'enseignement moral et civique, l'éducation aux médias et à l'information, et en abordant des thèmes comme la laïcité, l'égalité, la lutte contre les discriminations, l'éducation à l'environnement et au développement durable.

Le CESCE contribue pleinement à ce parcours par son rôle dans l'éducation à la citoyenneté et à l'environnement.

Les actions de prévention des discriminations, la promotion de l'égalité (notamment filles-garçons, en lien avec les chargés de mission académiques à l'égalité), l'éducation au développement durable (en lien avec le label E3D et l'ADEME) et la prévention de la radicalisation sont des aspects que le CESCE peut intégrer dans ses actions.

La participation des élèves à la vie de l'établissement, encouragée par le CESCE, est également un élément clé du parcours citoyen.

En mobilisant les ressources propres de l'EPLE et en sollicitant des subventions auprès de divers partenaires (collectivités, préfecture, MILDECA, ADEME, ARS, etc.), le CESCE dispose de moyens pour mettre en œuvre des actions concrètes qui nourrissent et enrichissent les quatre parcours éducatifs, contribuant ainsi à une éducation globale et cohérente pour les élèves.

I remember hearing about 4Chan for the first time and how I saw it as an unfiltered early version of Reddit. However, I didn't know about 8Kun, and how it was supposed to be a "less restrictive" public board, even though this "less restrictive" action is the brewing stage for conspiracy theories and other harmful content.

This section highlights how 4Chan became a hub of internet creativity and a breeding ground for harmful subcultures. It serves as an example of the fine line that separates encouraging edgy humor from toxic behavior.

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Welcome back and in this lesson I'm going to be covering a topic which is probably slightly beyond what you need for the Solutions Architect Associate exam, but additional understanding of CloudFormation is never a bad thing and it will help you answer any automation style questions in the exam, and so I'm going to talk about it anyway. CloudFormation in it is a way that you can pass complex bootstrapping instructions into an EC2 instance, and it's much more complex than the simple user data example that you saw in the previous lesson.

Now we do have a lot to cover, so let's jump in and step through the theory before we move to another demo lesson. In the previous lesson I showed you how CloudFormation handled user data, and it works in a similar way to the console UI where you pass in base 64 encoded data into the instance operating system and it runs as a shell script. Now there's another way to configure EC2 instances, a way which is much more powerful.

It's called cfn-init, and it's officially referred to by AWS as a helper script which is installed on EC2 operating systems such as Amazon Linux 2. Now cfn-init is actually much more than a simple helper script — it's much more like a simple configuration management system. User data is what's known as procedural; it's a script, it's run by the operating system line by line. Now cfn-init can also be procedural — it can be used to run commands just like user data — but it can also be desired state, where you direct it how you want something to be.

What's the desired state of an EC2 instance, and it will perform whatever is required to move the instance into that desired state. So for example, you can tell cfn-init that you want a certain version of the Apache web server to be installed, and if that's already the case — if Apache is already installed and it's the same version — then nothing is done. However, if Apache is not installed then cfn-init will install it, or it will update any older versions to that version.

cfn-init can do lots of pretty powerful things — it can make sure packages are installed even with an awareness of versions, it can manipulate operating system groups and users, it can download sources and extract them onto the local instance even using authentication, it can create files with certain contents, permissions, and ownerships, it can run commands and test that certain conditions are true after the commands have run, and it can even control services on an instance — so in ensuring that a particular service is started or enabled to be started on the boot of the OS.

cfn-init is executed like any other command by being passed into the instance as part of the user data, and it retrieves its directives from the CloudFormation stack, and you define this data in a special part of each logical resource inside CloudFormation templates called aws double colon cloud formation double colon init — and don't worry, you'll get a chance to see this very soon in the demo.

So the instance runs cfn-init, it pulls this desired state data from the CloudFormation stack that you put in there via the CloudFormation template, and then it implements the desired state that's specified by you in that data. So let's quickly look at this architecture visually — the way that cfn-init works is probably going to be easier to understand if we do take a look at it visually, and once you see the individual components it's a lot simpler than I've made it sound on the previous screen.

It all starts off with a CloudFormation template, and this one creates an EC2 instance — and you'll see this in action yourself very soon. Now the template has a logical resource inside it called EC2 instance, which is to create an EC2 instance, and it has this new special component: metadata and aws double colon cloud formation double colon init — and this is where the cfn init configuration is stored. The cfn init command itself is executed from the user data that's passed into that instance.

So the CloudFormation template is used to create a stack which itself creates an EC2 instance, and the cfn-init line in the user data at the bottom here is executed by the instance. This should make sense now — anything in the user data is executed when the instance is first launched. Now if you look at the command for cfn-init, you'll notice that it specifies a few variables — specifically a stack ID and a region.

Remember, this instance is being created using CloudFormation, and so these variables are actually replaced for the actual values before this ends up inside the EC2 instance — so the region will be replaced with the actual region that the stack is created in, and the stack ID is the actual stack ID that's being created by this template, and these are all passed in to cfn-init. This allows cfn-init to communicate with the CloudFormation service and receive its configuration, and it can do that because of those variables passed into the user data by CloudFormation.

Once cfn-init has this configuration, then because it's a desired state system, it can implement the desired state that's specified inside the CloudFormation by you. And another amazing thing about this process, or about cfn-init and its associated tools, is that it can also work with stack updates.

Remember that the user data works once, while cfn-init can be configured to watch for updates to the metadata on an object in a template, and if that metadata changes, then cfn-init can be executed again and it will update the configuration of that instance to the desired state specified inside the template — it's really powerful. Now this is not something that user data can do — user data only works the once when you launch the instance.

Now in the demo lesson which immediately follows this one, you're going to experience just how cool this cfn-init process is. The WordPress CloudFormation template that you used in the previous demo — which included some user data — I've updated that and I've supplied a new version which uses this CloudFormation in its process or cfn-init, so you'll get to see how it's different and exactly how that looks when you apply it into your AWS account.

Now there's one more really important feature of CloudFormation which I want to cover — as you start performing more advanced bootstrapping, it will start to matter more and more. This feature is called CloudFormation creation policies and CloudFormation signals — so let's look at that next.

On the previous example there was another line passed into the user data — the bottom line: cfn-signal. Without this, the resource creation process inside CloudFormation is actually pretty dumb. You have a template which is used to create a stack which creates an EC2 instance — let's say you pass in some user data, this runs, and then the instance is marked as complete.

The problem though is we don't actually know if the resource actually completed successfully — CloudFormation has created the resource and passed in the user data, but I've already said that CloudFormation doesn't understand the user data, it just passes it in. So if the user data has a problem — if the instance bootstrapping process fails and from a customer perspective the instance doesn't really work — CloudFormation won't know. The instance is going to be marked as complete regardless of how the configuration is inside that instance.

Now this is fine when we're creating resources like a blank EC2 instance when there is no post-launch configuration — if EC2 reports to CloudFormation that it's successfully provisioned an instance then we can rely on that; if we're creating an S3 bucket and S3 reports to CloudFormation that it's worked okay then it's worked okay. But what if there's extra configuration happening inside the resource such as this bootstrapping process?

We need a better way — a way that the resource itself, the EC2 instance in this case, can inform CloudFormation if it's being configured correctly or not. This is how creation policies work, and this is a creation policy. A creation policy is something which is added to a logical resource inside a CloudFormation template — you create it and you supply a timeout value.

This one has 15 minutes, and this is used to create a stack which creates an instance. So far the process is the same — but at this point CloudFormation waits. It doesn't move the instance into a create complete status when EC2 signals that it's been created successfully — instead it waits for a signal, a signal from the resource itself.

So even though EC2 has launched the instance, even though its status checks pass and it's told CloudFormation that the instance is provisioned and ready to go — CloudFormation waits. It waits for a signal from the resource itself. The CFN signal command at the bottom is given the stack ID, the resource name, and the region — and these are passed in by the CloudFormation stack when the resource is created.

So the CFN signal command understands how to communicate with the specific CloudFormation stack that it's running inside. The -e $? part of that command represents the state of the previous command — so in this case the CFN init command is going to perform this desired state configuration, and if the output of that command is an OK state, then the OK is sent as a signal by CFN signal.

If CFN init reports an error code, then this is sent using CFN signal to the CloudFormation stack — so CFN signal is reporting to CloudFormation the success or not of the CFN init bootstrapping, and this is reported to the CloudFormation stack. If it's a success code — so if CFN init worked as intended — then the resource is moved into a create complete state; if CFN signal reports an error, the resource in CloudFormation shows an error.

If nothing happens for 15 minutes — the timeout value — then CloudFormation assumes it's erred and doesn't let the stack create successfully — the resource will generate an error. Now you'll see creation policies feature in more complex CloudFormation templates either within EC2 instance resources or within auto scaling groups that we'll be covering later in the course.

Now you won't need to know the technical implementation details of this for the Solutions Architect Associate exam, but I do expect the knowledge of this architecture will help you in any automation related questions. And now it's time for a quick demonstration — I just want you to have some experience in using a template which uses CFN init and also one which uses the creation policy, so I hope this theory has been useful to you and when you're ready for the demo, go ahead and complete this video and you can join me in the next.

Welcome back. In this lesson, I want to cover a little bit more theory, something which you'll need to understand from now on in the course because the topics that we'll discuss and the examples that we'll use to fully understand those topics will become ever more complex. Horizontal and vertical scaling are two different ways that a system can scale to handle increasing or, in some cases, decreasing load placed on that system, so let's quickly step through the difference and look at some of the pros, cons, and requirements of each. Scaling is what happens when systems need to grow or shrink in response to increases or decreases of load placed upon them by your customers. From a technical perspective, you're adding or removing resources to a system. A system can in some cases be a single compute device such as an EC2 instance, but in some cases could be hundreds, thousands, tens of thousands, or even hundreds of thousands, or more of individual devices.

Vertical scaling is one way of achieving this increase in capacity, so this increase of resource allocation. The way it works is simple. Let's say, for example, we have an application and it's running on an EC2 instance, and let's say that it's a T3.large, which provides two virtual CPUs and eight GiB of memory. The instance will service a certain level of incoming load from our customers, but at some point, assuming the load keeps increasing, the size of this instance will be unable to cope, and the experience for our customers will begin to decrease. Customers might experience delays, unreliability, or even outright system crashes. So the commonly understood solution is to use a bigger server. In the virtual world of EC2, this means resizing the EC2 instance. We have lots of sizes to choose from. We might pick a T3.extralarge, which doubles the virtual CPU and memory, or if the rate of increase is significant, we could go even further and pick another size up, a T3.2 XL, which doubles that again to eight virtual CPUs and 32 GiB of memory.

Let's talk about a few of the finer points though of vertical scaling. When you're actually performing vertical scaling with EC2, you're resizing an EC2 instance, and because of that, there's downtime, often a restart during the resize process, which can potentially cause customer disruption. But it goes beyond this because of this disruption, it means that you can only scale generally during pre-agreed times, so within outage windows. If incoming load on a system changes rapidly, then this restriction of only being able to scale during outage windows limits how quickly you can react, how quickly you can respond to these changes by scaling up or down. Now as load increases on a system, you can scale up, but larger instances often carry a price premium. So the increasing cost going larger and larger is often not linear towards the top end. And because you're scaling individual instances, there's always going to be an upper cap on performance. And this cap is the maximum instance size. While AWS are always improving EC2, there will always be a maximum possible instance size, and so with vertical scaling, this will always be the cap on the scaling of an individual compute resource.

Now there are benefits of vertical scaling. It's really simple and it doesn't need any application modification. If an application can run on an instance, then it can run on a bigger instance. Vertical scaling works for all applications, even monolithic ones, where the whole code base is one single application because it all runs on one instance and that one instance can increase in size. Horizontal scaling is designed to address some of the issues with vertical scaling, so let's have a look at that next. Horizontal scaling is still designed to cope with changes to incoming load on a system, but instead of increasing the size of an individual instance, horizontal scaling just adds more instances. The original one instance turns into two as load increases, maybe two more added. Eventually, maybe eight instances are required. As the load increases on a system, horizontal scaling just adds additional capacity.

The key thing to understand with horizontal scaling is that with this architecture, instead of one running copy of your application, you might have two or 10 or hundreds of copies, each of them running on smaller compute instances. This means they all need to work together, all need to take their share of incoming load placed on the system by customers, and this generally means some form of load balancer. A load balancer is an appliance which sits between your servers, in this case instances, and your customers. When customers attempt to access the system, all that incoming load is distributed across all of the instances running your application. Each instance gets a fair amount of the load and for a given customer, every mouse click, every interaction with the application, could be on the same instance or randomized across any of the available instances.

Horizontal scaling is great, but there are a few really important things that you need to be aware of as a solutions architect. When you think about horizontal scaling, sessions are everything. When you log into an application, think about YouTube, about Netflix, about your email. The state of your interaction with that application is called a session. You're using this training site right now and if I deleted your session right at this moment, then the next time you interacted with the site, you would be logged out. You might lose the position of the video that you're currently watching. On amazon.com or your home grocery shopping site, the session stores what items are in your cart. With a single application running on a single server, the sessions of all customers are generally stored on that server. With horizontal scaling, this won't work. If you're shopping on your home grocery site and you add some cat cookies to your cart, this might be using instance one. When you add your weekly selection of donuts, you might be using instance 10. Without changes, every time you moved between instances for a horizontally scaled application, you would have a different session or no session. You would be logged out, the application, put simply, would be unusable. With horizontal scaling, you can be shifting between instances constantly. That's one of the benefits. It evens out the load. And so horizontal scaling needs either application support or what's known as off-host sessions.

If you use off-host sessions, then your session data is stored in another place, an external database. And this means that the servers are what's called stateless. They're just dumb instances of your application. The application doesn't care which instance you connect to because your session is externally hosted somewhere else. That's really the key consideration with horizontal scaling. It requires thought and design so that your application supports it. But if it does support it, then you get all of the benefits. The first one of those benefits is that you have no disruption while you're scaling because all you're doing is just adding instances. The existing ones aren't being impacted. So customer connections remain unaffected. Even if you're scaling in, so removing instances because sessions should be off-host, so externally hosted, connections can be moved between instances, leaving customers unaffected. So that's a really powerful feature of having externally hosted sessions together with horizontal scaling. It means all of the individual instances are just dumb instances. It doesn't matter to which instance a particular customer connects to at a particular time because the sessions are hosted externally. They'll always have access to their particular state in the application.

And there's no real limits to horizontal scaling because you're using lots of smaller, more common instances. You can just keep adding them. There isn't the single instance size cap which vertical scaling suffers from. Horizontal scaling is also often less expensive. You're using smaller commodity instances, not the larger ones which carry a premium. So it can be significantly cheaper to operate a platform using horizontal scaling. And finally, it can allow you to be more granular in how you scale. With vertical scaling, if you have a large instance and go to an extra-large, which is one step above it, you're pretty much doubling the amount of resources allocated to that system. With horizontal scaling, if you're currently using five small instances and you add one more, then you're scaling by around 20%. The smaller instances that you use, the better granularity that you have with horizontal scaling.

Now, there's a lot more to this. Later in the course, in the high availability and scaling section, I'll introduce elasticity and how we can use horizontal scaling as a component of highly available and fault-tolerant designs. But for now, I'll leave you with a visual exam power-up. Visuals often make things easier to understand, and they help especially with memory recall. So when it comes to remembering the different types of scaling methods, picture this, two types of scaling. First, horizontal scaling, and this adds and removes things. So if we're scaling Bob, one of our regular course guest stars, then scaling Bob in a horizontal way would mean moving to two Bob, which is scary enough. But if the load required it, we might even have to move to four Bob. And if we needed huge amounts of capacity, if four Bob wasn't enough, if you needed more and more and more, and even more Bob, then horizontal scaling has you covered. There isn't really a limit. We can scale Bob infinitely. In this case, we can have so many Bobs. We can scale Bob up to a near-infinite level as long as we're using horizontal scaling. Scaling Bob in a vertical way, that starts off with a small Bob, then moves to a medium Bob, and if we really need more Bob, then we can scale to a large Bob. In the exam, when you're struggling to remember the difference between horizontal scaling and vertical scaling, picture this image. I guarantee with this, you will not forget it. But at this point, that's all of the theory that I wanted to cover. Go ahead, complete the video, and when you're ready, you can join me in the next.

Welcome back and in this first lesson of the EC2 section of the course, I want to cover the basics of virtualization as briefly as possible. EC2 provides virtualization as a service. It's an infrastructure as a service or I/O product. To understand all the value it provides and why some of the features work the way that they do, understanding the fundamentals of virtualization is essential. So that's what this lesson aims to do.

Now, I want to be super clear about one thing. This is an introduction level lesson. There's a lot more to virtualization than I can talk about in this brief lesson. This lesson is just enough to get you started, but I will include a lot of links in the lesson description if you want to learn more. So let's get started.

We do have a fair amount of theory to get through, but I promise when it comes to understanding how EC2 actually works, this lesson will be really beneficial. Virtualization is the process of running more than one operating system on a piece of physical hardware, a server. Before virtualization, the architecture looked something like this. A server had a collection of physical resources, so CPU and memory, network cards and maybe other logical devices such as storage. And on top of this runs a special piece of software known as an operating system.

That operating system runs with a special level of access to the hardware. It runs in privilege mode, or more specifically, a small part of the operating system runs in privilege mode, known as the kernel. The kernel is the only part of the operating system, the only piece of software on the server that's able to directly interact with the hardware. Some of the operating system doesn't need this privilege level of access, but some of it does. Now, the operating system can allow other software to run such as applications, but these run in user mode or unprivileged mode. They cannot directly interact with the hardware, they have to go through the operating system.

So if Bob or Julie are attempting to do something with an application, which needs to use the system hardware, that application needs to go through the operating system. It needs to make a system call. If anything but the operating system attempts to make a privileged call, so tries to interact with the hardware directly, the system will detect it and cause a system-wide error, generally crashing the whole system or at minimum the application. This is how it works without virtualization.

Virtualization is how this is changed into this. A single piece of hardware running multiple operating systems. Each operating system is separate, each runs its own applications. But there's a problem, CPU at least at this point in time, could only have one thing running as privileged. A privileged process member has direct access to the hardware. And all of these operating systems, if they're running in their unmodified state, they expect to be running on their own in a privileged state. They contain privileged instructions. And so trying to run three or four or more different operating systems in this way will cause system crashes.

Virtualization was created as a solution to this problem, allowing multiple different privileged applications to run on the same hardware. But initially, virtualization was really inefficient, because the hardware wasn't aware of it. Virtualization had to be done in software, and it was done in one of two ways. The first type was known as emulated virtualization or software virtualization. With this method, a host operating system still ran on the hardware and included additional capability known as a hypervisor. The software ran in privileged mode, and so it had full access to the hardware on the host server.

Now, around the multiple other operating systems, which we'll now refer to as guest operating systems, were wrapped a container of sorts called a virtual machine. Each virtual machine was an unmodified operating system, such as Windows or Linux, with a virtual allocation of resources such as CPU, memory and local disk space. Virtual machines also had devices mapped into them, such as network cards, graphics cards and other local devices such as storage. The guest operating systems believed these to be real. They had drivers installed, just like physical devices, but they weren't real hardware. They were all emulated, fake information provided by the hypervisor to make the guest operating systems believe that they were real.

The crucial thing to understand about emulator virtualization is that the guest operating systems still believe that they were running on real hardware, and so they still attempt to make privileged calls. They tried to take control of the CPU, they tried to directly read and write to what they think of as their memory and their disk, which are actually not real, they're just areas of physical memory and disk that have been allocated to them by the hypervisor. Without special arrangements, the system would at best crash, and at worst, all of the guests would be overriding each other's memory and disk areas.

So the hypervisor, it performs a process known as binary translation. Any privileged operations which the guests attempt to make, they're intercepted and translated on the fly in software by the hypervisor. Now, the binary translation in software is the key part of this. It means that the guest operating systems need no modification, but it's really, really slow. It can actually halve the speed of the guest operating systems or even worse. Emulated virtualization was a cool set of features for its time, but it never achieved widespread adoption for demanding workloads because of this performance penalty.

But there was another way that virtualization was initially handled, and this is called para-virtualization. With para-virtualization, the guest operating systems are still running in the same virtual machine containers with virtual resources allocated to them, but instead of the slow binary translation which is done by the hypervisor, another approach is used. Para-virtualization only works on a small subset of operating systems, operating systems which can be modified. Because with para-virtualization, there are areas of the guest operating systems which attempt to make privileged calls, and these are modified. They're modified to make them user calls, but instead of directly calling on the hardware, they're calls to the hypervisor called hypercalls.

So areas of the operating systems which would traditionally make privileged calls directly to the hardware, they're actually modified. So the source code of the operating system is modified to call the hypervisor rather than the hardware. So the operating systems now need to be modified specifically for the particular hypervisor that's in use. It's no longer just generic virtualization, the operating systems are modified for the particular vendor performing this para-virtualization. By modifying the operating system this way, and using para-virtual drivers in the operating system for network cards and storage, it means that the operating system became almost virtualization aware, and this massively improved performance. But it was still a set of software processors designed to trick the operating system and/or the hardware into believing that nothing had changed.

The major improvement in virtualization came when the physical hardware started to become virtualization aware. This allows for hardware virtualization, also known as hardware assisted virtualization. With hardware assisted virtualization, hardware itself has become virtualization aware. The CPU contains specific instructions and capabilities so that the hypervisor can directly control and configure this support, so the CPU itself is aware that it's performing virtualization. Essentially, the CPU knows that virtualization exists.

What this means is that when guest operating systems attempt to run any privileged instructions, they're trapped by the CPU, which knows to expect them from these guest operating systems, so the system as a whole doesn't halt. But these instructions can't be executed as is because the guest operating system still thinks that it's running directly on the hardware, and so they're redirected to the hypervisor by the hardware. The hypervisor handles how these are executed. And this means very little performance degradation over running the operating system directly on the hardware.

The problem, though, is while this method does help a lot, what actually matters about a virtual machine tends to be the input/output operation, so network transfer and disk I/O. The virtual machines, they have what they think is physical hardware, for example, a network card. But these cards are just logical devices using a driver, which actually connect back to a single physical piece of hardware which sits in the host. The hardware, everything is running on.

Unless you have a physical network card per virtual machine, there's always going to be some level of software getting in the way, and when you're performing highly transactional activities such as network I/O or disk I/O, this really impacts performance, and it consumes a lot of CPU cycles on the host.

The final iteration that I want to talk about is where the hardware devices themselves become virtualization aware, such as network cards. This process is called S-R-I-O-V, single root I/O virtualization. Now, I could talk about this process for hours about exactly what it does and how it works, because it's a very complex and feature-rich set of standards. But at a very high level, it allows a network card or any other add-on card to present itself, not just one single card, but almost a several mini-cards.

Because this is supported in hardware, these are fully unique cards, as far as the hardware is concerned, and these are directly presented to the guest operating system as real cards dedicated for its use. And this means no translation has to happen by the hypervisor. The guest operating system can directly use its card whenever it wants. Now, the physical card which supports S-R-I-O-V, it handles this process end-to-end. It makes sure that when the guest operating system is used, there are logical mini-network cards that they have physical access to the physical network connection when required.

In EC2, this feature is called enhanced networking, and it means that the network performance is massively improved. It means faster speeds. It means lower latency. And more importantly, it means consistent lower latency, even at high loads. It means less CPU usage for the host CPU, even when all of the guest operating systems are consuming high amounts of consistent I/O.

Many of the features that you'll see EC2 using are actually based on AWS implementing some of the more advanced virtualization techniques that have been developed across the industry. AWS do have their own hypervisor stack now called Nitro, and I'll be talking about that in much more detail in an upcoming lesson, because that's what enables a lot of the higher-end EC2 features.

But that's all the theory I wanted to cover. I just wanted to introduce virtualization at a high level and get you to the point where you understand what S-R-I-O-V is, because S-R-I-O-V is used for enhanced networking right now, but it's also a feature that can be used outside of just network cards. It can help hardware manufacturers design cards, which, whilst they're a physical single card, can be split up into logical cards that can be presented to guest operating systems. It essentially makes any hardware virtualization aware, and any of the advanced EC2 features that you'll come across within this course will be taking advantage of S-R-I-O-V.

At this point, though, we've completed all of the theory I wanted to cover, so go ahead, complete the slicing when you're ready. You can join me in the next.

Reviewer #3 (Public Review):

Summary:

This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

Strengths:

The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

Original Weaknesses:

I have three major concerns:

(1) Low N / no single subject results/statistics: The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

(2) Separation between V1-V3 and dorsolateral electrodes: Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

(3) Alpha pRFs are larger than broadband pRFs: first, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility. Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

[Editors' note: the authors have addressed the original concerns.]

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary

In this study, the authors build upon previous research that utilized non-invasive EEG and MEG by analyzing intracranial human ECoG data with high spatial resolution. They employed a receptive field mapping task to infer the retinotopic organization of the human visual system. The results present compelling evidence that the spatial distribution of human alpha oscillations is highly specific and functionally relevant, as it provides information about the position of a stimulus within the visual field.

Using state-of-the-art modeling approaches, the authors not only strengthen the existing evidence for the spatial specificity of the human dominant rhythm but also provide new quantification of its functional utility, specifically in terms of the size of the receptive field relative to the one estimated based on broad band activity.

We thank the reviewer for their positive summary.

Weakness 1.1

The present manuscript currently omits the complementary view that the retinotopic map of the visual system might be related to eye movement control. Previous research in non-human primates using microelectrode stimulation has clearly shown that neuronal circuits in the visual system possess motor properties (e.g. Schiller and Styker 1972, Schiller and Tehovnik 2001). More recent work utilizing Utah arrays, receptive field mapping, and electrical stimulation further supports this perspective, demonstrating that the retinotopic map functions as a motor map. In other words, neurons within a specific area responding to a particular stimulus location also trigger eye movements towards that location when electrically stimulated (e.g. Chen et al. 2020).

Similarly, recent studies in humans have established a link between the retinotopic variation of human alpha oscillations and eye movements (e.g., Quax et al. 2019, Popov et al. 2021, Celli et al. 2022, Liu et al. 2023, Popov et al. 2023). Therefore, it would be valuable to discuss and acknowledge this complementary perspective on the functional relevance of the presented evidence in the discussion section.

The reviewer notes that we do not discuss the oculomotor system and alpha oscillations. We agree that the literature relating eye movements and alpha oscillations are relevant.

At the Reviewer’s suggestion, we added a paragraph on this topic to the first section of the Discussion (section 3.1, “Other studies have proposed … “).

Reviewer #2 (Public Review):

Summary:

In this work, Yuasa et al. aimed to study the spatial resolution of modulations in alpha frequency oscillations (~10Hz) within the human occipital lobe. Specifically, the authors examined the receptive field (RF) tuning properties of alpha oscillations, using retinotopic mapping and invasive electroencephalogram (iEEG) recordings. The authors employ established approaches for population RF mapping, together with a careful approach to isolating and dissociating overlapping, but distinct, activities in the frequency domain. Whereby, the authors dissociate genuine changes in alpha oscillation amplitude from other superimposed changes occurring over a broadband range of the power spectrum. Together, the authors used this approach to test how spatially tuned estimated RFs were when based on alpha range activity, vs. broadband activities (focused on 70-180Hz). Consistent with a large body of work, the authors report clear evidence of spatially precise RFs based on changes in alpha range activity. However, the size of these RFs were far larger than those reliably estimated using broadband range activity at the same recording site. Overall, the work reflects a rigorous approach to a previously examined question, for which improved characterization leads to improved consistency in findings and some advance of prior work.

We thank the reviewer for the summary.

Strengths:

Overall, the authors take a careful and well-motivated approach to data analyses. The authors successfully test a clear question with a rigorous approach and provide strong supportive findings. Firstly, well-established methods are used for modeling population RFs. Secondly, the authors employ contemporary methods for dissociating unique changes in alpha power from superimposed and concomitant broadband frequency range changes. This is an important confound in estimating changes in alpha power not employed in prior studies. The authors show this approach produces more consistent and robust findings than standard band-filtering approaches. As noted below, this approach may also account for more subtle differences when compared to prior work studying similar effects.

We thank the reviewer for the positive comments.

Weaknesses:

Weakness 2.1 Theoretical framing:

The authors frame their study as testing between two alternative views on the organization, and putative functions, of occipital alpha oscillations: i) alpha oscillation amplitude reflects broad shifts in arousal state, with large spatial coherence and uniformity across cortex; ii) alpha oscillation amplitude reflects more specific perceptual processes and can be modulated at local spatial scales. However, in the introduction this framing seems mostly focused on comparing some of the first observations of alpha with more contemporary observations. Therefore, I read their introduction to more reflect the progress in studying alpha oscillations from Berger's initial observations to the present. I am not aware of a modern alternative in the literature that posits alpha to lack spatially specific modulations. I also note this framing isn't particularly returned to in the discussion.

This was helpful feedback. We have rewritten nearly the entire Introduction to frame the study differently. The emphasis is now on the fact that several intracranial studies of spatial tuning of alpha (in both human and macaque) tend to show increases in alpha due to visual stimulation, in contrast to a century of MEG/EEG studies, from Berger to the present, showing decreases. We believe that the discrepancy is due to an interaction between measurement type and brain signals. Specifically, intracranial measurements sum decreases in alpha oscillations and increases in broadband power on the same trials, and both signals can be large. In contrast, extracranial measures are less sensitive to the broadband signals and mostly just measure the alpha oscillation. Our study reconciles this discrepancy by removing the baseline broadband power increases, thereby isolating the alpha oscillation, and showing that with iEEG spatial analyses, the alpha oscillation decreases with visual stimulation, consistent with EEG and MEG results.

Weakness 2.2 A second important variable here is the spatial scale of measurement.

It follows that EEG based studies will capture changes in alpha activity up to the limits of spatial resolution of the method (i.e. limited in ability to map RFs). This methodological distinction isn't as clearly mentioned in the introduction, but is part of the author's motivation. Finally, as noted below, there are several studies in the literature specifically addressing the authors question, but they are not discussed in the introduction.

The new Introduction now explicitly contrasts EEG/MEG with intracranial studies and refers to the studies below.

Weakness 2.3 Prior studies:

There are important findings in the literature preceding the author's work that are not sufficiently highlighted or cited. In general terms, the spatio-temporal properties of the EEG/iEEG spectrum are well known (i.e. that changes in high frequency activity are more focal than changes in lower frequencies). Therefore, the observations of spatially larger RFs for alpha activities is highly predicted. Specifically, prior work has examined the impact of using different frequency ranges to estimate RF properties, for example ECoG studies in the macaque by Takura et al. NeuroImage (2016) [PubMed: 26363347], as well as prior ECoG work by the author's team of collaborators (Harvey et al., NeuroImage (2013) [PubMed: 23085107]), as well as more recent findings from other groups (Luo et al., (2022) BioRxiv: https://doi.org/10.1101/2022.08.28.505627). Also, a related literature exists for invasively examining RF mapping in the time-voltage domain, which provides some insight into the author's findings (as this signal will be dominated by low-frequency effects). The authors should provide a more modern framing of our current understanding of the spatial organization of the EEG/iEEG spectrum, including prior studies examining these properties within the context of visual cortex and RF mapping. Finally, I do note that the author's approach to these questions do reflect an important test of prior findings, via an improved approach to RF characterization and iEEG frequency isolation, which suggests some important differences with prior work.

Thank you for these references and suggestions. Some of the references were already included, and the others have been added.

There is one issue where we disagree with the Reviewer, namely that “the observations of spatially larger RFs for alpha activities is highly predicted”. We agree that alpha oscillations and other low frequency rhythms tend to be less focal than high frequency responses, but there are also low frequency non-rhythmic signals, and these can be spatially focal. We show this by demonstrating that pRFs solved using low frequency responses outside the alpha band (both below and above the alpha frequency) are small, similar to high frequency broadband pRFs, but differing from the large pRFs associated with alpha oscillations. Hence we believe the degree to which signals are focal is more related to the degree of rhythmicity than to the temporal frequency per se. While some of these results were already in the supplement, we now address the issue more directly in the main text in a new section called, “2.5 The difference in pRF size is not due to a difference in temporal frequency.”

We incorporated additional references into the Introduction, added a new section on low frequency broadband responses to the Results (section 2.5), and expanded the Discussion (section 3.2) to address these new references.

Weakness 2.4 Statistical testing:

The authors employ many important controls in their processing of data. However, for many results there is only a qualitative description or summary metric. It appears very little statistical testing was performed to establish reported differences. Related to this point, the iEEG data is highly nested, with multiple electrodes (observations) coming from each subject, how was this nesting addressed to avoid bias?

We reviewed the primary claims made in the manuscript and for each claim, we specify the supporting analyses and, where appropriate, how we address the issue of nesting. Although some of these analyses were already in the manuscript, many of them are new, including all of the analyses concerning nesting. We believe that putting this information in one place will be useful to the reader, and we now include this text as a new section in supplement, Graphical and statistical support for primary claims.

Reviewer #2 (Recommendations For The Authors):

Recommendation 2.1:

Data presentation: In several places, the authors discuss important features of cortical responses as measured with iEEG that need to be carefully considered. This is totally appropriate and a strength of the author's work, however, I feel the reader would benefit from more depiction of the time-domain responses, to help better understand the authors frequency domain approach. For example, Figure 1 would benefit from showing some form of voltage trace (ERP) and spectrogram, not just the power spectra. In addition, part (a) of Figure 1 could convey some basic information about the timing of the experimental paradigm.

We changed panel A of Figure 1 to include the timing of the experimental paradigm, and we added panels C and D to show the electrode time series before and after regression out of the ERP.

Recommendation 2.2

Update introduction to include references to prior EEG/iEEG work on spatial distribution across frequency spectrum, and importantly, prior work mapping RFs with different frequencies.

We have addressed this issue and re-written our introduction. Please refer to our response in Public Review for further details.

Recommendation 2.3

Figure 3 has several panels and should be labeled to make it easier to follow.The dashed line in lower power spectra isn't defined in a legend and is missing from the upper panel - please clarify.

We updated Figure 3 and reordered the panels to clarify how we computed the summary metrics in broadband and alpha for each stimulus location (i.e., the “ratio” values plotted in panel B). We also simplified the plot of the alpha power spectrum. It now shows a dashed line representing a baseline-corrected response to the mapping stimulus, which is defined in the legend and explained in the caption.

Recommendation 2.4

Power spectra are always shown without error shading, but they are mean estimates.

We added error shading to Figures 1, 2 and 3.

Recommendation 2.5

The authors deal with voltage transients in response to visual stimulation, by subtracting out the trail averaged mean (commonly performed). However, the efficacy of this approach depends on signal quality and so some form of depiction for this processing step is needed.

We added a depiction of the processing steps for regressing out the averaged responses in Figure 1 in an example electrode (panels C and D). We also show in the supplement the effect of regressing out the ERP on all the electrode pRFs. We have added Supplementary Figure 1-2.

Recommendation 2.6

I have a similar request for the authors latency correction of their data, where they identified a timing error and re-aligned the data without ground truth. Again, this is appropriate, but some depiction of the success of this correction is very critical for confirming the integrity of the data.

We now report more detail on the latency correction, and also point out that any small error in the estimate would not affect our conclusions (4.6 ECoG data analysis | Data epoching). The correction was important for a prior paper on temporal dynamics (Groen et al, 2022), which used data from the same participants and estimated the latency of responses. In this paper, our analyses are in the spectral domain (and discard phase), so small temporal shifts are not critical. We now also link to the public code associated with that paper, which implemented the adjustment and quantified the uncertainty in the latency adjustment.

More details on latency adjustment provided in section 4.6.

Recommendation 2.7

In many places the authors report their data shows a 'summary' value, please clarify if this means averaging or summation over a range.

For both broadband and alpha, we derive one summary value (a scalar) for trial for each stimulus. For broadband, the summary metric is the ratio of power during a given trial and power during blanks, where power in a trial is the geometric mean of the power at each frequency within the defined band). This is equation 3 in the methods, which is now referred to the first time that summary metrics are mentioned in the results.  For alpha, the summary metric is the height of the Gaussian from our model-based approach. This is in equations 1 and 2, and is also now referred to the first time summary metrics are mentioned in the results.

We added explanation of the summary metrics in the figure captions and results where they are first used, and also referred to the equations in the methods where they are defined.

Recommendation 2.8

The authors conclude: "we have discovered that spectral power changes in the alpha range reflect both suppression of alpha oscillations and elevation of broadband power." It might not have been the intention, but 'discovered' seems overstated.

We agree and changed this sentence.

Recommendation 2.9

Supp Fig 9 is a great effort by the authors to convey their findings to the reader, it should be a main figure.

We are glad you found Supplementary Figure 9 valuable. We moved this figure to the main text.

Reviewer #3 (Public Review):

Summary:

This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

We thank the reviewer for the summary.

Strengths:

The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

We thank the reviewer for the positive comments.

Weaknesses:

I have three major concerns:

Weakness 3.1. Low N / no single subject results/statistics:

The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

We have added substantial changes to deal with subject specific effects, including new results and new figures.

• Figure 4 now shows variance explained by the alpha pRF broken down by each participant for electrodes in V1 to V3. We also now show a similar figure for dorsolateral electrodes in Supplementary Figure 4-2.

• Figure 5, which shows results from individual electrodes in V1 to V3, now includes color coding of electrodes by participant to make it clear how the electrodes group with participant. Similarly, for dorsolateral electrodes, we show electrodes grouped by participant in Supplementary Figure 5-1. Same for Supplementary Figure 6-2.

• Supplementary Figure 7-2 now shows the benefits of our model-based approach for estimating alpha broken down by individual participants.

• We also now include a new section in the supplement that summarizes for every major claim, what the supporting data are and how we addressed the issue of nesting electrodes by participant, section Graphical and statistical support for primary claims.

Weakness 3.2. Separation between V1-V3 and dorsolateral electrodes:

Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

Electrode assignment was probabilistic to reflect uncertainty in the mapping between location and retinotopic map. The probabilistic assignment is handled in two ways.

(1) For visualizing results of single electrodes, we simply go with the maximum probability, so no electrode is visualized for both groups of data. For example, Figure 5a (V1-V3) and supplementary Figure 5-1a (dorsolateral electrodes) have no electrodes in common: no electrode is in both plots.

(2) For quantitative summaries, we sample the electrodes probabilistically (for example Figures 4, 5c). So, if for example, an electrode has a 20% chance of being in V1 to V3, and 30% chance of being in dorsolateral maps, and a 50% chance of being in neither, the data from that electrode is used in only 20% of V1-V3 calculations and 30% of dorsolateral calculations. In 50% of calculations, it is not used at all. This process ensures that an electrode with uncertain assignment makes no more contribution to the results than an electrode with certain assignment. An electrode with a low probability of being in, say, V1-V3, makes little contribution to any reported results about V1-V3. This procedure is essentially a weighted mean, which the reviewer suggests in the recommendations. Thus, we believe there is not a problem of “double counting”.

The alternative would have been to use maximum probability for all calculations. However, we think that doing so would be misleading, since it would not take into account uncertainty of assignment, and would thus overstate differences in results between the maps.

We now clarify in the Results that for probabilistic calculations, the contribution of an electrode is limited by the likelihood of assignment (Section 2.3). We also now explain in the methods why we think probabilistic sampling is important.

Weakness 3.3. Alpha pRFs are larger than broadband pRFs:

First, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility.

We addressed this question in a new paragraph in Discussion section 3.1 (“What is the function of the large alpha pRFs?”, paragraph beginning… “Another possible interpretation is that the poorer model fit in the alpha pRF is due to lower signal-to-noise”). This paragraph both refers to prior work on the relationship between noise and pRF size and to our own control analyses (Supplementary Figure 5-2).

Weakness 3.4 Statistics

Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

We agree that it is important for the reader to be able to link specific results and analyses to specific claims. We are not convinced that null hypothesis statistical testing is always the best approach. This is a topic of active debate in the scientific community.

We added a new section that concisely states each major claim and explicitly annotates the supporting evidence. (Section 4.7). Please also refer to our responses to Reviewer #2 regarding statistical testing (Reviewer weakness 2.4 “Statistical testing”)

Weakness 3.5 Summary

While I judge these issues as crucial, I can also appreciate the considerable effort and thoughtfulness that went into this study. I think that addressing these concerns will substantially raise the confidence of the readership in the study's findings, which are potentially important and interesting.

We again thank the reviewer for the positive comments.

Reviewer #3 (Recommendations For The Authors):

Suggestions for how to address the three major concerns:

Suggestion 3.1.

I am very well aware that it's very hard to have n=30 in a visual cortex ECOG study. That's fine. Best practice would be to have a linear mixed effects model with patients as a random effect. However, for some figures with just 3-4 patients (Figure 4,5) the sample size might be too small even for that. At the very minimum, I would expect to show in figures/describe in text all results per patient (perhaps one can do statistics within each patient, and show for each patient that the effect is significant). Even in primate studies with just two subjects it is expected to show that the results replicate for subject A and B. It is necessary to show that your results don't depend on a single unrepresentative subject. And if they do, at least be transparent about it.

We have addressed this thoroughly. Please see response to Weakness 3.1 (“Low N / no single subject results/statistics”).

Suggestion 3.2.

I just don't get it. I would simply assign an electrode to V1-V3 or dorsolateral cortex based on which area has the highest probability. It doesn't make sense to me that an electrode that has 60% of being in dorsolateral cortex and only 10% to be in V1-V3 would be assigned as both V1-V3 and dorsolateral. Also, what's the rationale to include such electrode in the analysis for let's say V1-V3 (we have weak evidence to believe it's there)? I would either assign electrodes based on the highest probability, or alternatively do a weighted mean based on the probability of each electrode belonging to each region group (e.g. electrode with 40% to be in V1-V3, will get twice the weight as an electrode who has 20% to be in V1-V3) but this is more complicated.

We have addressed this issue. Please refer to our response in Public Review (“Weakness 3.2 Separation between V1-V3 and dorsolateral”) for details.

Suggestion 3.3.

First, to exclude the possibility that alpha pRF are larger simply because they have a worse fit to the neural data, I would show if there is a correlation between the goodnessof-fit and pRF size (for alpha and broadband signals, separately). No [negative] correlation between goodness-of-fit and pRF size would be a good sign. I would also compare alpha & broadband receptive field size when controlling for the goodness-of-fit (selecting electrodes with similar goodness-of-fit for both signals). If the results replicate this way it would be convincing.

Second, there are no statistical tests in section 2.4, possibly also in others. Even if you employ bootstrap / Monte-Carlo resampling methods you can extract a p-value.

We have addressed this issue. Please refer to our response in Public Review Point 3.3 (“Alpha pRFs are larger than broadband pRFs”) for further details.

Suggestion 3.4.

Also, I don't understand the resampling procedure described in lines 652-660: "17.7 electrodes were assigned to V1-V3, 23.2 to dorsolateral, and 53 to either " - but 17.7 + 23.2 doesn't add up to 53. It also seems as if you assign visual areas differently in this resampling procedure than in the real data - "and randomly assigned each electrode to a visual area according to the Wang full probability distributions". If you assign in your actual data 27 electrodes to both visual areas, the same should be done in the resampling procedure (I would expect exactly 35 V1-V3 and 45 dorsolateral electrodes in every resampling, just the pRFs will be shuffled across electrodes).

We apologize for the confusion.

We fixed the sentence above, clarified the caption to Table 2, and also explained the overall strategy of probabilistic resampling better. See response to Public Review point 3.2 for details.

Suggestion 3.5.

These are rather technical comments but I believe they are crucial points to address in order to support your claims. I genuinely think your results are potentially interesting and important but these issues need to be first addressed in a revision. I also think your study may carry implications beyond just the visual domain, as alpha suppression is observed for different sensory modalities and cortical regions. Might be useful to discuss this in the discussion section.

Agree. We added a paragraph on this point to the Discussion (very end of 3.2).

Is your analysis code available (as a github repo or otherwise)?

Reviewer #1 (Public review):

This work presents data from three species (mice, rats, and humans) performing an evidence accumulation task, that has been designed to be as similar as possible between species (and is based on a solid foundation of previous work on decision-making). The tasks are well-designed, and the analyses are solid and clearly presented - showing that there are differences in the overall parameters of the decision-making process between the species. This is valuable to neuroscientists who aim to translate behavioral and neuroscientific findings from rodents to humans and offers a word of caution for the field in readily claiming that behavioral strategies and computations are representative of all mammals. The dataset would be of great interest to the community and may be a source of further modelling of across-species behavior, but unfortunately, neither data or code are currently shared.

A few other questions remain, that make the conclusions of the paper a bit hard to assess:

(1) The main weakness is that the authors claim that all species rely on evidence accumulation as a strategy, but this is not tested against other models (see e.g. Stine et al. https://elifesciences.org/articles/55365): the fact that the DDM fits rather well does not mean that this is the strategy that each species was carrying out.

(2) In all main analyses, it is unclear what the effect is of the generative flash rate and how this has been calibrated between species. Only in Figure 6C do we see basic psychometric functions, but these should presumably also feature as a crucial variable dominating the accuracy and RTs (chronometric functions) across species. The very easy trials are useful to constrain the basic sensorimotor differences that may account for RT variability, e.g. perhaps the small body of mice requires them to move a relatively longer distance to trigger the response.

(3) The GLM-HMM results (that mice are not engaged in all trials) are very important, but they imply that mouse DDM fits may well be more similar to rats and humans if done only on engaged trials. Could it be that the main species differences are driven by different engagement state occupations?

(4) It would be very helpful if the authors could present a comprehensive overview (perhaps a table) of the factors that may be relevant for explaining the observed species differences. This may include contextual/experimental variables (age range (adolescent humans vs. mice/rats, see https://www.jax.org/news-and-insights/jax-blog/2017/november/when-are-mice-considered-old; reward source, etc) and also outcomes (e.g. training time required to learn the task, # trials per session and in total).

This is a good example of how the NPPES taxonomy code is being relied on in the public.

Reviewer #1 (Public review):

Summary:

The authors investigate the role of different specific dopaminergic neurons in the mushroom body of Drosophila larvae for learning and innate behavior. All the tested neurons are thought to be involved in punishment learning. The authors discover that artificial activation of single DANs in training leads to safety learning, but not punishment learning. Furthermore, activation of single DANs can lead to changes in locomotion behavior, which can affect light preference. The authors provide a deeper understanding of the functional diversity of single dopamine neurons; however, it is unclear how translatable these findings are to learning experiments with real punishment stimuli.

Strengths:

The authors attempt to disentangle what kind of memories are formed with the activation of different dopamine neurons - safety learning, and punishment learning, will the US be required to test for recall or not? They do indeed find differences and the results will be of interest to the learning and memory community.

Interestingly, optogenetic activation of a single DAN during training leads to safety memory, but not punishment memory. Furthermore, DAN activation also affects innate locomotion, and the authors can show that optogenetic activation of different DANs affects locomotion differently.

Weaknesses:<br /> All experiments in the manuscript use optogenetic activation of DANs, thus it is not clear what kind of memories are formed. Several stimuli can be used as punishment, such as electric shock, salt, bitter, and light - it is not clear what kind of memory the authors investigate here. The findings could be discussed in the context of what DANs respond to. Furthermore, studies in adults and larvae showed that most DANs can code for both valences - etc., aversive DANs can be activated by punishment, and inhibited by reward. Thus, safety learning might be a result of a decrease in activity in DANs during odor presentation. The authors also do not discuss possible feedback loops from MBONs to DANs across compartments. Could such connections allow for safety learning in larvae?

The authors show that artificial activation with different light intensities can form different memories and that increasing the light intensity sometimes leads to no memories. Also, using different optogenetic tools reveals different results. This again raises the question of how applicable the results will be for learning with real stimuli. Is there a natural stimulus that only induces safety learning, but no punishment learning?<br /> The authors provide a detailed behavioral analysis of locomotion behavior; however, the detailed analysis seems unnecessary for that dataset. Modulation of speed and bending rate has been described before with simpler methods (specifically for MBONs). The revealed locomotion phenotypes probably affect larval locomotion during memory recall with light activation, thus the authors should show that larvae are potentially able to move during light-on memory tests.

Overview & Motivation

Repeatedly failed to write a post, realizing it should be a talk:

“It turns out that I wasn’t really writing a post; I was actually preparing a talk.”

Central Topic: React Server Components and distributed computations between two machines using React concepts.

“It’s about everyone’s favorite topic, React Server Components.”

Act 1: Recipes (Imperative) vs. Blueprints (Declarative)

Tags vs. Function Calls:

Visual and structural differences:

“< and > are hard and spiky and ( and ) are soft and round.”

Similarities:

Both reference named operations (functions or tags) and accept arguments.

Both allow nesting.

“Clearly, function calls and tags are very similar...they let us elaborate by nesting further.”

Differences:

Tags (declarative):

Often nouns; represent timeless structures (blueprints).

Convenient for deep nesting, clearly marking structure.

Time-independent, passive descriptions.

“Tags tend to be nouns rather than verbs... nouns are easier to decompose.”

Function calls (imperative):

Often verbs; represent sequential actions (recipes).

Execution order critical.

“A recipe prescribes a sequence of steps to be performed in order.”

Remote Procedure Calls (RPC) and Async/Await

Problem: Calling Functions Across Computers

RPC concept introduced: Functions across network boundaries.

async/await: Simplifies asynchronous calls but still has limitations (coupling, losing direct references).

“An async function...may pause execution...async and await propagate upwards.”

Import RPC idea: Extends importing to remote function calls while maintaining references and type-checking.

“Let’s invent a special syntax...import rpc because what we’ve described here has been known for decades as RPC.”

Potential Calls (Tags as Deferred RPCs)

"Potential function calls": Represented by tags; calls that might happen in the future.

“It’s a blueprint of a function call.”

Nested tags: Express dependencies naturally.

“Dependencies between potential calls...should be expressed by embedding these calls inside each other.”

Splitting Computation in Time and Space

Computation split in time: Returning partial functions that capture necessary data (closures).

Computation split across space (client-server): Splitting execution between two computers, handling data passing explicitly.

“It’s an interesting shape—a program returning the rest of itself...closure over the network.”

Two Types of Operations: Components vs. Primitives

Components (Capitalized): "Brains" of a program; flexible, timeless, and declarative, embedding tags without introspection.

“Components are truly timeless...they accept tags as arguments.”

Primitives (lowercase): "Muscles"; introspect arguments, execution order sensitive, imperative, execute last.

“Primitives introspect arguments...they must know all their arguments.”

Execution Phases:

1. Interpret (thinking): Processes Components freely without strict order.

2. Perform (doing): Executes Primitives strictly inside-out.

“First, you need to think...then you need to do.”

Act 2: Reflections and Dialog

Meta-dialog: Reflection on the writing process itself; writer and reader dialogue, acknowledging uncertainty and experimental nature of content.

“The Writer: I have a rough idea, but truthfully, I’m pretty much winging it.”

Core Conceptual Innovations

Tags as code/data pairs: Potential function calls represented explicitly as data (tags), allowing deferred execution across contexts.

Program as distributed computation: A single conceptual function spanning multiple runtime environments (Early and Late worlds).

Timelessness and Flexibility: Components allow arbitrary computation ordering; Primitives enforce execution order.

Key Quotes & Ideas:

Blueprints vs. Recipes:

“A blueprint describes what nouns a thing is made of...a recipe prescribes a sequence of steps to be performed.”

RPC and Potential Calls:

“A tag is like a function call but passive, inert, open to interpretation.”

Components and Primitives Separation:

“Components are the ‘brains’...Primitives are the ‘muscles’.”

Importance of Introspection vs. Embedding:

“If a function only embeds an argument without introspection, you can delay computing it.”

Conclusion (Conceptual Breakthroughs)

Distributed React Model: Redefining client-server interaction as React component structures.

Future implications: Suggests moving common primitives into lower-level implementations to optimize distributed computation.

“If many programs used the same Primitives...move their implementation to Rust or C++.”

Reviewer #1 (Public review):

Summary:

The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine mapping.

Strengths:

The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

Weaknesses:

(1) Although the authors employ state-of-the-art analytical approaches for the identification of SVs, the overall accuracy remains suboptimal, as indicated by an F1 score of 74.0%, particularly in tandem repeat regions. To enhance accuracy, it would be beneficial to explore alternative SV detection methods or develop novel approaches. Given the value of the reference panel and the fact that improved SV accuracy would lead to more precise SV imputation and GWAS results, investing effort in methodological refinement is highly encouraged.

(2) From the Methods section, it appears that the authors employed Beagle for both the "leave-one-out" imputation and the UK Biobank imputation. It would be better to explicitly clarify this in the Results section and provide a detailed description of the corresponding procedures and parameters in the Methods section for both analyses, as this represents a key aspect of the study. Additionally, Beagle is not specifically designed for SV imputation, the imputation quality of SVs is generally lower than that of SNVs. Exploring strategies to improve SV imputation, such as developing a novel method with reference panel data, may enhance performance. It is also important to assess how this reduced imputation quality may influence GWAS results. For instance, it would be useful to examine whether associated SVs exhibit higher imputation quality and whether SVs with lower quality are less likely to achieve significant association signals. In addition, the lower imputation quality observed for INV, DUP, and BND variants (Figure 3) may be due to their greater lengths (Figure 2). It is better to investigate the relationship between SV length and imputation quality.

(3) All examples presented in the manuscript focus on SVs that overlap with genes. It may also be valuable to investigate SVs that do not overlap with genes but intersect with enhancer regions. SVs can contribute to disease by altering regulatory elements, such as enhancers, which play a crucial role in gene expression. Including such analyses would further demonstrate the utility of SV-GWAS and provide deeper insights into the functional impact of SVs.

(4) The data availability link currently provides only a VCF file ("sniffles2_joint_sv_calls.vcf.gz") containing the identified SVs. It would be beneficial for the authors to make all raw sequencing data (FASTQ files) and key processed datasets (such as alignment results and merged SV and SNV files) available. Providing these resources would enable other researchers to develop improved SV detection and imputation methods or conduct further genetic analyses. Furthermore, establishing a dedicated website for data access, along with a genome browser for SV visualization, could significantly enhance the impact and accessibility of the study. Additionally, all code, particularly the SV imputation pipeline accompanied by a detailed tutorial, should be deposited in a public repository such as GitHub. This would support researchers in imputing SVs and conducting SV-GWAS on their own datasets.

This sounds like a terrible idea. Using your monitoring solution as part of the underlying code functionality...

What does this mean? When I tried this, Lwt allowed utop to read an entire line, not just a single character. Furthermore, isn't that exactly what's expected from this code? What is confusing about it? Also, utop interpreted read_line and stdin as referring to the functions in Stdlib when I tried it, so I needed to prefix both of them with Lwt_io to get this code to work.

Reviewer #1 (Public review):

Summary:

In this study, the authors re-analyzed a public dataset (Rademaker et al, 2019, Nature Neuroscience) which includes fMRI and behavioral data recorded while participants held an oriented grating in visual working memory (WM) and performed a delayed recall task at the end of an extended delay period. In that experiment, participants were pre-cued on each trial as to whether there would be a distracting visual stimulus presented during the delay period (filtered noise or randomly-oriented grating). In this manuscript, the authors focused on identifying whether the neural code in retinotopic cortex for remembered orientation was 'stable' over the delay period, such that the format of the code remained the same, or whether the code was dynamic, such that information was present, but encoded in an alternative format. They identify some timepoints - especially towards the beginning/end of the delay - where the multivariate activation pattern fails to generalize to other timepoints, and interpret this as evidence for a dynamic code. Additionally, the authors compare the representational format of remembered orientation in the presence vs absence of a distracting stimulus, averaged over the delay period. This analysis suggested a 'rotation' of the representational subspace between distracting orientations and remembered orientations, which may help preserve simultaneous representations of both remembered and viewed stimuli. Intriguingly, this rotation was a bit smaller for Expt 2, in which the orientation distractor had a greater behavioral impact on the participants' behavioral working memory recall performance, suggesting that more separation between subspaces is critical for preserving intact working memory representations.

Strengths:

(1) Direct comparisons of coding subspaces/manifolds between timepoints, task conditions, and experiments is an innovative and useful approach for understanding how neural representations are transformed to support cognition

(2) Re-use of existing dataset substantially goes beyond the authors' previous findings by comparing geometry of representational spaces between conditions and timepoints, and by looking explicitly for dynamic neural representations

(3) Simulations testing whether dynamic codes can be explained purely by changes in data SNR are an important contribution, as this rules out a category of explanations for the dynamic coding results observed

Weaknesses:

(1) Primary evidence for 'dynamic coding', especially in early visual cortex, appears to be related to the transition between encoding/maintenance and maintenance/recall, but the delay period representations seem overall stable, consistent with some previous findings. However, given the simulation results, the general result that representations may change in their format appears solid, though the contribution of different trial phases remains important for considering the overall result.

(2) Converting a continuous decoding metric (angular error) to "% decoding accuracy" serves to obfuscate the units of the actual results. Decoding precision (e.g., sd of decoding error histogram) would be more interpretable and better related to both the previous study and behavioral measures of WM performance.

在时空数据库的背景下，时空依赖的函数（Spatio-Temporal Functional Dependency, STFD） 是一种描述数据中时空属性之间关系的数学工具。它扩展了传统数据库中的函数依赖（Functional Dependency, FD）概念，将时间和空间维度同时纳入考虑，用于刻画数据在时空变化中的规律性。以下是对其核心概念和意义的详细解释：

1. 时空依赖的函数的基本定义

在传统数据库中，函数依赖 \( X \rightarrow Y \) 表示属性集 \( X \) 的值唯一确定属性集 \( Y \) 的值。而在时空数据库中，时空函数依赖需要同时考虑时间（T）和空间（S）的维度。例如： - 时空函数依赖 \( X \rightarrow Y \) 可能表示：在某一时刻 \( t \) 和某一空间位置 \( s \)，属性 \( X \) 的值唯一决定属性 \( Y \) 的值。 - 更严格的定义可能涉及时空键（Spatio-Temporal Key）或时空码（Spatio-Temporal Code），即一组属性（包括时间、空间和其他属性）能够唯一标识一条时空记录。

2. 时空依赖的函数的特点

(1) 多维性

时空依赖同时考虑时间、空间和属性的关联，例如： - 示例：在物流系统中，一辆货车的实时位置（空间坐标）和时间戳（时间）共同决定了其行驶状态（如速度、货物状态）。 - 数学表达：可能表示为 \( (T, S) \rightarrow A \)，其中 \( T \) 是时间，\( S \) 是空间坐标，\( A \) 是其他属性（如温度、速度）。

(2) 动态性

时空数据是动态变化的，因此函数依赖可能随时间或空间的变化而变化： - 示例：某地区的气温（属性 \( A \)）在不同时间（\( T \)）和不同地理位置（\( S \)）下可能有不同的依赖关系。例如，\( (T, S) \rightarrow A \) 表示气温由时间和空间唯一决定。

(3) 约束冗余

时空函数依赖用于减少数据冗余和保持数据一致性。例如： - 如果 \( (T, S) \rightarrow A \) 成立，则在任何时刻 \( t \) 和位置 \( s \)，属性 \( A \) 的值必须唯一，避免重复或矛盾的数据记录。

3. 时空依赖的函数在数据库规范化中的作用

时空数据库的规范化（如时空一范式、二范式等）依赖于时空函数依赖的分析，以消除冗余并确保数据完整性。例如： - 时空一范式（ST-1NF）：要求所有时空数据以原子形式存储，消除嵌套或重复的时空记录。 - 时空二范式（ST-2NF）：在满足 ST-1NF 的基础上，确保不存在非主属性对时空键的部分依赖。 - 时空三范式（ST-3NF）：进一步消除非主属性之间的传递依赖，例如 \( T \rightarrow S \)，而 \( S \rightarrow A \)，则 \( T \rightarrow A \) 可能形成传递依赖，需通过范式化处理。

4. 时空依赖的函数的应用场景

(1) 物流与交通管理

• 示例：追踪货车的实时位置（空间）和时间，确保每辆车的行驶路径（属性）由时空键唯一确定。
• 依赖关系：\( (T, 车辆ID) \rightarrow 路径 \)，表示同一时间、同一车辆的路径唯一。

2) 环境监测

• 示例：大气污染传感器的监测数据（如PM2.5浓度）需记录时间、地理位置，且浓度值由时空键唯一决定： [ (T, 经度, 纬度) \rightarrow PM2.5 ]

(3) 社交媒体与位置服务

• 示例：用户的位置（空间）和时间戳（时间）共同决定其活动状态（如签到地点、动态内容）。

5. 时空依赖与传统函数依赖的区别

| 特性 | 传统函数依赖（FD） | 时空函数依赖（STFD） | |-------------------|---------------------------------------|----------------------------------------| | 维度 | 仅涉及属性间的依赖 | 同时涉及时间、空间和属性的依赖 | | 动态性 | 静态关系，不随时间变化 | 动态关系，可能随时间或空间变化 | | 冗余控制 | 通过消除属性冗余减少数据重复 | 通过时空键控制时空维度的冗余 | | 规范化目标 | 优化关系数据库的结构 | 优化时空数据的存储与查询效率 |

6. 时空依赖的函数的挑战

1. 复杂性：时空数据的高维性增加了依赖关系的分析难度。
2. 动态维护：时空数据随时间变化，依赖关系可能需要动态更新。
3. 存储与查询效率：时空函数依赖需平衡数据冗余与查询性能，例如索引设计需同时考虑时空维度。

总结

时空依赖的函数是时空数据库中用于描述数据在时空维度上规律性的重要工具。它通过时空键和函数依赖规则，帮助设计高效、一致的时空数据模型，减少冗余，并支持复杂时空查询（如轨迹分析、时空模式挖掘）。其核心在于将时间、空间与属性的关联纳入统一框架，为处理动态变化的地理信息、物流轨迹、环境监测等场景提供了理论基础。

Welcome back and in this lesson I want to cover S3 object storage classes. Now this is something which is equally as important at the associate and the professional level. You need to understand the costs relative to each other, the technical features and compromises, as well as the types of situations where you would and wouldn't use each of the storage classes. Now we've got a lot to cover so let's jump in and get started.

The default storage class available within S3 is known as S3 Standard. So with S3 Standard when Bob stores his cat pictures on S3 using the S3 API, the objects are stored across at least three availability zones. And this level of replication means that S3 Standard is able to cope with multiple availability zone failure while still safeguarding data. So start with this as a foundation when comparing other storage classes because this is a massively important part of the choice between different S3 storage classes.

Now this level of replication means that S3 Standard provides 11 nines of durability and this means if you store 10 million objects within an S3 bucket, then on average you might lose one object every 10,000 years. The replication uses MD5 checksums together with cyclic redundancy checks known as CRCs to detect and resolve any data issues. Now when objects which are uploaded to S3 have been stored durably, S3 responds with a HTTP 1.1 200 OK status. This is important to remember for the exam if you see this status, if S3 responds with a 200 code then you know that your data has been stored durably within the product.

With S3 Standard there are a number of components to how you'll build for the product. You'll build a gigabyte per month fee for data stored within S3, a dollar per gigabyte charge for transfer of data out of S3 and transfer into S3 is free and then finally you have a price per 1,000 requests made to the product. There are no specific retrieval fees, no minimum duration for objects stored and no minimum object sizes. Now this isn't true for the other storage classes so this is something to focus on as a solutions architect and in the exam.

With S3 Standard you aren't penalized in any way. You don't get any discounts but it's the most balanced class of storage when you look at the dollar cost versus the features and compromises. Now S3 Standard makes data accessible immediately. It has a first byte latency of milliseconds and this means that when data is requested it's available within milliseconds and objects can be made publicly available. This is either using S3 permissions or if you enable static website hosting and make all of the contents of the bucket available to the public internet. If you're doing that then S3 Standard supports both of these access architectures.

So for the exam the critical point to remember is that S3 Standard should be used for frequently accessed data which is important and non-replaceable. It should be your default and you should only investigate moving to other storage classes when you have a specific reason to do so.

Now let's move on and look at another storage class available within S3 and the next class I want to cover is S3 Standard in frequent access known as S3 Standard - IA. So Standard in frequent access shares most of the architecture and characteristics of S3 Standard. Data is still replicated over at least three availability zones in the region. The durability is the same, the availability is the same, the first byte latency is the same and objects can still be made publicly available.

You also have the same basic cost model starting with a storage cost but the storage costs for this class are much cheaper than S3 Standard about half at the price at the time of creating this lesson. So it's much more cost effective to store data using Standard in frequent access. You also have a per request charge and a data transfer out cost which is the same as S3 Standard and like other AWS services, data transfer in is free of charge.

So this reduction in storage cost is a substantial benefit to using in frequent access but in exchange for this benefit there are some compromises which are made. First, Standard in frequent access has a new cost component which is a retrieval fee. For every gigabyte of data retrieved from the product where the objects are stored using this storage class, there is a cost to retrieve that data and that's in addition to the transfer fee. So while the costs of storage for this class are much less than S3 Standard, that cost efficacy is reduced the more that you access the data which is why this class is designed for infrequently accessed data.

Now additionally, there is a minimum duration charge for objects using this class. However long you store objects, you'll build for a minimum duration of 30 days and however small the objects that you store within this class, you'll build a minimum of 128 kb in size per object. So this class is cost effective for data as long as you don't access the data very often or you don't need to store it short term or you don't need to store lots of tiny objects.

For the exam remember this, S3 Standard in frequent access should be used for long lived data which is important or irreplaceable but where data access is infrequent. Don't use it for lots of small files, don't use it for temporary data, don't use it for data which is constantly accessed and don't use it for data which isn't important or which can be easily replaced because there's a better cheaper option for that and that's what we're going to be covering next.

The next storage class which I want to talk about is S3 One Zone Infrequent Access and this is similar to Standard Infrequent Access in many ways. The starting point is that it's cheaper than S3 Standard or S3 Infrequent Access and there is a significant compromise for that cost reduction which I'll talk about soon.

Now this storage class shares many of the minimums and other considerations as S3 Infrequent Access. There's still the retrieval fee, there's still the minimum 30 day build storage duration and there's still the 128kb minimum capacity charge per object.

The big difference between S3 Infrequent Access and One Zone Infrequent Access and you can probably guess this from the name is that data stored using this class is only stored in one availability zone within the region so it doesn't have the replication across those additional availability zones. So you get cheaper access to storage but you take on additional risk of data loss if the AZ that the data is stored in fails.

Now oddly enough you do get the same level of durability so 11 nines of durability but that's assuming that the availability zone that your data is stored in doesn't fail during that time period. Data is still replicated within the availability zone so you have multiple copies of the data but only crucially within one availability zone.

Now for the exam this storage class should be used for long lived data because you still have the size and duration minimums. It should be used for data which is infrequently accessed because you still have the retrieval fee and and this is specific to this class for data which is non-critical or data which can be easily replaced. So this means things like replica copies so if you're using same or cross region replication then you can use this class for your replicated copy or if you're generating intermediate data that you can afford to lose then this storage class offers great value.

Don't use this for your only copy of data because it's too risky. Don't use this for critical data because it's also too risky. Don't use this for data which is frequently accessed, frequently changed or temporary data because you'll be penalized by the duration and size minimums that this storage class is affected by.

Okay so this is the end of part one of this lesson. It was getting a little bit on the long side and I wanted to give you the opportunity to take a small break. Maybe stretch your legs or make a coffee. Now part two will continue immediately from this point so go ahead complete this video and when you're ready I look forward to you joining me in part two.

Remove this section

go over

The W3Schools HTML Introduction page explains that HTML is the basic language used to build web pages. It talks about how HTML uses tags (like labels) to mark things like titles, headings, and paragraphs. For example, you use one tag type to create a heading and another for a paragraph. It even shows a simple example of what a basic web page looks like in code. It also mentions that your web browser (like Chrome or Safari) reads this code and turns it into the websites you see. And there’s a special line at the top of the page that helps the browser understand it’s working with HTML.

I'm curious as to what the flaws of utilitarianism are. I know very little about it besides what was described in the reading, but it seems like a logical and helpful way to go about solving problems ethically and morally. The only thing I could see about it that could be problematic is that it might ignore the potential negative aspects of certain outcomes if it only focuses on what the best outcome is. Still, I wonder how this applies to social media and data found through social media. The connection I draw between utilitarianism and such data on social media is that data could be incorrect or misleading, which could then lead to assumptions or allegedly ethical solutions to issues that weren't reported properly in the first place.

These paragraphs tell us that it is important to collect comprehensive data, think about the impact of relevant parties, and considering the groups that are easily overlooked before making decisions. This reminds me of cyberbullying in the society today. Lots of people only listen to one side of the story. They get emotionally stirred up by comments on a popular influencer’s social post and end up participating in online bullying against the other group. This kind of behavior stems from a lack of critical thinking and the unwillingness to investigate the truth from multiple perspectives, which can have serious consequences.

The idea that utility calculus relies on the data we chose to include really made me think. It highlights how easy it is to justify decisions as ethical when we only focus on the data that supports our goals. For example, social media platforms might prioritise engagement but they often overlook the negative impact on users mental health

Commenting out or commenting on a line stops the code from executing, it's like a way to disable the code and leave notes for yourself.

Keep calm and reassess the code before making any big changes.

Always test you code with multiple inputs as it may not work for all inputs. You need to be able to determine the range and figure out what input works best for the solutions.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Revision Plan (Response to Reviewers)

1. General Statements [optional]

Response: We are pleased the reviewers appreciate the power of this novel proteomics methodology that allowed us to uncover new depths on the complexity of the ribosome ubiquitination code in response to stress. We also appreciate that the reviewers think that this is a "very timely" study and "interesting to a broad audience" that can change the models of translation control currently adopted in the field. Characterizing complex cellular processes is critical to advance scientific knowledge and our work is the first of its kind using targeted proteomics methods to unveil the integrated complexity of ribosome ubiquitin signals in eukaryotic systems. We also appreciate the fairness of the comments received and below we offer a comprehensive revision plan substantially addressing the main points raised by the reviewers. According to the reviewers' suggestions, we will also expand our studies to two additional E3 ligases (Mag2 and Not4) known to ubiquitinate ribosomes, which will create an even more complete perspective of ubiquitin roles in translation regulation.

2. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

        __Response: __We appreciate the evaluation of our work and that the power of our proteomics method established a good foundation for the study. We also understand the reviewer's concerns and we will detail below a plan to enhance quantification and increase systematic comparisons. The experiments presented here were conducted with biological replicates, but in several instances, we focused on presence and absence of bands, or their pattern (mono vs poly-ub) because of the semi-quantitative nature of immunoblots. We will revise the figures and present their quantification and statistical analyses. In additional, we did not intend to use these stressors interchangeably, but instead, to use select conditions to highlight the complexity the stress response. In particular, we followed up with H2O2 *versus* 4-NQO because both chemicals are considered sources of oxidative stress. Even though it is unfeasible to compare every single stress condition in every strain background, in the revised version, we will include additional controls to increase the cohesion of the narrative, and expand the comparison between MMS, H2O2, and 4-NQO, as suggested. Details below.

To strengthen the work, the following revisions are essential:

R1.1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.

        __Response: __As requested, we will display quantification and statistical analysis of the suggested and new immunoblots that will be conducted during the revision period.

R1.3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.

        __Response: __We will follow the reviewers' suggestion and redesign the analysis to increase consistency and prioritize data under identical conditions. To increase confidence in the mRNA data analysis, we intend to perform follow up experiments and analyze protein abundance of *ARG proteins* and *CTT1 *under different conditions. The remaining data using non-parallel comparisons will be moved to supplemental material and de-emphasized in the final version of the manuscript.

R1.4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

        __Response: __To ensure a better comparison across strains and conditions, we will re-run several experiments and focus on our main stress conditions. Specifically:

• 3D: We plan to re-run this experiment and include MMS

• 3E: We plan to perform the same panel of experiments in rad6D ,and display WT data as main figure.

• 4A-B: We plan to perform translation output (HPG incorporation) experiments with MMS as suggested

• 4C: We plan to re-run blots for p-eIF2a under MMS for improved comparison.

Reviewer #1 (Significance (Required)):

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6. It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted.

Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

        __Response: __We value the positive evaluation of our work. We also appreciate the notion that it meaningfully expands the knowledge on the complexity of the ribosome ubiquitination code, challenges the current models of translation control, and conducted well-performed, and nicely controlled experiments. To address the main concern of the reviewer, we will expand our work by studying two additional enzymes involved in ribosome ubiquitination (Mag2 and Not4) and provide a more comprehensive picture of this integrated system. Specifically, we will generate yeast strains deleted for *MAG2* and *NOT4*, and evaluate their impact in ribosome ubiquitination under our main conditions of stress. We will investigate the role of these additional E3s in translation output (HPG incorporation), and in inducing the integrated stress response via phosphorylated eIF2α and Gcn4 expression. Additional follow up experiments will be performed according to our initial results.

Reviewer #2 (Significance (Required)):

In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience. However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

        __Response: __We appreciate the comments and the balanced view that studies like ours will still be impactful and contribute to a number of fields in multiple and meaningful ways. With the new experiments proposed here, and used of additional mutants and strains, we intend to propose and provide a more unified model that explain this complex and dynamic relationship.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

Major Comments:

I consider the additional experiments essential to support the claims of the paper.

R3.1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.

        __Response: __We understand the importance of the suggested experiment. We have already requested and kindly received strains expressing these mutations, which will reduce the time required to successfully address this point. We will perform our translation and ISR assays such as the one referred by the reviewer in Figs. 4A-C and 5E, and results will determine the role of individual ribosome ubiquitination sites in translation control.

R3.2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

        __Response: __As was also requested by reviewer 1 and discussed above (point R1.1), we will conduct quantification and display statistical analyses for our immunoblots. In addition, we will re-run the aforementioned experiments to improve quantification following the reviewers' request (same gel & diluted control samples).

Reviewer #3 (Significance (Required)):

• General assessment:

Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

• Advance:

In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

• Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

• The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

__ Response:__ We appreciate that our work will be valuable to a number of fields in protein dynamics and that our method advances the field by measuring ribosome ubiquitination relatively and stoichiometrically in response to stress.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Response: All requested changes require experiments and data analyses, and a complete revision plan is delineated above in section #2.

• *

4. Description of analyses that authors prefer not to carry out

• *

R1.2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.

        __Response: __Although we understand the interest on the proposed result for consistency, this is the only requested experiment that we do not intend to conduct. Because of the lack of overall ubiquitination of ribosomal proteins in *rad6**D* in response to H2O2 (e.g., Silva et al., 2015, Simoes et al., 2022), we believe that this PRM experiment in unlikely to produce meaningful insight on the ubiquitination code. In this context, we expected that sites regulated by Hel2 will be the ones largely modified in rad6*D *and we followed up on them via immunoblot. Moreover, this experiment would not be time or cost-effective, and resources and efforts could be used to strengthen other important areas of the manuscript, such as including the E3's Mag2 and Not4 into our work.

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Referee #3

Evidence, reproducibility and clarity

Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

Major Comments:

I consider the additional experiments essential to support the claims of the paper.

1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.
2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

Significance

General assessment:

Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

Advance:

In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6.

It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted. Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

Significance

In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience.

However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

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Referee #1

Evidence, reproducibility and clarity

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

To strengthen the work, the following revisions are essential:

1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.
2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.
3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.
4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

Significance

The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

The present study aims to associate reproduction with age-related disease as support of the antagonistic pleiotropy hypothesis of ageing, predominantly using Mendelian Randomization. The authors found evidence that early-life reproductive success is associated with advanced ageing.

Strengths:

Large sample size. Many analyses.

Weaknesses:

There are some errors in the methodology, that require revisions.

In particular, the main conclusions drawn by the authors refer to the Mendelian Randomization analyses. However, the authors made a few errors here that need to be reconsidered:

(1) Many of the outcomes investigated by the authors are continuous outcomes, while the authors report odds ratios. This is not correct and should be revised.

Thank you for your observation. We have revised the manuscript to ensure that the results for continuous outcomes are appropriately reported using beta coefficients, which indicate the change in the outcome per unit increase in exposure. This will accurately reflect the nature of the analysis and provide a clearer interpretation of continuous outcomes (lines 56-109).

(2) Some of the odds ratios (for example the one for osteoporosis) are really small, while still reaching the level of statistical significance. After some checking, I found the GWAS data used to generate these MR estimates were processed by the program BOLT-LLM. This program is a linear mixed model program, which requires the transformation of the beta estimates to be useful for dichotomous outcomes. The authors should check the manual of BOLT-LLM and recalculate the beta estimates of the SNP-outcome associations prior to the Mendelian Randomization analyses. This should be checked for all outcomes as it doesn't apply to all.

Thank you for your detailed feedback. We have reviewed all the GWAS data used in our MR analyses and confirmed that all GWAS of continuous traits have already been processed using the BOLT-LMM, including age at menarche, age at first birth, BMI, frailty index, father's age at death, mother's age at death, DNA methylation GrimAge acceleration, age at menopause, eye age, and facial aging. Most of the dichotomous outcomes have not been processed by BOLT-LMM, including late-onset Alzheimer's disease, type 2 diabetes, chronic heart failure, essential hypertension, cirrhosis, chronic kidney disease, early onset chronic obstructive pulmonary disease, breast cancer, ovarian cancer, endometrial cancer, and cervical cancer, except osteoporosis. We have reprocessed the GWAS beta values of osteoporosis and re-conducted the MR analysis (lines 74-75; lines 366-373).

(3) The authors should follow the MR-Strobe guidelines for presentation.

Thank you for your suggestion to follow the MR-STROBE guidelines for the presentation of our study. We appreciate the importance of adhering to these standardized guidelines to ensure clarity and transparency in reporting Mendelian Randomization (MR) analyses. We confirm that the MR components of our research are structured and presented following the MR-STROBE checklist. In addition to the MR analyses, our study also integrates Colocalization analysis, Genetic correlation analysis, Ingenuity Pathway Analysis (IPA), and population validation to provide a more comprehensive understanding of the genetic and biological context. While these analyses are not strictly covered by MR-STROBE guidelines, they complement the MR results by offering additional validation and mechanistic insights.

We have structured our manuscript to separate these complementary analyses from the core MR results, maintaining alignment with MR-STROBE for the MR-specific components. The additional analyses are discussed in dedicated sections to highlight their unique contributions and avoid conflating them with the MR findings.

(4) The authors should report data in the text with a 95% confidence interval.

Thank you for your feedback. We have added the 95% confidence intervals for the reported data within the main text to enhance clarity and provide comprehensive context (lines 56-109). Additionally, the complete analysis data, including all detailed results, can be found in Table S3.

(5) The authors should consider correction for multiple testing

Thank you for your comment regarding the need to consider correction for multiple testing. We agree that correcting for multiple comparisons is an important step to control for the possibility of false-positive findings, particularly in studies involving large numbers of statistical tests. In our study, we carefully considered the issue of multiple testing and adopted the following approach:

Context of Multiple Testing: The tests we conducted were hypothesis-driven, focusing on specific relationships (e.g., genetic correlation, colocalization, and Mendelian Randomization). These analyses are based on priori hypotheses supported by existing literature or biological relevance.

Statistical Methods: Where applicable, we applied appropriate measures to account for multiple tests. For instance, in Mendelian Randomization, sensitivity analyses serve to validate the robustness of the results.

We believe that the methodology and corrections applied in our study appropriately address concerns about multiple testing, given the hypothesis-driven nature of our analyses and the rigorous steps taken to validate our findings. If you feel that additional corrections are required for specific parts of the analysis, we would be happy to further clarify or revise as needed.

Reviewer #2 (Public review):

Summary:

The authors present an interesting paper where they test the antagonistic pleiotropy theory. Based on this theory they hypothesize that genetic variants associated with later onset of age at menarche and age at first birth have a positive causal effect on a multitude of health outcomes later in life, such as epigenetic aging and prevalence of chronic diseases. Using a mendelian randomization and colocalization approach, the authors show that SNPs associated with later age at menarche are associated with delayed aging measurements, such as slower epigenetic aging and reduced facial aging, and a lower risk of chronic diseases, such as type 2 diabetes and hypertension. Moreover, they identified 128 fertility-related SNPs that are associated with age-related outcomes and they identified BMI as a mediating factor for disease risk, discussing this finding in the context of evolutionary theory.

Strengths:

The major strength of this manuscript is that it addresses the antagonistic pleiotropy theory in aging. Aging theories are not frequently empirically tested although this is highly necessary. The work is therefore relevant for the aging field as well as beyond this field, as the antagonistic pleiotropy theory addresses the link between fitness (early life health and reproduction) and aging.

Points that have to be clarified/addressed:

(1) The antagonistic pleiotropy is an evolutionary theory pointing to the possibility that mutations that are beneficial for fitness (early life health and reproduction) may be detrimental later in life. As it concerns an evolutionary process and the authors focus on contemporary data from a single generation, more context is necessary on how this theory is accurately testable. For example, why and how much natural variation is there for fitness outcomes in humans?

Thank you for these insightful questions. We appreciate the opportunity to clarify how we approach the testing of AP theory within a contemporary human cohort and address the evolutionary context and comparative considerations with the disposable soma theory.

We recognize that modern human populations experience selection pressures that differ from those in the past, which may affect how well certain genetic variants reflect historical fitness benefits. Nonetheless, the genetic variation present today still offers valuable insights into potential AP mechanisms through statistical associations in contemporary cohorts. We believe that AP can indeed be explored in current populations by examining genetic links between reproductive traits and age-related health outcomes. In our study, we investigate whether certain genetic variants linked to reproductive timing—such as age at menarche and age at first birth—also correlate with late-life health risks. By identifying SNPs associated with both early-life reproductive success and adverse aging outcomes, we aim to capture the evolutionary trade-offs that AP theory suggests.

Despite contemporary selection pressures that differ from historical conditions, there remains natural genetic variation in traits like reproductive timing and longevity in humans today. This diversity allows us to apply MR to test causal relationships between reproductive traits and aging outcomes, providing insights into potential AP mechanisms. Prior studies have demonstrated that reproductive behaviors exhibit significant heritability and have identified genetic loci associated with reproductive timing (1,2). This genetic variation facilitates causal inference in modern cohorts, despite environmental and healthcare advances that might modulate these associations (3). By leveraging genetic risk scores for reproductive timing, our study captures the necessary variability to assess potential AP effects, thus providing valuable insights into how evolutionary trade-offs may continue to influence human health outcomes.

How do genetic risk score distributions of the exposure data look like?

Thank you for your question. Our study is focused on Mendelian Randomization (MR) analysis, which aims to infer causal relationships between exposures and outcomes. While genetic risk scores (GRS) provide valuable insights at an individual level, they do not directly align with our study's objective, which is centered on population-level causal inference rather than individual-level genetic risk assessment. In MR, we use genetic variants as instrumental variables to determine the causal effect of an exposure on an outcome. GRS analysis typically focuses on summarizing an individual's risk based on multiple genetic variants, which is outside the scope of our current research. Therefore, we did not perform or analyze the distribution of genetic risk scores, as our primary goal was to understand broader causal relationships using established genetic instruments.

Also, how can the authors distinguish in their data between the antagonistic pleiotropy theory and the disposable soma theory, which considers a trade-off between investment in reproduction and somatic maintenance and can be used to derive similar hypotheses? There is just a very brief mention of the disposable soma theory in lines 196-198.

In our manuscript, we test AP theory specifically by examining genetic variants associated with reproductive timing and their association with age-related health risks in later life. MR and genetic risk scores allow us to assess these associations, directly testing the hypothesis that certain alleles enhancing reproductive success might have adverse effects on aging outcomes. This gene-centered approach aligns with AP’s premise of genetic trade-offs, enabling us to observe whether alleles associated with early-life reproductive traits correlate with increased risks of age-related diseases. Distinguishing from disposable soma theory, which would predict a general trade-off in energy allocation affecting somatic maintenance and not specific genetic effects, our data focuses on how certain alleles have differential impacts across life stages. Our findings thus support AP theory over disposable soma by highlighting the effects of specific genetic loci on both reproductive and aging phenotypes. However, future research could indeed explore the intersection of these theories, for example, by examining how resource allocation and genetic predispositions interact to influence longevity in various environmental contexts.

(2) The antagonistic pleiotropy theory, used to derive the hypothesis, does not necessarily distinguish between male and female fitness. Would the authors expect that their results extrapolate to males as well? And can they test that?

Emerging evidence suggests that early puberty in males is linked to adverse health outcomes, such as an increased risk of cardiovascular disease, type 2 diabetes, and hypertension in later life (4). A Mendelian randomization study also reported a genetic association between the timing of male puberty and reduced lifespan (5). These findings support the hypothesis that genetic variants associated with delayed reproductive timing in males might similarly confer health benefits or improved longevity, akin to the patterns observed in females. This would suggest that similar mechanisms of antagonistic pleiotropy could operate in males as well.

In our study, BMI was identified as a mediator between reproductive timing and disease risk. Given that BMI is a common risk factor for age-related diseases in both males and females (6-9), it is plausible that similar mechanisms involving BMI, reproductive timing, and disease risk could exist in males. This shared mediator points to the possibility that, while reproductive timelines may differ, the pathways through which these traits influence aging outcomes may be consistent across genders.

AP theory could potentially be tested in males, as the principles of the theory may extend to analogous reproductive traits in males, such as age at puberty and testosterone levels, which could similarly influence health outcomes later in life. However, as our current study focuses specifically on female reproductive traits, testing the AP theory in males is outside the scope of this work. We acknowledge the importance of exploring these mechanisms in males, and we hope that future research will address this by investigating male-specific reproductive traits and their relationship to aging and health outcomes.

(3) There is no statistical analyses section providing the exact equations that are tested. Hence it's not clear how many tests were performed and if correction for multiple testing is necessary. It is also not clear what type of analyses have been done and why they have been done. For example in the section starting at line 47, Odds Ratios are presented, indicating that logistic regression analyses have been performed. As it's not clear how the outcomes are defined (genotype or phenotype, cross-sectional or longitudinal, etc.) it's also not clear why logistic regression analysis was used for the analyses.

Thank you for your thoughtful comments regarding the statistical analyses and the clarification of methods and variables used in the study.

Statistical Analyses Section: We have included a detailed explanation of all statistical analyses in the Methods section (lines 291–408), specifying the rationale for the choice of methods, the variables analyzed, and their relationships. Additionally, we have provided the relevant equations or statistical models used where appropriate to ensure transparency.

Beta Values and Odds Ratios: In the Results section (starting at line 56), both Beta values and Odds Ratios are presented: Beta values were used for analyses of continuous outcomes to quantify the linear relationship between predictors and outcomes. Odds Ratios (ORs) were calculated for binary or categorical disease outcomes to describe the relative odds of an outcome given specific exposures or independent variables.

Validation and Regression Analyses: For further validation of the MR results, we conducted analyses using the UK Biobank dataset (starting at line 162). Logistic regression analysis was then employed for disease risk assessments involving categorical outcomes (e.g., diseased or not).

We hope that this clarifies the methods and their applicability to our study, as well as the rationale for the presentation of Beta values and Odds Ratios. If further details or refinements are required, we are happy to incorporate them.

(4) Mendelian Randomization is an important part of the analyses done in the manuscript. It is not clear to what extent the MR assumptions are met, how the assumptions were tested, and if/what sensitivity analyses are performed; e.g. reverse MR, biological knowledge of the studied traits, etc. Can the authors explain to what extent the genetic instruments represent their targets (applicable expression/protein levels) well?

Thank you for your insightful comments regarding the Mendelian Randomization (MR) analysis and the evaluation of its assumptions. Below, we provide additional clarification on how the MR assumptions were addressed, sensitivity analyses performed, and the representativeness of the genetic instruments (starting at line 314):

Relevance Assumption (Genetic instruments are associated with the exposure): “We identified single nucleotide polymorphisms (SNPs) associated with exposure datasets with p < 5 × 10^-8 (10,11). In this case, 249 SNPs and 67 SNPs were selected as eligible instrumental variables (IVs) for exposures of age at menarche and age at first birth, respectively. All selected SNPs for every exposure would be clumped to avoid the linkage disequilibrium (r² = 0.001 and kb = 10,000).” “During the harmonization process, we aligned the alleles to the human genome reference sequence and removed incompatible SNPs. Subsequent analyses were based on the merged exposure-outcome dataset. We calculated the F statistics to quantify the strength of IVs for each exposure with a threshold of F>10 (12).”

Independence Assumption (Genetic instruments are not associated with confounders, Genetic instruments affect the outcome only through the exposure): Then we identified whether there were potential confounders of IVs associated with the outcomes based on a database of human genotype-phenotype associations, PhenoScanner V2 (13,14) (http://www.phenoscanner.medschl.cam.ac.uk/), with a threshold of p < 1 × 10^-5. IVs associated with education, smoking, alcohol, activity, and other confounders related to outcomes would be excluded.

Sensitivity Analyses Performed: A pleiotropy test was used to check if the IVs influence the outcome through pathways other than the exposure of interest. A heterogeneity test was applied to ensure whether there is a variation in the causal effect estimates across different IVs. Significant heterogeneity test results indicate that some instruments are invalid or that the causal effect varies depending on the IVs used. MRPRESSO was applied to detect and correct potential outliers of IVs with NbDistribution = 10,000 and threshold p = 0.05. Outliers would be excluded for repeated analysis. The causal estimates were given as odds ratios (ORs) and 95% confidence intervals (CI). A leave-one-out analysis was conducted to ensure the robustness of the results by sequentially excluding each IV and confirming the direction and statistical significance of the remained remaining SNPs.

Supplemental post-GWAS analysis: Colocalization analysis (starting at line 356), Genetic correlation analysis (starting at line 366).

Our MR analysis adheres to the guidelines for causal inference in MR studies. By combining multiple sensitivity analyses and ensuring the quality of genetic instruments, we demonstrate that the results are robust and unlikely to be driven by confounding or pleiotropy.

(5) It is not clear what reference genome is used and if or what imputation panel is used. It is also not clear what QC steps are applied to the genotype data in order to construct the genetic instruments of MR.

Starting in line 314, the steps of SNPs selection were included in the Methods part. “We identified single nucleotide polymorphisms (SNPs) associated with exposure datasets with p < 5 × 10^-8 (10,11). In this case, 249 SNPs and 67 SNPs were selected as eligible instrumental variables (IVs) for exposures of age at menarche and age at first birth, respectively. All selected SNPs for every exposure would be clumped to avoid the linkage disequilibrium (r² = 0.001 and kb = 10,000). Then we identified whether there were potential confounders of IVs associated with the outcomes based on a database of human genotype-phenotype associations, PhenoScanner V2 (13,14) (http://www.phenoscanner.medschl.cam.ac.uk/), with a threshold of p < 1 × 10^-5. IVs associated with education, smoking, alcohol, activity, and other confounders related to outcomes would be excluded. During the harmonization process, we aligned the alleles to the human genome reference sequence and removed incompatible SNPs. Subsequent analyses were based on the merged exposure-outcome dataset. We calculated the F statistics to quantify the strength of IVs for each exposure with a threshold of F>10 (12). If the effect allele frequency (EAF) was missing in the primary dataset, EAF would be collected from dsSNP (https://www.ncbi.nlm.nih.gov/snp/) based on the population to calculate the F value.” The SNP numbers of exposures for each outcome and F statistics results were listed in supplemental table S2.

(6) A code availability statement is missing. It is understandable that data cannot always be shared, but code should be openly accessible.

We have added it to the manuscript (starting at line 410).

Reviewer #2 (Recommendations for the authors):

(1) The outcomes seem to be genotypes (lines 274-288). In MR, genotypes are used as an instrument, representing an exposure, which is then associated with an outcome that is typically observed and measured at a later moment in time than the predictors. If both exposure and outcome are genotypes it is not clear how this works in terms of causality; it would rather reflect a genetic correlation. One would expect the genotypes that function as instruments for the exposure to have a functional cascade of (age-related) effects, leading to an (age-related) outcome. From line 149 the outcomes seem to be phenotypes. Can the authors please clearly explain in each section what is analyzed, how the analyses were done, and why the analyses were done that way?

Thank you for your insightful comment. We understand the concern regarding the use of genotypes as both exposures and outcomes and the implications this has for interpreting causality versus genetic correlation. To clarify, in our study, the outcomes analyzed in the MR framework are indeed genotypes, starting from line 47. We use genotypes as instrumental variables for exposures, which are then linked to phenotypic outcomes observed at a later stage, in line with standard MR principles.

To improve the robustness of the MR results, we validated the genetic associations in the population with phenotype data from UK Biobank (lines 162-203), and the detailed methods were listed in lines 385-408.

(2) Overall, the English writing is good. However, some small errors slipped in. Please check the manuscript for small grammar mistakes like in sentences 10 (punctuation) and 33 (grammar).

Thank you for your feedback. We appreciate your careful review and attention to detail. We thoroughly rechecked the manuscript for any grammatical errors, including punctuation and sentence structure, especially in sentences 11 and 35 in revised manuscript, as suggested.

(3) There is currently no results and discussion section.

The manuscript was submitted as Short Reports article type with a combined Results and Discussion section. We have added the section title of Discussion.

(4) Why did the authors not include SNPs associated with age at menopausal onset? See for example: https://www.nature.com/articles/s41586-021-03779-7https://urldefense.com/v3/__https://www.nature.com/articles/s41586-021-03779-7__;!!HYjtAOY1tjP_!Kl_ZKCmWOQEnvEbl46TG0TuhlsxapwvFdAFfZJkMvz8z7XhX5VEA1cT8CVvNu8xrv9k679Kl0XTrxwSajUeiXWm04XP4$.

Thank you for your information. Our manuscript focuses on the antagonistic pleiotropy theory, which posits that inherent trade-off in natural selection, where genes beneficial for early survival and reproduction (like menarche and childbirth) may have costly consequences later. So, we only included age at menarche and age at first childbirth as exposures in our research.

(5) Can the authors include genetic correlations between menarche, age at first child, BMI, and preferably menopause?

Thank you for your suggestion. We acknowledge that including genetic correlations between age at menarche, age at first childbirth, BMI, and menopause can provide valuable context to our analysis. While our current MR study sets age at menarche and age at first childbirth as exposures and menopause as the outcome, and we have already included results that account for BMI-related SNPs before and after correction, we recognize the importance of assessing genetic correlations.

To address this, we calculated the genetic correlations between these traits to provide insight into their shared genetic architecture. This analysis helps clarify whether there is a significant genetic overlap between the two exposures and between exposure and outcome, which can inform and support the interpretation of our MR results. We appreciate your suggestion and include these calculations to enhance the robustness and comprehensiveness of our study. In the genetic correlations analysis, LDSC software was applied and the genetic correlation values for all pairwise comparisons among age at menarche, age at first birth, BMI, and age at menopause onset were calculated(15,16). The results are listed in Table S6.

(6) Line 39-40: that is not entirely true. There is also amounting evidence that socioeconomic factors cause earlier onset of menarche through stress-related mechanisms: https://doi.org/10.1016/j.annepidem.2010.08.006https://urldefense.com/v3/__https://doi.org/10.1016/j.annepidem.2010.08.006__;!!HYjtAOY1tjP_!Kl_ZKCmWOQEnvEbl46TG0TuhlsxapwvFdAFfZJkMvz8z7XhX5VEA1cT8CVvNu8xrv9k679Kl0XTrxwSajUeiXZ4vbX0y$

Thank you so much for your information. We changed it to “Considering reproductive events are partly regulated by genetic factors that can manifest the physiological outcome later in life”.

(7) Why did the authors choose to work with studies derived from IEU Open GWAS? as it is often does not contain the most recent and relevant GWAS for a specific trait.

We chose to work with studies derived from the IEU Open GWAS database after careful consideration of several sources, including the GWAS Catalog database and recently published GWAS papers. Our selection criteria focused on publicly available GWAS with large sample sizes and a higher number of SNPs to ensure robust analysis. For specific traits such as late-onset Alzheimer's disease and eye aging, we used GWAS data published in scientific articles to ensure that our research reflects the latest findings in the field.

(1) Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 48, 1462-1472 (2016). https://doi.org/10.1038/ng.3698

(2) Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat Hum Behav 1, 757-765 (2017). https://doi.org/10.1038/s41562-017-0195-1

(3) Stearns, S. C., Byars, S. G., Govindaraju, D. R. & Ewbank, D. Measuring selection in contemporary human populations. Nat Rev Genet 11, 611-622 (2010). https://doi.org/10.1038/nrg2831

(4) Day, F. R., Elks, C. E., Murray, A., Ong, K. K. & Perry, J. R. Puberty timing associated with diabetes, cardiovascular disease and also diverse health outcomes in men and women: the UK Biobank study. Sci Rep 5, 11208 (2015). https://doi.org/10.1038/srep11208

(5) Hollis, B. et al. Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan. Nat Commun 11, 1536 (2020). https://doi.org/10.1038/s41467-020-14451-5

(6) Field, A. E. et al. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med 161, 1581-1586 (2001). https://doi.org/10.1001/archinte.161.13.1581

(7) Singh, G. M. et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One 8, e65174 (2013). https://doi.org/10.1371/journal.pone.0065174

(8) Kivimaki, M. et al. Obesity and risk of diseases associated with hallmarks of cellular ageing: a multicohort study. Lancet Healthy Longev 5, e454-e463 (2024). https://doi.org/10.1016/S2666-7568(24)00087-4

(9) Kivimaki, M. et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol 10, 253-263 (2022). https://doi.org/10.1016/S2213-8587(22)00033-X

(10) Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50, 912-919 (2018). https://doi.org/10.1038/s41588-018-0152-6

(11) Gao, X. et al. The bidirectional causal relationships of insomnia with five major psychiatric disorders: A Mendelian randomization study. Eur Psychiatry 60, 79-85 (2019). https://doi.org/10.1016/j.eurpsy.2019.05.004

(12) Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res 26, 2333-2355 (2017). https://doi.org/10.1177/0962280215597579

(13) Staley, J. R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207-3209 (2016). https://doi.org/10.1093/bioinformatics/btw373

(14) Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 35, 4851-4853 (2019). https://doi.org/10.1093/bioinformatics/btz469

(15) Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236-1241 (2015). https://doi.org/10.1038/ng.3406

(16) Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-295 (2015). https://doi.org/10.1038/ng.3211

Reviewer #2 (Public review):

Summary:

It is generally believed that higher-order areas in the prefrontal cortex guide selection during working memory and attention through signals that selectively recruiting neuronal populations in sensory areas that encode the relevant feature. In this work, Parto-Dezfouli and colleagues tested how these prefrontal signals influence activity in visual area V4 using a spatial working memory task. They recorded neuronal activity from visual area V4 and found that information about visual features at the behaviorally relevant part of space during the memory period is carried in a spatially selective manner in the timing of spikes relative to a beta oscillation (phase coding) rather than in the average firing rate (rate code). The authors further tested whether there is a causal link between prefrontal input and the phase encoding of visual information during the memory period. They found that indeed inactivation of the frontal eye fields, a prefrontal area known to send spatial signal to V4, decreased beta oscillatory activity in V4 and information about the visual features. The authors went one step further to develop a neural model that replicated the experimental findings and suggested that changes in the average firing rate of individual neurons might be a result of small changes in the exact beta oscillation frequency within V4. These data provide important new insights on the possible mechanisms through which top-down signals can influence activity in hierarchically lower sensory areas and can therefore have a significant impact on the Systems, Cognitive and Computational Neuroscience fields.

Strengths:

This is a well-written paper with a well-thought-out experimental design. The authors used a smart variation of the memory-guided saccade task to assess how information about the visual features of stimuli is encoded during the memory period. By using a grating of various contrasts and orientations as the background the authors ensured that bottom-up visual input would drive responses in visual area V4 in the delay period, something that is not commonly done in experimental settings in the same task. Moreover, one of the major strengths of the study is the use of different approaches including analysis of electrophysiological data using advanced computational methods of analysis, manipulation of activity through inactivation of prefrontal cortex to establish causality of top-down signals on local activity signatures (beta oscillations, spike locking and information carried) as well as computational neuronal modeling. This has helped extend an observation into a possible mechanism well supported by the results.

Weaknesses:

Although the authors provide support for their conclusions from different approaches, a few conceptual gaps make it harder for the reader to appreciate the mechanisms that lead to the observed results and evaluate whether and how these may apply to other cases of top-down control. The fact that the visual features under study were behaviorally irrelevant make it difficult to appreciate the relevance of the finding and its relation to top-down spatial attention mechanisms that involve similar/overlapping circuits. In the same vein, the use of the memory-guided saccade task has certain disadvantages in the context of this study. Although delay activity is interpreted as memory activity by the authors, it is in principle possible that it reflects preparation for the upcoming saccade, spatial attention (particularly since there is a stimulus in the RF) etc. This could potentially change the conclusion and perspective.

Moreover, encoding of the two visual features that are manipulated in the context of the study (contrast and orientation) seems to be affected differently in certain cases, which leaves a reader wondering about the source of this variability.

Finally, although the study provides evidence in favor of a role of FEF in influencing phase coding of visual features in V4 in beta frequencies, important analysis that could have revealed the long-range mechanisms of such an effect including the analysis of intra-FEF and interareal (FEF-V4) neuronal interactions is missing from this paper

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This study investigates what happens to the stimulus-driven responses of V4 neurons when an item is held in working memory. Monkeys are trained to perform memory-guided saccades: they must remember the location of a visual cue and then, after a delay, make an eye movement to the remembered location. In addition, a background stimulus (a grating) is presented that varies in contrast and orientation across trials. This stimulus serves to probe the V4 responses, is present throughout the trial, and is task-irrelevant. Using this design, the authors report memory-driven changes in the LFP power spectrum, changes in synchronization between the V4 spikes and the ongoing LFP, and no significant changes in firing rate.

Strengths:

(1) The logic of the experiment is nicely laid out.

(2) The presentation is clear and concise.

(3) The analyses are thorough, careful, and yield unambiguous results.

(4) Together, the recording and inactivation data demonstrate quite convincingly that the signal stored in FEF is communicated to V4 and that, under the current experimental conditions, the impact from FEF manifests as variations in the timing of the stimulus-evoked V4 spikes and not in the intensity of the evoked activity (i.e., firing rate).

Weaknesses:

I think there are two limitations of the study that are important for evaluating the potential functional implications of the data. If these were acknowledged and discussed, it would be easier to situate these results in the broader context of the topic, and their importance would be conveyed more fairly and transparently.

(1) While it may be true that no firing rate modulations were observed in this case, this may have been because the probe stimuli in the task were behaviorally irrelevant; if anything, they might have served as distracters to the monkey's actual task (the MGS). From this perspective, the lack of rate modulation could simply mean that the monkeys were successful in attending the relevant cue and shielding their performance from the potentially distracting effect of the background gratings. Had the visual probes been in some way behaviorally relevant and/or spatially localized (instead of full field), the data might have looked very different.

Any task design involves tradeoffs; if the visual stimulus was behaviorally relevant, then any observed neurophysiological changes would be more confounded by possible attentional effects. We cannot exclude the possibility that a different task or different stimuli would produce different results; we ourselves have reported firing rate enhancements for other types of visual probes during an MGS task (Merrikhi et al. 2017). We have added an acknowledgement of these limitations in the discussion section (lines 323-330 in untracked version). At minimum, our results show a dissociation between the top-down modulation of phase coding, which is enhanced during WM even for these task-irrelevant stimuli, and rate coding. Establishing whether and how this phase coding is related to perception and behavior will be an important direction for future work.

With this in mind, it would be prudent to dial down the tone of the conclusions, which stretch well beyond the current experimental conditions (see recommendations).

We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

(2) Another point worth discussing is that although the FEF delay-period activity corresponds to a remembered location, it can also be interpreted as an attended location, or as a motor plan for the upcoming eye movement. These are overlapping constructs that are difficult to disentangle, but it would be important to mention them given prior studies of attentional or saccade-related modulation in V4. The firing rate modulations reported in some of those cases provide a stark contrast with the findings here, and I again suspect that the differences may be due at least in part to the differing experimental conditions, rather than a drastically different encoding mode or functional linkage between FEF and V4.

We have added a paragraph to the discussion section addressing links to attention and motor planning (lines 315-333), and specifically acknowledging the inherent difficulties of fully dissociating these effects when interpreting our results (lines 323-330).

Reviewer #2 (Public review):

Summary:

It is generally believed that higher-order areas in the prefrontal cortex guide selection during working memory and attention through signals that selectively recruit neuronal populations in sensory areas that encode the relevant feature. In this work, Parto-Dezfouli and colleagues tested how these prefrontal signals influence activity in visual area V4 using a spatial working memory task. They recorded neuronal activity from visual area V4 and found that information about visual features at the behaviorally relevant part of space during the memory period is carried in a spatially selective manner in the timing of spikes relative to a beta oscillation (phase coding) rather than in the average firing rate (rate code). The authors further tested whether there is a causal link between prefrontal input and the phase encoding of visual information during the memory period. They found that indeed inactivation of the frontal eye fields, a prefrontal area known to send spatial signals to V4, decreased beta oscillatory activity in V4 and information about the visual features. The authors went one step further to develop a neural model that replicated the experimental findings and suggested that changes in the average firing rate of individual neurons might be a result of small changes in the exact beta oscillation frequency within V4. These data provide important new insights into the possible mechanisms through which top-down signals can influence activity in hierarchically lower sensory areas and can therefore have a significant impact on the Systems, Cognitive, and Computational Neuroscience fields.

Strengths:

This is a well-written paper with a well-thought-out experimental design. The authors used a smart variation of the memory-guided saccade task to assess how information about the visual features of stimuli is encoded during the memory period. By using a grating of various contrasts and orientations as the background the authors ensured that bottom-up visual input would drive responses in visual area V4 in the delay period, something that is not commonly done in experimental settings in the same task. Moreover, one of the major strengths of the study is the use of different approaches including analysis of electrophysiological data using advanced computational methods of analysis, manipulation of activity through inactivation of the prefrontal cortex to establish causality of top-down signals on local activity signatures (beta oscillations, spike locking and information carried) as well as computational neuronal modeling. This has helped extend an observation into a possible mechanism well supported by the results.

Weaknesses:

Although the authors provide support for their conclusions from different approaches, I found that the selection of some of the analyses and statistical assessments made it harder for the reader to follow the comparison between a rate code and a phase code. Specifically, the authors wish to assess whether stimulus information is carried selectively for the relevant position through a firing rate or a phase code. Results for the rate code are shown in Figures 1B-G and for the phase code are shown in Figure 2. Whereas an F-statistic is shown over time in Figure 1F (and Figure S1) no such analysis is shown for LFP power. Similarly, following FEF inactivation there is no data on how that influences V4 firing rates and information carried by firing rates in the two conditions (for positions inside and outside the V4 RF). In the same vein, no data are shown on how the inactivation affects beta phase coding in the OUT condition.

Per the reviewer’s suggestion, we have added several new supplementary figures. We now show the F-statistic for discriminability over time for the LFP timecourse (Fig. S2), and as a function of power in various frequencies (Fig. S4). We have added before/after inactivation comparisons of the LFP and spiking activity, and their respective F-statistics for discrimination between contrasts and orientations in Fig. S9. Lastly, we added a supplementary figure evaluating the impact of FEF inactivation on beta phase coding in the OUT condition, showing no significant change (Fig. S11).

Moreover, some of the statistical assessments could be carried out differently including all conditions to provide more insight into mechanisms. For example, a two-way ANOVA followed by post hoc tests could be employed to include comparisons across both spatial (IN, OUT) and visual feature conditions (see results in Figures 2D, S4, etc.). Figure 2D suggests that the absence of selectivity in the OUT condition (no significant difference between high and low contrast stimuli) is mainly due to an increase in slope in the OUT condition for the low contrast stimulus compared to that for the same stimulus in the IN condition. If this turns out to be true it would provide important information that the authors should address.

We have updated the STA slope measurement, excluding the low contrast condition which lacks a clear peak in the STA. Additionally, we equalized the bin widths and aligned the x-axes for better visual comparability. Then, we performed a two-way ANOVA, analyzing the effects of spatial features (IN vs. OUT) and visual conditions (contrast and orientation). The results showed a significant effect of the visual feature on both orientation (F = 3.96, p=0.046) and contrast (F = 14.26, p<10^-3). However, neither the spatial feature nor the spatial-visual interaction exhibited significant effects for orientation (F = 0.52, p=0.473, F=1.56, p=0.212) or contrast (F = 2.19, p=0.139, F=1.15, p=0.283).

There are also a few conceptual gaps that leave the reader wondering whether the results and conclusion are general enough. Specifically,

(1) The authors used microstimulation in the FEF to determine RFs. It is thus possible that the FEF sites that were inactivated were largely more motor-related. Given that beta oscillations and motor preparatory activity have been found to be correlated and motor sites show increased beta oscillatory activity in the delay period, it is possible that the effect of FEF inactivation on V4 beta oscillations is due to inactivation of the main source of beta activity. Had the authors inactivated sites with a preponderance of visual neurons in the FEF would the results be different?

We do not believe this to be likely based on what is known anatomically and functionally about this circuitry. Anatomically, the projections from FEF to V4 arise primarily from the supragranular layers, not layers which contain the highest proportion of motor activity (Barone et al. 2000, Pouget et al. 2009, Markov et al. 2013). Functionally, based on electrical identification of V4-projecting FEF neurons, we know that FEF to V4 projections are predominantly characterized by delay rather than motor activity (Merrikhi et al. 2017). We have now tried to emphasize these points when we introduce the inactivation experiments (lines 185-186).

Experimentally, the spread of the pharmacological effect with our infusion system is quite large relative to any clustering of visual vs. motor neurons within the FEF, with behavioral consequences of inactivation spreading to cover a substantial portion of the visual hemifield (e.g., Noudoost et al. 2014, Clark et al. 2014), and so our manipulation lacks the spatial resolution to selectively target motor vs. other FEF neurons.

(2) Somewhat related to this point and given the prominence of low-frequency activity in deeper layers of the visual cortex according to some previous studies, it is not clear where the authors' V4 recordings were located. The authors report that they do have data from linear arrays, so it should be possible to address this.

Unfortunately, our chamber placement for V4 has produced linear array penetration angles which do not reliably allow identification of cortical layers. We are aware of previous results showing layer-specific effects of attention in V4 (e.g., Pettine et al. 2019, Buffalo et al. 2011), and it would indeed be interesting to determine whether our observed WM-driven changes follow similar patterns. We may be able to analyze a subset of the data with current source density analysis to look for layer-specific effects in the future, but are not able to provide any information at this time.

(3) The authors suggest that a change in the exact frequency of oscillation underlies the increase in firing rate for different stimulus features. However, the shift in frequency is prominent for contrast but not for orientation, something that raises questions about the general applicability of this observation for different visual features.

While the shift in peak frequency across contrasts is more prominent than that across orientations (Fig. S3A-B), the relationship between orientation and peak frequency is also significant (one-way ANOVA for peak frequency across contrasts, F_Contrast=10.72, p<10^-4; or across orientations, F_Orientation=3, p=0.030; stats have been added to Fig. S3 caption). This finding also aligns with previous studies, which reported slight peak frequency shifts (~1–2 Hz) in the context of attention (Fries, 2015). To address the question of whether the frequency-firing rate correlation generalizes to orientation-driven changes, we now examine the relationship between peak frequency and firing rate separately for each contrast level (Fig. S14). The average normalized response as a function of peak frequency, pooled across subsamples of trials from each of 145 V4 neurons (100 subsamples/neuron), IN vs. OUT conditions, shows a significant correlation during the delay period for each contrast (contrast low (F_Condition=0.03, p=0.867; F_Frequency=141.86, p<10^-18; F_Interaction=10.70, p=0.002, ANCOVA), contrast middle (F_Condition=7.18, p=0.009; F_Frequency=96.76, p<10^-14; F_Interaction=0.13, p=0.716, ANCOVA), contrast high (F_Condition=12.51, p=0.001; F_Frequency=333.74, p<10^-29; F_Interaction=7.91, p=0.006, ANCOVA).

(4) One of the major points of the study is the primacy of the phase code over the rate code during the delay period. Specifically, here it is shown that information about the visual features of a stimulus carried by the rate code is similar for relevant and irrelevant locations during the delay period. This contrasts with what several studies have shown for attention in which case information carried in firing rates about stimuli in the attended location is enhanced relative to that for stimuli in the unattended location. If we are to understand how top-down signals work in cognitive functions it is inevitable to compare working memory with attention. The possible source of this difference is not clear and is not discussed. The reader is left wondering whether perhaps a different measure or analysis (e.g. a percent explained variance analysis) might reveal differences during the delay period for different visual features across the two spatial conditions.

We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

The use of the memory-guided saccade task has certain disadvantages in the context of this study. Although delay activity is interpreted as memory activity by the authors, it is in principle possible that it reflects preparation for the upcoming saccade, spatial attention (particularly since there is a stimulus in the RF), etc. This could potentially change the conclusion and perspective.

We have added a new discussion paragraph addressing the relationship to attention and motor planning (lines 315-333). We have also moderated the language used to describe our conclusions throughout the manuscript in light of this ambiguity.

For the position outside the V4 RF, there is a decrease in both beta oscillations and the clustering of spikes at a specific phase. It is therefore possible that the decrease in information about the stimuli features is a byproduct of the decrease in beta power and phase locking. Decreased oscillatory activity and phase locking can result in less reliable estimates of phase, which could decrease the mutual information estimates.

Looking at the SNR as a ratio of power in the beta band to all other bands, there is no significant drop in SNR between conditions (SNRIN = 4.074+-984, SNROUT = 4.333+-0.834 OUT, p=0.341, Wilcoxon signed-rank). Therefore, we do not think that the change in phase coding is merely a result of less reliable phase estimates.

The authors propose that coherent oscillations could be the mechanism through which the prefrontal cortex influences beta activity in V4. I assume they mean coherent oscillations between the prefrontal cortex and V4. Given that they do have simultaneous recordings from the two areas they could test this hypothesis on their own data, however, they do not provide any results on that.

This paper only includes inactivation data. We are working on analyzing the simultaneous recording data for a future publication.

The authors make a strong point about the relevance of changes in the oscillation frequency and how this may result in an increase in firing rate although it could also be the reverse - an increase in firing rate leading to an increase in the frequency peak. It is not clear at all how these changes in frequency could come about. A more nuanced discussion based on both experimental and modeling data is necessary to appreciate the source and role (if any) of this observation.

As the reviewer notes, it is difficult to determine whether the frequency changes drive the rate changes, vice versa, or whether both are generated in parallel by a common source. We have adjusted our language to reflect this (lines 291-293). Future modeling work may be able to shed more light on the causal relationships between various neural signatures.

Reviewer #3 (Public review):

Summary:

In this report, the authors test the necessity of prefrontal cortex (specifically, FEF) activity in driving changes in oscillatory power, spike rate, and spike timing of extrastriate visual cortex neurons during a visual-spatial working memory (WM) task. The authors recorded LFP and spikes in V4 while macaques remembered a single spatial location over a delay period during which task-irrelevant background gratings were displayed on the screen with varying orientation and contrast. V4 oscillations (in the beta range) scaled with WM maintenance, and the information encoded by spike timing relative to beta band LFP about the task-irrelevant background orientation depended on remembered location. They also compared recorded signals in V4 with and without muscimol inactivation of FEF, demonstrating the importance of FEF input for WM-induced changes in oscillatory amplitude, phase coding, and information encoded about background orientations. Finally, they built a network model that can account for some of these results. Together, these results show that FEF provides meaningful input to the visual cortex that is used to alter neural activity and that these signals can impact information coding of task-irrelevant information during a WM delay.

Strengths:

(1) Elegant and robust experiment that allows for clear tests for the necessity of FEF activity in WM-induced changes in V4 activity.

(2) Comprehensive and broad analyses of interactions between LFP and spike timing provide compelling evidence for FEF-modulated phase coding of task-irrelevant stimuli at remembered location.

(3) Convincing modeling efforts.

Weaknesses:

(1) 0% contrast background data (standard memory-guided saccade task) are not reported in the manuscript. While these data cannot be used to consider information content of spike rate/time about task-irrelevant background stimuli, this condition is still informative as a 'baseline' (and a more typical example of a WM task).

We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL when remembering the V4 RF location) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

(2) Throughout the manuscript, the primary measurements of neural coding pertain to task-irrelevant stimuli (the orientation/contrast of the background, which is unrelated to the animal's task to remember a spatial location). The remembered location impacts the coding of these stimulus variables, but it's unclear how this relates to WM representations themselves.

Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

As mentioned above, the two points I raised in the public review merit a bit of development in the Discussion. In addition, the authors should revise some of their conclusions.

For instance (L217):

"The finding that WM mainly modulates phase coded information within extrastriate areas fundamentally shifts our understanding of how the top-down influence of prefrontal cortex shapes the neural representation, suggesting that inducing oscillations is the main way WM recruits sensory areas."

In my opinion, this one is over-the-top on various counts.

Here is another exaggerated instance (L298):

"...leading us to conclude that representations based on the average firing rate of neurons are not the primary way that top-down signals enhance sensory processing."

Again, as noted above, the problem is that one could make the case that the top-down signals are, in fact, highly effective, since they are completely quashing any distracter-related modulation in firing rate across RFs. There is only so much that one can conclude from responses to stimuli that are task-irrelevant, uniform across space, and constant over the course of a trial.

I think even the title goes too far. What the work shows, by all accounts, is that the sustained activity in FEF has a definitive impact on V4 *even* with respect to a sustained, irrelevant background stimulus. The result is very robust in this sense. However, this is quite different from saying that the *primary* means of functional control for FEF is via phase coding. Establishing that would require ruling out other forms of control (i.e., rate coding) in all or a wide range of experimental conditions. That is far from the restricted set of conditions tested here and is also at variance with many other experiments demonstrating effects of attention or even FEF microstimulation on V4 firing activity.

To reiterate, in my opinion, the work is carefully executed and the data are interesting and largely unambiguous. I simply take issue with what can be reliably concluded, and how the results fit with the rest of the literature. Revisions along these lines would improve the readability of the paper considerably.

We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

Reviewer #3 (Recommendations for the authors):

(1) My primary comment that came up multiple times as I read the manuscript (and which is summarized above) is that I wasn't ever sure why the authors are focused on analyzing neural coding of task-irrelevant sensory information during a WM task as a function of WM contents (remembered location). Most studies of neural codes supporting WM often focus on coding the remembered information - not other information. Conceptually, it seems that the brain would want to suppress - or at least not enhance - representations of task-irrelevant information when performing a demanding task, especially when there is no search requirement, and when there is no feature correspondence between the remembered and viewed stimuli. (i.e., the interaction between WM and visual input is more obvious for visual search for a remembered target). Why, in theory, would a visual region need to improve its coding of non-remembered information as a function of WM? This isn't meant to detract from the results, which are indeed very interesting and I think quite informative. The authors are correct that this is certainly relevant for sensory recruitment models of WM - there's clear evidence for a role of feedback from PFC to extrastriate cortex - but what role, specifically, each region plays in this task is critical to describe clearly, especially given the task-irrelevance of the input. Put another way: what if the animal was remembering an oriented grating? In that case, MI between spike-based measures and orientation would be directly relevant to questions of neural WM representations, as the remembered feature is itself being modeled. But here, the focus seems to be on incidental coding.

Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

Whether similar phase coding is also used to represent the content of object WM (for example, if the animal was remembering an oriented grating), or whether phase coding is only observed for WM’s modulation of the representation of incoming sensory signals, is an important question to be addressed in future work.

(2) Related to the above, the phrasing of the second sentence of the Discussion (lines 291-292) is ambiguous - do the authors mean that the FEF sends signals that carry WM content to V4, or that FEF sends projections to V4, and V4 has the WM content? As presently phrased, either of these are reasonable interpretations, yet they're directly opposing one another (the next sentence clarifies, but I imagine the authors want to minimize any confusion).

We have edited this sentence to read, “Within prefrontal areas, FEF sends direct projections to extrastriate visual areas, and activity in these projections reflects the content of WM.”

(3) I'm curious about how the authors consider the spatial WM task here different from a cued spatial attention task. Indeed, both require sustained use of a location for further task performance. The section of the Discussion addressing similar results with attention (lines 307-311) presently just summarizes the similarities of results but doesn't offer a theoretical perspective for how/why these different types of tasks would be expected to show similar neural mechanisms.

We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

(4) As far as I can tell, there is no consideration of behavioral performance on the memory-guided saccade task (RT, precision) across the different stimulus background conditions. This should be reported for completeness, and to determine whether there is an impact of the (likely) task-irrelevant background on task performance. This analysis should also be reported for Figure 3's results characterizing how FEF inactivation disrupts behavior (if background conditions were varied, see point 7 below).

We have added the effect of inactivation on behavioral RT and % correct across the different stimulus background conditions (Fig. S8). Background contrast and orientation did not impact either RT or % correct.

(5) Results from Figure 2 (especially Figures 2A-B) concerning phase-locked spiking in V4 should be shown for 0%-contrast trials as well, as these trials better align with 'typical' WM tasks.

We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

(6) The magnitude of SPL difference in aggregate (Figure 2B) is much, much smaller than that of the example site shown (Figure 2A), such that Figure 2A's neuron doesn't appear to be visible on Figure 2B's scatterplot. Perhaps a more representative sample could be shown? Or, the full range of x/y axes in Figure 2B could be plotted to illustrate the full distribution.

We have updated Fig. 2A with a more representative sample neuron.

(7) I'm a bit confused about the FEF inactivation experiments. In the Methods (lines 512-513), the authors mention there was no background stimulus presented during the inactivation experiment, and instead, a typical 8-location MGS task was employed. However, in the results on pg 8 (Lines 201-214), and Figure 3G, the authors quantify a phase code MI. The previous phase code MI analysis was looking at MI between each spike's phase and the background stimulus - but if there's no background, what's used to compute phase code MI? Perhaps what they meant to write was that, in addition to the primary task with a manipulation of background properties, an 8-location MGS task was additionally employed.

The reviewer is correct that both tasks were used after inactivation (the 8-location task to assess the spread of the behavioral effect of inactivation, and the MGS-background task for measuring MI). We have edited the methods text to clarify.

(8) How is % Correct defined for the MGS task? (what is the error threshold? Especially for the results described in lines 192-193).

The % correct is defined as correct completed trials divided by the total number of trials; the target window was a circle with radius of 2 or 4 dva (depending on cue eccentricity). These details have been added to the Methods.

(9) The paragraph from lines 183-200 describes a number of behavioral results concerning "scatter" and "RT" - the RT shown seems extremely high, and perhaps is normalized. Details of this normalization should be included in the Methods. The "scatter" is listed as dva, but it's not clear how scatter is quantified (std dev of endpoint distribution? Mean absolute error), nor how target eccentricity is incorporated (as scatter is likely higher for greater target eccentricity).

We have renamed ‘scatter’ to ‘saccade error’ in the text to match the figure, and now provide details in the Methods section. Both RT and saccade error are normalized for each session, details are now provided in the Methods. Since error was normalized for each session before performing population statistics, no other adjustment for eccentricity was made.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Summary:

The authors propose a new model of biologically realistic reinforcement learning in the direct and indirect pathway spiny projection neurons in the striatum. These pathways are widely considered to provide a neural substrate for reinforcement learning in the brain. However, we do not yet have a full understanding of mechanistic learning rules that would allow successful reinforcement learning like computations in these circuits. The authors outline some key limitations of current models and propose an interesting solution by leveraging learning with efferent inputs of selected actions. They show that the model simulations are able to recapitulate experimental findings about the activity profile in these populations of mice during spontaneous behavior. They also show how their model is able to implement off-policy reinforcement learning.

Strengths:

The manuscript has been very clearly written and the results have been presented in a readily digestible manner. The limitations of existing models, that motivate the presented work, have been clearly presented and the proposed solution seems very interesting. The novel contribution of the proposed model is the idea that different patterns of activity drive current action selection and learning. Not only does this allow the model is able to implement reinforcement learning computations well, but this suggestion may have interesting implications regarding why some processes selectively affect ongoing behavior and others affect learning. The model is able to recapitulate some interesting experimental findings about various activity characteristics of dSPN and iSPN pathway neuronal populations in spontaneously behaving mice. The authors also show that their proposed model can implement off-policy reinforcement learning algorithms with biologically realistic learning rules. This is interesting since off-policy learning provides some unique computational benefits and it is very likely that learning in neural circuits may, at least to some extent, implement such computations.

We thank the reviewer for the positive comments.

Weaknesses:

A weakness in this work is that it isn’t clear how a key component in the model - an efferent copy of selected actions - would be accessible to these striatal populations. The authors propose several plausible candidates, but future work may clarify the feasibility of this proposal.

We agree that the biological substrate of the efference copy remains a key open question. We discuss potential pathways in the Discussion section of our manuscript and hope that future experimental studies clarify the question.

Reviewer #2:

Summary:

The basal ganglia is often understood within a reinforcement learning (RL) framework, where dopamine neurons convey a reward prediction error that modulates cortico-striatal connections onto spiny projection neurons (SPNS) in the striatum. However, current models of plasticity rules are inconsistent with learning in a reinforcement learning framework.

This paper proposes a new model that describes how distinct learning rules in direct and indirect pathway striatal neurons allow them to implement reinforcement learning models. It proposes that two distinct components of striatal activity affect action selection and learning. They show that the proposed implementation allows learning in simple tasks and is consistent with experimental data from calcium imaging data in direct and indirect SPNs in freely moving mice.

Strengths:

Despite the success of reward prediction errors at characterizing the responses of dopamine neurons as the temporal difference error within an RL framework, the implementation of RL algorithms in the rest of the basal ganglia has been unclear. A key missing aspect has been the lack of a RL implementation that is consistent with the distinction of direct- and indirect SPNs. This paper proposes a new model that is able to learn successfully in simple RL tasks and explains recent experimental results.

The author shows that their proposed model, unlike previous implementations, this model can perform well in RL tasks. The new model allows them to make experimental predictions. They test some of these predictions and show that the dynamics of dSPNs and iSPNs correspond to model predictions.

More generally, this new model can be used to understand striatal dynamics across direct and indirect SPNs in future experiments.

We thank the reviewer for the positive comments.

Weaknesses:

The authors could characterize better the reliability of their experimental predictions and the description of the parameters of some of the simulations.

In addition to the descriptions in the Methods, we have provided code implementing the key features of our simulations, which should contribute to reproducibility of our results.

The authors propose some ideas about how the specificity of the striatal efferent inputs but should highlight better that this is a key feature of the model whose anatomical implementation has yet to be resolved.

We have clarified in the Discussion section “Biological substrates of striatal efferent inputs” that these represent assumptions or predictions that have not yet been demonstrated experimentally.

Reviewer #3:

Summary:

This paper points out an inconsistency of the roles of the striatal spiny neurons projecting to the indirect pathway (iSPN) and the synaptic plasticity rule of those neurons expressing dopamine D2 receptors and proposes a novel, intriguing mechanisms that iSPNs are activated by the efference copy of the chosen action that they are supposed to inhibit.

The proposed model was supported by simulations and analysis of the neural recording data during spontaneous behaviors.

Strengths:

Previous models suggested that the striatal neurons learn action-value functions, but how the information about the chosen action is fed back to the striatum for learning was not clear. The author pointed out that this is a fundamental problem for iSPNs that are supposed to inhibit specific actions and its synaptic inputs are potentiated with dopamine dips.

The authors propose a novel hypothesis that iSPNs are activated by efference copy of the selected action which they are supposed to inhibit during action selection. Even though intriguing and seemingly unnatural, the authors demonstrated that the model based on the hypothesis can circumvent the problem of iSPNs learning to disinhibit the actions associated with negative reward errors. They further showed by analyzing the cell-type specific neural recording data by Markowitz et al. (2018) that iSPN activities tend to be anti-correlated before and after action selection.

We thank the reviewer for the positive comments.

Weaknesses:

It is not correct to call the action value learning using the externally-selected action as “offpolicy.” Both off-policy algorithm Q-learning and on-policy algorithm SARSA update the action value of the chosen action, which can be different from the greedy action implicated by the present action values. In standard reinforcement learning terminology, on-policy or off-policy is regarding the actions in the subsequent state, whether to use the next action value of (to be) chosen action or that of greedy choice as in equation (7).

It is worth noting that this paper suggested that dopamine neurons encode on-policy TD errors: Morris G, Nevet A, Arkadir D, Vaadia E, Bergman H (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9, 1057-63. https://doi.org/10.1038/nn1743.

We regret that we do not completely follow the reviewer’s comment. We use “off-policy” to refer to the fact that, considered in isolation, the basal ganglia reinforcement learning system that we model learns a target policy that may be distinct from the behavioral policy of the organism as a whole.

It is also confusing to contract TD learning and Q-learning, as the latter is considered as one type of TD learning. In the TD error signal by state value function (6) is dependent on the chosen action at−1 implicitly in rt and st based on the reward and state transition function.

We agree that this was confusing. We have therefore changed the places in our paper where we intended to refer to “TD learning of a value function V (s)” to specifically mention V (s), rather than just “TD learning.”

It is not clear why interferences of the activities for action selection and learning can be avoided, especially when actions are taken with short intervals or even temporal overlaps. How can the efference copy activation for the previous action be dissociated with the sensory cued activation for the next action selection?

The non-interference arises from the orthogonality of the difference (action selection) and sum (efference copy) modes, as described in Figure 3. However, we agree with the reviewer that the problem of temporal credit assignment, when many actions are taken before reward feedback is obtained, is present in our model, as in any standard RL model.

Although it may be difficult to single out the neural pathway that carries the efference copy signal to the striatum, it is desired to consider their requirements and difference possibilities. A major issue is that the time delay from actions to reward feedback can be highly variable.

An interesting candidate is the long-latency neurons in the CM thalamus projecting to striatal cholinergic interneurons, which are activated following low-reward actions: Minamimoto T, Hori Y, Kimura M (2005). Complementary process to response bias in the centromedian nucleus of the thalamus. Science, 308, 1798-801. https://doi.org/10.1126/science.1109154.

We are grateful for the interesting suggestion and reference, which we have added to the manuscript. However, we note that the issue of delayed reward feedback may also be partially addressed by using a sufficiently long eligibility trace.

In the paragraph before Eq. (3), Eq. (1) should be Eq. (2) for the iSPN.

Corrected.

stakblitz

search = online code develipment javascript

LLM's (Language Models) are like the human brain and use similar mechanisms.

Language models have improved to perform highly diverse tasks instead to older language models, which could only process text.

this sources talks about object oriented programming, which is programming around an object within the program itself. This contains both data and code to modify data

I believe pseudocode is a way of sorting out the logic and a good start of writing the code. It is hard for people to write the complete and logic-clear code at one time, but with the help of pseudocode, they will understand the general outline of the logic and what should be filled in inside the framework.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study aimed to investigate the effects of optically stimulating the A13 region in healthy mice and a unilateral 6-OHDA mouse model of Parkinson's disease (PD). The primary objectives were to assess changes in locomotion, motor behaviors, and the neural connectome. For this, the authors examined the dopaminergic loss induced by 6-OHDA lesioning. They found a significant loss of tyrosine hydroxylase (TH+) neurons in the substantia nigra pars compacta (SNc) while the dopaminergic cells in the A13 region were largely preserved. Then, they optically stimulated the A13 region using a viral vector to deliver the channelrhodopsine (CamKII promoter). In both sham and PD model mice, optogenetic stimulation of the A13 region induced pro-locomotor effects, including increased locomotion, more locomotion bouts, longer durations of locomotion, and higher movement speeds. Additionally, PD model mice exhibited increased ipsi lesional turning during A13 region photoactivation. Lastly, the authors used whole-brain imaging to explore changes in the A13 region's connectome after 6-OHDA lesions. These alterations involved a complex rewiring of neural circuits, impacting both afferent and efferent projections. In summary, this study unveiled the pro-locomotor effects of A13 region photoactivation in both healthy and PD model mice. The study also indicates the preservation of A13 dopaminergic cells and the anatomical changes in neural circuitry following PD-like lesions that represent the anatomical substrate for a parallel motor pathway.

Strengths:

These findings hold significant relevance for the field of motor control, providing valuable insights into the organization of the motor system in mammals. Additionally, they offer potential avenues for addressing motor deficits in Parkinson's disease (PD). The study fills a crucial knowledge gap, underscoring its importance, and the results bolster its clinical relevance and overall strength.

The authors adeptly set the stage for their research by framing the central questions in the introduction, and they provide thoughtful interpretations of the data in the discussion section. The results section, while straightforward, effectively supports the study's primary conclusion - the pro-locomotor effects of A13 region stimulation, both in normal motor control and in the 6-OHDA model of brain damage.

We thank the reviewer for their positive comments.

Weaknesses:

(1) Anatomical investigation. I have a major concern regarding the anatomical investigation of plastic changes in the A13 connectome (Figures 4 and 5). While the methodology employed to assess the connectome is technically advanced and powerful, the results lack mechanistic insight at the cell or circuit level into the pro-locomotor effects of A13 region stimulation in both physiological and pathological conditions. This concern is exacerbated by a textual description of results that doesn't pinpoint precise brain areas or subareas but instead references large brain portions like the cortical plate, making it challenging to discern the implications for A13 stimulation. Lastly, the study is generally well-written with a smooth and straightforward style, but the connectome section presents challenges in readability and comprehension. The presentation of results, particularly the correlation matrices and correlation strength, doesn't facilitate biological understanding. It would be beneficial to explore specific pathways responsible for driving the locomotor effects of A13 stimulation, including examining the strength of connections to well-known locomotor-associated regions like the Pedunculopontine nucleus, Cuneiformis nucleus, LPGi, and others in the diencephalon, midbrain, pons, and medulla.

We initially considered two approaches. The first was to look at specific projections to the motor regions, focusing on the MLR. The second was to utilize a whole-brain analysis, which is presented here. Given what we know about the zona incerta, especially its integrative role, we felt that examining the full connectome was a reasonable starting point.

The value of the whole-brain approach is that it provides a high-level overview of the afferents and efferents to the region. The changes in the brain that occur following Parkinson-like lesions, such as those in the nigrostriatal pathway, are complex and can affect neighbouring regions such as the A13. Therefore, we wished to highlight the A13, which we considered a therapeutic target, and examine changes in connectivity that could occur following acute lesions affecting the SNc. We acknowledge that this study does not provide a causal link, but it presents the fundamental background information for subsequent hypothesis-driven, focused, region-specific analysis.

The terms provided were taken from the Allen Brain Atlas terminology and presented as abbreviations. We have added two new figures focusing on motor regions to make the information more comprehensible (new Figures 4 and 5) and rewrote the connectomics section to make it easier to understand.

Additionally, identifying the primary inputs to A13 associated with motor function would enhance the study's clarity and relevance.

This is a great point to help simplify the whole-brain results. We have presented the motor-related inputs and outputs as part of a new figure in the main paper (Figure 5) and added accompanying text in the results section. We have also updated the correlation matrices to concentrate on motor regions (Figure 4). This highlights possible therapeutic pathways. We have also enhanced our discussion of these motor-related pathways. We have retained the entire dataset and added it to our data repository for those interested.

The study raises intriguing questions about compensatory mechanisms in Parkinson's disease and a new perspective on the preservation of dopaminergic cells in A13, despite the SNc degeneration, and the plastic changes to input/output matrices. To gain inspiration for a more straightforward reanalysis and discussion of the results, I recommend the authors refer to the paper titled "Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon from the David Kleinfeld laboratory." This could guide the authors in investigating motor pathways across different brain regions.

Thank you for the advice. As pointed out, Kleinfeld’s group presented their data in a nice, focused way. For the connectomic piece, we have added Figure 5, which provides a better representation than our previous submission.

(2) Description of locomotor performance. Figure 3 provides valuable data on the locomotor effects of A13 region photoactivation in both control and 6-OHDA mice. However, a more detailed analysis of the changes in locomotion during stimulation would enhance our understanding of the pro-locomotor effects, especially in the context of 6-OHDA lesions. For example, it would be informative to explore whether the probability of locomotion changes during stimulation in the control and 6-OHDA groups. Investigating reaction time, speed, total distance, and could reveal how A13 is influencing locomotion, particularly after 6-OHDA lesions. The laboratory of Whelan has a deep knowledge of locomotion and the neural circuits driving it so these features may be instructive to infer insights on the neural circuits driving movement. On the same line, examining features like the frequency or power of stimulation related to walking patterns may help elucidate whether A13 is engaging with the Mesencephalic Locomotor Region (MLR) to drive the pro-locomotor effects. These insights would provide a more comprehensive understanding of the mechanisms underlying A13-mediated locomotor changes in both healthy and pathological conditions.

Thank you for these suggestions. We have reorganized Figure 3 to highlight the metrics by separating the 6-OHDA from the Sham experiments (3F-J, which highlights distance travelled, average speed and duration). We have also added additional text to highlight these metrics better in the text. We have relabelled Supplementary Figure S3, which presents reaction time as latency to initiate locomotion and updated the main text to address the reviewers' points.

Reviewer #2 (Public Review):

Summary:

The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection. The study suggests that if the remodeling of the A13 region connectome does not promote recovery following chronic dopaminergic depletion, photostimulation of the A13 region restores locomotor functions.

Strengths:

Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients.

Weaknesses:

Electrical stimulation of the medial Zona Incerta, in which the A13 region is located, has been previously reported to promote locomotion (Grossman et al., 1958). Recent mouse studies have shown that if optogenetic or chemogenetic stimulation of GABAergic neurons of the Zona Incerta promotes and restores locomotor functions after 6-OHDA injection (Chen et al., 2023), stimulation of glutamatergic ZI neurons worsens motor symptoms after 6-OHDA (Lie et al., 2022).

Thank you - we have added this reference. It is helpful as Grossman did stimulate the zona incerta in the cat and elicit locomotion, suggesting that stimulation of the area in normal mice has external validity. Grossman’s results prompted a later clinical examination of the zona incerta, but it concentrated on the zona incerta regions close to the subthalamic regions (Ossowska 2019), further caudal to the area we focused on. Chen et al. (2023) targeted the area in the lateral aspect of central/medial zona incerta, formed by dorsal and ventral zona incerta, which may account for the differing results. Our data were robust for stimulation of the medial aspect of the rostromedial zona incerta. The thigmotactic behaviour that we observed in our work that focused on CamKII neurons has not been observed with chemogenetic, optogenetic activation or with photoinhibition of GABAergic central/medial ZI (Chen et al. 2023).

GABAergic activation of mZI to Cuneiform projections (Sharma et al. 2024) also did not produce thigmotactic behavior. We have added these points to the discussion.

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, behavioral results of this study raise questions about the neuronal population targeted in the vicinity of the A13 region. Moreover, if YFP and CHR2-YFP neurons express dopamine (TH) within the A13 region (Fig. 2), there is also a large population of transduced neurons within and outside of the A13 region that do not, thus suggesting the recruitment of other neuronal cell types that could be GABAergic or glutamatergic.

We found that CamKII transfection of the A13 region was extremely effective in promoting locomotor activity, which was critical for our work in exploring its possible therapeutic potential. We have since quantified the cell number, we found that the c-fos cell number was increased following ChR2 activation. There is evidence of TH activation - but the data suggest that other cell types contribute. C-fos alone is a blunt tool to assess specificity - rather, it is better at showing overall photostimulus efficacy - which we have demonstrated. Moreover, there is evidence that cell types are not purely dopaminergic, with GABA co-localized (Negishi et al. 2020). We acknowledge that specific viral approaches that target the GABAergic, glutamatergic, and dopaminergic circuits would be very useful. The range of tools to target A13 dopaminergic circuits is more limited than the SNc, for example, because the A13 region lacks DAT, and TH-IRES-Cre approaches, while helpful, are less specific than DAT-Cre mouse models. Intersectional approaches targeting multiple transmitters (glutamate & dopamine, for example) may be one solution as we do not expect that a single transmitter-specific pathway would work, as well as broad targeting of the A13 region. Our recent work suggests that GABAergic neuron activation may have more general effects on behaviour rather than control of ongoing locomotor parameters (Sharma et al. 2024). Recent work shows a positive valence effect of dopamine A13 activation on motivated food-seeking behavior, which differs from consummatory behavior observed with GABAergic modulation (Ye, Nunez, and Zhang 2023). Chemogenetic inactivation and ablation of dopaminergic A13 revealed that they contribute to grip strength and prehensile movements, uncoupling food-seeking grasping behavior from motivational factors (Garau et al. 2023). Overall, this suggests differing effects of GABA compared to DA and/or glutamatergic cell types, consistent with our effects of stimulating CamKII. The discussion has been updated.

Regarding the analysis of interregional connectivity of the A13 region, there is a lack of specificity (the viral approach did not specifically target the A13 region), the number of mice is low for such correlation analyses (2 sham and 3 6-OHDA mice), and there are no statistics comparing 6-OHDA versus sham (Fig. 4) or contra- versus ipsilesional sides (Fig. 5). Moreover, the data are too processed, and the color matrices (Fig. 4) are too packed in the current format to enable proper visualization of the data. The A13 afferents/efferents analysis is based on normalized relative values; absolute values should also be presented to support the claim about their upregulation or downregulation.

Generally, papers using tissue-clearing imaging approaches have low sample sizes due to technical complexity and challenges. The technical challenges of obtaining these data were substantial in both collection and analysis. There are multiple technical complexities arising from dual injections (A13 and MFB coordinates) and targeting the area correctly. The A13 region is difficult to target as it spans only around 300 µm in the anterior-posterior axis. While clearing the brain takes weeks, and light-sheet imaging also takes time, the time necessary to analyze the tissue using whole-brain quantification is labor intensive, especially with a lack of a standardized analysis pipeline from atlas registrations, signal segmentations, and quantifications. The field is still relatively new, requiring additional time to refine pipelines.

Correlation matrices are often used in analyzing connectivity patterns on a brain-wide scale, as they can identify any observable patterns within a large amount of data. We used correlation matrices to display estimated correlation coefficients between the afferent and efferent proportions from one brain subregion to another across 251 brain regions in total in a pairwise manner (not for hypothesis testing). We provided descriptive statistics (mean and error bars) in the original Figure 5C and G. As mentioned in comments for Reviewer 1, we have now presented the data in revised Figure 4 and 5 that focuses specifically on motor-related pathways to provide information on possible pathways. The has simplified the correlation matrices and highlighted the differences in 6-OHDA efferent data especially. As suggested, raw values are shared in a supplemental file on our data repository.

In the absence of changes in the number of dopaminergic A13 neurons after 6-OHDA injection, results from this correlation analysis are difficult to interpret as they might reflect changes from various impaired brain regions independently of the A13 region.

We acknowledge that models of Parkinson’s disease, particularly those using 6-OHDA, induce plasticity in various regions, which may subsequently affect A13 connectivity. We aim to emphasize the residual, intact A13 pathways that could serve as therapeutic targets in future investigations. This emphasis is pertinent in the context of potential clinical applications, as the overall input and output to the region fundamentally dictate the significance of the A13 region in lesioned nigrostriatal models. We agree with the reviewer that the changes certainly can be independent of A13; however, the fact that there was a significant change in the connectome post-6-OHDA injection and striatonigral degeneration is in and of itself important to document. We have added a sentence acknowledging this limitation to the discussion.

There is no causal link between anatomical and behavioral data, which raises questions about the relevance of the anatomical data.

This point was also addressed earlier in response to a comment from Reviewer 1. Focusing on specific motor pathways is one avenue to explore. However, given that the zona incerta acts as an integrative hub, we believed it is prudent to initially examine both afferent and efferent pathways using a brain-wide approach. For instance, without employing this methodology, the potential significance of cortical interconnectivity to the A13 region might not have been fully appreciated. As mentioned previously, we will place additional emphasis on motor-related regions in our revised paper, thereby enhancing the relevance of the anatomical data presented. With these modifications, we anticipate that our data will underscore specific motor-related targets for future exploration, employing optogenetic targeting to assess necessity and sufficiency.

Overall, the study does not take advantage of genetic tools accessible in the mouse to address the direct or indirect behavioral and anatomical contributions of the A13 region to motor control and recovery after 6-OHDA injection.

Our study has not specifically targeted neurons that express dopaminergic, glutamatergic, or GABAergic properties (refer to earlier comment for more detail). However, like others, we find that targeting one neuronal population often does not result in a pure transmitter phenotype. For instance, evidence suggests co-localization of dopamine neurons with a subpopulation of GABA neurons in the A13/medial zona incerta (Negishi et al. 2020). In the hypothalamus, research by Deisseroth and colleagues (Romanov et al. 2017) indicates the presence of multiple classes of dopamine cells, each containing different ratios of co-localized peptides and/or fast neurotransmitters. Consequently, we believe our work lays the foundation for the investigations suggested by the reviewer. Furthermore, if one considers this work in the context of a preclinical study to determine whether the A13 might be a target in human Parkinson's disease, the existing technology that could be utilized is deep brain stimulation (DBS) or electrical modulation, which would also affect different neuronal populations in a non-specific manner.

While optogenetic stimulation therapy is longer term, using CamKII combined with the DJ hybrid AAV could be a translatable strategy for targeting A13 neuronal populations in non-human primates (Watakabe et al. 2015; Watanabe et al. 2020). We have added to the discussion.

Reviewer #3 (Public Review):

Kim, Lognon et al. present an important finding on pro-locomotor effects of optogenetic activation of the A13 region, which they identify as a dopamine-containing area of the medial zona incerta that undergoes profound remodeling in terms of afferent and efferent connectivity after administration of 6-OHDA to the MFB. The authors claim to address a model of PD-related gait dysfunction, a contentious problem that can be difficult to treat with dopaminergic medication or DBS in conventional targets. They make use of an impressive array of technologies to gain insight into the role of A13 remodeling in the 6-OHDA model of PD. The evidence provided is solid and the paper is well written, but there are several general issues that reduce the value of the paper in its current form, and a number of specific, more minor ones. Also, some suggestions, that may improve the paper compared to its recent form, come to mind.

Thank you for the suggestions and careful consideration of our work - it is appreciated.

The most fundamental issue that needs to be addressed is the relation of the structural to the behavioral findings. It would be very interesting to see whether the structural heterogeneity in afferent/effects projections induced by 6-OHDA is related to the degree of symptom severity and motor improvement during A13 stimulation.

As mentioned in comments for Reviewer 1, we have performed additional analysis and present this in Figure 5. We have also revised Figure 4, focusing on motor regions. Our work will provide a roadmap for future studies to disentangle divergent or convergent A13 pathways that are involved in different or all PD-related motor symptoms. Because we could not measure behavioural change in the same animals studied with the anatomic study (essentially because the optrode would have significantly disrupted the connectome we are measuring), we cannot directly compare behaviour to structure.

The authors provide extensive interrogation of large-scale changes in the organization of the A13 region afferent and efferent distributions. It remains unclear how many animals were included to produce Fig 4 and 5. Fig S5 suggests that only 3 animals were used, is that correct? Please provide details about the heterogeneity between animals. Please provide a table detailing how many animals were used for which experiment. Were the same animals used for several experiments?

The behavioral set and the anatomical set were necessarily distinct. In the anatomical experiments, we employed both anterograde and retrograde viral approaches to target the afferent and efferent A13 populations with fluorescent proteins. For the behavioral approach, a single ChR2 opsin was utilized to photostimulate the A13 region; hence combining the two populations was not feasible. We were also concerned that the optrode itself would interfere with connectomics. A lower number of animals were used for the whole-brain work due to technical limitations described earlier. We have now provided additional information regarding numbers in all figures and the text. Using Spearman’s correlation analysis, we found afferent and efferent proportions across animals to be consistent, with an average correlation of 0.91, which is reported in Figure S6.

While the authors provide evidence that photoactivation of the A13 is sufficient in driving locomotion in the OFT, this pro-locomotor effect seems to be independent of 6-OHDA-induced pathophysiology. Only in the pole test do they find that there seems to be a difference between Sham vs 6-OHDA concerning the effects of photoactivation of the A13. Because of these behavioral findings, optogenic activation of A13 may represent a gain of function rather than disease-specific rescue. This needs to be highlighted more explicitly in the title, abstract, and conclusion.

Optogenetic activation of A13 may represent a gain of function in both healthy and 6-OHDA mice, highlighting a parallel descending motor pathway that remains intact. 6-OHDA lesions have multiple effects on motor and cognitive function. This makes a single pathway unlikely to rescue all deficits observed in 6-OHDA models. The lack of locomotion observed in 6-OHDA models can be reversed by A13 region photostimulation. Therefore, this is a reversal of a loss of function, in this case. However, the increase in turning represents a gain of function. We have highlighted this as suggested in the discussion.

The authors claim that A13 may be a possible target for DBS to treat gait dysfunction. However, the experimental evidence provided (in particular the lack of disease-specific changes in the OFT) seems insufficient to draw such conclusions. It needs to be highlighted that optogenetic activation does not necessarily have the same effects as DBS (see the recent review from Neumann et al. in Brain: https://pubmed.ncbi.nlm.nih.gov/37450573/). This is important because ZI-DBS so far had very mixed clinical effects. The authors should provide plausible reasons for these discrepancies. Is cell-specificity, which only optogenetic interventions can achieve, necessary? Can new forms of cyclic burst DBS achieve similar specificity (Spix et al, Science 2021)? Please comment.

Thank you for the valuable comments. They have been incorporated into the discussion.

Our study highlights a parallel motor pathway provided by the A13 region that remains intact in 6-OHDA mice and can be sufficiently driven to rescue the hypolocomotor pathology observed in the OFT and overcome bradykinesia and akinesia. The photoactivation of ipsilesional A13 also has an overall additive effect on ipsiversive circling, representing a gain of function on the intact side that contributes to the magnitude of overall motor asymmetry against the lesioned side. The effects of DBS are rather complex, ranging from micro-, meso-, to macro-scales, involving activation, inhibition, and informational lesioning, and network interactions. This could contribute to the mixed clinical effects observed with ZI-DBS, in addition to differences in targeting and DBS programming among the studies (see review (Ossowska 2019) ). Also the DBS studies targeting ZI have never targeted the rostromedial ZI which extends towards the hypothalamus and contains the A13. Furthermore, DBS and electrical stimulation of neural tissue, in general, are always limited by current spread and lower thresholds of activation of axons (e.g., axons of passage), both of which can reduce the specificity of the true therapeutic target. Optogenetic studies have provided mechanistic insights that could be leveraged in overcoming some of the limitations in targeting with conventional DBS approaches. Spix et al. (2021) provided an interesting approach highlighting these advancements. They devised burst stimulation to facilitate population-specific neuromodulation within the external globus pallidus. Moreover, they found a complementary role for optogenetics in exploring the pathway-specific activation of neurons activated by DBS. To ascertain whether A13 DBS may be a viable therapy for PD gait, it will be necessary to perform many more preclinical experiments, and tuning of DBS parameters could be facilitated by optogenetic stimulation in these murine models. We have added to the discussion.

In a recent study, Jeon et al (Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus, 2022, Cell Reports) provided evidence on the topographically graded organization of STN afferents and McElvain et al. (Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon, 2021, Neuron) have shown similar topographical resolution for SNr efferents. Can a similar topographical organization of efferents and afferents be derived for the A13/ ZI in total?

The ZI can be subdivided into four subregions in the antero-posterior axis: rostral (ZIr), dorsal (ZId), ventral (ZIv), and caudal (ZIc) regions. The dorsal and ventral ZI is also referred together as central/medial/intermediate ZI. There are topographical gradients in different cell types and connectivity across these subregions (see reviews: (Mitrofanis 2005; Monosov et al. 2022; Ossowska 2019). Recent work by Yang and colleagues (2022) demonstrated a topographical organization among the inputs and outputs of GABAergic (VGAT) populations across four ZI subregions. Given that A13 region encompasses a smaller portion (the medial aspect) of both rostral and medial/central ZI (three of four ZI subregions) and coexpress VGAT, A13 region likely falls under rostral and intermediate medial ZI dataset found in Yang et al. (2022). With our data, we would not be able to capture the breadth of topographical organization shown in Yang et al (2022).

In conclusion, this is an interesting study that can be improved by taking into consideration the points mentioned above.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Figure 2 indeed presents valuable information regarding the effects of A13 region photoactivation. To enhance the comprehensiveness of this figure and gain a deeper understanding of the neurons driving the pro-locomotor effect of stimulation, it would be beneficial to include quantifications of various cell types:

• cFos-Positive Cells/TH-Positive Cells: it can help determine the impact of A13 stimulation on dopaminergic neurons and the associated pro-locomotor effect in the healthy condition and especially in the context of Parkinson's disease (PD) modeling.

• cFos-Positive Cells /TH-Negative Cells: Investigating the number of TH-negative cells activated by stimulation is also important, as it may reveal non-dopaminergic neurons that play a role in locomotor responses. Identifying the location and characteristics of these TH-negative cells can provide insights into their functional significance.

We have completed this analysis. The data is presented in Figure 2F, where we show increased c-fos intensity with photoactivation. We observed an increase in the number of cells activated in the A13 region. However, we did not definitively see increases in TH+ cells, suggesting a heterogeneous set of neurons responsible for the effects—possibly glutamatergic neurons.

Incorporating these quantifications into Figure 2 would enhance the figure's informativeness and provide a more comprehensive view of the neuronal populations involved in the locomotor effects of A13 stimulation.

We have added text and a new graph.

(2) Refer to Figure 3. In the main text (page 5) when describing the animal with 6-OHDA the wrong panels are indicated. It is indicated in Figure 2A-E but it should be replaced with 3A-E.

Please do that.

Done, and we have updated the figure to improve readability, by separating the 6-OHDA findings from sham in all graphs.

Reviewer #2 (Recommendations For The Authors):

Abstract

Page 1: Inhibitory or lesion studies will be necessary to support the claim that the global remodeling of afferent and efferent projections of the A13 region highlights the Zona Incerta's role as a crucial hub for the rapid selection of motor function.

Overall, there is quite a bit of evidence that the zona incerta is a hub for afferent/efferents.

Mitrofanis (2005) and, more recently, Wang et al. (2020) summarize some of the evidence. Yang (2022) illustrates that the zona incerta shows multiple inputs to GABAergic neurons and outputs to diverse regions. Recent work suggests that the zona incerta contributes to various motor functions such as hunting, exploratory locomotion, and integrating multiple modalities (Zhao et al. 2019; Wang et al. 2019; Monosov et al. 2022; Chometton et al. 2017). The introduction has been updated.

Introduction

Page 2, paragraph 2: "However, little attention has been placed on the medial zona incerta (mZI), particularly the A13, the only dopamine-containing region of the rostral ZI" Is the A13 region located in the rostral or medial ZI or both?

It should have been written “rostromedial” ZI. The A13 is located in the medial aspect of rostromedial ZI. Introduction has been updated.

Page 2, para 3: Li et al (2021) used a mini-endoscope to record the GCaMP6 signal. Masini and Kiehn, 2022 transiently blocked the dopaminergic transmission; they never used 6-OHDA.

Please correct through the text.

Corrected.

Page 2, para 4: the A13 connectome encompasses the cerebral cortex,... MLR. The MLR is a functional region, correct this for the CNF and PPN.

Corrected.

Page 3, the last paragraph of the introduction could be clarified by presenting the behavioral data first, followed by the anatomy.

This has been corrected

Figure 1 is nice and clear, and well summarizes the experimental design.

Thank you.

Figure 2 shows an example of the extent of the ChR2-YFP expression and the position of an optical fiber tip above the dopaminergic A13 region from a mouse. Without any quantification, these images could be included in Figure 1. Despite a very small volume (36.8nL) of AAV, the extent of ChR2-YFP expression is quite large and includes dopaminergic and unidentified neurons within the A13 region but also a large population of unidentified neurons outside of it, thus raising questions about the volume and the types of neurons recruited.

This is an important consideration. The issue of viral spread is complex and depends on factors including tissue type, serotype, and promotor of the virus. Li et al. (2021), for example, used different virus serotypes and promotors, injecting 150nL, whereas we used AAV DJ, injecting 36.8nL. AAV-DJ is a hybrid viral type consisting of multiple serotypes. It has a high transduction efficiency, which leads to greater gene delivery than single-serotype AAV viral constructs (Mao et al. 2016). A secondary consideration regarding translation was that AAV-DJ could effectively transduce non-primate neurons (Watanabe et al. 2020). We have addressed the issue of neurons recruited earlier, provided c-Fos quantification, and provided a new supplementary figure showing viral spread (Figure S1).

Anatomical reconstruction of the extent of the ChR2-YFP expression and the location of the tip of the optical fiber will be necessary to confirm that ChR2-YFP expression was restricted to the A13 region.

We will provide additional information regarding viral spread, ferrule tip placement, and c-fos cell counts. This has been done in Figure 2 and we also present a new Figure S1 where we have quantified the viral spread.

Page 5, 1st para: Double-check the references, as not all of them are 6-OHDA injections in the MLF.

Corrected. Removed Kiehn reference.

Page 5, 1st para, 4th line: Replace ferrule with optical canula or fiber.

Done

Page 5, 1st para, 9th line: Replace Figure 2 with Figure 3.

Done

Page 5, 2nd para: About the refractory decrease in traveled distance by sham-ChR2 mice: is this significant?

It was not significant (Figure S1C, 1-way RM ANOVA: F5,25 = 0.486, P \= 0.783). This has been updated in the text.

Figure 3 showing behavioral assessments is nice, but the stats are not always clear. In Fig 3A, are each of the off and on boxes 1 minute long? The figure legend states the test lasts 1 min, but isn't it 4 minutes? In Figure 3B-E and 3J-M, what are the differences? Do the stats identify a significant difference only during the stimulation phase? Fig. 3F-I are nice and could have been presented as primary examples prior to data analysis in Fig. 3B-E. Group labels above the graph would help.

Yes, the off-on boxes are 1 minute long. The error is corrected in the legend. Great suggestion for F-I - they have been moved ahead of the summary figures. We have also updated new Fig 3F-,I, J, L, M) to make the differences between 6-OHDA and sham graphs easier to visualize. The stats do indicate a significant difference during the stimulation phase. We have added group labels, and reorganized the figure, and it is much easier to read now.

Fig. 3L-M, what do PreSur, Post, and Ferrule mean? I assume that Ferrule refers to mice tested with the optical fiber without stimulation, whereas Stim. refers to the stimulation. It would be helpful to standardize the format of stats in Fig. 3B-E and 3-J-M. What are time points a, b, and c referring to?

We have renamed the figure names to be more intuitive. We have standardized the presentation of statistics in the figure, and eliminated the a,b,c nomenclature. We have also updated the caption to provide descriptions of the tests in Fig 3 L-M.

Figure S2A: the higher variability in 6-OHDA-YFP mice in comparison to 6-OHDA-ChR2 mice prior to stimulation suggests that 6-OHDA-YFP mice were less impaired. Why use boxplots only for these data? Would a pairwise comparison be more appropriate?

We have removed these plots from Figure S2. We now present the Baseline to Pre values across the experimental timespan to illustrate the fact that distance travelled returned to baseline values for all trials conducted.

Fig. S2B: add the statistical marker.

We have removed this from Figure S2.

Page 7, para 1, line 8: to add "in comparison to 6-OHDA-YFP and YFP mice" to during photostimulation... (Figure 3E).

Done

Page 7, para 3, line 5: about larger improvement, replace "sham ChR2" with "6-OHDA."

Done

Page 8, para 1, line 4: Perier et al., 2000 reported that 6-OHDA injection increased the firing frequency of the ZI over a month.

Added the timeframe to this sentence.

Page 8, para 2, line 1: Since the results were expected, add some references.

Done.

Page 8, para 3, line 4. Double-check the reference.

Corrected.

Page 8: About large-scale changes in the A13 region, the relevance of correlation matrices is difficult to grasp. Analysis of local connectivity would have been more informative in the context of GABAergic and glutamatergic neurons of the ZI in the vicinity of the A13 region.

We have updated the figures for connectivity throughout the manuscript. Overall, there are new Figures 4 and 5 in the main text. We also provide a revised Supplementary Figure 8. Unfortunately, we could not do that experiment regarding local connectivity. In light of our new work (Sharma et al. 2024), it is clear that this will be critical going forward.

Page 8, para 3, line: given Fig. 2, there is concern about the claim that only the A13 region was targeted. The time of the analysis after 6-OHDA should be mentioned. Some sections of the paragraph could be moved to methods.

We have provided more information about the viral spread in the text and Supplementary Figure 1. The functional and anatomical experiments are separate, which we realize caused confusion. We have mentioned analysis time after 6-OHDA and inserted this into the text.

Fig. 4: The color code helps the reader visualize distribution differences. However, statistical analyses comparing 6-OHDA versus sham should be included. Quantification per region would greatly help readers visualize the data and support the conclusion. The relationship between the type of correlation (positive or negative) and absolute change (increase or decrease) is unknown in the current format, which limits the interpretation of the data. Moreover, examples of raw images of axons and cells should be presented for several brain regions. The experimental design with a timeline, as in Fig. 1, would be helpful. The legend for Fig. 4 is a bit long. Some sections are very descriptive, whereas others are more interpretive.

We have provided a new Figure 5 where we present quantification per region, and the correlation matrices have been updated in Figure 4. We have also focused on motor regions as mentioned earlier. We also provide examples of raw regions in Supplementary Figure 8. Raw values are shared on our data repository.

Page 10, para 1, line 1: add "afferent" to "changes in -afferent and- projection patterns."

Done

Page 10, para 1, line 9: remove the 2nd "compared to sham" in the sentence.

Done

Page 10, para 1, line 10: remove "coordinated" in "several regions showed a coordinated reduction in afferent density." We cannot say anything about the timing of events, as there is only info at 1 month.

Done

Page 10, para 2: the section should be written in the past tense.

Done

Page 13, para 2, the last sentence is overstated. Please remove "cells" and refer to the A13 region instead.

Done

About differential remodelling of the A13 region connectome: Figure 5C and 5G: The proportion of total afferents ipsi- and contralateral to 6-OHDA injection argues that the A13 region primarily receives inputs from the cortical plate and the striatum. Unfortunately, there are no statistics.

Due to the small sample size, we provided descriptive statistics (mean and error bars) in Figure 5A. As mentioned in comments for Reviewers 1 and 2, we have revised Figure 5 to present data focusing on motor-related pathways to provide clarity. In addition, absolute values are shared on our data repository.

Figure 5 D and 5H: Changes in the proportion of total afferents/projections are relatively modest (less than 10% of the whole population for the highest changes). There is no standard deviation for these data and no statistics. Do they reflect real changes or variability from the injection site?

The changes are relatively modest (less than 10%) since a small brain region usually provides a small proportion of total input (McElvain et al. 2021; Yang et al. 2022). The changes in the proportions reflect real differences between average proportions observed in sham and 6-OHDA mice. The variability in the total labelling of neurons and fibers was minimized by normalizing individual regional counts against total counts found in each animal. This figure has been updated as reviewers requested.

Fig 5F and H: The example in F shows a huge decrease in the striatum, but H indicates only a 2% change, which makes the example not very representative. Absolute values would be helpful.

While a 2% change may seem small, it represents a relatively large change in the A13 efferent connectome. To provide further clarity, we have provided absolute values as suggested in our new supplemental table.

Figure 6 is inaccurate and unnecessary.

Figure 6 has been removed.

Discussion

Although interesting, the discussion is too long.

The discussion has been reduced by about three quarters of a page.

Methods

Page 17, para 1: include the stereotaxic coordinates of the optical cannula above the A13 region.

Added.

References

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This reminds me of topics that I've learned in previous classes- Specifically surveillance capitalism and the digital panopticon. These notions, in relation to the ethics of bots, creates a rather interesting contrast because on the one hand, the argument can be made in favor of the bots as the bots themselves are nothing more than programs that are following a code while surveillance capitalism is a form of business done by advertisers and scientists with express disregard for privacy. Both should definitely be regulated, especially based on how they're used.

Personally I believe that this paragraph here is very realistic but also a bit frightening at the same time to think that humans can create bots to cause trouble either through simple trolling or as harsh as impersonation. I am wondering though if we would consider accounts that automatically impersonate and contact accounts as bots? If a human messages through the bot but the account and impersonation is created through code, is it still considered a bot?

for - prevent download of pdf

I have a question about the ethics of bots. In this section and chapter, we talked about the intentions and actions of those who operate bots, as well as the bots themselves. Is there a code of ethics that both the bot and the human need to follow?

Bots are automated to perform a specific task. Human users in comparison are subject to emotions and error. These emotions allow users routines on social media to vary and change, while a bot complete a specific task without be influenced by anything other than code. This makes them great for executing tasks like deleting old tweets or identifying something.

I found this interesting and felt as though it tied back to the Code of Hammurabi of how there was an offense for each crime. Then, there was rules and curfews that citizens must follow or be punished.

Please provide at least one specific example from each FW placement to illustrate the Code of Ethics in action. This paragraph is an introduction to the topic.

In my own language (Latvian) I have family members from parts of Latvia who do similar things in their spoken language regarding plural endings and past tense as African-American dialects. They make "mistakes" that digress from General Latvian all over the place. It speaks to the different pockets of culture diversity within my culture, and I appreciate that. I know Latvians who can code switch from those dialects into General Latvian. So I understand how this all works and feel I can relate my experience to other dialect systems in other languages. However, I struggle with whether or not my students understand that I respect their home language system. I am not African-American, so do I come across as a white outsider, correcting them because I think they are wrong and need to be fixed?, as opposed to a randomly-assigned teacher who holds no opinion or judgment about their home language and is just there to teach them General English without trying to disturb or diminish their home language system? I wonder about this.

I witness that code switching in my kids. When some of them leave the classroom, their language patterns and intonation change. It is very interesting and cool to me that they know which "language" is expected and appropriate in which setting.

I've had this conversation with both MS and HS students over the years: code-switching. We talked about whether or not it is authentic or a fake version of ourselves. When is it okay to use slang or dialect that isn't "school appropriate"? I used myself as an example and we discussed how I can be the various true versions of myself, but I know when to switch between how I express myself.

I highlighted the word noncontroversial, because in the current political climate. Everything seems to be controversial. Honesty is dictated by cancel culture and free speech. Integrity can uphold vastly different values. Justice to one may not mean justice to another. Even the value of freedom is a contested and controversial topic.

They included Staten Island hehe

Order of multi-labels seems to have no real meaning of significance or precedence

This is surprising to me because I would have thought that Superman, even in his earlier days would have had some sort of moral code. Although Superman was for the people, it seems as though his aggressive ways are what set him aprt from today's Superman.

There is no secret because we all know the real anwser. To learn a language you need to open up your mind and be okay with new information. That is the only way to improve. Trying to directly translate word for word harms our mental state and learning capabilities

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (public review): 

This manuscript presents SAVEMONEY, a computational tool designed to enhance the utilization of Oxford Nanopore Technologies (ONT) long-read sequencing for the design and analysis of plasmid sequencing experiments. In the past few years, with the improvement in both sequencing length and accuracy, ONT sequencing is being rapidly extended to almost all omics analyses which are dominated by short-read sequencing (e.g., Illumina). However, relatively higher sequencing errors of long-read sequencing techniques including PacBio and ONT is still a major obstacle for plasmid/clone-based sequencing service that aims to achieve single base/nucleotide accuracy. This work provides a guideline for sequencing multiple plasmids together using the same ONT run without molecular barcoding, followed by data deconvolution. The whole algorithm framework is well-designed, and some real data and simulation data are utilized to support the conclusions. The tool SAVEMONEY is proposed to target users who have their own ONT sequencers and perform library preparation and sequencing by themselves, rather than relying on commercial services. As we know and discussed by the authors, in the real world, to ensure accuracy, the researchers will routinely pick up multiple colonies in the same plasmid construction and submit for Sanger sequencing. However, SAVEMONEY is not able to support the simultaneous analysis of multiple colonies in the same run, as compared to the barcoding-based approaches. This is a major limitation in the significance of this work. Encouraging computational ePorts in ONT data debarcoding for mixed-plasmid or even single-cell sequencing would be more valuable in the field. 

We thank the reviewer for the positive response to our manuscript and the helpful comments.

The tool SAVEMONEY is proposed to target users who have their own ONT sequencers and perform library preparation and sequencing by themselves, rather than relying on commercial services.

We apologize that we were not clear enough in the manuscript. Our tool is designed for users who rely on commercial services (i.e., those who cannot include a barcode by themselves). However, it can also benefit those performing library preparation, as SAVEMONEY can be applied after standard barcode-based sequencing and de-multiplexing. The combination of standard barcodes with SAVEMONEY would significantly expands the scope of sequencing applications. For example, it would enable sequencing of more plasmid types than the number of available barcodes and, in some cases, it may even eliminate the need for barcode introduction. Because we do not own ONT equipment and because the primary target audience for the SAVEMONEY algorithm are users without ONT equipment, we were not able to conduct experiments using ONT. However, to clarify these possibilities, we added a dedicated paragraph describing these issues (3rd paragraph in the discussion section).

However, SAVEMONEY is not able to support the simultaneous analysis of multiple colonies in the same run, as compared to the barcoding-based approaches.

We agree with the reviewer about this limitation of SAVEMONEY, as it does not allow mixing of plasmids from multiple colonies in the same cloning run. However, that does not necessarily mean that SAVEMONEY cannot reduce sequencing costs in cloning. For example, when sequencing two colonies from each of three diPerent constructs (six plasmids in total), the standard approach would require sequencing costs for six samples. However, with SAVEMONEY, up to three plasmids can be mixed per sample, allowing them to be sequenced as just two samples. As a result, the sequencing cost per plasmid is reduced to one-third. The greatest benefits can be realized when SAVEMONEY is used at the laboratory level or by multiple researchers. To make this point clearer, we have added sentences in the 5th paragraph of the discussion section.

(1) To provide more comprehensive information for users who care about the cost, the Introduction section should include a cost comparison between Sanger and ONT, with more details, such as diPerent ONT platforms (MinION, PromethION, FlongIe), chemistries (flow cells) and kits. This additional information will be more helpful and informative for the users who have their own sequencers and are the target audience for SAVEMONEY. 

We thank the reviewer for pointing this out. Since we do not own ONT equipment, we are unable to provide a total cost for using the ONT platform. However, we have included the price per sample (~$15 per plasmid) for the commercial service we have used, as well as the equipment that they employ (V14 chemistry on a PromethION with an R10.4.1 flow cell) and the number of reads obtained per plasmid (~100–1000) in the 4th paragraph of the introduction section.     Though these costs will inevitably change over time, this information should still be helpful for those who own ONT sequencers in estimating the costs.

(2) In "Overview of the algorithm" (Pages 3-4) under the Results section, instead of stating "However, coverage varies from ~100-1000 and is diPicult to predict because each nanopore flow cell has diPerent properties.", it will be beneficial to provide more detailed information, such as sequencing length, yield/read count per flow cell of diPerent platforms. This information will assist users in designing their own experiments ePectively. 

We thank the reviewer for the comment. As mentioned in the previous response, we are unable to provide sequencing length, yield/read count per flow cell because we do not own ONT equipment. However, we apologize if it was not clear in "Overview of the algorithm" section that we are discussing the use of results obtained from commercial services, and therefore we need to provide more detailed information about the results from the commercial service. We have now clarified in the sentence pointed out by the reviewr that the numbers are derived from the information provided by commercial sequencing services. In addition, we have also added that typical examples of the result properties, i.e., read length and quality score distribution, can be found in Fig. 2 at the end of the same paragraph.

(3) While this study optimized and evaluated the tool using a total of 14 plasmids, it may not provide suPicient power to represent the diversity of the plasmid world. Consideration should be given to expanding the dataset to include a broader range of plasmids in future studies to enhance the robustness and generalizability of the tool. 

We are grateful to the reviewer for their valuable input. It is very reasonable that we had to expect that a larger number of plasmids should be used, even though the main target of SAVEMONEY is those who utilize commercial services. In the previous version of SAVEMONEY, it was not possible to process in a reasonable amount of time if too many plasmids were provided, though the algorithm itself does not have no restrictions based on the number of plasmids. Therefore, we have changed the underlying code to improve the algorithm, making it more than 20 times faster than the previous version (the benchmark time mentioned in the 3rd paragraph of the discussion section was improved to 3.1 minutes from the previous 65 minutes, using the same dataset and the same computer). Additionally, SAVEMONEY is now compatible with multiprocessing. The processing time is expected to decrease approximately inversely proportional to the number of CPU cores used. We have added these updates at the end of the 3rd paragraph in the discussion section.

(4) If applicable and feasible, including a comparison or benchmark of SAVEMONEY against other similar tools would further strengthen the manuscript. This comparison would allow users to evaluate the advantages and disadvantages of diPerent tools for their specific needs. 

We thank the reviewer for the suggestion. We have added the benchmark using the similar tool, On-Ramp, with the exact same set of plasmids and FASTQ data used for our benchmark (4th paragraph in the discussion section). Because the machine specifications used in the On-Ramp web server are unknown, a direct comparison is not possible. However, using only laptop-level computational resources, SAVEMONEY was able to process the data 38% faster than On-Ramp. When using mini-PC level computational resources, the processing time was 64% faster than on-RAMP.

(5) The importance of pre-filtering raw sequencing reads should be emphasized as noisy reads can significantly impact the overall performance of the tool. It is essential to clarify whether any pre-filtering steps were performed in this study, such as filtering based on quality scores, read length, or other relevant factors. 

We apologize for not being clear. Unfortunately, the commercial sequencing service we used did not provide the information regarding pre-filtering. However, the impact of the quality of pre-filtering based on quality score and read length on the quality of the final results is theoretically minimal in SAVEMONEY. First, during the initial step of the post-analysis, the classification step, short reads compared to the full plasmid length can be excluded based on the user-defined “score_threshold”. Simultaneously, low-quality reads with poor alignment to the plasmid can also be excluded, because “score_threshold” is related to the normalized alignment score. Even if there are low-quality reads that are not excluded at this stage, the ePect can be minimized during the final step of the post-analysis that generates consensus sequences. This is because our Bayesian analysis considers not only the base calling but also the q-scores to determine the consensus. Therefore, we believe the overall impact of pre-filtering on the final results is negligible.

(6) The statement regarding the number of required reads per plasmid (20-30) and the maximum number of plasmids (up to six) that can be mixed in a single run may become outdated due to the rapid advancements in ONT technology. In the Discussion section, instead of assuming specific numbers, it would be more beneficial to provide information based on the current state of ONT sequencing, such as the number of reads per MinION flow cell that can be produced.

We thank the reviewer for pointing this out. Because the number of required reads per plasmid depends on the accuracy of each read (i.e., the number of required reads can be reduced if the accuracy increases), we have added the description of these points to the last paragraph of the discussion section.

Reviewer #2 (public review):  

The authors developed an algorithm that allows for deconvoluting of plasmid sequences from a mixture of plasmids that have been sequenced by nanopore long read technology. As library preparations and barcoding of individual samples increase sequencing costs, the algorithm bypasses this need and thus decreases time on sample prep and sequencing costs. In the first step, the tool assesses which of the plasmid constructions can be mixed in a single library preparation by calculating a distance matrix between the reference plasmid and the constructions producing sequence clusters. The user is given groups of plasmids, from diPerent clusters, to be pooled together for sequencing. After sequencing, the algorithm deconvolutes the reads by classifying them based on alignments to the reference sequence. A Bayesian analysis approach is used to obtain a consensus sequence and quality scores. 

Strengths 

The authors exploit one of the main advantages of long-read sequencing which is to accurately resolve regions of high complexity, as regularly found in plasmids, and developed a tool that can validate plasmid constructions by reducing sequencing costs. Multiple plasmids (up to six) can be analyzed simultaneously in a single library without the need for sample barcoding, also reducing sample preparation time. Although inserts must be diPerent, just 2 bases diPerence would be enough for a correct assignation. It maximizes cost-ePiciency for projects that require large amounts of plasmid constructions and highthroughput validation. 

We thank the reviewer for the positive response to our manuscript and the helpful comments.

Weaknesses 

The method proposed by the authors requires prior knowledge of plasmid sequences (i.e., blueprints or plasmid reference) and is not suitable for small experiments. The plasmid inserts or backbones must be diPerent e.g., multiple colonies from the same plasmid construction ePort cannot be submitted together. 

As also discussed in the response to reviewer 1, we agree with the reviewer that SAVEMONEY does not allow you the analysis of plasmids from multiple colonies in the same cloning experiment. However, that does not necessarily mean that SAVEMONEY cannot reduce the sequencing cost. For example, when sequencing two colonies from each of three diPerent constructs (six plasmids in total), the standard approach would require sequencing costs for six samples. However, with SAVEMONEY, up to three plasmids can be mixed per sample, allowing them to be sequenced as just two samples. As a result, the sequencing cost per plasmid is reduced to one-third. The greatest benefits can be realized when SAVEMONEY is used at the laboratory level or by multiple researchers. To make this point clearer, we have added sentences in the 5th paragraph of the discussion section.

The reviewer also expressed concern that SAVEMONEY is not suitable for experiments at a small scale. To put it more precisely, SAVEMONEY cannot be used when the experiment size is minimal, such as in a lab that consistently constructs only a single plasmid at a time. That said, the strength of SAVEMONEY lies in its scalability. Even in labs where plasmid construction is typically limited to one at a time, there may be occasional instances where two or more plasmids are created simultaneously. In such cases, SAVEMONEY can be used to reduce sequencing costs. Moreover, in a typical molecular biology lab where multiple plasmids are constructed every week, SAVEMONEY can be particularly ePective. Given its adaptability and cost-saving potential and widespread use since its initial publication on bioRxiv and on Google Colab, we are confident that SAVEMONEY will continue to be a valuable tool for a wide range of researchers.

Recommendations For The Authors:

Reviewer #2 (Recommendations For The Authors): 

The manucript assumes all samples are sent out for sequencing at a specific company. This could be generalized for a much broader use since many labs now own nanopore sequencers. In turn, the advantage of reducing hands-on sample prep becomes more evident. 

We thank the reviewer for pointing this out. We agree that SAVEMONEY can also benefit those performing library preparation. Combination of standard barcodes with SAVEMONEY significantly expands the scope of sequencing applications. For example, it enables sequencing of more plasmid types than the number of available barcodes and, in some cases, may even eliminate the need for the sample prep step to introduce barcode. Because we do not own ONT equipment, we could not conduct experiments using ONT. However, to clarify these possibilities, we added a dedicated paragraph (3rd paragraph in the discussion section).

The base calling model (high accuracy, super accuracy) used by Plasmidsaurus and tested here should be mentioned.  

We thank the reviewer for the suggestion. The description about the base calling model (HAC) was added in Materials and Methods section.

Other modifications to the revised manuscript 

Beyond changes made in response to reviewer comments above, we have also through our continued use and improvement of SAVEMONEY, made additional changes to the algorithm and therefore to the manuscript. Those changes are outlined below. Improvements in the pre-survey step

(1) The pre-survey algorithm was reduced to a Zero-One Integer Linear Programming Problem to guarantee the optimal combinations, as previous versions did not ensure an optimal solution. Relatedly, the explanation of the algorithm in the main manuscript was updated.

(2) The algorithm was modified to ensure that the number of plasmids distributed to each group is balanced. A new feature was also added to allow users to specify the number of groups, which is beneficial when balancing between cost and quality.

(3) An error was corrected in Fig. 2, where the distance calculation method for the hierarchical clustering step for group formation was Farthest Point Algorithm, which calculates distance between two clusters based on the farthest pair of plasmids. The correct method is the Nearest Point Algorithm. This error was present only in Fig. 2, while other implementations, including source code of SAVEMONEY and Google Colab page, were correct from the beginning. We have corrected the error in Fig. 2.

Modifications in figures, manuscripts, and other aspects

(1) Fig. 3 was updated to reflect the update of SAVEMONEY, although it did not show any important diPerences.

(2) Parameter names were updated as follows:

“threshold (pre)” -> “distance_threshold”

“threshold (post)” -> “score_threshold” Added “number_of_groups”

(3) The order of elements was rearranged in Fig. 4.

(4) Incorrect calculations were fixed in Fig. 4g, h, and i (old Fig. 4d, h, and l). Related to that, Fig. 4j, k, and l and Table 1 were added, in addition to the explanation in the main manuscript.

(5) SAVEMONEY was packaged and was released on PyPI to facilitate easy installation and integration by other developers.

(6) SAVEMONEY was updated and expanded to accommodate linear DNA fragments, such as PCR amplicons and long synthetic DNA. Users can select the topology of DNA by specifying that as an option. A description of this new capability was added at the end of “Overview of the algorithm” section.

chronologie détaillée et la liste des personnages principaux mentionnés dans la source :

Chronologie des événements principaux :

• Années 70-90 : Progressivement, l'information sur la reproduction et la physiologie s'élargit pour devenir une éducation à la sexualité, intégrant des dimensions psychologiques (respect de soi, intimité), affectives et sociales (égalité fille-garçon, prévention des violences, droits des personnes).

• Années 80-90 : On passe véritablement à une éducation à la sexualité centrée sur la tolérance, la liberté, le respect de soi et d'autrui, et l'adoption d'attitudes responsables.

• 2001 : La loi relative à l'interruption volontaire de grossesse et à la contraception prévoit l'éducation à la sexualité comme une obligation dans le cadre scolaire. Cette loi est déclinée dans le code de l'éducation, rendant l'éducation à la sexualité obligatoire à l'école.

• Depuis 2001 : Le Planning Familial attend la pleine application de la loi de 2001 et continue d'intervenir en milieu scolaire depuis des décennies, fort d'un agrément.

• Juin 2023 : Le Conseil Supérieur des Programmes (CSP) est saisi pour élaborer un programme d'éducation à la sexualité.

• Fin janvier 2025 : Le projet de programme d'éducation à la vie affective et relationnelle et à la sexualité est adopté par le CSP.

• 3 février 2025 : Le programme est publié suite à la demande de la ministre.

• Période suivant la publication : Mise en place d'un vaste programme d'accompagnement et de formation des professionnels au niveau national et académique. Création de ressources pédagogiques mises à disposition des professionnels (et potentiellement consultables par tous).

Cast des personnages principaux et leurs brèves bios : * Marc Pelletier : Un des intervenants de la discussion ("bi" mentionné au début), prenant la parole en premier pour répondre à la question de l'évolution de l'éducation à la sexualité et de son caractère éducatif. Il semble être un expert ou une figure institutionnelle impliquée dans les questions d'éducation.

• Sarah Durocher : La deuxième intervenante à prendre la parole. Elle représente le Planning Familial et apporte le point de vue d'une association de terrain impliquée depuis longtemps dans l'éducation à la sexualité. Elle a 15 ans d'expérience d'intervention au sein du Planning Familial.

• Adeline : L'animatrice ou la modératrice de la discussion ("bi"). Elle introduit les intervenants, pose les questions et gère le déroulement de l'échange.

• Samira : Une personne posant des questions lors de la discussion, notamment sur la formation des intervenants, le consentement et l'intimité. Elle semble relayer des préoccupations du public.

• La Ministre (mentionnée) : La ministre ayant demandé la publication du programme d'éducation à la sexualité le 3 février. Bien que non nommée, elle représente le niveau gouvernemental ayant validé et rendu public le programme.

• Ancienne Ministre de l'Éducation Nationale (mentionnée) : A déclaré que la "théorie du genre" n'existait pas et n'était pas présente dans les programmes. Cette mention vise à rassurer sur le contenu de l'éducation à la sexualité.

Syntax highlighting if it's gonna be much closer to actual code than pseudocode?

This is funny to me because I always hear cases where public schools are underfunded and lack resources to provide certain courses or even college counselors (for college application prep) that help students pursue higher education. One's experience at a public school is also greatly impacted by where one lives. For instance, if your area code is in a more affluent neighbourhood like Palo Alto, you might get large auditoriums, better teachers, unique programs and opportunities, among other aspects that are far superior to the average public school.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

In this study, Osiurak and colleagues investigate the neurocognitive basis of technical reasoning. They use multiple tasks from two neuroimaging studies and overlap analysis to show that the area PF is central for reasoning, and plays an essential role in tool-use and non-tool-use physical problem-solving, as well as both conditions of mentalizing task. They also demonstrate the specificity of the technical reasoning and find that the area PF is not involved in the fluid-cognition task or the mentalizing network (INT+PHYS vs. PHYS-only). This work suggests an understanding of the neurocognitive basis of technical reasoning that supports advanced technologies.

Strengths:

-The topic this study focuses on is intriguing and can help us understand the neurocognitive processes involved in technical reasoning and advanced technologies.

-The researchers obtained fMRI data from multiple tasks. The data is rich and encompasses the mechanical problem-solving task, psychotechnical task, fluid-cognition task, and mentalizing task.

-The article is well written.

We sincerely thank Reviewer 1 for their positive and very helpful comments, which helped us improve the MS. Thank you.

Weaknesses:

- Limitations of the overlap analysis method: there are multiple reasons why two tasks might activate the same brain regions. For instance, the two tasks might share cognitive mechanisms, the activated regions of the two tasks might be adjacent but not overlapping at finer resolutions, or the tasks might recruit the same regions for different cognition functions.

Thus, although overlap analysis can provide valuable information, it also has limitations.

Further analyses that capture the common cognitive components of activation across different

tasks are warranted, such as correlating the activation across different tasks within subjects for a region of interest (i.e. the PF).

We thank Reviewer 1 for this comment. We added new analyses to address the two alternative interpretations stressed here by Reviewer 1, namely, the same-region-but-differentfonction interpretation and the adjacency interpretation. The new analyses ruled out both alternative interpretations, thereby reinforcing our interpretation.

“The conjunction analysis reported was subject to at least two key limitations that needed to be overcome to assure a correct interpretation of our findings. The first was that the tasks could recruit the same regions for different cognition functions (same-region-but-different-function interpretation). The second was that the activated regions of the different tasks could be adjacent but did not overlap at finer resolutions (adjacency interpretation). We tested the same-region-but-different-function interpretation by conducting additional ROI analyses, which consisted of correlating the specific activation of the left area PF (i.e., difference in terms of mean Blood-Oxygen Level Dependent [BOLD] parameter estimates between the experimental condition minus the control condition) in the psychotechnical task, the fluid-cognition task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. This analysis did not include the mechanical problem-solving task because the sample of participants was not the same for this task. As shown in Fig. 5, we found significant correlations between all the tasks that were hypothesized as recruiting technical reasoning, i.e., the psychotechnical task and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .05). By contrast, no significant correlation was obtained between these three tasks and the fluid-cognition task (all p > .15). This finding invalidates the same-region-but-different-function interpretation by revealing a coherent pattern in the activation of the left area PF in situations in which participants were supposed to reason technically. We examined the adjacency interpretation by analysing the specific locations of individual peak activations within the left area PF ROI for the mechanical problemsolving task, the psychotechnical task, the fluid-cognition task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. These peaks, which corresponded to the maximum value of activation obtained for each participant within the left area PF ROI, are reported in Fig. 6. As can be seen, the peaks of the fluid-cognition task were located more anteriorly, in the left area PFt (Parietal Ft) and the postcentral cortex, compared to the peaks of the other four tasks, which were more posterior, in the left area PF. Statistical analyses based on the y coordinates of the individual activation peaks confirmed this description (Fig. 6). Indeed, the y coordinates of the peaks of the mechanical problem-solving task, the psychotechnical task and the PHYS-Only and INT+PHYS conditions of the mentalizing task were posterior to the y coordinates of the peaks of the fluid-cognition task (all p < .05), whereas no significant differences were reported between the four tasks (all p > .05). These findings speak against the adjacency interpretation by revealing that participants recruited the same part of the left area PF to perform tasks involving technical reasoning.” (p. 11-13)

Control tasks may be inadequate: the tasks may involve other factors, such as motor/ actionrelated information. For the psychotechnical task, fluid-cognition task, and mentalizing task, the experiment tasks need not only care about technical-cognition information but also motor-related information, whereas the control tasks do not need to consider motor-related information (mainly visual shape information). Additionally, there may be no difference in motor-related information between the conditions of the fluid-cognition task. Therefore, the regions of interest may be sensitive to motor-related information, affecting the research conclusion.

We thank Reviewer 1 for this comment. We added a specific section in the discussion that addresses this limitation.

“The second limitation concerns the alternative interpretation that the left area PF is not central to technical reasoning but to the storage of sensorimotor programs about the prototypical manipulation of common tools. Here we show that the left area PF is recruited even in situations in which participants do not have to process common manipulable tools. For instance, some items of the psychotechnical task consisted of pictures of tractor, boat, pulley, or cannon. The fact that we found a common activation of the left area PF in such tasks as well as in the mechanical problem-solving task, in which participants could nevertheless simulate the motor actions of manipulating novel tools, indicates that this brain area is not central to tool manipulation but to physical understanding. That being said, some may suggest that viewing a boat or a cannon is enough to incite the simulation of motor actions, so our tasks were not equipped to distinguish between the manipulation-based approach and the reasoning-based approach. We have already shown that the left area PF is more involved in tasks that focus on the mechanical dimension of the tool-use action (e.g., the mechanical interaction between a tool and an object) than its motor dimension (i.e., the interaction between the tool and the effector [e.g., 24, 40]). Nevertheless, we recognize that future research is still needed to test the predictions derived from these two approaches.” (p. 18-19)

-Negative results require further validation: the cognitive results for the fluid-cognition task in the study may need more refinement. For instance, when performing ROI analysis, are there any differences between the conditions? Bayesian statistics might also be helpful to account for the negative results.

We agree that our negative results required further validation. We conducted the ROI analyses suggested by Reviewer 1, which confirmed the initial whole-brain analyses.

“Region of interest (ROI) results. We conducted additional analyses to test the robustness of our findings. One of our results was that we did not report any specific activation of the left area PF in the fluid-cognition task contrary to the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. However, this negative result needed exploration at the ROI level. Therefore, we created a spherical ROI of the left area PF with a radius of 12 mm in the MNI standard space (–59; –31; 40). This ROI was literature-defined to ensure the independence of its selection (40). ROI results are shown in Fig. 4. The analyses confirmed the results obtained with the whole-brain analyses by indicating a greater activation of the left area PF in the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .001), but not in the fluid-cognition task (p \= .35).” (p. 10-11)

Reviewer #1 (Recommendations For The Authors):

(1) I may not fully grasp some of the arguments. In the abstract, what does the term "intermediate-level" mean, and why is it an intermediate-level state? In the sentence "the existence of a specific cognitive module in the human brain dedicated to materiality", I cannot see a clear link between technical cognition and the word "materiality".

We used the term materiality to refer to a potential human trait that allows us to shape the physical world according to our ends, by using, making tools and transmiting them to others. This is a reference to Allen et al. (2020; PNAS): “We hope this empirical domain and modeling framework can provide the foundations for future research on this quintessentially human trait: using, making, and reasoning about tools and more generally shaping the physical world to our ends” (p. 29309). Scientists (including archaeologists, economists, psychologists, neuroscientists) interested in human materiality have tended to focus on how we manipulate things according to our thought (motor cognition) or how we conceptualize our behaviour to transmit it to others (language, social cognition). However, little has been said on the intermediate level, that is, technical cognition. We added the term “technical cognition” here, which should help to make the connection more quickly.

“Yet, little has been said about the intermediate-level cognitive processes that are directly involved in mastering this materiality, that is, technical cognition.” (p. 2)

(2) The introduction could provide more details on why the issue of "generalizability and specificity" is important to address, to clarify the significance of the research question.

We followed this comment and added a sentence to explain why it is important to address this research question. Again, we thank Reviewer 1 for their helpful comments.

“Here we focus on two key aspects of the technical-reasoning hypothesis that remain to be addressed: Generalizability and specificity. If technical reasoning is a specific form of reasoning oriented towards the physical world, then it should be implicated in all (the generalizability question) and only (the specificity question) the situations in which we need to think about the physical properties of our world.” (p. 5)

Reviewer #2 (Public Review):

Summary:

The goal of this project was to test the hypothesis that a common neuroanatomic substrate in the left inferior parietal lobule (area PF) underlies reasoning about the physical properties of actions and objects. Four functional MRI (fMRI) experiments were created to test this hypothesis. Group contrast maps were then obtained for each task, and overlap among the tasks was computed at the voxel level. The principal finding is that the left PF exhibited differentially greater BOLD response in tasks requiring participants to reason about the physical properties of actions and objects (referred to as technical reasoning). In contrast, there was no differential BOLD response in the left PF when participants engaged in fMRI variant of the Raven's progressive matrices to assess fluid cognition.

Strengths:

This is a well-written manuscript that builds from extensive prior work from this group mapping the brain areas and cognitive mechanisms underlying object manipulation, technical reasoning, and problem-solving. Major strengths of this manuscript include the use of control conditions to demonstrate there are differentially greater BOLD responses in area PF over and above the baseline condition of each task. Another strength is the demonstration that area PF is not responsive in tasks assessing fluid cognition - e.g., it may just be that PF responds to a greater extent in a harder condition relative to an easy condition of a task. The analysis of data from Task 3 rules out this alternative interpretation. The methods and analysis are sufficiently written for others to replicate the study, and the materials and code for data analysis are publicly available.

We sincerely thank Reviewer 2 for their precious comments, which helped us improve the MS. 

Weaknesses:

The first weakness is that the conclusions of the manuscript rely on there being overlap among group-level contrast maps presented in Figure 2. The problem with this conclusion is that different participants engaged in different tasks. Never is an analysis performed to demonstrate that the PF region identified in e.g., participant 1 in Task 2 is the same PF region identified in Participant 1 in Task 4.

We added new analyses that demonstrated that “the PF region identified in e.g., participant 1 in Task 2 is the same PF region identified in Participant 1 in Task 4”. We thank Reviewer 2 for this comment, because these new analyses reinforced our interpretation.

“The conjunction analysis reported was subject to at least two key limitations that needed to be overcome to assure a correct interpretation of our findings. The first was that the tasks could recruit the same regions for different cognition functions (same-region-but-different-function interpretation). The second was that the activated regions of the different tasks could be adjacent but did not overlap at finer resolutions (adjacency interpretation). We tested the same-region-but-different-function interpretation by conducting additional ROI analyses, which consisted of correlating the specific activation of the left area PF (i.e., difference in terms of mean Blood-Oxygen Level Dependent [BOLD] parameter estimates between the experimental condition minus the control condition) in the psychotechnical task, the fluid-cognition task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. This analysis did not include the mechanical problem-solving task because the sample of participants was not the same for this task. As shown in Fig. 5, we found significant correlations between all the tasks that were hypothesized as recruiting technical reasoning, i.e., the psychotechnical task and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .05). By contrast, no significant correlation was obtained between these three tasks and the fluid-cognition task (all p > .15). This finding invalidates the same-region-but-different-function interpretation by revealing a coherent pattern in the activation of the left area PF in situations in which participants were supposed to reason technically. We examined the adjacency interpretation by analysing the specific locations of individual peak activations within the left area PF ROI for the mechanical problemsolving task, the psychotechnical task, the fluid-cognition task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. These peaks, which corresponded to the maximum value of activation obtained for each participant within the left area PF ROI, are reported in Fig. 6. As can be seen, the peaks of the fluid-cognition task were located more anteriorly, in the left area PFt (Parietal Ft) and the postcentral cortex, compared to the peaks of the other four tasks, which were more posterior, in the left area PF. Statistical analyses based on the y coordinates of the individual activation peaks confirmed this description (Fig. 6). Indeed, the y coordinates of the peaks of the mechanical problem-solving task, the psychotechnical task and the PHYS-Only and INT+PHYS conditions of the mentalizing task were posterior to the y coordinates of the peaks of the fluid-cognition task (all p < .05), whereas no significant differences were reported between the four tasks (all p > .05). These findings speak against the adjacency interpretation by revealing that participants recruited the same part of the left area PF to perform tasks involving technical reasoning.” (p. 11-13)

A second weakness is that there is a variance in accuracy between tasks that are not addressed. It is clear from the plots in the supplemental materials that some participants score below chance (~ 50%). This means that half (or more) of the fMRI trials of some participants are incorrect. The methods section does not mention how inaccurate trials were handled. Moreover, if 50% is chance, it suggests that some participants did not understand task instructions and were systematically selecting the incorrect item.

It is true that the experimental conditions were more difficult than the control conditions, with some participants who performed at or below 50% in the experimental conditions. We added a section in the MS to stress this aspect. To examine whether this potential difficulty effect biased our interpretation, we conducted new ROI analyses by removing all the participants who performed at or below the chance level. These analyses revealed the same results as when no participant was excluded, suggesting that this did not bias our interpretation.

“As mentioned above, the experimental conditions of all the tasks were more difficult than their control conditions. As a result, the specific activation of the left area PF documented above could simply reflect that this area responds to a greater extent in a harder condition relative to an easy condition of a task. This interpretation is nevertheless ruled out by the results obtained with the fluid-cognition task. We did not report a specific activation of the left area PF in this task while its experimental condition was more difficult than its control condition. To test more directly this effect of difficulty, we conducted new ROI analyses by removing all the participants who performed at or below 50% (Fig. S2). These new analyses replicated the initial analyses by showing a greater activation of the left area PF in the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .001), but not in the fluid-cognition task (p \= .48). In sum, the ROI analyses corroborated the wholebrain analyses and ruled out the potential effect of difficulty.” (p. 11)

A third weakness is related to the fluid cognition task. In the fMRI task developed here, the participant must press a left or right button to select between 2 rows of 3 stimuli while only one of the 3 stimuli is the correct target. This means that within a 10-second window, the participant must identify the pattern in the 3x3 grid and then separately discriminate among 6 possible shapes to find the matching stimulus. This is a hard task that is qualitatively different from the other tasks in terms of the content being manipulated and the time constraints.

We acknowledge that the fluid-cognition task involved a design that differed from the other tasks. However, this was also true for the other tasks, as the design also differed between the mechanical problem-solving task, the psychotechnical task, and the mentalizing task. Nevertheless, despite these distinctions, we found a consistent activation of the left area PF in these tasks with different designs including in the psychotechnical task, which seemed as difficult as the fluid-cognition task.

“Region of interest (ROI) results. We conducted additional analyses to test the robustness of our findings. One of our results was that we did not report any specific activation of the left area PF in the fluid-cognition task contrary to the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task. However, this negative result needed exploration at the ROI level. Therefore, we created a spherical ROI of the left area PF with a radius of 12 mm in the MNI standard space (–59; –31; 40). This ROI was literature-defined to ensure the independence of its selection (40). ROI results are shown in Fig. 4. The analyses confirmed the results obtained with the whole-brain analyses by indicating a greater activation of the left area PF in the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .001), but not in the fluid-cognition task (p \= .35).” (p. 10-11)

In sum, this is an interesting study that tests a neuro-cognitive model whereby the left PF forms a key node in a network of brain regions supporting technical reasoning for tool and non-tool-based tasks. Localizing area PF at the level of single participants and managing variance in accuracy is critically important before testing the proposed hypotheses.

We thank Reviewer 2 for this positive evaluation and their suggestions. As detailed in our response, our revision took into consideration both the localization of the left area PF at the level of single participants and the variance in accuracy. 

Reviewer #2 (Recommendations For The Authors):

Did the fMRI data undergo high-pass temporal filtering prior to modeling the effects of interest? Participants engaged in a long (17-24 minutes) run of fMRI data collection. Highpass filtering of the data is critically important when managing temporal autocorrelation in the fMRI response (e.g., see Shinn et al., 2023, Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience).

Yes. We added this information.

“Regressors of non-interest resulting from 3D head motion estimation (x, y, z translation and three axes of rotation) and a set of cosine regressors for high-pass filtering were added to the design matrix.” (p. 25-26)

Including scales in Figure 2 would help the reader interpret the magnitude of the BOLD effects.

We added this information in Figure 3 (Figure 2 in the initial version of the MS).

It was difficult to inspect the small thumbnail images of the task stimuli in Figure 1. Higher resolution versions of those stimuli would help facilitate understanding of the task design and trial structure.

We changed both Figure 1 and Figure S1.

Reviewer #3 (Public Review):

Summary:

This manuscript reports two neuroimaging experiments assessing commonalities and differences in activation loci across mechanical problem-solving, technical reasoning, fluid cognition, and "mentalizing" tasks. Each task includes a control task. Conjunction analyses are performed to identify regions in common across tasks. As Area PF (a part of the supramarginal gyrus of the inferior parietal lobe) is involved across 3 of the 4 tasks, the investigators claim that it is the hub of technical cognition.

Strengths:

The aim of finding commonalities and differences across related problem-solving tasks is a useful and interesting one.

The experimental tasks themselves appear relatively well-thought-out, aside from the concern that they are differentially difficult.

The imaging pipeline appears appropriate.

We thank Reviewer 3 for their constructive comments, which helped us improve the MS.

Weaknesses:

(1) Methodological

As indicated in the supplementary tables and figures, the experimental tasks employed differ markedly in 1) difficulty and 2) experimental trial time. Response latencies are not reported (but are of additional concern given the variance in difficulty). There is concern that at least some of the differences in activation patterns across tasks are the result of these fundamental differences in how hard various brain regions have to work to solve the tasks and/or how much of the trial epoch is actually consumed by "on-task" behavior. These difficulty issues should be controlled for by 1) separating correct and incorrect trials, and 2) for correct trials, entering response latency as a regressor in the Generalized Linear Models, 3) entering trial duration in the GLMs.

We thank Reviewer 3 for this comment. It is true that the experimental conditions were more difficult than the control conditions, with some participants who performed at or below 50% in the experimental conditions. We added a section in the MS to stress this aspect. We could not conduct new analyses by separating correct and incorrect trials because, for each task, participants had to respond only on the last item of the block. Therefore, we did not record a response for each event. Nevertheless, we could examine whether this potential difficulty effect biased our interpretation, by conducting new ROI analyses in which we removed all the participants who performed at or below the chance level. These analyses revealed the same results as when no participant was excluded, suggesting that this did not bias our interpretation. 

“As mentioned above, the experimental conditions of all the tasks were more difficult than their control conditions. As a result, the specific activation of the left area PF documented above could simply reflect that this area responds to a greater extent in a harder condition relative to an easy condition of a task. This interpretation is nevertheless ruled out by the results obtained with the fluid-cognition task. We did not report a specific activation of the left area PF in this task while its experimental condition was more difficult than its control condition. To test more directly this effect of difficulty, we conducted new ROI analyses by removing all the participants who performed at or below 50% (Fig. S2). These new analyses replicated the initial analyses by showing a greater activation of the left area PF in the mechanical problem-solving task, the psychotechnical task, and the PHYS-Only and INT+PHYS conditions of the mentalizing task (all p < .001), but not in the fluid-cognition task (p \= .48). In sum, the ROI analyses corroborated the wholebrain analyses and ruled out the potential effect of difficulty.” (p. 11)

A related concern is that the control tasks also differ markedly in the degree to which they were easier and faster than their corresponding experimental task. Thus, some of the control tasks seem to control much better for difficulty and time on task than others. For example, the control task for the psychotechnical task simply requires the indication of which array contains a simple square shape (i.e., it is much easier than the psychotechnical task), whereas the control task for mechanical problem-solving requires mentally fitting a shape into a design, much like solving a jigsaw puzzle (i.e., it is only slightly easier than the experimental task).

It is true that some control conditions could be easier than other ones. These differences reinforced the common activation found in the left area PF in the tasks hypothesized as involving technical reasoning, because this activation survived irrespective of the differences in terms of experimental design. For us, the rationale is the same as for a meta-analysis, in which we try to find what is common to a great variety of tasks. The only detrimental consequence we identified here is that this difference explained why we did not report a specific activation of the left area PF in the fluid-cognition task, as if the left area PF was more responsive when the task was difficult. This possibility assumes that the experimental condition of the fluid-cognition task is much more difficult than its control condition compared to what can be seen in the other tasks. As Reviewer 2 stressed in Point 1, this interpretation is unlikely, because the differences between the experimental and control conditions were similar to the fluid-cognition task in the mechanical problem-solving and psychotechnical tasks. In addition, again, the new ROI analyses in which we removed all the participants who performed at or below the chance level in expetimental conditions reproduced our initital results.

(2) Theoretical 

The investigators seem to overlook prior research that does not support their perspective and their writing seems to lack scientific objectivity in places. At times they over-reach in the claims that can be made based on the present data. Some claims need to be revised/softened.

As this comment is also mentioned below, please find our response to it below.

Reviewer #3 (Recommendations For The Authors):

(1) Because of the high level of detail, Figures 1 and S2 (particularly the mentalizing task and mechanical problem-solving task, and their controls) are very hard to parse, even when examined relatively closely. It is suggested that these figures be broken down into separate panels for Experiment 1 and Experiment 2 to facilitate understanding.

We changed both Figure 1 and Figure S1.

(2) The behavioral data (including response latencies) should be reported in the main results section of the paper and not in a supplement.

The behavioural data are now reported in the main results. We did not report response latencies because participants were not prompted to respond as quickly as possible.

“Behavioural results. All the behavioural results are given in Fig. 2. As shown, scores were higher in the experimental conditions than for the control conditions for all the tasks (all p < .05). In other words, the experimental conditions were more difficult than the control conditions. This difference in terms of difficulty can also be illustrated by the fact that some participants performed at or below the chance level in the experimental conditions whereas none did so in the control conditions.” (p. 8)

(3) The investigators seem to overlook prior research that does not support their perspective and their writing seems to lack scientific objectivity in places. At times they over-reach in the claims that can be made based on the present data. For example, claims that need to be revised/softened include:

Abstract: "Area PF... can work along with social-cognitive skills to resolve day-to-day interactions that combine social and physical constraints". This statement is overly speculative.

This statement is based on the fact that we reported a combined activation of the technical-reasoning network and the mentalizing network in the INT+PHYS condition of the mentalizing task. This suggests that both networks need to work together for solving a day-today problem in which both the physical constraints of the situation and the intention of the individual must be integrated. Our findings replicated previous ones with a similar task (e.g., Brunet et al. 2000; Völlm et al., 2006), in which the authors gave an interpretation similar to ours in considering that this task requires understanding physical and social causes. Perhaps that the reference to the results of the mentalizing task was not explicit enough. We added “dayto-day” before “problem” in the part of the discussion in which we discuss this possibility to make this aspect clearer.

“In broad terms, the results of the mentalizing task indicate that causal reasoning has distinct forms and that it recruits distinct networks of the human brain (Social domain: Mentalizing; Physical domain: Technical reasoning), which can nevertheless interact together to solve day-to-day problems in which several domains are involved, such as in the INT+PHYS condition of the mentalizing task.” (p. 16)

Introduction: "The manipulation-based approach... remains silent on the more general cognitive mechanisms...that must also encompass the use of unfamiliar or novel tools". This statement seems to be based on an overly selective literature review. There are a number of studies in which the relationship between a novel and familiar tool selection/use has been explored (e.g., Buchman & Randerath, 2017; Mizelle & Wheaton, 2010; Silveri & Ciccarelli, 2009; Stoll, Finkel et al., 2022; Foerster, 2023; Foerster, Borghi, & Goslin, 2020; Seidel, Rijntjes et al., 2023).

We thank Reviewer 3 for this comment. Even if we accept the idea that we possess specific sensorimotor programs about tool manipulation, it remains that these programs cannot explain how an individual decides to bend a wire to make a hook or to pour water in a recipient to retrieve a target. As a matter of fact, such behaviour has been reported in nonhuman animals, such as crows (Weir et al., 2002, Nature) or orangutans (Mendes et al., 2007, Biology Letters). In these studies, the question is whether these nonhuman animals understand the physical causes or not, but the question of sensorimotor programs is never addressed (to our knowledge). This is also true in developmental studies on tool use (e.g., Beck et al., 2011, Cognition; Cutting et al., 2011, Journal of Experimental Child Psychology). This is what we meant here, that is, the manipulation-based approach is not equipped to explain how people solve physical problems by using or making tools – or any object – or by building constructions or producing technical innovations. However, we agree that some papers have been interested in exploring the link between common and novel tool use and have suggested that both could recruit common sensorimotor programs. It is noteworthy that these studies do not test the predictions from the manipulation-based approach versus the reasoning-based approach, so both interpretations are generally viable as stressed by Seidel et al. (2023), one of the papers recommended by Reviewer 3.

“Apparently, the presentation of a graspable object that is recognizable as a tool is sufficient to provoke SMG activation, whether one tends to see the function of SMG to be either “technical reasoning” (Osiurak and Badets 2016; Reynaud et al. 2016; Lesourd et al. 2018; Reynaud et al. 2019) or “manipulation knowledge” (Sakreida et al. 2016; Buxbaum 2017; Garcea et al. 2019b).” (Seidel et al., 2023; p. 9)

Regardless, as suggested by Reviewer 3, these papers deserve to be cited and this part needed to be rewritten to insist on the “making, construction, and innovation” dimension more than on the “unfamiliar and novel tool use” dimension to avoid any ambiguity.

“This manipulation-based approach has provided interesting insights (12–16) and even elegant attempts to explain how these sensorimotor programs could support the use of both unfamiliar or novel tools (17–20), but remains silent on the more general cognitive mechanisms behind human technology that include the use of common and unfamiliar or novel tools but must also encompass tool making, construction behaviour, technical innovations, and transmission of technical content.” (p. 3)

Introduction: "Here we focus on two important questions... to promote the technicalreasoning hypothesis as a comprehensive cognitive framework..."(italics added). This and other similar statements should be rewritten as testable scientific hypotheses rather than implying that the point of the research is to promote the investigators' preferred view.

We agree that our phrasing could seem inappropriate here. What we meant here is that the technical-reasoning hypothesis could become an interesting framework for the study of the cognitive bases of human technology only if we are able to verify some of its key facets. As suggested, we rewrote this part. We also rewrote the abstract and the first paragraph of the discussion.

“Here we focus on two key aspects of the technical-reasoning hypothesis that remain to be addressed: Generalizability and specificity. If technical reasoning is a specific form of reasoning oriented towards the physical world, then it should be implicated in all (the generalizability question) and only (the specificity question) the situations in which we need to think about the physical properties of our world.” (p. 5)

Introduction: The Goldenberg and Hagmann paper cited actually shows that familiar tool use may be based either on retrieval from semantic memory or by inferring function from structure (mechanical problem solving); in other words, the investigators saw a role for both kinds of information, and the relationship between mechanical problem solving and familiar tool use was actually relatively weak. This requires correction.

We disagree with Reviewer 3 on this point. The whole sentence is as follows:

“This silence has been initially broken by a series of studies initiated by Goldenberg and Hagmann (9), which has documented a behavioural link in left brain-damaged patients between common tool use and the ability to solve mechanical problems by using and even sometimes making novel tools (e.g., extracting a target out from a box by bending a wire to create a hook) (9, 17).” (p. 3-4)

We did not mention the interpretations given by Goldenberg and Hagmann about the link with the pantomime task, but only focused on the link they reported between common tool use and novel tool use. This is factual. In addition, we also disagree that the link between common tool use and novel tool use was weak.

“The hypothesis put forward in the introduction predicts that knowledge about prototypical tool use assessed by pantomime of tool use and the ability to infer function from structure assessed by novel tool selection can both contribute to the use of familiar tools. Indeed results of both tests correlated signicantly with the use of familiar tools pantomime of tool use: r \= 0.77, novel tool selection: r \= 0.62; both P < 0.001), but there was also a signicant correlation between the two tests r \= 0.64, P < 0.001).” (Goldenberg & Hagmann, 1998; p. 585)

As can be seen in this quote, they reported a significant correlation between novel tool selection and the use of familiar tools. It is also noteworthy that the novel tool selection test and the pantomime test correlated together. Georg Goldenberg told one of the authors (F. Osiurak; personal communication) that this result incited him to revise its idea that pantomime could assess “semantic knowledge”, which explains why he did not use it again as a measure of semantic knowledge. Instead, he preferred to use a classical semantic matching task in his 2009 Brain paper with Josef Spatt, in which they found a clearer dissociation between semantic knowledge and common/novel tool use not only at the behavioral level but also at the cerebral level.

Introduction: Please expand and clarify this sentence "However, this involvement seems to be task-dependent, contrary to the systematic involvement of left are PF. The IFG and LOTC activations observed in prior studies are of interest as well. Were they indeed all taskdependent in these studies?

We agree that this sentence is confusing. We meant that, in the studies reported just above in the paragraph, these regions were not systematically reported contrary to the left area PF. As we think that this information was not crucial for the logic of the paper, we preferred to remove it. 

Introduction: If implicit mechanical knowledge is acquired through interactions with objects, how is that implicit knowledge conveyed to pass on the material culture to others?

We thank Reviewer 3 for this comment. Although mechanical knowledge is implicit, it can be indirectly transmitted to other individuals, as shown in two papers we published in Nature Human Behaviour (Osiurak et al., 2021) and Science Advances (Osiurak et al., 2022). Actually, verbal teaching is not the only way to transmit information. There are many other ways of transmitting information such as gestural teaching (e.g., pointing the important aspects of a task to make them salient to the learner), observation without teaching (i.e., when we observe someone unbeknown to them) or reverse engineering (i.e., scrutinizing an artifact made by someone else). We have shown that even in reverse-engineering conditions, participants can benefit from what previous participants have done to increase their understanding of a physical system. In other words, all these forms of transmission allow the learners to understand new physical relationships without waiting that these relationships randomly occur in the environment. There is a wide literature on social learning, which describes very well how knowledge can be transmitted without using explicit communication. In fact, it is very likely that such forms of transmission were already present in our ancestors, allowing them to start accumulating knowledge without using symbolic language. We did not add this information in the MS because we think that this was a little bit beyond the scope of the MS. Nevetheless, we cited relevant literature on the topic to help the reader find it if interested in the topic.

“Yet, recent accounts have proposed that non-social cognitive skills such as causal understanding or technical reasoning might have played a crucial role in cumulative technological culture (6, 29, 66). Support for these accounts comes from micro-society experiments, which have demonstrated that the improvement of technology over generations is accompanied by an increase in its understanding (67, 68), or that learners’ technical-reasoning skills are a good predictor of cumulative performance in such micro-societies (33, 69).” (p. 19)

What distinguishes this implicit mechanical knowledge from stored knowledge about object manipulation? Are these two conceptualizations really demonstrably (testably) different?

We agree that it is complex to distinguish between these two hypotheses as suggested by Seidel et al. (2023) cited above (see Reviewer 3 Point 8). We have conducted several studies to test the opposite predictions derived from each hypothesis. The main distinction concerns the understanding of physical materials and forces, which is central to the technical-reasoning hypothesis but not to the manipulation-based approach. Indeed, sensorimotor programs about tool manipulation are not assumed to contain information about physical materials and forces. In the present study, the understanding of physical materials and forces was needed in the four tasks hypothesized as requiring technical reasoning, i.e., the mechanical problem-solving task, the psychotechnical task and the PHYS-Only and INT+PHYS conditions of the mentalizing task. We can illustrate this aspect with items of each of these tasks. Figure 1A is of the mechanical problem-solving task. 

As explained in the MS, participants had memorized the five possible tools before the scanner session. Thus, for 4 seconds, they had to imagine which of these tools could be used to extract the target out from the box. We did so to incit them to reason about mechanical solutions based on the physical properties of the problem. Then, they had 3 seconds to select the tool with the appropriate shape, here the right one. In this case, the motor action remains the same (i.e., pulling). Another illustration can be given, with the psychotechnical task (Figure 1B).

In this task, the participant had to reason as to whether the boat-tractor connection was better in the left picture or in the right picture. This needs to reason about physical forces, but there is no need to recruit sensorimotor programs about tool manipulation. Finally, a last example can be given with the PHYS-Only condition of the mentalizing task (but the logic is the same for the INT+PHYS condition except that the character’s intentions must also be taken into consideration) Figure 1D).

Here the participant must reason about which picture shows what is physically possible. In this task, there is no need to recruit sensorimotor programs about tool manipulation. In sum, what is common between these three tasks is the requirement to reason about physical materials and forces. We do not ignore that motor actions could be simulated in the mechanical problemsolving task, but no motor action needed to be simulated in the other three tasks. Therefore, what was common between all these tasks was the potential involvement of technical reasoning but not of sensorimotor programs about tool manipulation. Of course, an alternative is to consider that motor actions are always needed in all the situations, including situations where no “manipulable tool” is presented, such as a tractor and a boat, a pulley, or a cannon. We cannot rule out this alternative, which is nevertheless, for us, prejudicial because it implies that it becomes difficult to test the manipulation-based approach as motor actions would be everywhere. We voluntarily decided not to introduce a debate between the reasoning-based approach and the manipulation-based approach and preferred a more positive writing by stressing the insights from the present study. Note that we stressed the merits of the manipulation-based approach in the introduction because we sincerely think that this approach has provided interesting insights. However, we voluntarily did not discuss the debate between the two approaches. Given Reviewer 3’s comment (see also Reviewer 1 Point 2), we understand and agree that some words must be nevertheless said to discuss how the manipulation-based approach could interpret our results, thus stressing the potential limitations of our interpretations. Therefore, we added a specific section in the discussion in which we discussed this aspect in more details.

“The second limitation concerns the alternative interpretation that the left area PF is not central to technical reasoning but to the storage of sensorimotor programs about the prototypical manipulation of common tools. Here we show that the left area PF is recruited even in situations in which participants do not have to process common manipulable tools. For instance, some items of the psychotechnical task consisted of pictures of tractor, boat, pulley, or cannon. The fact that we found a common activation of the left area PF in such tasks as well as in the mechanical problem-solving task, in which participants could nevertheless simulate the motor actions of manipulating novel tools, indicates that this brain area is not central to tool manipulation but to physical understanding. That being said, some may suggest that viewing a boat or a cannon is enough to incite the simulation of motor actions, so our tasks were not equipped to distinguish between the manipulation-based approach and the reasoning-based approach. We have already shown that the left area PF is more involved in tasks that focus on the mechanical dimension of the tool-use action (e.g., the mechanical interaction between a tool and an object) than its motor dimension (i.e., the interaction between the tool and the effector [e.g., 24, 40]). Nevertheless, we recognize that future research is still needed to test the predictions derived from these two approaches.” (p. 18-19)

Introduction and throughout: The framing of left Area PF as a special area for technical reasoning is overly reductionistic from a functional neuroanatomic perspective in that it ignores a large relevant literature showing that the region is involved with many other tasks that seem not to require anything like technical cognition. Indeed, entering the coordinates - 56, -29, 36 (reported as the peak coordinates in common across the studied tasks) in Neurosynth reveals that 59 imaging studies report activations within 3 mm of those coordinates; few are action-related (a brief review indicated studies of verbal creativity, texture processing, reading, somatosensory processing, stress reactions, attentional selection etc). Please acknowledge the difficulty of claiming that a large brain region should be labeled the brain's technical reasoning area when it seems to also participate in so much else. The left IPL (including area PF) is densely connected to the ventral premotor cortex, and this network is activated in language and calculation tasks as well as tool use tasks (e.g., Matsumoto, Nair, et al., 2012). What other constructs might be able to unite this disparate literature, and are any of these alternative constructs ruled out by the present data? Lacking this objective discussion, the manuscript does read as a promotion of the investigators' preferred viewpoint.

We thank Reviewer 3 for this comment. As stressed in the initial version of the MS, we did not write that the left area PF is sufficient but central to the network that allows us to reason about the physical world. Regardless, we agree that an objective discussion was needed on this aspect to help the reader not misunderstand our purpose. We added a section in this aspect as suggested. 

“Before concluding, we would like to point out two potential limitations of the present study. The first limitation concerns the fact that the literature has documented the recruitment of the left area PF in many neuroimaging experiments in which there was no need to reason about physical events (e.g., language tasks). This can be easily illustrated by entering the left area PF coordinates in the Neurosynth database.

This finding could be enough to refute the idea that this brain area is specific to technical reasoning. Although this limitation deserves to be recognized, it is also true for many other findings. For instance, sensory or motor brain regions such as the precentral or the postcentral cortex have been found activated in many non-motor tasks, the visual word form area in non-language tasks, or the Heschl’s gyrus in nonmusical tasks. This remains a major challenge for scientists, the question being how to solve these inconsistencies that can result from statistical errors or stress that considerable effort is needed to understand the very functional nature of these brain areas. Thus, understanding that the left area PF is central to physical understanding can be viewed as a first essential step before discovering its fundamental function, as suggested by the functional polyhedral approach (56).” (p. 18)

Discussion: The discussion of a small cluster in the IFG (pars opercularis) that nearly survived statistical correction is noteworthy in light of the above point. This further underscores the importance of discussing networks and not just single brain regions (such as area PF) when examining complex processes. The investigators note, "a plausible hypothesis is that the left IFG integrates the multiple constraints posed by the physical situation to set the ground for a correct reasoning process, such as it could be involved in syntactic language processing". In fact, the hypothesis that the IFG and SMG are together related to resolving competition has been previously proposed, as has the more specific hypothesis that the SMG buffers actions and that the context-appropriate action is then selected by the IFG (e.g., Buxbaum & Randerath, 2018). The parallels with the way the SMG is engaged with competing lexical or phonological alternatives (e.g., Peramunage, Blumstein et al., 2011) have also been previously noted.

We added the Buxbaum and Randerath (2018)’s reference in this section.

“The functional role of the left IFG in the context of tool use has been previously discussed (24) and a plausible hypothesis is that the left IFG integrates the multiple constraints posed by the physical situation to set the ground for a correct reasoning process, such as it could be involved in syntactic language processing (for a somewhat similar view, see [51]).” (p. 16-17)

Introduction and Discussion: Please clarify how the technical reasoning network overlaps with or is distinct from the tool-use network reported by many previous investigators.

We added a couple of sentences in the discussion to clarify this point.

“It should be clear here that we do not advocate the localizationist position simply stating that activation in the left area PF is the necessary and sufficient condition for technical reasoning. We rather defend the view according to which it requires a network of interacting brain areas, one of them – and of major importance – being the left area PF. This allows the engagement of different configurations of cerebral areas in different technical-reasoning tasks, but with a central process acting as a stable component: The left area PF. Thus, when people intend to use physical tools, it can work in concert with brain regions specific to object manipulation and motor control, thereby forming another network, the tool-use network. It can also interact with brain regions specific to intentional gestures to form a “social-learning” network that allows people to enhance their understanding about the physical aspects of a technical task (e.g., the making of a tool) through communicative gestures such as pointing gestures (42). The major challenge for future research is to specify the nature of the cognitive process supported by the left area PF and that might be involved in the broad understanding of the physical world.” (p. 14)

Discussion: All of the experimental tasks require a response from a difficult choice in an array, and all of the tasks except for the fluid cognition task are likely to require prediction or simulation of a motion trajectory-whether an embodied or disembodied trajectory is unclear. The Discussion does mention the related (but distinct) idea of an "intuitive physics engine", a "kind of simulator", Please clarify how this study can rule out these alternative interpretations of the data. If the study cannot rule out these alternatives, the claims of the study (and the paper title which labels PF as a technical cognition area) should be scaled back considerably. 

We thank Reviewer 3 for this comment. The authors of the papers on intuitive physics engine or associative learning do not suggest that these processes are embodied. As discussed above, we clarified our perspective on the role of the left area PF and hope that these modifications help the reader better understand it. We warmly thank Reviewer 3 for their comments, which considerably helped us improve the MS.

Voici un sommaire de la vidéo avec des indications temporelles basées sur le déroulement du contenu :

• Introduction (Début de la vidéo) : L'introduction est faite par Elena, la fondatrice de Toadhouse Games. Elle explique que ce tutoriel est conçu pour les débutants qui n'ont aucune connaissance en codage et que les premières vidéos seront gratuites sur YouTube. Elle présente Ren'Py comme un moteur de roman visuel utilisé par des milliers de créateurs.

• Qu'est-ce que Ren'Py ? (Environ 0:00 - 1:00) : Ren'Py est un moteur pour créer des romans visuels et de la fiction interactive. Bien qu'il fonctionne avec du code Python, il n'est pas nécessaire de savoir coder pour l'utiliser. Le logiciel fournit tout ce dont vous avez besoin, y compris des éditeurs de texte.

• Téléchargement de Ren'Py (Environ 1:00 - 2:00) : Il faut se rendre sur ri.org et cliquer sur le bouton de téléchargement. Différentes versions sont disponibles pour Windows, Mac, Linux, Android et iOS. Une fois le fichier téléchargé, il faut l'exécuter et extraire les fichiers dans le dossier de votre choix.

• Ouverture et présentation du lanceur Ren'Py (Environ 2:00 - 4:00) : Dans le dossier extrait, double-cliquez sur l'application Ren'Py (l'icône avec un anime) pour ouvrir le lanceur. Le lanceur affiche les projets ouverts (tutoriel et question par défaut) et les fichiers associés à chaque projet. Sur la droite, l'option "script" permet d'accéder aux fichiers de code, qui peuvent être édités dans un éditeur de texte comme Atom. Ren'Py peut télécharger et installer Atom pour vous.

• Exploration des fichiers du projet (Environ 4:00 - 5:00) : Le dossier "game" contient tous les fichiers du jeu (audio, musique, images, etc.). Un raccourci vers le dossier "images" est également disponible. Le fichier "script" contient le code du jeu, y compris les dialogues, les transitions, la musique et les scènes. Les options et les écrans (screens) permettent de personnaliser l'apparence du jeu.

• Construction et distribution du jeu (Environ 5:00 - 5:30) : L'option "build distributions" permet de créer une version jouable de votre jeu pour la partager avec d'autres sur différentes plateformes comme PC, Linux, Mac, itch.io ou Steam.

• Exercice pratique avec le projet "The Question" (Environ 5:30 - 8:00) : Il est recommandé de sélectionner le projet "the question" et de lancer le projet pour jouer au jeu. Ensuite, ouvrez le script du projet "the question". L'exercice consiste à jouer au jeu tout en regardant le code correspondant dans l'éditeur de texte. Cela permet de comprendre comment le code contrôle le déroulement du jeu (musique, scènes, dialogues, choix). Il est possible de faire de petites modifications dans le script et de recharger le jeu pour voir les changements.

• Présentation de Scrivener (Environ 8:00 - 9:00) : Scrivener est un logiciel optionnel qui peut être utilisé pour écrire le dialogue et organiser le contenu de votre roman visuel. Un modèle Ren'Py pour Scrivener créé par Toadhouse Games est disponible. Scrivener propose des conseils d'écriture de base et des modèles pour les profils de personnages et le code Ren'Py.

• Conclusion (Environ 9:00 - Fin de la vidéo) : Elena encourage les spectateurs à commencer à expérimenter avec Ren'Py en modifiant le projet "the question". Des tutoriels plus avancés sur les "flags" et les choix seront proposés ultérieurement. Des ressources d'aide sont disponibles sur Twitter, par e-mail (teamtoadhouse@gmail.com), sur les subreddits et les forums Ren'Py, ainsi que sur le Discord de Toadhouse Games.

Voici un sommaire avec des indications temporelles basées sur le déroulement de la vidéo :

• Introduction (Début) : Elena Linaire, fondatrice et directrice créative de Team Toad House et Toad House Games, présente le studio et annonce un game jam de visual novels sur itch.io.

Elle mentionne des ateliers animés par des professionnels de Toad House pour aider à la création de visual novels. L'objectif est de rendre la création de jeux accessible aux débutants.

• Téléchargement de Ren'Py (Environ 2-3 minutes) : Elena explique comment télécharger Ren'Py depuis le site renpy.org.

Elle précise que Ren'Py est un moteur de jeu open source et gratuit spécialement conçu pour les visual novels. Elle cite d'autres moteurs de jeu comme Unity, Unreal, Game Maker, Godot et Twine, notant qu'ils sont adaptés à différents types de jeux.

Elle souligne que la connaissance de Python n'est pas nécessaire pour utiliser Ren'Py, bien que le "pi" dans Ren'Py fasse référence à Python.

• Documentation et Ressources (Environ 4-5 minutes) :

Elena mentionne que le site de Ren'Py contient de la documentation, qui est parfois considérée comme peu conviviale.

Elle recommande également le serveur Discord Ren'Py et le forum Lumisoft comme ressources d'aide.

La documentation couvre les bases et les utilisations plus spécifiques de Ren'Py, y compris les systèmes de dates, de monnaie et d'inventaire.

• Lancement et Création d'un Nouveau Projet (Environ 6-10 minutes) :

Elena montre l'interface du lanceur Ren'Py, affichant des projets existants comme ceux de Toad House Games.

Elle explique comment créer un nouveau projet, choisir la langue (avec des options comme le pig latin) et sélectionner un éditeur de texte (recommandant Adam, qui peut être téléchargé directement depuis Ren'Py).

Il est possible de choisir la résolution du projet, avec 1280x720 comme valeur par défaut, et un schéma de couleurs clair ou foncé pour l'interface (GUI).

• Structure des Fichiers d'un Projet (Environ 11-13 minutes) :

Elena présente la structure des dossiers créés pour un nouveau projet Ren'Py, notamment les dossiers game (contenant images, audio, gui, saves), audio cache et autres.

Elle explique que le fichier script.rpy est l'endroit où le code du jeu est écrit.

Elle montre comment remplacer l'icône de l'application et modifier les éléments de l'interface graphique dans le dossier gui.

• Jeu Ren'Py par Défaut et Code de Base (Environ 14-16 minutes) :

Elena lance le projet par défaut de Ren'Py pour montrer les fonctionnalités intégrées comme les sauvegardes, les chargements, les préférences (volume, plein écran, saut, etc.) et l'écran "À propos". Elle exécute le court jeu par défaut pour illustrer la structure de base : arrière-plan (bg), sprites et dialogue.

Elle ouvre ensuite le fichier script.rpy dans Adam pour montrer le code correspondant, expliquant les déclarations de personnages (define) et le point de départ du jeu (label start).

• Définir des Personnages et Écrire du Dialogue (Environ 17-19 minutes) : Elena explique comment définir des personnages avec un nom et une couleur de texte.

Elle montre comment écrire du dialogue en utilisant le nom du personnage défini. Elle aborde la question de la gestion de plusieurs personnages avec des noms similaires.

• Outil Narratif Scrivener (Environ 19-22 minutes) : Elena présente Scrivener comme un outil utile pour la planification et l'écriture du récit d'un visual novel, permettant d'organiser l'intrigue, les dialogues et même d'intégrer des éléments de code de base avant de les copier-coller dans Ren'Py.

• Narration et Positionnement du Texte (Environ 22-24 minutes) : Elena explique comment gérer le texte de narration (sans nom de personnage), souvent utilisé pour les pensées internes.

Elle mentionne les deux modes de texte principaux dans Ren'Py : en bas de l'écran et en plein écran (NVL). Elle déconseille de placer le texte narratif ailleurs à cause de la complexité du code.

• Choix, Sauts (Jump), Appels (Call) et Drapeaux (Flags) (Environ 25-41 minutes) :

Elena démontre comment créer des choix (menu) dans Ren'Py, en utilisant les mots-clés menu, les options et les actions à entreprendre (texte, jump vers un autre label, call à un autre label).

Elle explique la différence entre jump (saut sans retour) et call (appel avec retour après un return).

Elle introduit le concept de drapeaux (flags), qui sont des variables utilisées pour suivre les décisions du joueur et influencer le déroulement de l'histoire (default nom_du_drapeau = False, \$ nom_du_drapeau = True, if nom_du_drapeau:).

Elle montre comment les drapeaux peuvent être utilisés avec des instructions if pour afficher du contenu conditionnel.

• Analyse des Jeux Tutoriel et "The Question" (Environ 41-47 minutes) :

Elena examine le jeu tutoriel inclus avec Ren'Py, soulignant ses fonctionnalités (sauvegarde, chargement, préférences, rollback, historique) et son contenu éducatif sur les bases de Ren'Py.

Elle explore ensuite le jeu d'exemple "The Question", attirant l'attention sur l'analyse du jeu du point de vue d'un développeur (apparence des sprites, positionnement, expressions, choix).

Elle montre comment le code du jeu "The Question" utilise la définition de personnages avec des couleurs de texte personnalisées (codes hexadécimaux), les drapeaux et la structure des labels pour créer des choix et des embranchements narratifs.

• Exemple de Code de "Good Looking Home Cooking" (Environ 47-1 heure 17 minutes) :

Elena présente le code de son jeu "Good Looking Home Cooking" comme un exemple plus complexe, montrant l'utilisation de définitions pour les sons, les curseurs et les personnages (avec des propriétés comme la couleur et le texte alternatif pour la synthèse vocale).

Elle explique l'utilisation de variables pour suivre les choix importants (drapeaux) et comment ces drapeaux sont utilisés dans les menus et les instructions conditionnelles pour créer différents embranchements et fins.

Elle illustre l'utilisation de transformations et de positionnement pour les sprites, les transitions (dissolve, fade to black), et la création d'une séquence de crédits animée à partir d'une image PNG défilante. Elle discute des différentes fins possibles dans un visual novel (bonne, mauvaise, tiède).

• Taille des Images et Arrière-Plans (Environ 1 heure 17 minutes - 1 heure 21 minutes) :

Elena aborde la question de la taille des images, expliquant qu'elle est déterminée par essai et erreur pour correspondre à la résolution du projet et à l'aspect souhaité. Elle montre des exemples d'arrière-plans filtrés dans le jeu Ren'Py "Karashojo".

• Conseils de Gestion du Temps et Conclusion (Environ 1 heure 21 minutes - Fin) :

Elena termine en donnant des conseils pour la gestion du temps et éviter le "crunch" pendant le game jam, notamment en se fixant des mini-échéances régulières et en étant réaliste quant à la portée du projet.

Elle encourage les participants à utiliser des outils comme Scrivener et à rejoindre la communauté Discord pour trouver de l'aide et des collaborateurs.

Elle rappelle l'importance d'inclure la mention légale de Ren'Py dans le jeu.

This work has been peer reviewed in GigaScience (https://doi.org/10.1093/gigascience/giaf015), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Vanessa Marcelino

The manuscript proposes a new method to distinguish between healthy and diseased human gut microbiomes. The topic is timely, as to date, there is no consensus on what constitutes a healthy microbiome. The key conceptual advance of this study is the integration of functional microbiome features to define health. Their new computational approach, q2-predict-dysbiosis (Q2PD), is open source and available on GitHub.

While the manuscript is conceptually innovative and interesting for the scientific community, there are several major limitations in the current version of this study.

1. To develop the Q2PD, they define features associated with health by comparing it with microbiome samples from IBD patients. There are many more non-healthy/dysbiotic phenotypes beyond IBD, therefore it is not accurate to use IBD as synonymous of dysbiosis as done throughout this version of the paper.

2. The study initially tests the performance of Q2PD against other gut microbiome health indexes (GMHI and hiPCA) using the same data that was used to select the health-associated features of Q2PD. Model performance should be assessed on independent data. On a separate analysis, they do use different datasets (from GMHI and hiPCA), but these datasets seem to be incomplete - GMHI and hiPCA publications have included 10 or more disease categories, and it is unclear why only 4 categories are shown in this study.

3. While Q2PD does provide visible improvements in differentiating some diseases from healthy phenotypes, the accuracy and sensitivity of Q2PD isn't clear. To adopt Q2PD, I would like to know what are the chances that the classification results will be correct.

4. There is very little documentation on how to use Q2PD. What are the expect outputs for example, do we need to chose a threshold to define health? Is the method completely dependent on Humann and Metaphlan outputs, or other formats are accepted? The test data contain some samples with zero counts. I got an error when trying it with the test data (ValueError: node array from the pickle has an incompatible dtype…).

Therefore, I recommend including a range of disease categories to develop Q2PD and use independent datasets to validate the model in terms of accuracy and sensitivity. Alternatively, consider focusing this contribution on IBD. Making the code more user friendly will drastically increase the adoption of Q2PD by the community.

Please also use page and line numbers when submitting the next version. Other suggestions:

Abstract: I recommend replacing 'attributed' with 'linked', as 'attributed' suggests that dysbiosis may be causing (rather than reflecting) disease.

Results: Please indicate what it is meant by 'function' here - it will be good to clarify that this method uses Metaphlan's read-based approach to identify metabolic pathways. What is used, pathway completeness or abundance?

Results regarding Figure 3a are difficult to interpret. Is 'non-negatively correlated' the same as 'positively correlated'? What does the colour gradient represent - their abundance in those groups, or the strength of their correlation?

"We observed that the prevalence of the pairs positively correlated in health was higher than in a number of disease-associated groups (Figure 3b)" . This is a very generalised statement considering that only half of the comparisons were significant. How co-occurring species were selected?

"To test this, we compared the contributions of MDFS-identified species to "core functions" in different groups (Supplementary Figure 4)." How was this comparison made, based on species correlations? The caption of these figures could include more detail - it just says 'Top species contributions to functions.' but how do you define 'top' ? What do the colours represent?

'This finding was congruent with our earlier suspicions of functional plasticity; modulation of function and thus altered connectivity in the interaction network, shifting towards less abundant, non-core functions upon perturbation of homeostasis.' This is reasonable, but I don't understand how you can draw this conclusion from these figures where there seems to be no significant difference between health and disease.

Section 'Testing q2-predict-dysbiosis, GMHI and hiPCA accuracy of prediction for healthy and IBD individuals'

What is the difference between fraction of "core functions" found the fraction of "core functions" among all functions?

"Most importantly, Q2PD produced visually the highest scores for all healthy in comparison to unhealthy cohorts" . This was not statistically significant. In fact, GMHI finds more significant differences between health and disease than Q2PD.

Sup. Figure 7 - would be informative to add the name/description of these metabolites not just their ID).

'Although the threshold of 0.6 as determinant of health by the Q2PD was not applicable to the new datasets'. Does the threshold to define health with Q2PD change depending on the dataset? What are the implications of this for the applicability of this index?

Effects of sequencing depth - this is a very good addition to the paper, the effects of sequencing depth can be profound but are ignored in most studies, so I commend the authors for doing this here. It would be even better, in my opinion, if this was done with the same datasets used to test/compare Q2PD with other methods, as using a different dataset here adds a new layer of confounding factors.

'the GMHI and the hiPCA produced the opposite trend, wrongly indicating patient recovery.' The difference here is striking, what is driving this trend?

The Gut Microbiome Wellness Index 2 (GMWI2) is now published. I don't think it needs to be part of the benchmarking, but it could be acknowledged/cited here.

Methods: More information on how the data was processed is needed - how were the abundance tables normalized? Which output from Humann was used for downstream analyses?

To ensure reproducibility, please provide the scripts/code used for analyses and figures.

While reading, I wondered how students can avoid accidental plagiarism, especially if they don't know how to properly cite their sources. The text specifies that plagiarism can be intentional or by mistake, but I want to better understand how to check if my work is properly cited. I'm also wondering if all universities have the same rules and penalties for plagiarism, or if they differ. I'd also like to learn how to paraphrase correctly while still attributing the source, so I don't accidentally copy someone's ideas.

Valentinus says that Christians subject themselves to the law, and are foolish since they must then follow all the laws. However, he himself says that those who are saved do not sin at all. Such a high bar.

Reading this section reminded me of another group that was likely shaped by virtue ethics, knights of the middle ages. Aristotle’s works had a resurgence in the 11th/12th centuries, and his thoughts reshaped culture/education. One piece of this being major changes in the way university education worked, with these changes reflected in the way we learn in universities today. At the same time, knights were operating under a chivalric virtue code - kind of a “bro code” for how they interacted with each other. These virtues were: honor, prowess, loyalty, and largesse (generosity). I hadn’t made the connection to how Aristotle’s ethics likely influenced this code, but I don’t think it would be a stretch to make that connection considering his influence at that time and the similar group virtues listed in this section.

I feel like this needs substantiation that I'm not getting anywhere in the piece so far?

注意：这里是将字符对应的 unicode 码位 (code points) 转换为整数类型，而不是基于 UTF-8 编码进行转换。

Một dạng ám dụng IR phổ biển được sử dụng hiện nay là truy xuất cô đặc (dense retrieval). Bên cạnh đó, mô hình embedding cũng có thể được áp dụng cho các task IR khác, như truy xuất da vector (multi-vector) mà trong đó, độ liên quan giữa tài liệu và truy vấn được tính dựa trên tương tác của nhiều vector embedding

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

The study by Osato and Hamada aims at computationally identifying a set of novel putative insulator-associated DNA binding proteins (DBPs) via estimation of their contribution to the expression of genes in the same chromosome region of their binding sites (+- 1Mbp from TSS). To achieve this, the authors leverage a deep learning architecture already published via which ChIP-seq peaks of DBPs in the TSS of a given gene are used to predict its expression level in four human cell lines.

Building on this, the authors used another tool called DeepLIFT to evaluate the weight of each DBP binding site on the final gene expression value. Hence they made the assumption that if a given DBP had an insulator function they could restrict the prediction of the gene's expression to the region included between pairs of that DBP binding sites, and evaluate the pair's motif directionality bias in the distribution of weights. They exemplify their approach's validity by the fact that they can predict the known directionality bias of CTCF/cohesin-bound sites as the highest of the lot, with the F-R orientation of the pairs the most enriched, recapitulating what already known in literature: i.e., that F-R chromatin interaction peaks are the most enriched. In addition, they find several new DBPs showing significant directionality bias; hence they could be candidates for insulation activity. They then provide correlation between these putative insulator binding sites and sites of transition between euchromatin and heterochromatin by independently using histone mark and gene expression datasets. This, of course, is not surprising because (a) there is insulation between regions with heterotypic chromatin identities, and (b) it was already known from the first papers describing insulated chromatin domains that their boundaries were well-enriched for active transcription and transcriptional regulators (e.g., Dixon et al, Nature 2012).

Finally, they use chromatin interaction (looping) sites to check the overlap between CTCF and all other DBPs and define a subset of putative insulator DBPs not overlapping CTCF peaks, suggesting potentially new insulatory mechanisms. These factors were all known transcriptional activators, but this part of the findings carry most of the novelty in the work and have the potential of opening up new directions for research in chromatin organization.

Overall, the methodology applied here is adequate, clear, and reproducible. The major issue, in our view, is that the entire manuscript's findings relies on the usage of deepLIFT, a tool which was not benchmarked previously or by the current study. In fact, deepLIFT is public as regards its code, and also appears as a preprint from 2017 on biorXiv and published in the Proceedings of Machine Learning Research conference. Also, this key tool was developed by the Kundaje lab (who produce high quality alogrithms), and not by the authors. Therefore, the manuscript is predominantly based on the execution of existing workflows to publicly-available data. This does not take anything away from the interesting question posed here, but at the same time does not provide the community with any new algorithm/workflow.

Finally, although I appreciate that the authors are purely computational and have likely no capacity for experimental validation of their claims of new DBPs having insulator roles, I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning. Using this kind of data, effects on gene expression can at least be tested in regard to the authors' predictions. Moreover, in terms of validation, Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As secondary issues, we would point out that:

• The suggested alternative transcripts function, also highlighted in the manuscript;s abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
• Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
• Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Significance

The scientific novelty of the work lies primarily in the identification of a set of DBPs that are proposed to confer insulator activity genome-wide. This has been long sought after in human data (whilst it is well understood and defined in Drosophila). The authors produce a quantitative ranking of the putative insulation effect of these DBPs and, most importantly, go on to identify a smaller subset that are apparently non-overlapping with anchors of CTCF-cohesin loop anchors; the presence of strong motif orientation biases in many DBPs can also be of broad interest, especially those that cannot be trivially ascribable to the loop extrusion process.

However, although these findings open the way for speculation on multiple insulation mechanisms via proteins with multiple regulatory functions, the manuscript provide no experimental or computational means to test the proposed roles of these DBPs - and, as such, this limits the potential impact of the work and mostly targets researchers in the field of genome organization that can test these findings. Having said this, if validated, this work can significantly broaden our understanding of how chromatin is organized in 3D nuclear space.

I typically identify myself to the authors: A. Papantonis, expertise in 3D genome architecture, chromatin biology, and genomics/bioinformatics.

Reviewer #1 (Public review):

Summary:

This study presents findings on dual TCR regulatory T cells (Tregs) using previously published single-cell RNA and TCR sequencing datasets. The authors aimed to quantify dual TCR Tregs in different tissues and analyze their characteristics. Rather than perform the difficult experiments needed to ascertain the functional role of dual receptors, this study relies entirely on scRNA-VDJ-seq data published by two other groups. The findings primarily confirm prior work rather than provide new insights, and the methodology has significant weaknesses that limit the study's impact. We have concerns about the scientific integrity of this work.

Strengths:

(1) The use of single-cell RNA and TCR sequencing is appropriate for addressing potential relationships between gene expression and dual TCR.

(2) The data confirm the presence of dual TCR Tregs in various tissues, with proportions ranging from 10.1% to 21.4%, aligning with earlier observations in αβ T cells.

(3) Tissue-specific patterns of TCR gene usage are reported, which could be of interest to researchers studying T cell adaptation, although these were more rigorously analyzed in the original works.

Weaknesses

(1) Lack of Novelty: The primary findings do not substantially advance our understanding of dual TCR expression, as similar results have been reported previously in other contexts.

(2) Incomplete Evidence: The claims about tissue-specific differences lack sufficient controls (e.g., comparison with conventional T cells) and functional validation (e.g., cell surface expression of dual TCRs).

(3) Methodological Weaknesses: The diversity analysis does not account for sample size differences, and the clonal analysis conflates counts and clonotypes, leading to potential misinterpretation.

(4) Insufficient Transparency: The sequence analysis pipeline is inadequately described, and the study lacks reproducibility features such as shared code and data.

(5) Weak Gene Expression Analysis: No statistical validation is provided for differential gene expression, and the UMAP plots fail to reveal meaningful clustering patterns.

(6) A quick online search reveals that the same authors have repeated their approach of reanalysing other scientists' publicly available scRNA-VDJ-seq data in six other publications:

(1) Peng, Q., Xu, Y. & Yao, X. scRNA+ TCR-seq revealed dual TCR T cells antitumor response in the TME of NSCLC. J Immunother Cancer 12 (2024). https://doi.org:10.1136/jitc-2024-009376

(2) Wang, H., Li, J., Xu, Y. & Yao, X. scRNA + BCR-seq identifies proportions and characteristics of dual BCR B cells in the peritoneal cavity of mice and peripheral blood of healthy human donors across different ages. Immun Ageing 21, 90 (2024). https://doi.org:10.1186/s12979-024-00493-6

(3) Xu, Y. et al. scRNA+TCR-seq reveals the pivotal role of dual receptor T lymphocytes in the pathogenesis of Kawasaki disease and during IVIG treatment. Front Immunol 15, 1457687 (2024). https://doi.org:10.3389/fimmu.2024.1457687

(4) Yuanyuanxu, Qipeng, Qingqingma & Yao, X. scRNA + TCR-seq revealed the dual TCR pTh17 and Treg T cells involvement in autoimmune response in ankylosing spondylitis. Int Immunopharmacol 135, 112279 (2024). https://doi.org:10.1016/j.intimp.2024.112279

(5) Zhu, L. et al. scRNA-seq revealed the special TCR beta & alpha V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells. Cell Death Dis 14, 487 (2023). https://doi.org:10.1038/s41419-023-06004-7

(6) Zhu, L., Peng, Q., Wu, Y. & Yao, X. scBCR-seq revealed a special and novel IG H&L V(D)J allelic inclusion rearrangement and the high proportion dual BCR expressing B cells. Cell Mol Life Sci 80, 319 (2023). https://doi.org:10.1007/s00018-023-04973-8

In other words, the approach used here seems to be focused on quick re-analyses of publicly available data without further validation and/or exploration

Appraisal of the Study's Aims and Conclusions:

The authors set out to analyze dual TCR Tregs across tissues, but the lack of robust controls, incomplete analyses, and insufficient novelty limit the study's ability to achieve its aims. The results confirm prior findings but do not provide compelling evidence to support the broader claims about the characteristics or significance of dual TCR Tregs.

Impact and Utility:

While the study provides a descriptive analysis of dual TCR Tregs, its limited novelty and methodological weaknesses reduce its likely impact on the field. The methods and data could have utility for researchers interested in tissue-specific TCR gene usage, but additional rigor is required to make the findings broadly applicable.