lagwAjfeoweifjwaroefiawjrerofigeajwogfaiwejgoaweijgo Mwah ahahaha hahahahahahaha!

This shows how unconscious biases can be so harmful in terms of data. When a system is being built to fit specific data moldings and algorithms used for those moldings are biased or not designed to be thoughtful about all types of people, thats when data storing can be seen as harmful

But he is unaware that he is in False Barn County, and that this is the one real barn

But isn't the belief that he isn't in False Barn County a false belief?

But aren't some false beliefs more important than others?

In my opinion, no

In my opinion, no

Connection: This discussion of equity resonated with the current material presented in my 5351 course. More specifically, I would like to draw ties to “The African-Centered Worldview: Toward a Paradigm for Social Work” written by Mekada J. Graham for the Journal of Black Studies. The article highlights the critical failures of social work theory in that it is foundationally ethnocentric. More specifically, social work theory is Eurocentric as its knowledge base is formed by the systems that see an overrepresentation of Black clients. Simply put, practitioners of social work have historically applied ethnocentric theory and practice to their aid of all individuals and groups.

This being said, a line can be drawn here between equity v. equality and Afrocentric ideals v. Eurocentric ideals. Equality would seem to prescribe traditional social work theory to all, providing aid and care in a manner that is similar for each individual. However, an equity approach is analogous to the Afrocentric approach, as diverse groups and varied individuals require tailored social work that does not look the same for all. This can be further proven by examining the outcomes of a universally prescribed social work theory. Graham's article states that Black individuals are overrepresented in social work, meaning that they are most commonly in stages of disparity and seeking out aid. They are also underrepresented in preventative aid, meaning that the overrepresentation and magnified need could be alleviated if an equity approach, not an equality approach, is taken systematically. The equity approach is connected to the application of an Afrocentric approach for these reasons.

Questions 1. What steps can be implemented when a social worker is attempting to understand the intersectionality of the client while maintaining mutual respect? 2. As an African American social worker that serves majority Caucasian clients, explain how cultural humility can help shape cultural competency.

embraced

social nonconformism is the condition sine qua non of intellectual achievement

To me this example shows how much in the name of good causes people can forget that there are many ways we can forget that social media is very widespread and while there are many people with good intentions, there are just as many people with bad intentions. It also breaches many ideas of consent as because these kids are young they don't have a say in whether they can be posted or not.

This suggests the friction between industrialisation and tourism in perhaps a clearer way than mitskell

footnotes here actually recommends mitskell's argument. This is interesting and his praise for her work suggests that they possibly follow a similar line of thought. (blah) however does build upon her thesis (this chapter published 9 years after hers), while the differences in their chosen case studies does see some differentiation in their conclusions

Interesting that an industrial feature boosted tourism? Mitskell talks more of the conflict between the two and people's arguments

like mitskell, highlights the importance of the railway in the influx of tourism. Both authors draw a clear link between this industrial development and the development of tourism. This concept of the connecting power of railway is greater developed by (blah) as he notes how this then connected the industrial workers of the rhondda valleys to Barry. This further suggests a strong relationship between industry and leisure. Industrialisation had facilitated a new working (), which saw disposable income increase for many, while industrial action encouraged parliamentary acts such as the (factory act and bank holiday acts with dates) which increased the free-time that workers could use to engage in the leisure industry, resorts like swansea and Barry, as both authors note, becoming places to do it (altho swansea's clientele was a little more posh

Places importance of the dock for the revival of the island in blah

Unlike Mitskell's swansea which saw tourism build upon their port, (blah) provides Barry as an example of a place, previously known for tourism, see itself revived through industrialisation (coal dock and railway for supplies), which then reinvigorated the tourist trade due to the ammenities provided initially for industrial purposes.

Unlike swansea, (blah's) discussion of Barry shows

Use of similar historians! He seems to be adding to the work of Mitskell this is a later piece so it will be interesting to see what he contributes to the historiography

Sita's purity departs from Vildi's verison (can't remember his name off the top of my head, supposedly the original version)

*found it... Valmiki is the name

How the Aryan invasion shaped Hinduism

first/second/third/finally are all evidence towards the argument

restates the reasoning behind all the arguments presented throughout the argument, as well as why continued inaction will negatively harm others

THIS IS THE ARGUMENT

this is the evidence for her thesis: facial recognition technology is not ready for mass deployment, and governments can't guarantee that it won't cause harm

yuck

reminds me a little bit about how black people weren't photographed in the early photography era because they didn't feel like figuring out how to more aptly design the process...the only reason it changed was because chocolate companies wanted pictures of their food in advertisements

technology is inherently biased. it is biased towards its creator and its creator's viewpoints. FOR EXAMPLE elon musk's grok

fuck ice fr

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

Learn more at Review Commons

Reply to the reviewers

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

Reply to the Reviewers

I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

In this work Neupane et al used large-scale robust CRISPR-based gene activation and ablation screens to identify novel regulators of α-synuclein pathology in synucleinopathies using as read-out p-αSyn129 signals by high-throughput fluorescence microscopy. The authors reveal that mitochondrial protein OXR1 promotes Ser129-phosphorylated αSyn aggregation, while ER-associated EMC4 suppresses it via enhanced autophagic clearance, highlighting new possible mechanistic pathways in disease progression of alpha-synucleinopathies.

Major comments:

1. As correctly pointed out by the authors in Introduction p-Syn is associated with aggregates, but its functional role is far to be clear and both neuroprotective or pro-aggregations effects have been proposed. Further it has been shown that, physiological neuronal activity augments Ser129-phospho αSyn, which is a trigger for protein-protein interactions, which in turn is necessary for mediating αSyn function at the synapse (https://doi.org/10.1016/j.neuron.2023.11.020). As a consequence modulation of p- p-αSyn as possible therapeutical target for PD and synucleinopathies is quite a complicate matter. The assumption, on which the whole paper is based, that increase in p-αSyn equates to αSyn aggregation and disease progression is rather weak to this reviewer, unless further validation of it is provided. Indeed while the authors performed experiments on human iPSC-derived cortical and dopaminergic neurons on p-αSyn analysis, any measurement of αSyn aggregates/oligomers, and neuronal degeneration is provided. It is recommended to provid this experiments ideally using different tecnique like αSyn-Proximity Ligation Assay for measurements of oligomers, as it has been largely validated in autoptic brains of PD, MSA and DLB patients (doi: 10.1007/s00401-025-02871-w.), as well as cell viability/apoptosis and neurites degeneration measurements upon OXR1 and EMC4 modulation in iPSC derived cortical and dopaminergic neurons.
2. The authors claims in Results page 5: "The absence of cytoplasmic pSyn129 signal in HEK293 cells lacking α-Syn overexpression demonstrates that elevated α-Syn levels are essential to drive robust and rapid aggregation. Moreover, it indicates that the 81A antibody selectively recognizes de novo aggregates rather than the recombinant seeds". The fact that ab81A recognize deNovo aggregates and not rec seeds is quite speculative, not supported by data, and might rather indicate that ab 81A does not recognize aggregates. Thus this further implays that other technology like for example Seeding amplification assays are being employed by the authors in addition to p-αSyn129 signals in validation experiments for example in genetic PD (ideally GBA1 or LRRK2) IPSC-derived dopaminergic neurons.

Minor comments:

1. The strain-specific effects especially from patients-derived fibrils of OXR1 activation and EMC4 depletion on pSyn levels is rather weak in comparison with RAB3 and PIKFYVE (fig 3F-G) and therefore the expected relevance of these results especially in vivo in patients should be better clarified and modulated in discussion
2. In discussion authors write "We observed that OXR1 activation preferentially increases α-Syn aggregates phosphorylation (EP1536Y) in neuronal somata, suggesting that mitochondrial dysfunction exacerbates α-Syn phosphorylation in later-stage aggregates." This is quite a surprising result since distal axonal endings are particularly susceptible to mitochondrial impairments for anatomical and physiologically reasons and if p-αSyn129 accumulation is driven by mithocondrial disfunction as suggested by this paper, this should be detected in neurites as well. Please clarify.
3. Authors say that they targeted mitochondrial, trafficking, and motility (MTM) genes in human cellular models. While mitochondrial and trafficking is clear in the context of Parkinson and neurodegnerative disease, less clear is the motility genes. Please expand on this.

Significance

This is a well written, comprehensive study with a well characterized, robust CRISPR-based gene activation and ablation screening pipeline to identify novel regulators of α-synuclein pathology. Methodology is rigorous and clearly described and results are well presented. The major limitation relays in the validation experiments where only one main read-out that is p-αSyn129 fluorescence signal is employed, limiting the significance and impact of the presented results. I believe that the basic science community might benefit principally of the proposed methodology of a large high-throughput screening to modulate a large set of genes, a platform that in principle might be used also for other scientific questions.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In the present study, Neupane et al. performed arrayed CRISPR activation and ablation screens, targeting genes related to mitochondria, trafficking and motility, to identify genes that modulate the presence of Ser 129 phosphorylated alpha-synuclein aggregates (pSyn129) upon administration of exogenous preformed alpha-synuclein fibrils. The screens have been performed in HEK cells stably overexpressing alpha-synuclein in two independent replicates, and hits have been further validated in induced pluripotent stem cell derived forebrain and dopaminergic neurons. Following functional validations, the authors conclude that enhancing the expression of OXR1 results in a modest increase in the number of pSyn129 puncta within cells, and their size, while partial loss of EMC4 expression reduces these puncta. To date some pre-print studies have used genome-wide CRISPR screening to identify modifiers of the accumulation of alpha-synuclein preformed fibrils in cells, suggesting the importance of uptake and endolysosomal trafficking for the propagation of alpha-synuclein aggregates in recipient cells. Although the topic is of interest in the field of Parkinson's disease and synucleinopathies in general, the readout of the present screen (presence of pSyn129) is not very sensitive and without investigating endogenous alpha-synuclein or cell homeostasis in neuronal models limits the stated conclusions.

Major comments:

• Please clarify whether the positive control genes RAB13 and PIKFYVE were nominated hits within the CRISPR screens. Specifically, the authors state that the positive control of the CRISPRa screen was RAB13, expected to reduce pSyn129 upon overexpression, nevertheless this gene does not appear as a hit in the CRISPRa volcano plot (although present in table S1 but not making the cutoff). In figure 2D, activation of RAB13 does not seem to impact the main readout phenotype. Moreover, in the CRISPRo screen, PIKFYVE was used, but this gene is also not presented as a hit linked to reducing pSyn129 in the CRISPRo plot. If these control genes do not come up as hits, it is difficult to support the conclusions of the screen.
• The effect size for screen hits presented in figure 2A/B is rather small. It is difficult to interpret the power of these findings in the absence of uptake efficiency controls, such as dextrans of appropriate molecular weights.
• The readout of the screen is not very sensitive, and it is unclear what it represents. Specifically, in Figure 2F, G the authors validate the hits OXR1 and EMC4, showing a small effect, albeit statistically significant. The authors should strengthen this data by adding more experiments addressing, for instance, what the pSyn spot area and spot intensity signify for the cell. Some experiments in a neuronal context are important, including SNCA KO as a negative control.
• It is unclear why the authors chose to follow up on the OXR1 and EMC4 hits. Please explain the rationale for follow-up studies.
• Generally, the notable difference in the number of pSyn129+ cells in the non-targeting across various experiments (including Fig.1G/I compared to Fig.2G/I or Fig. 3F/G or flow cytometry experiment) suggests the readout is not very sensitive.

For instance, in figure S3 it would be important to add an experiment controlling for cell number as opposed to LDH release, as the micrographs show some differences in cells number, e.g. in the ntg vs. EMC4 condition. - The data is not sufficient to suggest that OXR1 and EMC4 are strong modulators of alpha-synuclein aggregation, as the authors suggest based on figures 2 and 3 that show statistically significant difference and a rather small effect size. It is important to provide more insight into how these genes may affect endogenous alpha-synuclein and cellular homeostasis in more detail, especially in neuronal models. Further investigating the hits in this direction in additional genetic backgrounds would also increase the relevance of the findings, e.g. in SNCA triplication or GBA-PD neurons.<br /> In Fig. S8B the immunoblot analysis shows there may be an effect of EMC4 and OXR1 CRISPRa on α-synuclein levels; please quantify for both iPSC-derived cortical neurons and dopaminergic neurons. - The pattern of tyrosine hydroxylase staining in Figure 5F does not seem specific or as expected for iPSC-derived dopaminergic neurons. Furthermore, since endogenous SNCA expression is expected to be analogous to the expression of TH (with TH+ cells expressing higher SNCA), it would be important to compare pSyn129 between TH+ cells and/or relative to the TH+ area.

Minor comments:

• The authors report that RAB13 overexpression reduces pSyn129⁺ prevalence, whereas RAB13 ablation (CRISPRo screen) enhances pSyn129⁺ levels (Figures 2D-2E). Please revise as these specific figures show no effects for this gene.
• Please specify how many individual cells (approximately) were quantified in each figure legend.
• Figure 3F/G may be better as a supplemental figure since it does not add to the conclusions of the study.
• It would be good to clarify for the reader some of the genes that serve as positive controls for the screen's readout (as shown in Fig. S2D/G).
• It would be helpful to further clarify which cell type was used in each figure legend.

Significance

Important topic but their experimental design limits the significance of their findings. Hard to improve the work in a reasonable amount of time. Also many technical issues.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary:

This study by Neupane et al. investigates modulators of α-synuclein aggregation, focusing on Ser129-phosphorylated α-synuclein (pSyn129), a pathological hallmark of Parkinson's disease (PD). The authors performed high-content image-based, arrayed CRISPR activation (CRISPRa) and knockout (CRISPRo) screens targeting > 2300 genes related to mitochondrial function, intracellular trafficking, and cytoskeletal reorganization. Using α-Syn overexpressing HEK293 cells, they identified OXR1 and EMC4 as novel modulators of pSyn129 abundance. Key findings were that activation of the mitochondrial protein OXR1 increased pSyn129 by decreasing ATP levels, while ablation of the ER-associated protein EMC4 reduced pSyn129 by enhancing autophagic flux and lysosomal clearance. These findings were validated in human iPSC-derived cortical and dopaminergic neurons.

My major comments have to do with statistical methods and with significance of their findings.

Major comments:

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The claims and conclusions are generally well-supported by the presented data. The dual CRISPRa/CRISPRo screen provides a robust initial discovery platform, and the validation in iPSC-derived neurons strengthens the findings and their translational relevance. The mechanistic insights into OXR1 (ATP levels) and EMC4 (autophagic flux, lysosomal clearance) are supported by the described experiments. The use of two antibodies (81A and EP1536Y) for pSyn129 also enhances confidence in the measurements. I had a few questions about the statistical methods. The main concern I have about methodology for the screen is whether the authors have corrected for multiple hypotheses in their discovery screen. This is not clear from the text, methods, or legends (for Figures 2A/2B/2C).

• Figure 1B suggests a very large range of activation (multiple orders of magnitude) in the initial screen. What is the relationship between level of expression change and functional effect across the screen? How upregulated/downregulated are OXR1 and EMC4 at the mRNA and protein levels?
• Supplemental Figure S2D: Why do the non-targeting controls differ from the majority of the CRISPRa genes? If I am reading the figure correctly, it seems strange that the vast majority of the CRISPRa gene targets reduces pSyn pathology relative to the non-targeting controls (which is why I am wondering whether the level of increased expression correlates with the level of functional effect).
• In Figure 2A/B/C, is the p-value adjusted in any way for multiple comparisons? If so, this should be indicated in the legend. If not, why not? (The potential for false positives in a screen is very large and requires correction for multiple comparisons.)
• Figure 3: It's interesting that different seeding materials have different effects. However, it's quite surprising that the authors find less seeding with MSA-derived material in both the CRISPRa and CRISPRo context. This contradicts the work of Peng and coauthors (PMID 29743672) who find that MSA-derived material is much more potent in seeding aggregates in a number of different cell types. Do the authors have any thoughts about why this is the case?
• Figure 7A: pSyn129 image in the non-targeting control is poor - the very bright dots look like artifact. Not clear why the authors don't corroborate with EP1536Y antibody as they do in Figure 5.
• Overall methodology: Are the pSyn inclusions soluble? This could be easily determined by performing 1% TritonX extraction, for example, and it helps us understand how "pathological" the inclusions are.
• OPTIONAL: The authors perform some interesting experiments looking at genes affected downstream by, for example, OXR1 over-expression. It would be useful to understand whether the upstream effect is dependent on downstream effect. This could be tested by performing double perturbations (e.g. OXR1 overexpression and CCL8 knockout or ALDOC upregulation).
• OPTIONAL: The link between EMC4 ablation and enhanced ER-driven autophagic flux/lysosomal clearance could be corroborated with additional experiments. E.g.: Does EMC4 normally inhibit this pathway? Or only in the context of aSyn fibril seeding?

Are the suggested experiments realistic in terms of time and resources?

The OPTIONAL experiments are generally feasible as they employ methods that the lab is already using in this paper.

Are the experiments adequately replicated and statistical analysis adequate?

See comment about multiple hypothesis testing above.

Significance

This is a well-designed, difficult-to-accomplish study that expands the landscape of pS129Syn modulators. The validation of the primary hits identified in HEK293 cells in iPSC-derived neurons gives the findings greater relevance.

Strengths:

• Novelty: Using an unbiased and high-throughput approach, the study identifies two novel regulators of α-Syn aggregation, namely OXR1 and EMC4.
• Methodological Rigor: The use of arrayed CRISPRa/CRISPRo screens with high-content imaging is powerful and difficult to accomplish. Methodologically, this is a tour de force.
• Orthogonal Validation: The use of multiple α-Syn fibril polymorphs/strains and different antibodies (81A, EP1536Y) strengthens the robustness of the findings.

Limitations:

• It's not clear to me that pSyn129 is the ultimate readout. At a minimum, we should know something about the solubility of the inclusions. Some panels (e.g. Figure 7A) are not very informative in terms of what the authors are calling pSyn129+.
• The study relies on in vitro cellular models. While iPSC-derived neurons are relevant, the complexity of the brain environment, including glial cell interactions is not fully captured. This is fine for an initial report, but it does limit the significance.
• OXR1 and EMC4 seem to be very generic modulators. It's not clear to me that their effects are specific to aSyn or to PD in any way - they might just be effects on very basic cellular functions that would be applicable to a number of stressors or proteinopathies. Maybe that is fine (we probably need to get rid of tau aggregates, too!), but I don't think the authors can claim that they have identified "organelle-specific genetic nodes of aSyn pathology" since they biased their screen towards mitochondria and they don't test any other pathological aggregates. Moreover, from a translational perspective, it's not clear to me that implicating the antioxidant pathway or lysosomal/autophagosomal pathways in the pathogenesis of PD is new, and it's not clear that the specific genes identified would make good therapeutic targets.

I'll leave this as a general comment. But remember your resume is to showcase who you are and how you can bring energy and skill to a job. Your bullet points could use a bit more energy or insight. You're first year dont sweat too much but you have to start putting emphasis on bullet points like in details. Bullshit if you have to but make it believable obviously. Your resume has so much more potential.

Also as Dan said fill up as much as you can cuz that white space at the bottom is a lot.

Did you build a custom api for this or are you just scraping the environment canada website? If you made a custom api or even sanitized the data from it say it!

Quantify and I will keep stressing this. QUANTIFY! Like how much time did you save (estimate if you have to) when entering game results vs manual calculation. It's all metrics!

Then same with WEb-based front end using what and modern design etc something to stand out.

I like this but I feel liek you can quantify a bit more like how many MLB data sets? or how many games in a season are you analyzing or even weekly or daily.

Mention what you used for front-end to visualize, you can just say plain HTML/CSS/JS or you can add a framework.

If you automated generation of social media graphics how much time did you save? You can quantify that.

Please fix the formatting on this as if you look at it you're wasting so much space with the extra indentation of your projects compared to experience. Learn Latex or you can use those free Google Docs templates

Add more bullet points to this as I know there's not only one thing you learnt and front-end web development is a very BROAD topic.

DId you work with cross-functional teams? Did you improve a process that was slow initially by someone else on your team? Point out some languages you used.

Also idk this might be me nitpicking but idk if you're allowed to say the company project name as thats usualyl private but yet again sicne its a highschool i dont thinkl they care all that much.

Also I would rename this as "Software Developer Intern" or something because it looks like an internship seeing it spanned months. or Full Stack but since you said front end I'd just keep software developer intern or Front end developer intern.

Remove this or move it under the dates. As your dates should be above your location to follow the formatting you have in your resume

The date of this article's publication was a time when transgender people experienced hate crimes and widespread bigotry.

I wonder if when a child learns many languages during childhood, the brain treats the communicative systems as all a unified mechanism, as if it if was one whole language composed of several words that are synonyms and antonyms in the semantic spectrum. Under this logic, the benefit would be for adult learners who then would consider different languages as different communicative systems, therefore locating them in different regions of the brain and ultimately enhacing neuroplasticity and synapses.

I would not that much cost effective because learnuing a language takes time, and therefore, the money people pay for language courses will be deducted every month.

GAPS IN ANALYSIS - Only looking at Plenary voting. How would we measure influence on aspects like "agenda-setting, trilogue meetings, etc." - ALso does not say WHY radical right joins (often pro-EU, often progressive) winning coalitions. Says COULD be radical right's attempt at normalisation / "appearing responsible" -> work w/ national govts and voter attraction. - Maybe other parties have simply shifted rightward / become more palatable

"A longitudinal analysis could examine whether the ID group has indeed changed its strategy and/or whether the ideological positions of other groups have moved rightwards."

"focus on plenary voting does not allow for an assessment of the radical right's participation and influence in other aspects of the legislative process, such as agenda-setting, negotiations in plenary committees, or trialogue meetings."

OKAY -> takeaways - What Voting Influence tool reveals 1. ID must NOT be internally fractured in order to be pivotal (is not often the case) 2. For radical right to wield influence, THE PLENARY MUST BE VERY POLARISED 3. Overall voting influence remains limited in absolute terms -> usually joins coalitions that win by a LARGE majority, where it can therefore NOT wield a major influence. 4. In some cases where grand coalition is not formed, rad right is "crucial partner" for EPP (one of the mainstream preogressive parties)

"For the radical right group to hold voting influence, there must be a minimum level of polarisation in plenary, and the group itself must be internally cohesive."

WAS gap in litrature on how powerful ID was in EP -> voting influence tool helps us to see (that they are often part of coalitions but thats it)

"This article aims to address this gap by introducing a new measure of voting influence, thereby contributing to the existing literature on the impact of radical right parties."

CONCLUSION: - ID / radical right groups are OFTEN part of winning coalitions within the EP - This does NOT mean that the group is influential (I think because still of internal infighting? - Is a crucial partner of the EPP in "some cases"

So parties don't want to work w/ ID as opposed to CR (other less radical conservative party)

BUT voting share has increased much in past five years.

THIS article is going to MEASURE / EXAMINE THE VOTING INFLUENCE OF ID IN THE EP -> doing so by lookin at the "final stage of decision making in the EP" which is PLENARY VOTING

Voting influence (introduce term here): - "ability of a party group to sway the outcome of a vote" - Basically applies to PLENARY voting WITHIN the EP -. doesn't refer to ballot box.

SINCE 2019 -> Radical Right has gained MORE influence -> ID (identity / Dem) party has become 4th largest

Remains understudied

Why radical right has so far been ignored by schoalrs: - Cordon sanitaire -> coalition by EPP. Progressive Alliance of Socialists and Democrats (S&D) and Renew -> biggest three parties in EP to shut out their influence. (QUESTION -> is this WRITTEN policy (i.e., in those parties' constitutions) or is it just convention? - LACK OF TRANSNATIONAL SOLIDARITY by these groups -> i.e., no attempts at "internal cohesion" within the EP -> no reaching out and cooperatin (obviously, is this changing? QUESTION -> Is this because theyre all concerned w/ domestic issues only? Because they're concerned w/ domestic CULTURAL autonomy / issues? Just because they're Eurosceptic?

To me the story is this: NE always had some preference for liberal policies and for a while the party to affiliate with was less defined. After Reagan, the base of young and immigrant support FDR had grown was unable to find a home for their progressive mindsets in an increasingly evangelical republican party. Thus, they said: "We dippin" and deserted to the democrats.

I think the push factors were more important than the pull here

This seems to me a primary factor

In part whats gonna happen is the relative extremism is gonna make people who were once rep. democrats

Again progressivism rears its beautiful head

We just got the pull factors, now we will get the push. Reagans social conservatism will not be good for his new england base

Bringing new englanders into the democratic party

New engalnd holding strategic worth as country grows

The north was always more progressive, and there was a time when the republican part provided that, but as the population become younger and catholic and immigrant, the democrats were actually the party that had the opportunity for liberalism

Wonder if we will see parallels in modern politics

But isn't this not analogous to today since the views on states rights would flip which party it found a home under

ironic given that new england almost started the revolution with a want of states rights

But hasn't given new england the same voting power as the south

this is in the voice of the student

what does this tell you about the neighborhood she lives in? possible about her own SE status?

this reflects back to her being a TV kid

Now we're back to the opening line.

How can educators identify which children may be more sensitive to environmental influences without labeling them?

This sentence explains why early childhood is such an important time for learning and brain development.

This shows that children’s development is shaped by both their biology and their surroundings, not just one or the other.

This sentence stood out because it shows that a lack of responsive caregiving can have serious long term effects on a child’s growth and health.

This sentence explains how everyday interactions between a child and a caregiver directly support the brain development and showing that learning starts through simple responsive communication.

For example a square can be cut to pieces and arranged as a triangle as well.

I am very excited for this!

Such efforts to risk recapture to aid enslaved people is the kind of bravery that I could only hope to aspire to.

This passage makes me think of modern day America, with ICE raids and racism running rampant. The solution many are finding is to mock the ICE agents and make them ashamed of their actions.

Feels like such a healthy and authentic lens to take in the writing world!

:) Like this idea of staying true to yourself within the framework of traditional English assigments

:)

Very real feeling I have even felt myself in English classes- can make you feel unintelligent when in relaity you are just not fitting the rigid mold

This can lead to loss of culture as English is pritotized in the classroom and society

Interesting idea, although many speak only one language we are bilingual in the many versions we speak and suppress

Feels like a thesis or main idea for text- immigration effects english class

I've heard only three colors (RGB) are needed because these colors are enough to recreate what the human eye perceives. I think it’s interesting how such a wide range of colors and detailed images can be created just by changing the intensity of three simple values in each pixel.

OUI!

Une autre références intéressante pourrait être celle-là: Mueller Gavin, Breaking things at work: the Luddites are right about why you hate your job, London New York, Verso, 2021.

oui! Je ne vois pas de référence à A. Casilli, mai cela pourrait être pertinent de la rajouter.

Je pense particulièrement au rapport du diplab qu'il a co-écrit sur le digital labor dans deepseek: https://ip-paris.hal.science/hal-04952735/file/THE%20HUMAN%20COST%20OF%20DEEPSEEK-DiPLab%20Policy%20Memo%201%281%29.pdf

Disease: Von-willebrand Disease (Type 1)

Study: additional evaluation to interpret pathogenicity of a common VWF variant.

Note: 244 healthy controls from general population to compare to the patient cohorts. Study performed with subjects from across Canada.

Patients: 58 subjects with only the specified variant below were recruited from two cohort studies

Variant: VWF NM_000552.5 c:4751A>G p.(Y1584C)

According to this publication: using the rules of ACMG/AMP guidelines concluded the variant is LIKELY PATHOGENIC

CADD score (26)

AlphaMissense metric score (0.5865- Likely pathogenic)

Variant identified in 14% of index cases for Canadian type 1 VWD, in ClinVar databases is shown as conflicting interpretation of pathogenicity

Present in gnomAD, 1000 genomes, and UK BioBank with pop prevalence of 0.08%-0.27%. Highest prevalence is 0.43% in European Non-Finnish Population. Absent in East Asian and middle Eastern populations.

Cites a VWF mouse model for this variant that shows no change in protein clearance, decreased VWF antigen levels and mild reduction in multimers.

Interesting that CITIZENS of West Germany are allowed to enter -> blames government filled w/ Nazis and American "imperialism" -> again drawing link between Bonn govt and Nazism (directly, successor state, GDR only denazified state)

"Visits by peaceful citizens of West Berlin to the capital of the German Democratic Republic (the democratic Berlin) are possible upon presentation of a West Berlin identity card. Revanchist politicians and agents of West German militarism will not be allowed to set foot in the capital of the GDR (democratic Berlin)."

But then what about GDR citizens? -> Need "spevcial permission" which seems to betray a kind of anxiety ovr brain drain / undermined control.

Vulgate is latin, sepitgiunt is greek (and was translated into the vulgate), hebrew text is "original" (and was translated into greek)

Basically, being ok with the fact that there are differences

Canon because they wanted to politically oppose to keep power

Whats the difference? Don't obey them to a tee?

For people party it was on hand but it must have also been colonial motivations

DEI

Seems to blame border closure on West German aggression.

IS possible for women to contribute to national community (i.e., by participating in the labour economy). Hitler says women would be contributing as much as, say, husbands/fathers if they worked (strenuous) jobs AND were the only caretakers of their families.

"There may indeed be cases in which justice requires that a woman should receive the same income as a man if she is engaged in typical men's work, e.g. that of a heavy or very heavy worker and if, in addition, she has to care for children in place of a husband. But then one should achieve the equality not by increasing the basic wage, but through child allowances or, even better, through an appropriate reduction in taxes. For such a woman is contributing to the maintenance of the national community in the same way as a family man.'"

SUB argument / evidence for no equal pay for equal work -> because doesn't make economic sense: i.e., wages WON'T improve people's efficiency -> not how that works, also wartime economy cannot handle it. Specifically because it would only increase purchases on the "Black Market" Says, I guess, that they CAN externally improve women's performance in facotires, but only by increasing food supplies -> no consumer goods since war, so this would mean contributing to the Black Market.

"cannot anticipate a significant improvement in performance by equalizing the pay of men and women. Money is not worth as much as it used to be because there is a dearth of consumer goods which can be bought. "

"An increase in women's wages would in practice simply mean strengthening the black market. If one wanted to achieve a general improvement in performance that could only be done by improving food supplies and the supply of the most important commodities."

Equal pay for equal work is false

Why? BECAUSE women (again, according to Hitler, of all people) are meant to work "at home" to look after their families. Hitler doesn not consider this domestic labour to be as important as a husband/father's work in supporting the family and "national community" -> explicitly states that it is more important. "because they must make more sacrifices for the national community."

Framed in context of wages being paid NOT for performance but for "social reasons" -> namely, implicitly, workers should be paid accorindg to how much they need the money for a family / how much they contribute to "national community"

Hitler thinks that wages should NOT be paid on performance -> justifies this decision by saying that young (male) workers shouldn't be paid more than old ones even though they can likely work faster and for longer. Instead, older men should be paid more because, again, they have families to support and contribute more to the "national community". Younger men work faster but do not contribute as much because they are unattched.

Hitler, again, maintains that women both do not perform well compared to men AND have less to contribute to families / national community -> "only have themselves to look after" (in a patrarchal assumption)

In my opinion, America is so expensive now that even basic rent is hard to afford compared to people’s salary. If the monthly salary was at least three times higher than the rent, maybe people could actually survive, but right now after paying rent you still have to buy food and pay gas, internet, and electricity, so many people really cannot afford it. People are just working so hard every single day just to survive, repeating the same struggle until they cannot make it anymore, and sometimes that pressure pushes them into decisions they never really wanted to make.

Will we need to purchase this or is it provided on moodle?

Will rubrics be provided for the majority of assignments?

Can you clarify who this would be?

This is good to know if anyone is ill and cannot come to class because of being sick.

This is good to know if anyone may be in need of improving their grade.

Are the questions located after the chapter readings where it says discussion questions?

Where did the not adam and eve people come from?

All it took was one convo bruh?

Motor and psych development go hand in hand.

These projects are cool.

This is interesting, and I am wondering how much further would teachers have to work during outside contract hours to make this a reality. Is this a realistic thing to expect most teachers to do? Yes, it is good, but I worry about more things being added on teacher's plates without additional pay. Just additional work. Another question, if it IS additional work and no additional pay, would that STILL be worth it? If it increases career satisfaction?

Not only is art integration good for students, but it is also good for teachers. I think that is an excellent argument, however a question I might have is is it good for ALL teachers? Or just those who are more creatively inclined? I am very creatively inclined, however I wonder if arts integration would have the same affect on a person who has never had much interest in creative arts.

eLife Assessment

This study presents a platform to implement closed-loop experiments in mice based on auditory feedback. The authors provide convincing evidence that their platform enables a variety of closed-loop experiments using neural or movement signals, indicating that it will be a valuable resource to the neuroscience community. The paper could be strengthened by the addition of additional tutorials, such as on how to run an experiment.

Reviewer #1 (Public review):

Summary:

The authors provide a resource to the systems neuroscience community by offering their Python-based CLoPy platform for closed-loop feedback training. In addition to using neural feedback, as is common in these experiments, they include a capability to use real-time movement extracted from DeepLabCut as the control signal. The methods and repository are detailed for those who wish to use this resource. Furthermore, they demonstrate the efficacy of their system through a series of mesoscale calcium imaging experiments. These experiments use a large number of cortical regions for the control signal in the neural feedback setup, while the movement feedback experiments are analyzed more extensively. The revised preprint has improved substantially upon the previous submission.

Strengths:

The primary strength of the paper is the availability of their CLoPy platform. Currently, most closed-loop operant conditioning experiments are custom built by each lab, and carry a relatively large startup cost to get running. This platform lowers the barrier to entry for closed-loop operant conditioning experiments, in addition to making the experiments more accessible to those with less technical expertise.

Another strength of the paper is the use of many different cortical regions as control signals for the neurofeedback experiments. Rodent operant conditioning experiments typically record from the motor cortex, and maybe one other region. Here, the authors demonstrate that mice can volitionally control many different cortical regions not limited to those previously studied, recording across many regions in the same experiment. This demonstrates the relative flexibility of modulating neural dynamics, including in non-motor regions.

Finally, adapting the closed-loop platform to use real-time movement as a control signal is a nice addition. Incorporating movement kinematics into operant conditioning experiments has been a challenge due to the increased technical difficulties of extracting real-time kinematic data from video data at a latency where it can be used as a control signal for operant conditioning. In this paper, they demonstrate that the mice can learn the task using their forelimb position, at a rate that is quicker than the neurofeedback experiments.

Weaknesses:

Many of the original weaknesses have been addressed in the revised preprint.

While the dataset contains an impressive amount of animals and cortical regions for the neurofeedback experiment, my excitement for these experiments is tempered by the relative incompleteness of the dataset.

Additionally, adoption of the platform may be hindered by the absence of a tutorial on how to run a session.

Reviewer #2 (Public review):

Summary:

In this work, Gupta & Murphy present several parallel efforts. On one side, they present the hardware and software they use to build a head-fixed mouse experimental setup that they use to track in "real-time" the calcium activity in one or two spots at the surface of the cortex. On the other side, they present another setup that they use to take advantage of the "real-time" version of DeepLabCut with their mice. The hardware and software that they used/develop is described at length, both in the article and in a companion GitHub repository. Next, they present experimental work that they have done with these two setups, training mice to max out a virtual cursor to obtain a reward, by taking advantage of auditory tone feedback that is provided to the mice as they modulate either (1) their local cortical calcium activity, or (2) their limb position.

Strengths:

This work illustrates the fact that thanks to readily available experimental building blocks, body movement and calcium imaging can be carried out using readily available components, including imaging the brain using an incredibly cheap consumer electronics RGB camera (RGB Raspberry Pi Camera). It is a useful source of information for researchers that may be interested in building a similar setup, given the highly detailed overview of the system. Finally, it further confirms previous findings regarding the operant conditioning of the calcium dynamics at the surface of the cortex (Clancy et al. 2020) and suggests an alternative based on deeplabcut to the motor tasks that aim to image the brain at the mesoscale during forelimb movements (Quarta et al. 2022).

Weaknesses:

This work covers 3 separate research endeavors: (1) The development of two separate setups, their corresponding software. (2) A study that is highly inspired from the Clancy et al. 2021 paper on the modulation of the local cortical activity measured through a mesoscale calcium imaging setup. (3) A study of the mesoscale dynamics of the cortex during forelimb movements learning. Sadly, the analyses of the physiological data appears incomplete, and more generally, the paper shows weaknesses regarding several points:

The behavioral setups that are presented are representative of the state of the art in the field of mesoscale imaging/head fixed behavior community, rather than a highly innovative design. Still, they definitely have value as a starting point for laboratories interested in implementing such approaches.

Throughout the paper, there are several statements that point out how important it is to carry out this work in a closed-loop setting with an auditory feedback, but sadly there is no "no feedback" control in cortical conditioning experiments, while there is a no-feedback condition in the forelimb movement study, which shows that learning of the task can be achieved in the absence of feedback.

The analysis of the closed-loop neuronal data behavior lacks controls. Increased performance can be achieved by modulating actively only one of the two ROIs, this is not really analyzed, while this finding which does not match previous reports (Clancy et al. 2020) would be important to further examine.

Reviewer #3 (Public review):

Summary:

The study demonstrates the effectiveness of a cost-effective closed-loop feedback system for modulating brain activity and behavior in head-fixed mice. Authors have tested real-time closed-loop feedback system in head-fixed mice two types of graded feedback: 1) Closed-loop neurofeedback (CLNF), where feedback is derived from neuronal activity (calcium imaging), and 2) Closed-loop movement feedback (CLMF), where feedback is based on observed body movement. It is a python based opensource system, and the authors call it CLoPy. Authors also claim to provide all software, hardware schematics, and protocols to adapt it to various experimental scenarios. This system is capable and can be adapted for a wide use case scenarios.

Authors have shown that their system can control both positive (water drop) and negative reinforcement (buzzer-vibrator). This study also shows that using the closed-loop system, mice have shown to better performance, learnt arbitrary tasks and can adapt to changes in the rules as well. By integrating real-time feedback based on cortical GCaMP imaging and behavior tracking authors have provided strong evidence that such closed-loop systems can be instrumental in exploring the dynamic interplay between brain activity and behavior.

Strengths:

Simplicity of feedback systems design. Simplicity of implementation and potential adoption.

Weaknesses:

Long latencies, due to slow Ca2+ dynamics and slow imaging (15 FPS), may limit the application of the system.

Author response:

The following is the authors’ response to the original reviews.

Public reviews:

Reviewer #1 (Public review):

Summary: 

The authors provide a resource to the systems neuroscience community, by offering their Python-based CLoPy platform for closed-loop feedback training. In addition to using neural feedback, as is common in these experiments, they include a capability to use real-time movement extracted from DeepLabCut as the control signal. The methods and repository are detailed for those who wish to use this resource. Furthermore, they demonstrate the efficacy of their system through a series of mesoscale calcium imaging experiments. These experiments use a large number of cortical regions for the control signal in the neural feedback setup, while the movement feedback experiments are analyzed more extensively.

Strengths:

The primary strength of the paper is the availability of their CLoPy platform. Currently, most closed-loop operant conditioning experiments are custom built by each lab and carry a relatively large startup cost to get running. This platform lowers the barrier to entry for closed-loop operant conditioning experiments, in addition to making the experiments more accessible to those with less technical expertise.

Another strength of the paper is the use of many different cortical regions as control signals for the neurofeedback experiments. Rodent operant conditioning experiments typically record from the motor cortex and maybe one other region. Here, the authors demonstrate that mice can volitionally control many different cortical regions not limited to those previously studied, recording across many regions in the same experiment. This demonstrates the relative flexibility of modulating neural dynamics, including in non-motor regions.

Finally, adapting the closed-loop platform to use real-time movement as a control signal is a nice addition. Incorporating movement kinematics into operant conditioning experiments has been a challenge due to the increased technical difficulties of extracting real-time kinematic data from video data at a latency where it can be used as a control signal for operant conditioning. In this paper they demonstrate that the mice can learn the task using their forelimb position, at a rate that is quicker than the neurofeedback experiments.

Weaknesses:

There are several weaknesses in the paper that diminish the impact of its strengths. First, the value of the CLoPy platform is not clearly articulated to the systems neuroscience community. Similarly, the resource could be better positioned within the context of the broader open-source neuroscience community. For an example of how to better frame this resource in these contexts, I recommend consulting the pyControl paper. Improving this framing will likely increase the accessibility and interest of this paper to a less technical neuroscience audience, for instance by highlighting the types of experimental questions CLoPy can enable.

We appreciate the editor’s feedback regarding the clarity of the CLoPy platform's value and its positioning within the broader neuroscience community. We agree and understand the importance of effectively communicating the utility of CLoPy to both the systems neuroscience field and the wider open-source neuroscience community.

To address this, we have revised the introduction and discussion sections of the manuscript to more clearly articulate the unique contributions of the CLoPy platform. Specifically:

(1) We have emphasized how CLoPy can address experimental questions in systems neuroscience by highlighting its ability to enable real-time closed-loop experiments, such as investigating neural dynamics during behavior or studying adaptive cortical reorganization after injury. These examples are aimed at demonstrating its practical utility to the neuroscience audience.

(2) We have positioned CLoPy within the broader open-source neuroscience ecosystem, drawing comparisons to similar resources like pyControl. We describe how CLoPy complements existing tools by focusing on real-time optical feedback and integration with genetically encoded indicators, which are becoming increasingly popular in systems neuroscience. We also emphasize its modularity and ease of adoption in experimental settings with limited resources.

(3) To make the manuscript more accessible to a less technically inclined audience, we have restructured certain sections to focus on the types of experiments CLoPy enables, rather than the technical details of the implementation.

We have consulted the pyControl paper, as suggested, and have used it as a reference point to improve the framing of our resource. We believe these changes will increase the accessibility and appeal of the paper to a broader neuroscience audience.

While the dataset contains an impressive amount of animals and cortical regions for the neurofeedback experiment, and an analysis of the movement-feedback experiments, my excitement for these experiments is tempered by the relative incompleteness of the dataset, as well as its description and analysis in the text. For instance, in the neurofeedback experiment, many of these regions only have data from a single mouse, limiting the conclusions that can be drawn. Additionally, there is a lack of reporting of the quantitative results in the text of the document, which is needed to better understand the degree of the results. Finally, the writing of the results section could use some work, as it currently reads more like a methods section.

Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate the time and effort you took to review our work and provide detailed suggestions for improvement. Below, we address the key points raised in your review:

(1) Dataset Completeness: We acknowledge that some of the neurofeedback experiments include data from only a single mouse for some cortical regions while for some cortical regions, there are several animals. This was due to practical constraints during the study, and we understand the limitations this poses for drawing broad conclusions. We felt it was still important to include these data sets with smaller sample sizes as they might be useful for others pursuing this direction in the future. To address this, we have revised the text to explicitly acknowledge these limitations and clarify that the results for some regions are exploratory in nature. We believe our flexible tool will provide a means for our lab and others include more animals representing additional cortical regions in future studies. Importantly, we have included all raw and processed data as well as code for future analysis.

(2) Quantitative Results: We recognize the importance of reporting quantitative results in the text for better clarity and interpretation. In response, we have added more detailed description of the quantitative findings from both the neurofeedback and movement-feedback experiments. This will include effect sizes, statistical measures, and key numerical results to provide a clearer understanding of the degree and significance of the observed effects.

(3) Results Section Writing: We appreciate your observation that parts of the results section read more like a methods section. To improve clarity and focus, we have restructured the results section to present the findings in a more concise and interpretative manner, while moving overly detailed descriptions of experimental procedures to the methods section.

Suggestions for improved or additional experiments, data or analyses:

Not necessary for this paper, but it would be interesting to see if the CLNF group could learn without auditory feedback.

This is a great suggestion and certainly something that could be done in the future.

There are no quantitative results in the results section. I would add important results to help the reader better interpret the data. For example, in: "Our results indicated that both training paradigms were able to lead mice to obtain a significantly larger number of rewards over time," You could show a number, with an appropriate comparison or statistical test, to demonstrate that learning was observed.

Thank you for pointing this out. We have mentioned quantification values in the results now, along with being mentioned in the figure legends, and we are quoting it in following sentences. “A ΔF/F0 threshold value was calculated from a baseline session on day 0 that would have allowed 25% performance. Starting from this basal performance of around 25% on day 1, mice (CLNF No-rule-change, N=23, n=60 and CLNF Rule-change, N=17, n=60) were able to discover the task rule and perform above 80% over ten days of training (Figure 4A, RM ANOVA p=2.83e-5), and Rule-change mice even learned a change in ROIs or rule reversal (Figure 4A, RM ANOVA p=8.3e-10, Table 5 for different rule changes). There were no significant differences between male and female mice (Supplementary Figure 3A).”

For: "Performing this analysis indicated that the Raspberry Pi system could provide reliable graded feedback within ~63 {plus minus} 15 ms for CLNF experiments." The LED test shows the sending of the signal, but the actual delay for the audio generation might be longer. This is also longer than the 50 ms mentioned in the abstract.

We appreciate the reviewer’s insightful comment. The latency reported (~63ms) was measured using the LED test, which captures the time from signal detection to output triggering on the Raspberry Pi GPIO. We agree that the total delay for auditory feedback generation could include an additional latency component related to the digital-to-analog conversion and speaker response. In our setup, we employ a fast Audiostream library written in C to generate the audio signal and expect the delay contribution to be negligible compared to the GPIO latency. Though we did not do this, it can be confirmed by an oscilloscope-based pilot measurement (for additional delay calculation). We have updated the manuscript to clarify that the 63 ± 15 ms value reflects the GPIO-triggered output latency, and we have revised the abstract to accurately state the delay as “~63 ms” rather than 50 ms. This ensures consistency and avoids underestimation of the latency. We have corrected the LED latency for CLNF and CLMF experiments in the abstract as well.

It could be helpful to visualize an individual trial for each experiment type, for instance how the audio frequency changes as movement speed / calcium activity changes.

We have added Supplementary Figure 8 that contains this data where you can see the target cortical activity trace, target paw speed, rewards, along with the audio frequency generated.

The sample sizes are small (n=1) for a few groups. I am excited by the variety of regions recorded, so it could be beneficial for the authors to collect a few more animals to beef up the sample sizes.

We've acknowledged that some of the sample sizes are small. Importantly, we have included raw and processed data as well as code for future analysis. We felt it was still important to still include these data sets with smaller sample sizes as they might be useful for others pursuing this direction in the future.

I am curious as to why 60 trials sessions were used. Was it mostly for the convenience of a 30 min session, or were the animals getting satiated? If the former, would learning have occurred more rapidly with longer sessions?

This is a great observation and the answer is it was mostly due to logistical reasons. We tried to not keep animals headfixed for more than 45 minutes in each session as they become less engaged with long duration headfixed sessions. After headfixing them, it takes about 15 minutes to get the experiment going and therefore 30 - 40 minutes long recorded sessions seemed appropriate before they stop being engaged or before they get satiated in the task. We provided supplemental water after the sessions and we observed that they consumed water after the sessions so they were not fully satiated during the sessions even when they performed well in the task and got maximum rewards. We also had inter-trial rest periods of 10s that elongated the session duration. We think it would be interesting to explore the relationship between session duration(number of trials) and task learning progression over the days in a separate study.

Figure 4E is interesting, it seems like the changes in the distribution of deltaF was in both positive and negative directions, instead of just positive. I'd be curious as to the author's thoughts as to why this is the case. Relatedly, I don't see Figure 4E, and a few other subplots, mentioned in the text. As a general comment, I would address each subplot in the text.

We have split Figure 4 into two to keep the figures more readable. Previous Figure 4E-H are now Figure 5A-D in the revised manuscript. The online real-time CLNF sessions were using a moving window average to calculate ΔF/F₀  and the figures were generated by averaging the whole recorded sessions. We have added text in Methods under “Online ΔF/F₀calculation” and “Offline ΔF/F₀ calculation” sections making it clear about how we do our ΔF/F₀ normalization based on average fluorescence over the entire session. Using this method of normalization does increase the baseline so that some peaks appear to be below zero. Additionally, it is unclear what strategy animals are employing to achieve the rule specific target activity. The task did not constrain them to have a specific strategy for cortical activation - they were rewarded as long as they crossed the threshold in target ROI(s). For example, in 2-ROI experiments, to increase ROI1-ROI2 target activity, they could increase activity of ROI1 relative to ROI2 or decreased activity of ROI1 relative to ROI1 - both would have led to a reward as long as the result crossed the threshold.

We have now addressed and added reference to the figures in the text in Results under “Mice can explore and learn an arbitrary task, rule, and target conditions” and “Mice can rapidly adapt to changes in the task rule” sections - thanks for pointing this out.

For: "In general, all ROIs assessed that encompassed sensory, pre-motor, and motor areas were capable of supporting increased reward rates over time," I would provide a visual summary showing the learning curves for the different types of regions.

We have rewritten this section to emphasize that these conclusions were based on pooled data from multiple regions of interest. The sample sizes for each type of region are different and some are missing. We believe it would be incomplete and not comparable to present this as a regular analysis since the sample sizes were not balanced. We would be happy to dive deeper into this and point to the raw and processed dataset if anyone would like to explore this further by GitHub or other queries.

Relatedly, I would further explain the fast vs slow learners, and if they mapped onto certain regions.

Mice were categorized into fast or slow learners based on the slope of learning over days (reward progression over the days) as shown in Supplementary Figure 3C,D. Our initial aim was not to probe cortical regions that led to fast vs slow learning but this was a grouping we did afterwards. Based on the analysis we did, the fast learners included the sensory (V1), somatosensory (BC, HL), and motor (M1, M2) areas, while the slow learners included the motor (M1, M2), and higher order (TR, RL) cortical areas. Testing all dorsal cortical areas would be prudent to establish their role in fast or slow learning and it is an interesting future direction.

Also I would make the labels for these plots (e.g. Supp Fig3) more intuitive, versus the acronyms currently used.

We have made more expressive labels and explained the acronyms below the Supplementary Figure 3.

The CLMF animals showed a decrease in latency across learning, what about the CLNF animals? There is currently no mention in the text or figures.

We have now incorporated the CLNF task latency data into both the Results text and Figure 4C. Briefly, task latency decreased as performance improved, increased following a rule change, and then decreased again as the animals relearned the task. The previous Figure 4C has been updated to Figure 4D, and the former Figure 4D has been moved to Supplementary Figure 4E.

Reviewer #2 (Public review):

Summary:

In this work, Gupta & Murphy present several parallel efforts. On one side, they present the hardware and software they use to build a head-fixed mouse experimental setup that they use to track in "real-time" the calcium activity in one or two spots at the surface of the cortex. On the other side, the present another setup that they use to take advantage of the "real-time" version of DeepLabCut with their mice. The hardware and software that they used/develop is described at length, both in the article and in a companion GitHub repository. Next, they present experimental work that they have done with these two setups, training mice to max out a virtual cursor to obtain a reward, by taking advantage of auditory tone feedback that is provided to the mice as they modulate either (1) their local cortical calcium activity, or (2) their limb position.

Strengths:

This work illustrates the fact that thanks to readily available experimental building blocks, body movement and calcium imaging can be carried using readily available components, including imaging the brain using an incredibly cheap consumer electronics RGB camera (RGB Raspberry Pi Camera). It is a useful source of information for researchers that may be interested in building a similar setup, given the highly detailed overview of the system. Finally, it further confirms previous findings regarding the operant conditioning of the calcium dynamics at the surface of the cortex (Clancy et al. 2020) and suggests an alternative based on deeplabcut to the motor tasks that aim to image the brain at the mesoscale during forelimb movements (Quarta et al. 2022).

Weaknesses:

This work covers 3 separate research endeavors: (1) The development of two separate setups, their corresponding software. (2) A study that is highly inspired from the Clancy et al. 2020 paper on the modulation of the local cortical activity measured through a mesoscale calcium imaging setup. (3) A study of the mesoscale dynamics of the cortex during forelimb movements learning. Sadly, the analyses of the physiological data appears uncomplete, and more generally the paper tends to offer overstatements regarding several points:

In contrast to the introductory statements of the article, closed-loop physiology in rodents is a well-established research topic. Beyond auditory feedback, this includes optogenetic feedback (O'Connor et al. 2013, Abbasi et al. 2018, 2023), electrical feedback in hippocampus (Girardeau et al. 2009), and much more.

We have included and referenced these papers in our introduction section (quoted below) and rephrased the part where our previous text indicated there are fewer studies involving closed-loop physiology.

“Some related studies have demonstrated the feasibility of closed-loop feedback in rodents, including hippocampal electrical feedback to disrupt memory consolidation (Girardeau et al.2009), optogenetic perturbations of somatosensory circuits during behavior (O'Connor et al.2013), and more recent advances employing targeted optogenetic interventions to guide behavior (Abbasi et al. 2023).”

The behavioral setups that are presented are representative of the state of the art in the field of mesoscale imaging/head fixed behavior community, rather than a highly innovative design. In particular, the closed-loop latency that they achieve (>60 ms) may be perceived by the mice. This is in contrast with other available closed-loop setups.

We thank the reviewer for this thoughtful comment and fully agree that our closed-loop latency is larger than that achieved in some other contemporary setups. Our primary aim in presenting this work, however, is not to compete with the lowest possible latencies, but to provide an open-source, accessible, and flexible platform that can be readily adopted by a broad range of laboratories. By building on widely available and lower-cost components, our design lowers the barrier of entry for groups that wish to implement closed-loop imaging and behavioral experiments, while still achieving latencies well within the range that can support many biologically meaningful applications.

For example, our latency (~60 ms) remains compatible with experimental paradigms such as:

Motor learning and skill acquisition, where sensorimotor feedback on the scale of tens to hundreds of milliseconds is sufficient to modulate performance.

Operant conditioning and reward-based learning, in which reinforcement timing windows are typically broader and not critically dependent on sub-20 ms latencies.

Cortical state dependent modulation, where feedback linked to slower fluctuations in brain activity (hundreds of milliseconds to seconds) can provide valuable insight.

Studies of perception and decision-making, in which stimulus response associations often unfold on behavioral timescales longer than tens of milliseconds.

We believe that emphasizing openness, affordability, and flexibility will encourage widespread adoption and adaptation of our setup across laboratories with different research foci. In this way, our contribution complements rather than competes with ultra-low-latency closed-loop systems, providing a practical option for diverse experimental needs.

Through the paper, there are several statements that point out how important it is to carry out this work in a closed-loop setting with an auditory feedback, but sadly there is no "no feedback" control in cortical conditioning experiments, while there is a no-feedback condition in the forelimb movement study, which shows that learning of the task can be achieved in the absence of feedback.

We fully agree that such a control would provide valuable insight into the contribution of feedback to learning in the CLNF paradigm. In designing our initial experiments, we envisioned multiple potential control conditions, including No-feedback and Random-feedback. However, our first and primary objective was to establish whether mice could indeed learn to modulate cortical ROI activation through auditory feedback, and to further investigate this across multiple cortical regions. For this reason, we focused on implementing the CLNF paradigm directly, without the inclusion of these additional control groups. To broaden the applicability of the system, we subsequently adapted the platform to the CLMF experiments, where we did incorporate a No-feedback group. These results, as the reviewer notes, strengthen the evidence for the role of feedback in shaping task performance. We agree that the inclusion of a No-feedback control group in the CLNF paradigm will be crucial in future studies to further dissect the specific contribution of feedback to cortical conditioning.

The analysis of the closed-loop neuronal data behavior lacks controls. Increased performance can be achieved by modulating actively only one of the two ROIs, this is not clearly analyzed (for instance looking at the timing of the calcium signal modulation across the two ROIs. It seems that overall ROIs1 and 2 covariate, in contrast to Clancy et al. 2020. How can this be explained?

We agree that the possibility of increased performance being driven by modulation of a single ROI is an important consideration. Our study indeed began with 1-ROI closed-loop experiments. In those early experiments, while we did observe animals improving performance across days, we realized that daily variability in ongoing cortical GCaMP activity could lead to fluctuations in threshold-crossing events. The 2-ROI design was subsequently introduced to reduce this variability, as the target activity was defined as the relative activity between the two ROIs (e.g., ROI1 – ROI2). This approach offered a more stable signal by normalizing ongoing fluctuations. In our analysis of the early 2-ROI experiments, we observed that animals adopted diverging strategies to achieve threshold crossings. Specifically, some animals increased activity in ROI1 relative to ROI2, while others decreased activity in ROI2 to accomplish the same effect. Once discovered, each animal consistently adhered to its chosen strategy throughout subsequent training sessions. This was an early and intriguing observation, but as the experiments were not originally designed to systematically test this effect, we limited our presentation to the analysis of a small number of animals (shown in Figure 11). We have added details about this observation in our Results section as well, quoted below-

“In the 2-ROI experiment where the task rule required “ROI1 - ROI2” activity to cross a threshold for reward delivery, mice displayed divergent strategies. Some animals predominantly increased ROI1 activity, whereas others reduced ROI2 activity, both approaches leading to successful threshold crossing (Figure 11)”.

We hope this clarifies how the use of two ROIs helps explain the apparent covariation of the signals, and why some divergence from the observations of Clancy et al. (2020) may be expected.

Reviewer #3 (Public review):

Summary:

The study demonstrates the effectiveness of a cost-effective closed-loop feedback system for modulating brain activity and behavior in head-fixed mice. Authors have tested real-time closed-loop feedback system in head-fixed mice two types of graded feedback: 1) Closed-loop neurofeedback (CLNF), where feedback is derived from neuronal activity (calcium imaging), and 2) Closed-loop movement feedback (CLMF), where feedback is based on observed body movement. It is a python based opensource system, and authors call it CLoPy. The authors also claim to provide all software, hardware schematics, and protocols to adapt it to various experimental scenarios. This system is capable and can be adapted for a wide use case scenario.

Authors have shown that their system can control both positive (water drop) and negative reinforcement (buzzer-vibrator). This study also shows that using the close loop system mice have shown better performance, learnt arbitrary task and can adapt to change in the rule as well. By integrating real-time feedback based on cortical GCaMP imaging and behavior tracking authors have provided strong evidence that such closed-loop systems can be instrumental in exploring the dynamic interplay between brain activity and behavior.

Strengths:

Simplicity of feedback systems designed. Simplicity of implementation and potential adoption.

Weaknesses:

Long latencies, due to slow Ca2+ dynamics and slow imaging (15 FPS), may limit the application of the system.

We appreciate the reviewer’s comment and agree that latency is an important factor in our setup. The latency arises partly from the inherent slow kinetics of calcium signaling and GCaMP6s, and partly from the imaging rate of 15 FPS (every 66 ms). These limitations can be addressed in several ways: for example, using faster calcium indicators such as GCaMP8f, or adapting the system to electrophysiological signals, which would require additional processing capacity. In our implementation, image acquisition was fixed at 15 FPS to enable real-time frame processing (256 × 256 resolution) on Raspberry Pi 4B devices. With newer hardware, such as the Raspberry Pi 5, substantially higher acquisition and processing rates are feasible (although we have not yet benchmarked this extensively). More powerful platforms such as Nvidia Jetson or conventional PCs would further support much faster data acquisition and processing.

Major comments:

(1) Page 5 paragraph 1: "We tested our CLNF system on Raspberry Pi for its compactness, general-purpose input/output (GPIO) programmability, and wide community support, while the CLMF system was tested on an Nvidia Jetson GPU device." Can these programs and hardware be integrated with windows-based system and a microcontroller (Arduino/ Tency). As for the broad adaptability that's what a lot of labs would already have (please comment/discuss)?

While we tested our CLNF system on a Raspberry Pi (chosen for its compactness, GPIO programmability, and large user community) and our CLMF system on an Nvidia Jetson GPU device (to leverage real-time GPU-based inference), the underlying software is fully written in Python. This design choice makes the system broadly adaptable: it can be run on any device capable of executing Python scripts, including Windows-based PCs, Linux machines, and macOS systems. For hardware integration, we have confirmed that the framework works seamlessly with microcontrollers such as Arduino or Teensy, requiring only minor modifications to the main script to enable sending and receiving of GPIO signals through those boards. In fact, we are already using the same system in an in-house project on a Linux-based PC where an Arduino is connected to the computer to provide GPIO functionality. Furthermore, the system is not limited to Raspberry Pi or Arduino boards; it can be interfaced with any GPIO-capable devices, including those from Adafruit and other microcontroller platforms, depending on what is readily available in individual labs. Since many neuroscience and engineering laboratories already possess such hardware, we believe this design ensures broad accessibility and ease of integration across diverse experimental setups.

(2) Hardware Constraints: The reliance on Raspberry Pi and Nvidia Jetson (is expensive) for real-time processing could introduce latency issues (~63 ms for CLNF and ~67 ms for CLMF). This latency might limit precision for faster or more complex behaviors, which authors should discuss in the discussion section.

In our system, we measured latencies of approximately ~63 ms for CLNF and ~67 ms for CLMF. While such latencies indeed limit applications requiring millisecond precision, such as fast whisker movements, saccades, or fine-reaching kinematics, we emphasize that many relevant behaviors, including postural adjustments, limb movements, locomotion, and sustained cortical state changes, occur on timescales that are well within the capture range of our system. Thus, our platform is appropriate for a range of mesoscale behavioral studies that probably needs to be discussed more. It is also important to note that these latencies are not solely dictated by hardware constraints. A significant component arises from the inherent biological dynamics of the calcium indicator (GCaMP6s) and calcium signaling itself, which introduce slower temporal kinetics independent of processing delays. Newer variants, such as GCaMP8f, offer faster response times and could further reduce effective biological latency in future implementations.

With respect to hardware, we acknowledge that Raspberry Pi provides a low-cost solution but contributes to modest computational delays, while Nvidia Jetson offers faster inference at higher cost. Our choice reflects a balance between accessibility, cost-effectiveness, and performance, making the system deployable in many laboratories. Importantly, the modular and open-source design means the pipeline can readily be adapted to higher-performance GPUs or integrated with electrophysiological recordings, which provide higher temporal resolution. Finally, we agree with the reviewer that the issue of latency highlights deeper and interesting questions regarding the temporal requirements of behavior classification. Specifically, how much data (in time) is required to reliably identify a behavior, and what is the minimum feedback delay necessary to alter neural or behavioral trajectories? These are critical questions for the design of future closed-loop systems and ones that our work helps frame.

We have added a slightly modified version of our response above in the discussion section under “Experimental applications and implications”.

(3) Neurofeedback Specificity: The task focuses on mesoscale imaging and ignores finer spatiotemporal details. Sub-second events might be significant in more nuanced behaviors. Can this be discussed in the discussion section?

This is a great point  and we have added the following to the discussion section. “In the case of CLNF we have focused on regional cortical GCAMP signals that are relatively slow in kinetics. While such changes are well suited for transcranial mesoscale imaging assessment, it is possible that cellular 2-photon imaging (Yu et al. 2021) or preparations that employ cleared crystal skulls (Kim et al. 2016) could resolve more localized and higher frequency kinetic signatures.”

(4) The activity over 6s is being averaged to determine if the threshold is being crossed before the reward is delivered. This is a rather long duration of time during which the mice may be exhibiting stereotyped behaviors that may result in the changes in DFF that are being observed. It would be interesting for the authors to compare (if data is available) the behavior of the mice in trials where they successfully crossed the threshold for reward delivery and in those trials where the threshold was not breached. How is this different from spontaneous behavior and behaviors exhibited when they are performing the test with CLNF? 

We would like to emphasize that we are not directly averaging activity over 6 s to compare against the reward threshold. Instead, the preceding 6 s of activity is used solely to compute a dynamic baseline for ΔF/F₀ ( ΔF/F₀ = (F –F₀ )/F₀). Here, F₀is calculated as the mean fluorescence intensity over the prior 6 s window and is updated continuously throughout the session. This baseline is then subtracted from the instantaneous fluorescence signal to detect relative changes in activity. The reward threshold is therefore evaluated against these baseline-corrected ΔF/F₀ values at the current time point, not against an average over 6 s. This moving-window baseline correction is a standard approach in calcium imaging analyses, as it helps control for slow drifts in signal intensity, bleaching effects, or ongoing fluctuations unrelated to the behavior of interest. Thus, the 6-s window is not introducing a temporal lag in reward assignment but is instead providing a reference to detect rapid increases in cortical activity.  We have added the term dynamic baseline to the Methods to clarify.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

Additional suggestions for improved or additional experiments, data or analyses.

For: "Looking closely at their reward rate on day 5 (day of rule change), they had a higher reward rate in the second half of the session as compared to the first half, indicating they were adapting to the rule change within one session." It would be helpful to see this data, and would be good to see within-session learning on the rule change day

Thank you for pointing this out. We had missed referencing the figure in the text, and have now added a citation to Supplementary Figure 4A, which shows the cumulative rewards for each day of training. As seen in the plot for day 5, the cumulative rewards are comparable to those on day 1, with most rewards occurring during the second half of the session.

For: "These results suggest that motor learning led to less cortical activation across multiple regions, which may reflect more efficient processing of movement-related activity," it could also be the case that the behaviour became more stereotyped over learning, which would lead to more concentrated, correlated activity. To test this, it would be good to look at the limb variability across sessions. Similarly, if it is movement-related, there should be good decoding of limb kinematics.

Indeed, we observed that behavior became more stereotyped over the course of learning, as shown in Supplementary Figure 4C, 4D. One plausible explanation for the reduction in cortical activation across multiple regions is that behavior itself became more stereotyped, a possibility we have explored in the manuscript. Specifically, forelimb movements during the trial became increasingly correlated as mice improved on the task, particularly in the groups that received auditory feedback (Rule-change and No-rule-change groups; Figure 8). As movements became more correlated, overall body movements during trials decreased and aligned more closely with the task rule (Figure 9D). This suggests that reduced cortical activity may in part reflect changes in behavior. Importantly, however, in the Rule-change group, we observed that on the day of the rule switch (day 5), when the target shifted from the left to the right forelimb, cortical activity increased bilaterally (Figure 9A–C). This finding highlights our central point: groups that received feedback (Rule-change and No-rule-change) were able to identify the task rule more effectively, and both their behavior and cortical activity became more specifically aligned with the rule compared to the No-feedback group. We agree with the reviewers that additional analyses along these lines would be valuable future directions. To facilitate this, we have included the movement data for readers who may wish to pursue further analyses, details can be found under “Data and code availability” in Methods section. However, given the limited sample sizes in our dataset and the need to keep the manuscript focused on the central message, we felt that including these additional analyses here would risk obscuring the main findings.

For: "We believe the decrease in ΔF/F0peak is unlikely to be driven by changes in movement, as movement amplitudes did not decrease significantly during these periods (Figure 7D CLMF Rule-change)." I would formally compare the two conditions. This is an important control. Also, another way to see if the change in deltaF is related to movement would be to see if you can predict movement from the deltaF.

Figure 7D in the previous version is Figure 9D in the current revision of the manuscript. We've assessed this for the examples shown based on graphing the movement data, unfortunately there is not enough of that data to do a group analysis of movement magnitude. We would suggest that this would be an excellent future direction that would take advantage of the flexible open source nature of our tool.

Recommendations for improving the writing and presentation.

In the abstract there is no mention of the rationale for the project, or the resulting significance. I would modify this to increase readership by the behavioral neuroscience community. Similarly, the introduction also doesn't highlight the value of this resource for the field. Again, I think the pyControl paper does a good job of this. For readability, I would add more subheadings earlier in the results, to separate the different technical aspects of the system.

We have revised the introduction to include the rationale for the project, its potential implications, and its relevance for translational research. We have also framed the work within the broader context of the behavioral and systems neuroscience community. We greatly appreciate this suggestion, as we believe it enhances the clarity and accessibility of the manuscript for the community.

For: "While brain activity can be controlled through feedback, other variables such as movements have been less studied, in part because their analysis in real time is more challenging." I would highlight research that has studied the control of behavior through feedback, such as the Mathis paper where mice learn to pull a joystick to a virtual box, and adapt this motion to a force perturbation.

We have added a citation to the Mathis paper and describe this as an additional form of feedback. The text is quoted below:

“Opportunities also exist in extending real time pose classification (Forys et al. 2020; Kane et al. 2020) and movement perturbation (Mathis et al. 2017) to shape aspects of an animal’s motor repertoire.”

Some of the results content would be better suited for the methods, one example: "A previous version of the CLNF system was found to have non-linear audio generation above 10 kHz, partly due to problems in the audio generation library and partly due to the consumer-grade speaker hardware we were employing. This was fixed by switching to the Audiostream (https://github.com/kivy/audiostream) library for audio generation and testing the speakers to make sure they could output the commanded frequencies"

This is now moved to the Methods section.

For: "There are reports of cortical plasticity during motor learning tasks, both at cellular and mesoscopic scales (17-19), supporting the idea that neural efficiency could improve with learning," not sure I agree with this, the studies on cortical plasticity are usually to show a neural basis for the learning observed, efficiency is separate from this.

We have modified this statement to remove the concept of efficiency "There are reports of cortical plasticity during motor learning tasks, both at cellular and mesoscopic scales (17-19).”

The paragraph that opens "Distinct task- and reward-related cortical dynamics" that describes the experiment should appear in the previous section, as the data is introduced there.

We have moved the mentioned paragraphs in the previous section where we presented the data and other experiment details. This makes the text more readable and contextual.

I would present the different ROI rules with better descriptors and visualization to improve the readability.

We have added Supplementary Figure 7, which provides visualizations of the ROIs across all task rules used in the CLNF experiments.

Minor corrections to the text and figures.

Figure 1 is a little crowded, combining the CLNF and CLMF experiments, I would turn this into a 2 panel figure, one for each, similar to how you did figure 2.

We have revised Figure 1 to include two panels, one for CLNF and one for CLMF. The colored components indicate elements specific to each setup, while the uncolored components represent elements shared between CLNF and CLMF. Relevant text in the manuscript is updated to refer to these figures.

For Figure 2, the organization of the CLMF section is not intuitive for the reader. I would reorder it so it has a similar flow as the CLNF experiment.

We have revised the figure by updating the layout of panel B (CLMF) to align with panel A (CLNF), thereby creating a more intuitive and consistent flow between the panels. We appreciate this helpful suggestion, which we believe has substantially improved the clarity of the figure. The corresponding text in the manuscript has also been updated to reflect these changes.

For Figure 3, highlight that C and E are examples. They also seem a little out of place, so they could even be removed.

We have now explicitly labeled Figures 3C and 3E as representative examples (figure legend and on figure itself). We believe including these panels provides helpful context for readers: Figure 3C illustrates how the ROIs align on the dorsal cortical brain map with segmented cortical regions, while Figure 3E shows example paw trajectories in three dimensions, allowing visualization of the movement patterns observed during the trials.

In the plots, I would add sample sizes, for instance, in CLNF learning curve in Figure 4A, how many animals are in each group? 

We have labeled Figure 4 with number of animals used in CLNF (No-rule-change, N=23; Rule-change, N=17), and CLMF (Rule-change, N=8; No-rule-change, N=4; No-feedback, N=4).

Also, Figure 7 for example, which figures are single-sessions, versus across animals? For Figure 7c, what time bin is the data taken from?

We have clarified this now and mentioned it in all the figures. Figure 7 in the previous version is Figure 9 in the current updated manuscript. Figure 9A is from individual sessions on different days from the same mouse. Figure 9B is the group average reward centered ΔF/F₀ activity in different cortical regions (Rule-change, N=8; No-rule-change, N=4; No-feedback, N=4). Figure 9C shows average ΔF/F₀ peak values obtained within -1sec to +1sec centered around the reward point (N=8).

It says "punish" in Figure 3, but there is no punishment?

Yes, the task did not involve punishment. Each trial resulted in either a success, which is followed by a reward, or a failure, which is followed by a buzzer sound. To better reflect these outcomes, we have updated Figure 3 and replaced the labels “Reward” with “Success” and “Punish” with “Failure.”

The regression on 5c doesn't look quite right, also this panel is not mentioned in the text.

The figure referred to by the reviewer as Figure 5 is now presented as Figure 6 in the revised manuscript. Regarding the reviewer’s observation about the regression line in the left panel of Figure 5C, the apparent misalignment arises because the majority of the data points are densely clustered at the center of the scatter plot, where they overlap substantially. The regression line accurately reflects this concentration of overlapping data. To improve clarity, we have updated the figure and ensured that it is now appropriately referenced in the Results section.

Reviewer #2 (Recommendations for the authors):

(1) There would be many interesting observations and links between the peripheral and cortical studies if there was a body video available during the cortical study. Is there any such data available?

We agree that a detailed analysis of behavior during the CLNF task would be necessary to explore any behavior correlates with success in the task. Unfortunately, we do not have a sufficient video of the whole body to perform such an analysis.

(2) The text (p. 24) states: [intracortical GCAMP transients measured over days became more stereotyped in kinetics and were more correlated (to each other) as the task performance increased over the sessions (Figure 7E).] But I cannot find this quantification in the figures or text?

Figure 7 in the previous version of the manuscript now appears as Figure 9. In this figure, we present cortical activity across selected regions during trials, and in Figure 9E we highlight that this activity becomes more correlated. Since we did not formally quantify variability, we have removed the previous claim that the activity became stereotyped and revised the text in the updated manuscript accordingly.

Typos:

10-serest c (page 13)

Inverted color codes in figure 4E vs F

Reviewer #3 (Recommendations for the authors):

We have mostly attempted to limit the feedback to suggestions and posed a few questions that might be interesting to explore given the dataset the authors have collected.

Comments:

In close loop systems the latency is primary concern, and authors have successfully tested the latency of the system (Delay): from detection of an event to the reaction time was less than 67ms.

We have commented on the issues and limitations caused by latency, and potential future directions to overcome these challenges in responses to some of the previous comments.

Additional major comments:

"In general, all ROIs assessed that encompassed sensory, pre-motor, and motor areas were capable of supporting increased reward rates over time (Figure 4A, Animation 1)." Fig 4A is merely showing change in task performance over time and does not have information regarding the changes observed specific to CLNF for each ROI.

We acknowledge that the sample size for individual ROI rules was not sufficient for meaningful comparisons. To address this limitation, we pooled the data across all the rules tested. The manuscript includes a detailed list of the rules along with their corresponding sample sizes for transparency.

A ΔF/F₀ threshold value was calculated from a baseline session on day 0 that would have allowed 25% performance. Starting from this basal performance of around 25% on day 1, mice (CLNF No-rule-change, n=28 and CLNF Rule-change, n=13). It is unclear what the replicates here are. Trials or mice? The corresponding Figure legend has a much smaller n value.

Thank you for pointing this out. We realized that we had not indicated the sample replicates in the figure, and the use of n instead of N for the number of animals may have been misleading. We have now corrected the notation and clarified this information in the figure to resolve the discrepancy.

What were the replicates for each ROI pairs evaluated?

Each ROI rule and number of mice and trials are listed in Table 5 and Table 6.

Our analysis revealed that certain ROI rules (see description in methods) lead to a greater increase in success rate over time than others (Supplementary Figure 3D). The Supplementary figures 3C and 3D are blurry and could use higher resolution images. 

We have increased the font size of the text that was previously difficult to read and re-exported the figure at a higher resolution (300 DPI). We believe these changes will resolve the issue.

Also, It will help the reader is a visual representation of the ROI pairs are provided, instead of the text view. One interesting question is whether there are anatomical biases to fast vs slow learning pairs (Directionality - anterior/posterior, distance between the selected ROIs etc). This could be interesting to tease apart.

We have added Supplementary Figure 7, which provides visualizations of the ROIs across all task rules used in the CLNF experiments. While a detailed investigation of the anatomical basis of fast versus slow learning cortical ROIs is beyond the scope of the present study, we agree that this represents an exciting future direction for further research.

How distant should the ROIs be to achieve increased task performance?

We appreciate this insightful question. We did not specifically test this scenario. In our study, we selected 0.3 × 0.3 mm ROIs centered on the standard AIBS mouse brain atlas (CCF). At this resolution, ROIs do not overlap, regardless of their placement in a two-ROI experiment. Furthermore, because our threshold calculations are based on baseline recordings, we expect the system would function for any combination of ROI placements. Nonetheless, exploring this systematically would be an interesting avenue for future experiments.

Figures:

I would leave out some of the methodological details such as the protocol for water restriction (Fig. 3) out of the legend. This will help with readability.

We have removed some of the methodological details, including those mentioned above, from the legend of Figure 3 in the updated manuscript.

Fig 1 and Fig 2: In my opinion, It would be easier for the reader if the current Fig. 2, which provides a high level description of CLNF and CLBF is presented as Fig. 1. The current Fig. 1, goes into a lot of methodological implementation details, and also includes a lot of programming jargon that is being introduced early in the paper that is hard to digest early on in the paper's narrative.

Thank you for the suggestion. In the new manuscript, Figure 1 and Figure 2 have been swapped.

Higher-resolution images/ plots are needed in many instances. Unsure if this is the pdf compression done by the manuscript portal that is causing this.

All figures were prepared in vector graphics format using the open-source software Inkscape. For this manuscript, we exported the images at 300 DPI, which is generally sufficient for publication-quality documents. The submission portal may apply additional processing, which could have resulted in a reduction in image quality. We will carefully review the final submission files and ensure that all figures are clear and of high quality.

The authors repeatedly show ROI specific analysis M1_L, F1_R etc. It will be helpful to provide a key, even if redundant in all figures to help the reader.

We have now included keys to all such abbreviations in all the figures.

There are also instances of editorialization and interpretation e.g., "Surprisingly, the "Rule-change" mice were able to discover the change in rule and started performing above 70% within a day of the rule change, on day 6" that would be more appropriate in the main body of the paper.

Thank you for pointing this out in the figure legend, and we have removed it now since we already discussed this in the Results.

Minor comments

(1) The description of Figure 1 is hard to follow and can be described better based on how the information is processed and executed in the system from source to processing and back. Using separated colors (instead of shaded of grey) for the neuro feedback and movement feedback would help as well. Common components could have a different color. The specification like the description of the config file should come later.

Figure 1 in the previous version is Figure 2 in the updated version. We have taken suggestions from other reviewers and made the figure easier to understand and split it into two panels with color coding Green for CLNF, Pink for CLMF specific parts while common shared parts are left without any color.

(2) Page 20 last paragraph:

Authors are neglecting that the rule change is done one day prior and the results that you see in the second half on the 6th day are not just because of the first half of the 6th day instead combined training on the 5th day (rule change) and then the first half of the 6th day. Rephrasing this observation is essential.

We have revised the text for clarity to indicate that the performance increase observed on day 6 is not necessarily attributable to training on that day. In fact, we noted and mentioned that mice began to perform the task better during the second half of the session on day 5 itself.

(3)  The method section description of the CLMF setup (Page no 39 first paragraph) is more detailed, a diagram of this setup would make it easy to follow and a better read.

We have made changes to the CLMF setup (Figure 1B) and CLMF schematic (Figure 2B) to make it easier to understand parts of the setup and flow of control.

Conclusion p.28-29 - attitudes of landowners could delay development or could initiate development andprovide another terminous on the railway networks. when lanowners acquisesced in hthe developemtn without participating in planning, the resulting town tended to provide accommodation for the mass market - question of the availability of captial to deveop and extend a resort. the role of the merchants, millowners and commercial entreprenurs are evident. when capital failed to materialize, there was a hiatus in the development of the town. This dependence on capital from outside wales may be compared with the experience of industrialisation in nineteenth-century wales

in conclusion, he argues that agricultural wales was unabl or unwilling to fund the process of urbanisation, it wasn't unique to wales and needed capital injection from landowning elites or commercial capital from relatively new ndustries.

development had 3 major interlinking components - necessity to have incolvement of elites, capital to be invested for long periods before good rate of return, and railways to transport holiday makers quickly and cheaply two and from the resorts

could argue that different areas had different relationships with industrialisation swansea, with its catering towards higher class people and a bustling port, had a relationship that was often competative, with one building facilities which either aided for hindered the development of the other.

in the northern places, this article argues, industrialisation was significant in providing a working-class clientele who had sufficient funds to partake in tourism and aid its growth!

p.7 intitially it was the coastal shipping which began to open up the seaside tourist trade around the coast of britain and rhyl was no exception - direct quote it then talks of the boats. ig this could link to miskell and her talk of the existence of ports and stuff

pg. 6 talks of the importance of accesibility of large cotton and wollen conurbations that a market was made for such resorts

pg. 4 asa brings infers thatthe railways were responsible for the creation of popular seaside resorts. perkin's concurs.

he says howevrer that this oversimplifies the argumens, blackpool colwyn bay and llandudno wouldnt have been achieved without cheap means of transportation - but substantial tourist trade existed before railways were constructed and evidence that landowners constrained development for decades after railway developments

page 3 i guess for this one, the main argument would be that industrialisation and tourism had a relationship through industrialisations facilitation of tourism (it goes with the post it note i wrote tbh)

eLife Assessment

This is a valuable study that integrates behavioral and molecular approaches to identify neuromodulators influencing blood-feeding behavior in the disease vector Anopheles stephensi. Through gene expression analyses across blood-seeking life stages and RNA interference experiments, the authors present solid evidence that co-knockdown of the neuromodulators short Neuropeptide F and RYamide affects blood-seeking states in A. stephensi. However, evidence demonstrating that these neuropeptides are sufficient to promote host-seeking is lacking.

Reviewer #1 (Public review):

Summary:

Here Bansal et al., present a study on the fundamental blood and nectar feeding behaviors of the critical disease vector, Anopheles stephensi. The study encompasses not just the fundamental changes in blood feeding behaviors of the crucially understudied vector, but then use a transcriptomic approach to identify candidate neuromodulation path ways which influence blood feeding behavior in this mosquito species. The authors then provide evidence through RNAi knockdown of candidate pathways that the neuromodulators sNPF and Rya modulate feeding either via their physiological activity in the brain alone or through joint physiological activity along the brain-gut axis (but critically not the gut alone). Overall, I found this study to be built on tractable, well-designed behavioral experiments.

Their study begins with a well-structured experiment to assess how the feeding behaviors of A. stephensi changes over the course of its life history and in response to its age, mating and oviposition status. The authors are careful and validate their experimental paradigm in the more well-studied Ae. aegypti, and are able to recapitulate the results of prior studies which show that mating is pre-requisite for blood feeding behaviors in Ae. aegypt. Here they find A. stephensi like another Anopheline mosquitoes has a more nuanced regulation of its blood and nectar feeding behaviors.

The authors then go on to show in a Y- maze olfactometer that to some degree, changes in blood feeding status depend on behavioral modulation to host-cues, and this is not likely to be a simple change to the biting behaviors alone. I was especially struck by the swap in valence of the host-cues for the blood-fed and mated individuals which had not yet oviposited. This indicates that there is a change in behavior that is not simply desensitization to host-cues while navigating in flight, but something much more exciting happening.

The authors then use a transcriptomic approach to identify candidate genes in the blood feeding stages of the mosquito's life cycle to identify a list of 9 candidates which have a role in regulating the host-seeking status of A. stephensi. Then through investigations of gene knockdown of candidates they identify the dual action of RYa and sNPF and candidate neuromodulators of host-seeking in this species. Overrall, I found the experiments to be well-designed. I found the molecular approach to be sound. While I do not think the molecular approach is necessarily an all-encompassing mechanism identification (owing mostly to the fact that genetic resources are not yet available in A. stephensi as they are in other dipteran models), I think it sets up a rich lines of research questions for the neurobiology of mosquito behavioral plasticity and comparative evolution of neuromodulator action.

Strengths:

I am especially impressed by the authors' attention to small details in the course of this article. As I read and evaluated this article I continued to think how many crucial details I may have missed if I were the scientist conducting these experiments. That attention to detail paid off in spades and allowed the authors to carefully tease apart molecular candidates of blood-seeking stages. The authors top down approach to identifying RYamide and sNPF starting from first principles behavioral experiments is especially comprehensive. The results from both the behavioral and molecular target studies will have broad implications for the vectorial capacity of this species and comparative evolution of neural circuit modulation.

I believe the authors have adequately addressed all of my concerns; however, I think an accompanying figure to match the explained methods of the tissue-specific knockdown would help readers. The methods are now explicitly written for the timing and concentrations required to achieve tissue-specific knockdown, but seeing the data as a supplement would be especially reassuring given the critical nature of tissue-specific knockdown to the final interpretations of this paper.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated-females, but not unmated (virgin) females, exhibit suppression in their blood-feeding behaviour. Using brain transcriptomic analysis comparing sugar fed, blood fed and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding) although the impact was observed only after both neuropeptide genes underwent knockdown.

While the authors have addressed most of the concerns of the original manuscript, a few issues remain. Particularly, the following two points:

(5) Figure 4

The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well.

Perhaps we do not understand the reviewer's point or there has been a misunderstanding. In Figure 4D, we show that while there is more robust gene knockdown in unfed females, blood-fed females also showed modest but measurable knockdowns ranging from 5-40% for RYamide and 2-21% for sNPF.

NEW-

In both the dsRNA treatments where animals were fed, neither was significantly different from control. Therefore, there is no change, and indeed this is confirmed by the author's labelling of the figure stats in panel 4D.

In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data,...

In these qPCRs, we calculated relative mRNA expression using the delta-delta Ct method (see line 975). For each neuropeptide its respective control was used. For simplicity, we combined the RYa and sNPF control data into a single representation. The value of this control is invariant because this method sets the control baseline to a value of 1.

NEW-

The authors are claiming that there is no variation between individual qPCR experiments (particularly in their controls)? Normally, one uses a known standard value (or calibrator) across multiple experiments/plates so that variation across biological replicates can be assessed. This has an impact on statistical analyses since there is no variation in the control data. Indeed, this impacts all figures/datasets in the manuscript where qPCR data is presented. All the controls have zero variation!

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(3) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(4) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated and some conclusions appear premature based on the current data. The support for this conclusion would be strengthened with functional validation using peptide injection or genetic manipulation.

(2) The identification of candidate receptors is promising, but the manuscript would be significantly strengthened by testing whether receptor knockdowns phenocopy peptide knockdowns. Without this, it is difficult to conclude that the identified receptors mediate the behavioral effects.

(3) Some important caveats, such as variation in knockdown efficiency and the possibility of off-target effects, are not adequately discussed.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Bansal et al. present a study on the fundamental blood and nectar feeding behaviors of the critical disease vector, Anopheles stephensi. The study encompasses not just the fundamental changes in blood feeding behaviors of the crucially understudied vector, but then uses a transcriptomic approach to identify candidate neuromodulation pathways which influence blood feeding behavior in this mosquito species. The authors then provide evidence through RNAi knockdown of candidate pathways that the neuromodulators sNPF and Rya modulate feeding either via their physiological activity in the brain alone or through joint physiological activity along the brain-gut axis (but critically not the gut alone). Overall, I found this study to be built on tractable, well-designed behavioral experiments.

Their study begins with a well-structured experiment to assess how the feeding behaviors of A. stephensi change over the course of its life history and in response to its age, mating, and oviposition status. The authors are careful and validate their experimental paradigm in the more well-studied Ae. aegypti, and are able to recapitulate the results of prior studies, which show that mating is a prerequisite for blood feeding behaviors in Ae. aegypt. Here they find A. Stephensi, like other Anopheline mosquitoes, has a more nuanced regulation of its blood and nectar feeding behaviors.

The authors then go on to show in a Y-maze olfactometer that ,to some degree, changes in blood feeding status depend on behavioral modulation to host cues, and this is not likely to be a simple change to the biting behaviors alone. I was especially struck by the swap in valence of the host cues for the blood-fed and mated individuals, which had not yet oviposited. This indicates that there is a change in behavior that is not simply desensitization to host cues while navigating in flight, but something much more exciting is happening.

The authors then use a transcriptomic approach to identify candidate genes in the blood-feeding stages of the mosquito's life cycle to identify a list of 9 candidates that have a role in regulating the host-seeking status of A. stephensi. Then, through investigations of gene knockdown of candidates, they identify the dual action of RYa and sNPF and candidate neuromodulators of host-seeking in this species. Overall, I found the experiments to be well-designed. I found the molecular approach to be sound. While I do not think the molecular approach is necessarily an all-encompassing mechanism identification (owing mostly to the fact that genetic resources are not yet available in A. stephensi as they are in other dipteran models), I think it sets up a rich line of research questions for the neurobiology of mosquito behavioral plasticity and comparative evolution of neuromodulator action.

We appreciate the reviewer’s detailed summary of our work. We thank them for their positive comments and agree with them on the shortcomings of our approach.

Strengths:

I am especially impressed by the authors' attention to small details in the course of this article. As I read and evaluated this article, I continued to think about how many crucial details could potentially have been missed if this had not been the approach. The attention to detail paid off in spades and allowed the authors to carefully tease apart molecular candidates of blood-seeking stages. The authors' top-down approach to identifying RYamide and sNPF starting from first principles behavioral experiments is especially comprehensive. The results from both the behavioral and molecular target studies will have broad implications for the vectorial capacity of this species and comparative evolution of neural circuit modulation.

We really appreciate that the reviewer has recognised the attention to detail we have tried to put, thank you!

Weaknesses:

There are a few elements of data visualizations and methodological reporting that I found confusing on a first few read-throughs. Figure 1F, for example, was initially confusing as it made it seem as though there were multiple 2-choice assays for each of the conditions. I would recommend removing the "X" marker from the x-axis to indicate the mosquitoes did not feed from either nectar, blood, or neither in order to make it clear that there was one assay in which mosquitoes had access to both food sources, and the data quantify if they took both meals, one meal, or no meals.

We thank the reviewer for flagging the schematic in figure 1F. As suggested, we have removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose in the assay. For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data, as it does not capture the variability in the data.

I would also like to know more about how the authors achieved tissue-specific knockdown for RNAi experiments. I think this is an intriguing methodology, but I could not figure out from the methods why injections either had whole-body or abdomen-specific knockdown.

The tissue-specific knockdown (abdomen only or abdomen+head) emerged from initial standardisations where we were unable to achieve knockdown in the head unless we used higher concentrations of dsRNA and did the injections in older females. We realised that this gave us the opportunity to isolate the neuronal contribution of these neuropeptides in the phenotype produced. Further optimisations revealed that injecting dsRNA into 0-10h old females produced abdomen-specific knockdowns without affecting head expression, whereas injections into 4 days old females resulted in knockdowns in both tissues. Moreover, head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts.

We have mentioned the knockdown conditions- time of injection and the amount dsRNA injected- for tissue-specific knockdowns in methods but realise now that it does not explain this well enough. We have now edited it to state our methodology more clearly (see lines 932-948).

I also found some interpretations of the transcriptomic to be overly broad for what transcriptomes can actually tell us about the organism's state. For example, the authors mention, "Interestingly, we found that after a blood meal, glucose is neither spent nor stored, and that the female brain goes into a state of metabolic 'sugar rest', while actively processing proteins (Figure S2B, S3)".

This would require a physiological measurement to actually know. It certainly suggests that there are changes in carbohydrate metabolism, but there are too many alternative interpretations to make this broad claim from transcriptomic data alone.

We thank the reviewer for pointing this out and agree with them. We have now edited our statement to read:

“Instead, our data suggests altered carbohydrate metabolism after a blood meal, with the female brain potentially entering a state of metabolic 'sugar rest' while actively processing proteins (Figure S2B, S3). However, physiological measurements of carbohydrate and protein metabolism will be required to confirm whether glucose is indeed neither spent nor stored during this period.” See lines 271-277.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated females, but not unmated (virgin) females, exhibit suppression in their bloodfeeding behaviour. Using brain transcriptomic analysis comparing sugar-fed, blood-fed, and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools, including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding), although the impact was observed only after both neuropeptide genes underwent knockdown.

Strengths and/or weaknesses:

Overall, the manuscript was well-written; however, the authors should review carefully, as some sections would benefit from restructuring to improve clarity. Some statements need to be rectified as they are factually inaccurate.

Below are specific concerns and clarifications needed in the opinion of this reviewer:

(1) What does "central brains" refer to in abstract and in other sections of the manuscript (including methods and results)? This term is ambiguous, and the authors should more clearly define what specific components of the central nervous system was/were used in their study.

Central brain, or mid brain, is a commonly used term to refer to brain structures/neuropils without the optic lobes (For example: https://www.nature.com/articles/s41586-024-07686-5). In this study we have focused our analysis on the central brain circuits involved in modulating blood-feeding behaviour and have therefore excluded the optic lobes. As optic lobes account for nearly half of all the neurons in the mosquito brain (https://pmc.ncbi.nlm.nih.gov/articles/PMC8121336/), including them would have disproportionately skewed our transcriptomic data toward visual processing pathways. 

We have indicated this in figure 3A and in the methods (see lines 800-801, 812). We have now also clarified it in the results section for neurotranscriptomics to avoid confusion (see lines 236-237).

(2) The abstract states that two neuropeptides, sNPF and RYamide are working together, but no evidence is summarized for the latter in this section.

We thank the reviewer for pointing this out. We have now added a statement “This occurs in the context of the action of RYa in the brain” to end of the abstract, for a complete summary of our proposed model. 

(3) Figure 1

Panel A: This should include mating events in the reproductive cycle to demonstrate differences in the feeding behavior of Ae. aegypti.

Our data suggest that mating can occur at any time between eclosion and oviposition in An. stephensi and between eclosion and blood feeding in Ae. aegypti. Adding these into (already busy) 1A, would cloud the purpose of the schematic, which is to indicate the time points used in the behavioural assays and transcriptomics.

Panel F: In treatments where insects were not provided either blood or sugar, how is it that some females and males had fed? Also, it is unclear why the y-axis label is % fed when the caption indicates this is a choice assay. Also, it is interesting that sugar-starved females did not increase sugar intake. Is there any explanation for this (was it expected)?

We apologise for the confusion. The experiment is indeed a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. The x-axis indicates the choice made by the mosquitoes, not the choice provided in the assay, and the y-axis indicates the percentage of males or females that made each particular choice. We have now removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

In this assay, we scored females only for the presence or absence of each meal type (blood or sugar) and are therefore unable to comment on whether sugar-starved females consumed more sugar than sugarsated females. However, when sugar-starved, a higher proportion of females consumed both blood and sugar, while fewer fed on blood alone.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data as it does not capture the variability in the data.

(4) Figure 3

In the neurotranscriptome analysis of the (central) brain involving the two types of comparisons, can the authors clarify what "excluded in males" refers to? Does this imply that only genes not expressed in males were considered in the analysis? If so, what about co-expressed genes that have a specific function in female feeding behaviour?

This is indeed correct. We reasoned that since blood feeding is exclusive to females, we should focus our analysis on genes that were specifically upregulated in them. As the reviewer points out, it is very likely that genes commonly upregulated in males and females may also promote blood feeding and we will miss out on any such candidates based on our selection criteria. 

(5) Figure 4

The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well.

Perhaps we do not understand the reviewer’s point or there has been a misunderstanding. In figure 4D, we show that while there is more robust gene knockdown in unfed females, blood-fed females also showed modest but measurable knockdowns ranging from 5-40% for RYamide and 2-21% for sNPF. 

Relatedly, blood-feeding is decreased when both neuropeptide transcripts are targeted compared to uninjected (panel C) but not compared to dsGFP injected (panel E). Why is this the case if authors showed earlier in this figure (panel B) that dsGFP does not impact blood feeding?

We realise this concern stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens. 4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomens. We have now added a schematic in the plots to make this clearer.

In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data,…

In these qPCRs, we calculated relative mRNA expression using the delta-delta Ct method (see line 975). For each neuropeptide its respective control was used. For simplicity, we combined the RYa and sNPF control data into a single representation. The value of this control is invariant because this method sets the control baseline to a value of 1.

…and how do transcript levels of RYa and sNPF compare in the brain versus the abdomen (the presentation of data doesn't make this relationship clear).

The reviewer is correct in pointing out that we have not clarified this relationship in our current presentation. While we have not performed absolute mRNA quantifications, we extracted relative mRNA levels from qPCR data of 96h old unmanipulated control females. We observed that both sNPF and RYa transcripts are expressed at much lower levels in the abdomens, as compared to those in the heads, as shown in Author response Image 1 below. 

Author response image 1.

(6) As an overall comment, the figure captions are far too long and include redundant text presented in the methods and results sections.

We thank the reviewer for flagging this and have now edited the legends to remove redundancy.  

(7) Criteria used for identifying neuropeptides promoting blood-feeding: statement that reads "all neuropeptides, since these are known to regulate feeding behaviours". This is not accurate since not all neuropeptides govern feeding behaviors, while certainly a subset do play a role.

We agree with the reviewer that not all neuropeptides regulate feeding behaviours. Our statement refers to the screening approach we used: in our shortlist of candidates, we chose to validate all neuropeptides.

(8) In the section beginning with "Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels...", the authors state that there was no change in blood-feeding and later state the opposite. The wording should be clarified as it is unclear.

Thank you for pointing this out. We were referring to an unchanged proportion of the blood fed females. We have now edited the text to the following: 

“Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels in the heads but the proportion of females that took blood meals remained unchanged”. See lines 338-340.

(9) Just before the conclusions section, the statement that "neuropeptide receptors are often ligandpromiscuous" is unjustified. Indeed, many studies have shown in heterologous systems that high concentrations of structurally related peptides, which are not physiologically relevant, might cross-react and activate a receptor belonging to a different peptide family; however, the natural ligand is often many times more potent (in most cases, orders of magnitude) than structurally related peptides. This is certainly the case for various RYamide and sNPF receptors characterized in various insect species.

We agree with the reviewer and apologise for the mistake. We have now removed the statement.

(10) Methods

In the dsRNA-mediated gene knockdown section, the authors could more clearly describe how much dsRNA was injected per target. At the moment, the reader must carry out calculations based on the concentrations provided and the injected volume range provided later in this section.

We have now edited the section to reflect the amount of dsRNA injected per target. Please see lines 921-931.

It is also unclear how tissue-specific knockdown was achieved by performing injection on different days/times. The authors need to explain/support, and justify how temporal differences in injection lead to changes in tissue-specific expression. Does the blood-brain barrier limit knockdown in the brain instead, while leaving expression in the peripheral organs susceptible?

To achieve tissue-specific knockdowns of sNPF and RYa, we optimised both the time of injection as well as the dsRNA concentration to be injected. Injecting dsRNA into 0-10h females produced abdomen-specific knockdowns without affecting head expression, whereas injections into 96h old females resulted in knockdowns in both tissues. Head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts, reflecting the lower baseline expression of sNPF in abdomens compared to heads and the age-dependent increase in head expression (as confirmed by qPCR). It is possible that the blood-brain barrier also limits the dsRNA entering the brain, thereby requiring higher amounts to be injected for head knockdowns. 

We have now edited this section to state our methodology more clearly (see lines 932-948).

For example, in Figure 4, the data support that knockdown in the head/brain is only effective in unfed animals compared to uninjected animals, while there is no evidence of knockdown in the brain relative to dsGFP-injected animals. Comparatively, evidence appears to show stronger evidence of abdominal knockdown mostly for the RYa transcript (>90%) while still significantly for the sNPF transcript (>60%).

As we explained earlier, this concern likely stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens.  4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomen. We have now added a schematic in the plots to make this clearer.

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(4) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(5) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated (for example, with peptide injection or overexpression experiments).

Demonstrating sufficiency would require injecting sNPF peptide or its agonist. To date, no small-molecule agonists (or antagonists) that selectively mimic sNPF or RYa neuropeptides have been identified in insects. An NPY analogue, TM30335, has been reported to activate the Aedes aegypti NPY-like receptor 7 (NPYLR7; Duvall et al., 2019), which is also activated by sNPF peptides at higher doses (Liesch et al., 2013). Unfortunately, the compound is no longer available because its manufacturer, 7TM Pharma, has ceased operations. Synthesising the peptides is a possibility that we will explore in the future.

(2) The proposed model regarding central versus peripheral (gut) peptide action is inconsistently presented and lacks strong experimental support.

The best way to address this would be to conduct tissue-specific manipulations, the tools for which are not available in this species. Our approach to achieve head+abdomen and abdomen only knockdown was the closest we could get to achieving tissue specificity and allowed us to confirm that knockdown in the head was necessary for the phenotype. However, as the reviewer points out, this did not allow us to rule out any involvement of the abdomen. This point has been addressed in lines 364-371.

(3) Some conclusions appear premature based on the current data and would benefit from additional functional validation.

The most definitive way of demonstrating necessity of sNPF and RYa in blood feeding would be to generate mutant lines. While we are pursuing this line of experiments, they lie beyond the scope of a revision. In its absence, we relied on the knockdown of the genes using dsRNA. We would like to posit that despite only partial knockdown, mosquitoes do display defects in blood-feeding behaviour, without affecting sugar-feeding. We think this reflects the importance of sNPF in promoting blood feeding.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Overall, I found this manuscript to be well-prepared, visually the figures are great and clearly were carefully thought out and curated, and the research is impactful. It was a wonderful read from start to finish. I have the following recommendations:

Thank you very much, we are very pleased to hear that you enjoyed reading our manuscript!

(1) For future manuscripts, it would make things significantly easier on the reviewer side to submit a format that uses line numbers.

We sincerely apologise for the oversight. We have now incorporated line numbers in the revised manuscript.

(2) There are a few statements in the text that I think may need clarification or might be outside the bounds of what was actually studied here. For example, in the introduction "However, mating is dispensable in Anophelines even under conditions of nutritional satiety". I am uncertain what is meant by this statement - please clarify.

We apologise for the lack of clarity in the statement and have now deleted it since we felt it was not necessary.

(3) Typo/Grammatical minutiae:

(a) A small idiosyncrasy of using hyphens in compound words should also be fixed throughout. Typically, you don't hyphenate if the words are being used as a noun, as in the case: e.g. "Age affects blood feeding.". However, you would hyphenate if the two words are used as a compound adjective "Age affects blood-feeding behavior". This may not be an all-inclusive list, but here are some examples where hyphens need to either be removed or added. Some examples:

"Nutritional state also influences other internal state outputs on blood-feeding": blood-feeding -> blood feeding

"... the modulation of blood-feeding": blood-feeding -> blood feeding

"For example, whether virgin females take blood-meals...": blood-meals -> blood meals

".... how internal and external cues shape meal-choice"-> meal choice

"blood-meal" is often used throughout the text, but is correctly "blood meal" in the figures.

There are many more examples throughout.

We apologise for these errors and appreciate the reviewer’s keen eye. We have now fixed them throughout the manuscript.  

(b) Figure 1 Caption has a typo: "co-housed males were accessed for sugar-feeding" should be "co-housed males were assessed for sugar feeding"

We apologise for the typo and thank the reviewer for spotting it. We have now corrected this.  

(c) It would be helpful in some other figure captions to more clearly label which statement is relevant to which part of the text. For example, in Figure 4's caption.

"C,D. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head (C). Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected blood-fed and unfed females, as compared to that in uninjected females, analysed via qPCR (D)."

I found re-referencing C and D at the end of their statements makes it look as thought C precedes the "Relative mRNA expression" and on a first read through, I thought the figure captions were backwards. I'd recommend reformatting here and throughout consistently to only have the figure letter precede its relevant caption information, e.g.:

"C. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head. D. Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected bloodfed and unfed females, as compared to that in uninjected females, analysed via qPCR."

We have now edited the legends as suggested.

Reviewer #2 (Recommendations for the authors):

Separately from the clarifications and limitations listed above, the authors could strengthen their study and the conclusions drawn if they could rescue the behavioural phenotype observed following knockdown of sNPF and RYamide. This could be achieved by injection of either sNPF or RYa peptide independently or combined following knockdown to validate the role of these peptides in promoting blood-feeding in An. stephensi. Additionally, the apparent (but unclear) regionalized (or tissue-specific) knockdown of sNPF and RYamide transcripts could be visualized and verified by implementing HCR in situ hyb in knockdown animals (or immunohistochemistry using antibodies specific for these two neuropeptides). 

In a follow up of this work, we are generating mutants and peptides for these candidates and are planning to conduct exactly the experiments the reviewer suggests.

Reviewer #3 (Recommendations for the authors):

The loss-of-function data suggest necessity but not sufficiency. Synthetic peptide injection in non-hostseeking (blood-fed mated or juvenile) mosquitoes would provide direct evidence for peptide-induced behavioral activation. The lack of these experiments weakens the central claim of the paper that these neuropeptides directly promote blood feeding.

As noted above, we plan to synthesise the peptide to test rescue in a mutant background and sufficiency.  

Some of the claims about knockdown efficiency and interpretation are conflicting; the authors dismiss Hairy and Prp as candidates due to 30-35% knockdown, yet base major conclusions on sNPF and RYamide knockdowns with comparable efficiencies (25-40%). This inconsistency should be addressed, or the justification for different thresholds should be clearly stated.

We have not defined any specific knockdown efficacy thresholds in the manuscript, as these can vary considerably between genes, and in some cases, even modest reductions can be sufficient to produce detectable phenotypes. For example, knockdown efficiencies of even as low as about 25% - 40% gave us observable phenotypes for sNPF and RYa RNAi (Figure S9B-G).

No such phenotypes were observed for Hairy (30%) or Prp (35%) knockdowns. Either these genes are not involved in blood feeding, or the knockdown was not sufficient for these specific genes to induce phenotypes. We cannot distinguish between these scenarios. 

The observation that knockdown animals take smaller blood meals is interesting and could reflect a downstream effect of altered host-seeking or an independent physiological change. The relationship between meal size and host-seeking behavior should be clarified.

We agree with the reviewer that the reduced meal size observed in sNPF and RYa knockdown animals could result from their inability to seek a host or due to an independent effect on blood meal intake. Unfortunately, we did not measure host-seeking in these animals. We plan to distinguish between these possibilities using mutants in future work.

Several figures are difficult to interpret due to cluttered labeling and poorly distinguishable color schemes. Simplifying these and improving contrast (especially for co-housed vs. virgin conditions) would enhance readability. 

We regret that the reviewer found the figures difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition). Wherever mated females were used, we have now appended “(m)” to the annotations and consistently depicted these females with striped abdomens in all the schematics. We believe these changes will improve clarity and readability.

The manuscript does not clearly justify the use of whole-brain RNA sequencing to identify peptides involved in metabolic or peripheral processes. Given that anticipatory feeding signals are often peripheral, the logic for brain transcriptomics should be explained.

The reviewer is correct in pointing out that feeding signals could also emerge from peripheral tissues. Signals from these tissues – in response to both changing nutritional and reproductive states – are then integrated by the central brain to modulate feeding choices. For example, in Drosophila, increased protein intake is mediated by central brain circuitry including those in the SEZ and central complex (Munch et al., 2022; Liu et al., 2017; Goldschmidt et al., 202ti). In the context of mating, male-derived sex peptide further increases protein feeding by acting on a dedicated central brain circuitry (Walker et al., 2015). We, therefore focused on the central brain for our studies.

The proposed model suggests brain-derived peptides initiate feeding, while gut peptides provide feedback. However, gut-specific knockdowns had no effect, undermining this hypothesis. Conversely, the authors also suggest abdominal involvement based on RNAi results. These contradictions need to be resolved into a consistent model.

We thank the reviewer for raising this point and recognise their concern. Our reasons for invoking an involvement of the gut were two-fold:

(1) We find increased sNPF transcript expression in the entero-endocrine cells of the midgut in blood-hungry females, which returns to baseline after a blood-meal (Fig. 4L, M).

(2) While the abdomen-only knockdowns did not affect blood feeding, every effective head knockdown that affected blood feeding also abolished abdominal transcript levels (Fig. S9C, F). (Achieving a head-only reduction proved impossible because (i) systemic dsRNA delivery inevitably reaches the abdomen and (ii) abdominal expression of both peptides is low, leaving little dynamic range for selective manipulation.) Consequently, we can only conclude the following: 1) that brain expression is required for the behaviour, 2) that we cannot exclude a contributory role for gut-derived sNPF. We have discussed this in lines 364-371.

The identification of candidate receptors is promising, but the manuscript would be significantly strengthened by testing whether receptor knockdowns phenocopy peptide knockdowns. Without this, it is difficult to conclude that the identified receptors mediate the behavioral effects.

We agree that functional validation of the receptors would strengthen the evidence for sNPF and RYa-mediated control of blood feeding in An. stephensi. We selected these receptors based on sequence homology. A possibility remains that sNPF neuropeptides activate more than one receptor, each modulating a distinct circuit, as shown in the case of Drosophila Tachykinin (https://pmc.ncbi.nlm.nih.gov/articles/PMC10184743/). This will mean a systematic characterisation and knockdown of each of them to confirm their role. We are planning these experiments in the future.  

The authors compared the percentage changes in sugar-fed and blood-fed animals under sugar-sated or sugar-starved conditions. Figure 1F should reflect what was discussed in the results.

Perhaps this concern stems from our representation of the data in figure 1F? We have now edited the xaxis and revised its label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data because it does not capture the variability in the data.

Minor issues:

(1) The authors used mosquitoes with belly stripes to indicate mated females. To be consistent, the post-oviposition females should also have belly stripes.

We thank the reviewer for pointing this out. We have now edited all the figures as suggested.

(2) In the first paragraph on the right column of the second page, the authors state, "Since females took blood-meals regardless of their prior sugar-feeding status and only sugar-feeding was selectively suppressed by prior sugar access." Just because the well-fed animals ate less than the starved animals does not mean their feeding behavior was suppressed.

Perhaps there has been a misunderstanding in the experimental setup of figure 1F, probably stemming from our data representation. The experiment is a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. We scored females only for the presence or absence of each meal type (blood or sugar) and did not quantify the amount consumed.

(3) The figure legend for Figure 1A and the naming convention for different experimental groups are difficult to follow. A simplified or consistently abbreviated scheme would help readers navigate the figures and text.

We regret that the reviewer found the figure difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition).

(4) In the last paragraph of the Y-maze olfactory assay for host-seeking behaviour in An. stephensi in Methods, the authors state, "When testing blood-fed females, aged-matched sugar-fed females (bloodhungry) were included as positive controls where ever possible, with satisfactory results." The authors should explicitly describe what the criteria are for "satisfactory results".

We apologise for the lack of clarity. We have now edited the statement to read:

“When testing blood-fed females, age-matched sugar-fed females (blood-hungry) were included wherever possible as positive controls. These females consistently showed attraction to host cues, as expected.” See lines 786-790.

(5) In the first paragraph of the dsRNA-mediated gene knockdown section in Methods, dsRNA against GFP is used as a negative control for the injection itself, but not for the potential off-target effect.

We agree with the reviewer that dsGFP injections act as controls only for injection-related behavioural changes, and not for off-target effects of RNAi. We have now corrected the statement. See lines 919-920.

To control for off-target effects, we could have designed multiple dsRNAs targeting different parts of a given gene. We regret not including these controls for potential off-target effects of dsRNAs injected. 

(6) References numbers 48, 89, and 90 are not complete citations.

We thank the reviewer for spotting these. We have now corrected these citations.

eLife Assessment

This paper provides a useful new theory of the hallucinatory effects of 5-HT2A psychedelics. The authors present convincing evidence that a computational model trained with the Wake-Sleep algorithm can reproduce some features of hallucinations by varying the strength of top-down connections in the model, though it is not clear that this model applies to 5-HT2A hallucinogens in particular. The work will be of interest to researchers studying hallucinations or offline activity and plasticity more broadly.

Reviewer #1 (Public review):

Bredenberg et al. aim to model some of the visual and neural effects of psychedelics via the Wake-Sleep algorithm. This is an interesting study with findings that challenge certain mainstream ideas in psychedelic neuroscience.

While some of my concerns have been addressed in revision, I am still not convinced that this model applies to 5-HT2A hallucinogens, as opposed to a pharmacologically distinct hallucinogen. I think it is important to justify which class of hallucinogens this model applies to and distinguish it from other hallucinogens. While some researchers tend to group several hallucinogens together (e.g., 5-HT2A agonists, NMDA antagonists, kappa-opioids agonists), I'm not convinced this is warranted, when they have distinct subjective and cognitive effects (including quite different visual distortions, and again I point out that the kappa-opioid agonist salvinorin A, which is referred to as an "oneirogen," has been described as particularly dream-like, perhaps more so than 5-HT2A hallucinogens), as well as some differences in therapeutic outcomes (ketamine seems to not have as persisting of therapeutic effects, and kappa-opioid agonist have yet to be shown to be therapeutic). Their use patterns highlight this (e.g., 5-HT2A drugs are used less in non-festival/rave social settings compared to NMDA drugs like ketamine, which can be used frequently enough to the point of abuse; kappa-opioid agonists have quite mixed effects in terms of pleasurable outcomes, thereby rarely being used/abused and almost never to my knowledge being used recreationally).

In sum, more is needed to justify the claim that this work applies to 5-HT2A drugs in particular.

Reviewer #2 (Public review):

This work is a nice contribution to the literature in articulating a specific, testable theory of how psychedelics act to generate hallucinations and plasticity.

I believe my concerns from the first round of review have been addressed in this version.

Author response:

The following is the authors’ response to the original reviews.

First, we thank the reviewers for the valuable and constructive reviews. Thanks to these, we believe the article has been considerably improved. We have organized our response to address points that are relevant to both reviewers first, after which we address the unique concerns of each individual reviewer separately. We briefly paraphrase each concern and provide comments for clarification, outlining the precise changes that we have made to the text.

Common Concerns (R1 & R2):

Can you clarify how NREM and REM sleep relate to the oneirogen hypothesis?

Within the submission draft we tried to stay agnostic as to whether mechanistically similar replay events occur during NREM or REM sleep; however, upon a more thorough literature review, we think that there is moderately greater evidence in favor of Wake-Sleep-type replay occurring during REM sleep which is related to classical psychedelic drug mechanisms of action.

First, we should clarify that replay has been observed during both REM and NREM sleep, and dreams have been documented during both sleep stages, though the characteristics of dreams differ across stages, with NREM dreams being more closely tied to recent episodic experience and REM dreams being more bizarre/hallucinatory (see Stickgold et al., 2001 for a review). Replay during sleep has been studied most thoroughly during NREM sharp-wave ripple events, in which significant cortical-hippocampal coupling has been observed (Ji & Wilson, 2007). However, it is critical to note that the quantification methods used to identify replay events in the hippocampal literature usually focus on identifying what we term ‘episodic replay,’ which involves a near-identical recapitulation of neural trajectories that were recently experienced during waking experimental recordings (Tingley & Peyrach, 2020). In contrast, our model focuses on ‘generative replay,’ where one expects only a statistically similar reproduction of neural activity, without any particular bias towards recent or experimentally controlled experience. This latter form of replay may look closer to the ‘reactivation’ observed in cortex by many studies (e.g. Nguyen et al., 2024), where correlation structures of neural activity similar to those observed during stimulus-driven experience are recapitulated. Under experimental conditions in which an animal is experiencing highly stereotyped activity repeatedly, over extended periods of time, these two forms of replay may be difficult to dissociate.

Interestingly, though NREM replay has been shown to couple hippocampal and cortical activity, a similar study in waking animals administered psychedelics found hippocampal replay without any obvious coupling to cortical activity (Domenico et al., 2021). This could be because the coupling was not strong enough to produce full trajectories in the cortex (psychedelic administration did not increase ‘alpha’ enough), and that a causal manipulation of apical/basal influence in the cortex may be necessary to observe the increased coupling. Alternatively, as Reviewer 1 noted, it may be that psychedelics induce a form of hippocampus-decoupled replay, as one would expect from the REM stage of a recently proposed complementary learning systems model (Singh et al., 2022). 

Evidence in favor of a similarity between the mechanism of action of classical psychedelics and the mechanism of action of memory consolidation/learning during REM sleep is actually quite strong. In particular, studies have shown that REM sleep increases the activity of soma-targeting parvalbumin (PV) interneurons and decreases the activity of apical dendrite-targeting somatostatin (SOM) interneurons (Niethard et al., 2021), that this shift in balance is controlled by higher-order thalamic nuclei, and that this shift in balance is critical for synaptic consolidation of both monocular deprivation effects in early visual cortex (Zhou et al., 2020) and for the consolidation of auditory fear conditioning in the dorsal prefrontal cortex (Aime et al., 2022). These last studies were not discussed in our previous text–we have added them, in addition to a more nuanced description of the evidence connecting our model to NREM and REM replay. 

Relevant modifications: Page 4, 1st paragraph; Page 11, 1st paragraph.

Can you explain how synaptic plasticity induced by psychedelics within your model relates to learning at a behavioral level?

While the Wake-Sleep algorithm is a useful model for unsupervised statistical learning, it is not a model of reward or fear-based conditioning, which likely occur via different mechanisms in the brain (e.g. dopamine-dependent reinforcement learning or serotonin-dependent emotional learning). The Wake-Sleep algorithm is a ‘normative plasticity algorithm,’ that connects synaptic plasticity to the formation of structured neural representations, but it is not the case that all synaptic plasticity induced by psychedelic administration within our model should induce beneficial learning effects. According to the Wake-Sleep algorithm, plasticity at apical synapses is enhanced during the Wake phase, and plasticity at basal synapses is enhanced during the Sleep phase; under the oneirogen hypothesis, hallucinatory conditions (increased ‘alpha’) cause an increase in plasticity at both apical and basal sites. Because neural activity is in a fundamentally aberrant state when ‘alpha’ is increased, there are no theoretical guarantees that plasticity will improve performance on any objective: psychedelic-induced plasticity within our model could perhaps better be thought of as ‘noise’ that may have a positive or negative effect depending on the context.

In particular, such ‘noise’ may be beneficial for individuals or networks whose synapses have become locked in a suboptimal local minimum. The addition of large amounts of random plasticity could allow a system to extricate itself from such local minima over subsequent learning (or with careful selection of stimuli during psychedelic experience), similar to simulated annealing optimization approaches. If our model were fully validated, this view of psychedelic-induced plasticity as ‘noise’ could have relevance for efforts to alleviate the adverse effects of PTSD, early life trauma, or sensory deprivation; it may also provide a cautionary note against repeated use of psychedelic drugs within a short time frame, as the plasticity changes induced by psychedelic administration under our model are not guaranteed to be good or useful in-and-of themselves without subsequent re-learning and compensation.

We should also note that we have deliberately avoided connecting the oneirogen hypothesis model to fear extinction experimental results that have been observed through recordings of the hippocampus or the amygdala (Bombardi & Giovanni, 2013; Jiang et al., 2009; Kelly et al., 2024; Tiwari et al., 2024). Both regions receive extensive innervation directly from serotonergic synapses originating in the dorsal raphe nucleus, which have been shown to play an important role in emotional learning (Lesch & Waider, 2012); because classical psychedelics may play a more direct role in modulating this serotonergic innervation, it is possible that fear conditioning results (in addition to the anxiolytic effects of psychedelics) cannot be attributed to a shift in balance between apical and basal synapses induced by psychedelic administration. We have provided a more detailed review of these results in the text, as well as more clarity regarding their relation to our model.

Relevant modifications: Page 9, final paragraph; Page 12, final paragraph.

Reviewer 1 Concerns:

Is it reasonable to assign a scalar parameter ‘alpha’ to the effects of classical psychedelics? And is your proposed mechanism of action unique to classical psychedelics? E.g. Could this idea also apply to kappa opioid agonists, ketamine, or the neural mechanisms of hallucination disorders?

We have clarified that within our model ‘alpha’ is a parameter that reflects the balance between apical and basal synapses in determining the activity of neurons in the network. For the sake of simplicity we used a single ‘alpha’ parameter, but realistically, each neuron would have its own ‘alpha’ parameter, and different layers or individual neurons could be affected differentially by the administration of any particular drug; therefore, our scalar ‘alpha’ value can be thought of as a mean parameter for all neurons, disregarding heterogeneity across individual neurons.

There are many different mechanisms that could theoretically affect this ‘alpha’ parameter, including: 5-HT2a receptor agonism, kappa opioid receptor binding, ketamine administration, or possibly the effects of genetic mutations underlying the pathophysiology of complex developmental hallucination disorders. We focused exclusively on 5-HT2a receptor agonism for this study because the mechanism is comparatively simple and extensively characterized, but similar mechanisms may well be responsible for the hallucinatory symptoms of a variety of drugs and disorders.

Relevant modifications: Page 4, first paragraph; Page 13, first paragraph.

Can you clarify the role of 5-HT2a receptor expression on interneurons within your model?

While we mostly focused on the effects of 5-HT2a receptors on the apical dendrites of pyramidal neurons, these receptors are also expressed on soma-targeting parvalbumin (PV) interneurons. This expression on PV interneurons is consistent with our proposed psychedelic mechanism of action, because it could lead to a coordinated decrease in the influence of somatic and proximal dendritic inputs while increasing the influence of apical dendritic inputs. We have elaborated on this point, and moved the discussion earlier in the text.

Relevant modifications: Page 1, 1st paragraph; Page 4, 2nd paragraph.

Discussions of indigenous use of psychedelics over millenia may amount to over-romanticization.

We ultimately decided to remove these discussions from the main text, as they had little bearing on the content of our work. Within the Ethics Declarations section we softened our claims from “millenia” to “centuries,” as indigenous psychedelic use over this latter period of time is well-substantiated.

Relevant modifications: removed from introduction; modified Ethics Declarations

You isolate the 5-HT2a agonism as the mechanism of action underlying ‘alpha’ in your model, but there exist 5-HT2a agonists that do not have hallucinatory effects (e.g. lisuride). How do you explain this?

Lisuride has much-reduced hallucinatory effects compared to other psychedelic drugs at clinical doses (though it does indeed induce hallucinations at high doses; Marona-Lewicka et al., 2002), and we should note that serotonin (5-HT) itself is pervasive in the cortex without inducing hallucinatory effects during natural function. Similarly, MDMA is a partial agonist for 5-HT2a receptors, but it has much-reduced perceptual hallucination effects relative to classical psychedelics (Green et al., 2003) in addition to many other effects not induced by classical psychedelics.

Therefore, while we argue that 5-HT2a agonism induces an increase in influence of apical dendritic compartments and a decrease in influence of basal/somatic compartments, and that this change induces hallucinations, we also note that there are many other factors that control whether or not hallucinations are ultimately produced, so that not all 5-HT2a agonists are hallucinogenic. There are two possible additional factors that could contribute to this phenomenon: 5-HT receptor binding affinity and cellular membrane permeability.

Importantly, many 5-HT2a receptor agonists are also 5-HT1a receptor agonists (e.g. serotonin itself and lisuride), while MDMA has also been shown to increase serotonin, norepinephrine, and dopamine release (Green et al., 2003). While 5-HT2a receptor agonism has been shown to reduce sensory stimulus responses (Michaiel et al., 2019), 5-HT1a receptor agonism inhibits spontaneous cortical activity (Azimi et al., 2020); thus one might expect the net effect of administering serotonin or a nonselective 5-HT receptor agonist to be widespread inhibition of a circuit, as has been observed in visual cortex (Azimi et al., 2020). Therefore, selective 5-HT2a agonism is critical for the induction of hallucinations according to our model, though any intervention that jointly excites pyramidal neurons’ apical dendrites and inhibits their basal/somatic compartments across a broad enough area of cortex would be predicted to have a similar effect. Lisuride has a much higher binding affinity for 5-HT1a receptors than, for instance, LSD (Marona-Lewicka et al., 2002).

Secondly, it has recently been shown that both the head-twitch effect (a coarse behavioral readout of hallucinations in animals) and the plasticity effects of psychedelics are abolished when administering 5-HT2a agonists that are impermeable to the cellular membrane because of high polarity, and that these effects can be rescued by temporarily rendering the cellular membrane permeable (Vargas et al., 2023). This suggests that the critical hallucinatory effects of psychedelics (apical excitation according to our model) may be mediated by intracellular 5-HT2a receptors. Notably, serotonin itself is not membrane permeable in the cortex.

Therefore, either of these two properties could play a role in whether a given 5-HT2a agonist induces hallucinatory effects. We have provided an extended discussion of these nuances in our revision.

Relevant modifications: Page 1, paragraph 2.

Your model proposes that an increase in top-down influence on neural activity underlies the hallucinatory effects of psychedelics. How do you explain experimental results that show increases in bottom-up functional connectivity (either from early sensory areas or the thalamus)?

Firstly, we should note that our proposed increase in top-down influence is a causal, biophysical property, not necessarily a statistical/correlative one. As such, we will stress that the best way to test our model is via direct intervention in cortical microcircuitry, as opposed to correlative approaches taken by most fMRI studies, which have shown mixed results with regard to this particular question. Correlative approaches can be misleading due to dense recurrent coupling in the system, and due to the coarse temporal and spatial resolution provided by noninvasive recording technologies (changes in statistical/functional connectivity do not necessarily correspond to changes in causal/mechanistic connectivity, i.e. correlation does not imply causation).

There are two experimental results that appear to contradict our hypothesis that deserve special consideration. The first shows an increase in directional thalamic influence on the distributed cortical networks after psychedelic administration (Preller et al., 2018). To explain this, we note that this study does not distinguish between lower-order sensory thalamic nuclei (e.g. the lateral and medial geniculate nuclei receiving visual and auditory stimuli respectively) and the higher-order thalamic nuclei that participate in thalamocortical connectivity loops (Whyte et al., 2024). Subsequent more fine-grained studies have noted an increase in influence of higher order thalamic nuclei on the cortex (Pizzi et al., 2023; Gaddis et al., 2022), and in fact extensive causal intervention research has shown that classical psychedelics (and 5-HT2a agonism) decrease the influence of incoming sensory stimuli on the activity of early sensory cortical areas, indicating decoupling from the sensory thalamus (Evarts et al., 1955; Azimi et al., 2020; Michaiel et al. 2019). The increased influence of higher-order thalamic nuclei is consistent with both the cortico-striatal-thalamo-cortical (CTSC) model of psychedelic action as well as the oneirogen hypothesis, since higher-order thalamic inputs modulate the apical dendrites of pyramidal neurons in cortex (Whyte et al., 2024).

The second experimental result notes that DMT induces traveling waves during resting state activity that propagate from early visual cortex to deeper cortical layers (Alamia et al., 2020). There are several possibilities that could explain this phenomenon: 1) it could be due to the aforementioned difficulties associated with directed functional connectivity analyses, 2) it could be due to a possible high binding affinity for DMT in the visual cortex relative to other brain areas, or 3) it could be due to increases in apical influence on activity caused by local recurrent connectivity within the visual cortex which, in the absence of sensory input, could lead to propagation of neural activity from the visual cortex to the rest of the brain. This last possibility is closest to the model proposed by (Ermentrout & Cowan, 1979), and which we believe would be best explained within our framework by a topographically connected recurrent network architecture trained on video data; a potentially fruitful direction for future research.

Relevant modifications: Page 9, paragraph 1; Page 10, final paragraph; Page 11, final paragraph.

Shouldn’t the hallucinations generated by your model look more ‘psychedelic,’ like those produced by the DeepDream algorithm?

We believe that the differences in hallucination visualization quality between our Wake-Sleep-trained models and DeepDream are mostly due to differences in the scale and power of the models used across these two studies. We are confident that with more resources (and potentially theoretical innovations to improve the Wake-Sleep algorithm’s performance) the produced hallucination visualizations could become more realistic.

We note that more powerful generative models trained with backpropagation are able to produce surreal images of comparable quality (Rezende et al., 2014; Goodfellow et al., 2020; Vahdat & Kautz, 2020), though these have not yet been used as a model of psychedelic hallucinations. However, the DeepDream model operates on top of large pretrained image processing models, and does not provide an biologically mechanistic/testable interpretation of its hallucination effects. When training smaller models with a local synaptic plasticity rule (as opposed to backpropagation), the hallucination effects are less visually striking due to the reduced quality of our trained generative model, though they are still strongly tied to the statistics of sensory inputs, as quantified by our correlation similarity metric (Fig. 5b).

To demonstrate that our proposed hallucination mechanism is capable of producing more complex hallucinations in larger, more powerful models, we employed our same hallucination generation mechanism in a pretrained Very Deep Variational Autoencoder (VDVAE) (Child et al., 2021), which is a hierarchical variational autoencoder with a nearly identical structure compared to our Wake-Sleep-trained networks, with both a bottom-up inference pathway and a top-down generative pathway that maps cleanly onto our multicompartmental neuron model. VDVAEs are trained on the same objective function as our Wake-Sleep-trained networks, but using the backpropagation algorithm. The VDVAE models were able to generate much more complex hallucinations (emergence of complex geometric patterns, smooth deformations of objects and faces), whose complexity arguably exceeds those produced by the DeepDream algorithm. Therefore while the VDVAEs are less biologically realistic (they do not learn via local synaptic plasticity), they function as a valuable high-level model of hallucination generation that complements our Wake-Sleep-trained approach. As further validation, we were also able to replicate our key results and testable predictions with these models.

Relevant modifications: Results section “Modeling hallucinations in large-scale pretrained networks”; Figure 6, S7, S8; Page 12, paragraph 3; Methods section “Generating hallucinations in hierarchical variational autoencoders.”

Your model assumes domination by entirely bottom-up activity during the ‘wake’ phase, and domination entirely by top-down activity during ‘sleep,’ despite experimental evidence indicating that a mixture of top-down and bottom-up inputs influence neural activity during both stages in the brain. How do you explain this?

Our use of the Wake-Sleep algorithm, in which top-down inputs (Sleep) or bottom-up inputs (Wake) dominate network activity is an over-simplification made within our model for computational and theoretical reasons. Models that receive a mixture of top-down and bottom-up inputs during ‘Wake’ activity do exist (in particular the closely related Boltzmann machine (Ackley et al., 1985)), but these models are considerably more computationally costly to train due to a need to run extensive recurrent network relaxation dynamics for each input stimulus. Further, these models do not generalize as cleanly to processing temporal inputs. For this reason, we focused on the Wake-Sleep algorithm, at the cost of some biological realism, though we note that our model should certainly be extended to support mixed apical-basal waking regimes. We have added a discussion of this in our ‘Model Limitations’ section.

Relevant modifications: Page 12, paragraph 4.

Your model proposes that 5-HT2a agonism enhances glutamatergic transmission, but this is not true in the hippocampus, which shows decreases in glutamate after psychedelic administration.

We should note that our model suggests only compartment specific increases in glutamatergic transmission; as such, our model does not predict any particular directionality for measures of glutamatergic transmission that includes signaling at both apical and basal compartments in aggregate, as was measured in the provided study (Mason et al., 2020).

You claim that your model is consistent with the Entropic Brain theory, but you report increases in variance, not entropy. In fact, it has been shown that variance decreases while entropy increases under psychedelic administration. How do you explain this discrepancy?

Unfortunately, ‘entropy’ and ‘variance’ are heavily overloaded terms in the noninvasive imaging literature, and the particularities of the method employed can exert a strong influence on the reported effects. The reduction in variance reported by (Carhart-Harris et al., 2016) is a very particular measure: they are reporting the variance of resting state synchronous activity, averaged across a functional subnetwork that spans many voxels; as such, the reduction in variance in this case is a reduction in broad, synchronous activity. We do not have any resting state synchronous activity in our network due to the simplified nature of our model (particularly an absence of recurrent temporal dynamics), so we see no reduction in variance in our model due to these effects.

Other studies estimate ‘entropy’ or network state disorder via three different methods that we have been able to identify. 1) (Carhart-Harris et al., 2014) uses a different measure of variance: in this case, they subtract out synchronous activity within functional subnetworks, and calculate variability across units in the network. This measure reports increases in variance (Fig. 6), and is the closest measure to the one we employ in this study. 2) (Lebedev et al., 2016) uses sample entropy, which is a measure of temporal sequence predictability. It is specifically designed to disregard highly predictable signals, and so one might imagine that it is a measure that is robust to shared synchronous activity (e.g. resting state oscillations). 3) (Mediano et al., 2024) uses Lempel-Ziv complexity, which is, similar to sample entropy, a measure of sequence diversity; in this case the signal is binarized before calculation, which makes this method considerably different from ours. All three of the preceding methods report increases in sequence diversity, in agreement with our quantification method. Our strongest explanation for why the variance calculation in (Carhart-Harris et al., 2016) produces a variance reduction is therefore due to a reduction in low-rank synchronous activity in subnetworks during resting state.

As for whether the entropy increase is meaningful: we share Reviewer 1’s concern that increases in entropy could simply be due to a higher degree of cognitive engagement during resting state recordings, due to the presence of sensory hallucinations or due to an inability to fall asleep. This could explain why entropy increases are much more minimal relative to non-hallucinating conditions during audiovisual task performance (Siegel et al., 2024; Mediano et al., 2024). However, we can say that our model is consistent with the Entropic Brain Theory without including any form of ‘cognitive processing’: we observe increases in variability during resting state in our model, but we observe highly similar distributions of activity when averaging over a wide variety of sensory stimulus presentations (Fig. 5b-c). This is because variability in our model is not due to unstructured noise: it corresponds to an exploration of network states that would ordinarily be visited by some stimulus. Therefore, when averaging across a wide variety of stimuli, the distribution of network states under hallucinating or non-hallucinating conditions should be highly similar.

One final point of clarification: here we are distinguishing Entropic Brain Theory from the REBUS model–the oneirogen hypothesis is consistent with the increase in entropy observed experimentally, but in our model this entropy increase is not due to increased influence of bottom-up inputs (it is due instead to an increase in top-down influence). Therefore, one could view the oneirogen hypothesis as consistent with EBT, but inconsistent with REBUS.

Relevant modifications: Page 10, paragraph 1.

You relate your plasticity rule to behavioral-timescale plasticity (BTSP) in the hippocampus, but plasticity has been shown to be reduced in the hippocampus after psychedelic administration. Could you elaborate on this connection?

When we were establishing a connection between our ‘Wake-Sleep’ plasticity rule and BTSP learning, the intended connection was exclusively to the mathematical form of the plasticity rule, in which activity in the apical dendrites of pyramidal neurons functions as an instructive signal for plasticity in basal synapses (and vice versa): we will clarify this in the text. Similarly, we point out that such a plasticity rule tends to result in correlated tuning between apical and basal dendritic compartments, which has been observed in hippocampus and cortex: this is intended as a sanity check of our mapping of the Wake-Sleep algorithm to cortical microcircuitry, and has limited further bearing on the effects of psychedelics specifically.

Reduction in plasticity in the hippocampus after psychedelic administration could be due to a complementary learning systems-type model, in which the hippocampus becomes partly decoupled from the cortex during REM sleep (Singh et al., 2022); were this to be the case, it would not be incompatible with our model, which is mostly focused on the cortex. Notably, potentiating 5HT-2a receptors in the ventral hippocampus does not induce the head-twitch response, though it does produce anxiolytic effects (Tiwari et al., 2024), indicating that the hallucinatory and anxiolytic effects of classical psychedelics may be partly decoupled. 

Reviewer 2 Concerns:

Could you provide visualizations of the ‘ripple’ phenomenon that you’re referring to?

In our revised submission, ‘ripple’ phenomena are now visible in two places: Fig 2c-d, and Fig 6 (rows 2 and 3). Because the VDVAE models used to generate Figure 6 produce higher quality generated images, the ripples appearing in these plots are likely more prototypical, but it is not easy to evaluate the quality of these visualizations relative to subjective hallucination phenomena.

Could you provide a more nuanced description of alternative roles for top-down feedback, beyond being used exclusively for learning as depicted in your model?

For the sake of simplicity, we only treat top-down inputs in our model as a source of an instructive teaching signal, the originator of generative replay events during the Sleep phase, and as the mechanism of hallucination generation. However, as discussed in a response to a previous question, in the cortex pyramidal neurons receive and respond to a mixture of top-down and bottom-up processing.

There are a variety of theories for what role top-down inputs could play in determining network activity. To name several, top-down input could function as: 1) a denoising/pattern completion signal (Kadkhodaie & Simoncelli, 2021), 2) a feedback control signal (Podlaski & Machens, 2020), 3) an attention signal (Lindsay, 2020), 4) ordinary inputs for dynamic recurrent processing that play no specialized role distinct from bottom-up or lateral inputs except to provide inputs from higher-order association areas or other sensory modalities (Kar et al., 2019; Tugsbayar et al., 2025). Though our model does not include these features, they are perfectly consistent with our approach.

In particular, denoising/pattern completion signals in the predictive coding framework (closely related to the Wake-Sleep algorithm) also play a role as an instructive learning signal (Salvatori et al., 2021); and top-down control signals can play a similar role in some models (Gilra & Gerstner, 2017; Meulemans et al., 2021). Thus, options 1 and 2 are heavily overlapping with our approach, and are a natural consequence of many biologically plausible learning algorithms that minimize a variational free energy loss (Rao & Ballard, 1997; Ackley et al., 1985). Similarly, top-down attentional signals can exist alongside top-down learning signals, and some models have argued that such signals can be heavily overlapping or mutually interchangeable (Roelfsema & van Ooyen, 2005). Lastly, generic recurrent connectivity (from any source) can be incorporated into the Wake-Sleep algorithm (Dayan & Hinton, 1996), though we avoided doing this in the present study due to an absence of empirical architecture exploration in the literature and the computational complexity associated with training on time series data.

To conclude, there are a variety of alternative functions proposed for top-down inputs onto pyramidal neurons in the cortex, and we view these additional features as mutually compatible with our approach; for simplicity we did not include them in our Wake-Sleep-trained model, but we believe that these features are unlikely to interfere with our testable predictions or empirical results. In fact, the pretrained VDVAE models that we worked with do include top-down influence during the Wake-stage inference process, and these models recapitulated our key results and testable predictions (Fig. S8).

Relevant modifications: Fig. S8; Page 12, paragraph 4.

Which is why judicial and police sectors are also implicitly culture. Or rather, they are the chains of actually distributed culture. They are validated, invisibilised oppression.

Autonomous workers and cooperative groups should not be confused with autonomous managers (for-profit, expansive Shirky principle perpetuation angel investor mindset)! There is a difference, by design.

The Irish experiment worked! And the pilot cost €72 million to date but generated nearly €80 million in total benefits to the Irish economy. It is now being made permanent, although it only goes to 2000 people. Check it here: https://www.artnews.com/art-news/news/ireland-basic-income-artists-program-permanent-1234756981/

Still, I find caveats: These people going employed and/or working for monopolies, or else monopolies still squashing these people's liveability by means of sheer power in marketing or patents, political lobbying, or court judicial settlements.

indyweb.link/hyperpost/0/♖-hyperpost-0.html

This is the first instance of virtual indyweb.link that resolves via hypothesis to an ipns.dweb.link

indyweb.inks are virtual interpersonal interplanetary links

that rely on Open Commons Based Infrastructural constellations powered by hypothes.is, dweb.link IPF/NS and Peergos

这是我自己编出来的算法 return =a+b+c-min()-max()

Situation: You subscribe to a mobile plan. The telecom bill is collected automatically every month.

P&L means Profit & Loss (also called the Income Statement).

It’s a report that shows, over a period (month/quarter/year):

Revenue (money earned)

Expenses (money spent)

= Profit or Loss (what’s left)

Business Financial Management (BFM) is basically “PFM for companies” — the tools a business uses to control cash, track money in/out, stay compliant, and make decisions.

link to the barry article?

could link to the article about landowners and elite and stuff

cleverly compares primary sources with borsay, showing how his point about turnby works for swansea too - this is a linkage of welsh areas suggesting the relationship was seen further than just swansea that she focuses on

She places herself in historiography by naming Borsay, who she references multiple times. He is also the previous chapter in the same edited collection - it appears they have a similar view on the relationship of industry and tourism

Would this still be industrialisation? idk, need to think on this

BOOM really good link of relationship between industry and tourism aiding eachother

importance of railway again

reference to the importance of railways - as such, it was industrialisation which aided the boost of tourism. the railway was opened for industrial purposes, was then used for tourist ones - they kinda played off eachother, enabling each industry to grow!

together but seperate entities?

primary source usage for industry

Gives specific historian examples in footnotes - this is good and places her work within historiography

nice reference to specific historians

Primary source usage! analyse this possibly

So she does argue that industrialisation aided tourism - the facilities made for transporting industrial stuff eventually helped people

does she then argue that they co-existed but at the expense of the other?

to encourage english visitors and to advertise- quite a good use of evidence i think

Could be useful part of comparison if i went for the landowners article

she disagrees with this

The difference between data and metadata made it clear how much information there was outside of the actual data itself. Although it appears as though the pictures and texts contain the ‘main’ data, the metadata (engagement statistics, timing, etc.) could potentially be worth much more to platforms and researchers alike. The way that metadata can show patterns of behavior with little analysis necessary on the actual data is fascinating.

On the surface, this tweet appears to be purely innocent and cute, but in fact, there is quite a bit of information that can be gleaned from this tweet. Information such as the level of engagement, the posting of the tweet, images included in the tweet, and even the tone of the tweet can be analyzed for information about the type of tweet that works best.

eLife Assessment

This valuable study highlights the key role of NK cells and PD-L1+ neutrophils in worsening sepsis responses in the context of MASH (metabolic dysfunction-associated steatohepatitis). It focused on the role of neutrophils in mediating this effect, which is based on a choline-deficient high-fat diet model of various knockouts or selective ablation of immune cell types. While the data presented are of great interest, there are concerns around the reliability of the strength of the evidence provided, which is currently considered incomplete. The study may be of interest to researchers in immunopathological disease mechanisms once confirmatory studies have been completed.

[Editors' note: the authors no longer have access to the original flow cytometry data and plan to compile new datasets for further consideration.]

Reviewer #1 (Public review):

Summary:

By using an established NAFLD model, choline-deficient high-fat diet, Barros et al show that LPS challenge causes excessive IFN-γ production by hepatic NK cells which further induces recruitment and polarization of a PD-L1 positive neutrophil subset leading to massive TNFα production and increased host mortality. Genetic inhibition of IFN-γ or pharmacological blockade of PD-L1 decreases recruitment of these neutrophils and TNFα release, consequently preventing liver damage and decreasing host death.

Since NAFLD is often accompanied by chronic, low-grade inflammation, it can lead to an overactive but dysfunctional immune response and increase the body's overall susceptibility to infections, therefore this is very important research question.

Strengths:

The biggest strength of the manuscript is vast number of mouse strains used.

Weaknesses:

After the review, there are still some open questions from my side:

(1) I would like the authors to defend their choice of diet type since this has not been done in the review/response to authors. In case they cannot, we need additional proof (HFD or WD model).

(2) Since the authors used same control groups (chow and HFCD), as required by the animal ethics committee, they must have power analysis test to show that the number of controls (but also in other groups) they used is enough to see the effect. Please provide it.

Reviewer #2 (Public review):

Summary:

This is an extremely interesting mouse study, trying to understand how sepsis is tolerated during obesity/NAFLD. The researchers combine a well-established model of NASH (Choline-deficiency with High Fat Diet) with a sepsis model (IP injection of 10mg/kg LPS), leading to dramatic mortality in mice. Using this model, they characterize the complex contributions of immune cells. Specifically, they find that NK-cells and Neutrophils contribute the most to mortality in this model due to IFNG and PD-L1+ Neutrophils.

Strengths:

The biggest strength of the manuscript is how clear the primary phenotypes/endpoints of their model are. Within 6 hours of LPS injection, there is a stark elevation of liver inflammation and damage, which is exacerbated by a High Fat/CholineDeficient diet (HFCD). And after 1 day, almost all of the mice die. Using these endpoints, the authors were able to identify which cells were critical for mortality in the model and the specific mediators involved.

Comments on revisions:

I have no further comments.

Author response:

The following is the authors’ response to the original reviews.

We thank the editor and reviewers for their constructive questions, valuable feedback, and for approving our manuscript. We truly appreciate the opportunity to improve our work based on their insightful comments. Before addressing the editor’s and each referee’s remarks individually, we provide below a point-by-point response summarizing the revisions made.

Duplication of control groups across experiments

We appreciate the reviewers’ concern regarding the potential duplication of control groups. In the revised manuscript, we have explicitly clarified that independent groups of control mice were used for each experiment. These details are now clearly indicated in the Materials and Methods section to avoid any ambiguity and to reinforce the rigor of our experimental design (Page 15, Line 453-455): “Furthermore, knockout animals and those treated with pharmacological inhibitors or neutralizing antibodies shared the same control groups (chow and HFCD), as required by the animal ethics committee.”

Validation of the MASLD model

To strengthen the metabolic characterization of our MASLD model, we have now included additional parameters, including liver weight, Picrosirius staining and blood glucose measurements. These data are presented as new graphs in the revised manuscript and support the metabolic relevance of the HFCD diet model (Figure Suplementary S1). The corresponding description has been added to the Results section (Page 5, Lines 116-117) as follows: “Mice fed HFCD showed no increase in liver weight and collagen deposition as evidenced by Picrosirius staining (Fig. S1A and Fig. S1C)”

Assessment of liver injury in RagKO and anti-NK1.1 mice

We fully agree that assessment of liver injury is essential for these models. For mice treated with antiNK1.1, ALT levels are shown in Figure 4G, confirming increased liver injury after treatment. Regarding Rag⁻/⁻ mice, the animals exhibit exacerbation of liver injury when fed a HFCD diet and challenged with LPS (Page 7, Lines 183–184). The corresponding description has been added to the Results section (Page 7, Lines 175-176) as follows: “Interestingly, Rag1-deficient animals under the HFCD remained susceptible to the LPS challenge (Fig. 4C) with exacerbation of liver injury (Fig. 4D) ”

Discussion of limitations

We have expanded the Discussion section to provide a more comprehensive and balanced perspective on the limitations of our model and experimental approach (Page 13-14, Lines 401–414) “Our study presents several limitations that should be acknowledged and discussed. First, we cannot entirely rule out the possibility that our mice deficient in pro-inflammatory components exhibit reduced responsiveness to LPS. However, our ex vivo analyses using splenocytes from these animals revealed a preserved cytokine production following LPS stimulation. These results suggest that the in vivo differences observed are primarily driven by the MAFLD condition rather than by intrinsic defects in LPS sensitivity. Second, the absence of publicly available single-cell RNA-seq datasets from MAFLD subjects under endotoxemic or septic conditions limited our ability to perform direct translational comparisons. To overcome this, we analyzed existing MAFLD patients and experimental MAFLD datasets, which consistently demonstrated upregulation of IFN-y and TNF-α inflammatory pathways in MALFD. In line with these findings, our murine model revealed TNF-α⁺ myeloid and IFN-y⁺ NK cell populations, thereby reinforcing the validity and translational relevance of our results.”. This revision highlights the constraints of the MASLD model, the inherent variability among in vivo experiments, and the interpretative limitations related to immunodeficient mouse strains.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In Figure 4 the authors are showing the number of IFN+ positive CD4, CD8, and NK 1.1+ cells. Could they show from total IFNg production, how much it goes specifically on NK cells and how much on other cell populations since NK1.1 is NK but also NKT and gamma delta T cell marker? Also, in Figure 2E the authors see a substantial increase in IFNg signal in T cells.

While we did not specifically assess IFNγ production in NKT cells or other minor populations, our data indicate that the NK1.1+CD3+ cells (NKT cells) cited in Page 7, Lines  188-192 were essentially absent in the liver tissue of LPS-challenged animals, as shown in Supplementary Figures 3C and S10. The corresponding description has been added to the Results section (Page 7, Lines 188-192) as follows: “We observed that the number of NK cells increased in the liver tissue of PBS-treated MAFLD mice compared with mice fed a control diet (Fig. 4E). LPS challenge increased the accumulation of NK1.1+CD3− NK cells in the liver tissue of MAFLD mice and the absence of NK1.1+CD3+ NKT cells (Fig. S3C and 4E)”.

This absence was consistent across all experimental conditions, corroborating our focus on NK1.1+CD3− cells as the primary source of NK1.1-associated IFNγ production. Furthermore, data demonstrated in Figure 2E illustrate the presence of IFNγ primarily in NK cells. Therefore, the observed IFNγ signal, attributed to NK1.1+ cells, predominantly reflects conventional NK cells, with minimal contribution from NKT or γδ T cells.

(2) In Figure 4C, the authors state that the results suggest that T and B cells do not contribute to susceptibility to LPS challenge. However, they observe a drop in survival compared to chow+LPS. Are the authors certain there is no statistical significance there?

The observed decrease in survival is consistent with our expectations, as T and B cells are not the primary source of interferon-gamma (IFNγ) in this context. Even in their absence, animals remain susceptible to LPS challenge due to the presence of other IFNγ-producing cells that drive the observed lethality. We have carefully re-examined the statistical analysis and confirm that it was correctly performed.  

(3) Since the survival curve and rate are exactly the same (60%) in Figures 3F, 3G, 4C, 4F, 5G, and 5H I would just like to double-check that the authors used different controls for each experiment.

The number of mice used in each experiment was carefully determined to ensure sufficient statistical power while fully complying with the limits established by our institutional Animal Ethics Committee. To minimize animal use, the same control group was shared across multiple survival experiments. Despite using shared controls, the total number of animals per experimental group was adequate to produce robust and reproducible survival outcomes. All groups were properly randomized, and the shared control data were rigorously incorporated into statistical analyses. This strategy allowed us to maintain both ethical standards and the scientific rigor of our findings.

(4) In Figure 5 the authors are saying that it is neutrophils but not monocytes mediate susceptibility of animals with NAFLD to endotoxemia. However, CXCR2i depletion and CCR2 knock out mice affect both monocytes/macrophages and neutrophils. And in Figures 5E, 5G, and 5H they see that a) LPS+CXCR2i decreases liver damage more than LPS+anti Ly6G, b) HFCD mice challenged with LPS and treated with anti-LY6G do not rescue survival to levels of CHOW LPS and c) anti Ly6G treatment helps less than CXCR2i. Therefore, from both knock out mice and depletion experiments the authors can conclude that most likely monocytes (but potentially also other cells) together with neutrophils are substantial for the development of endotoxemic shock in choline-deficient high-fat diet model.

While neutrophils express CCR2, our data clearly show that CCR2 deficiency does not impair neutrophil migration, as demonstrated in Supplemental Figures 5A and 5B (added to the manuscript, page 8, lines 213–217). The corresponding description has been added to the Results section (Page 8, Lines 213217) as follows: ``Interestingly, animals deficient in monocyte migration (CCR2-/-) showed a high mortality rate compared to wild type after LPS challenge and neutrophil migration is not altered (Fig. 5SA and Fig. 5SB)``, In contrast, CCR2 deficiency primarily affects monocyte recruitment, yet in our experimental conditions, monocyte depletion or CCR2 knockout did not significantly alter the severity of endotoxemic shock, indicating that monocytes play a minimal role in mediating susceptibility in HFCD-fed mice.

To specifically investigate neutrophils, we used pharmacological blockade of CXCR2 to inhibit migration and antibody-mediated neutrophil depletion. Both approaches have consistently demonstrated that neutrophils are critical factors in endotoxemic shock.

These findings support our conclusion that neutrophils are the primary cellular contributors to susceptibility in HFCD-fed mice during endotoxemia, with monocytes making a negligible contribution under the tested conditions.

(6) In Figure 6A (but also others with PD-L1) did the authors do isotype control? And can they show how much of PD1+ population goes on neutrophils, and how much on all the other populations?

To address this issue, we performed additional analyses to assess the distribution of PD-L1 expression on CD45+CD11B+ leukocytes. These new results, detailed on Page 9, lines 245-250, and now presented in Supplemental Figure 6, demonstrate that PD-L1 expression is predominantly enriched in neutrophils compared to other immune subsets. This observation further reinforces our conclusion that neutrophils represent a major source of PD-L1 in our experimental model.

To ensure the robustness of these findings, we also included FMO controls for PD-L1 staining in the newly added Supplemental Figure S6. These controls validate the specificity of our gating strategy and confirm the reliability of the detected PD-L1 signal. The corresponding description has been added to the Results section (Page 9, Lines 245-250) as follows: ``First, we observed that only the MAFLD diet caused a significant increase in PD-L1 expression in CD45+CD11b+ leukocytes after LPS challenge (Fig. S6C). We observed that within this population, neutrophils predominate in their expression when compared to monocytes (Fig. 6SA, Fig. 6SB, and Fig. 6SD). Furthermore, PD-L+1 neutrophils showed an exacerbated migration of PD-L1+ neutrophils towards the liver (Fig. 6A and 6B)”

(7) In Figure 6D it is interesting that there is not an increase in PD-L1+ neutrophils in LPS HFCD IFNg+/+ mice in comparison to LPS chow IFNg+/+ mice, since those should be like WT mice (Figure 6A going from 50% to 97%) and so an increase should be seen?

The apparent difference between Figures 6A and 6D likely reflects inter-experimental variability rather than a biological discrepancy. Although the absolute percentages of PD-L1⁺ neutrophils varied slightly among independent experiments, the overall phenotype and trend were consistently maintained namely, that PD-L1 expression on neutrophils is enhanced in response to LPS stimulation and modulated by IFNγ signaling. Thus, the data shown in Figure 6D are representative of this consistent phenotype despite minor quantitative variation.

(8) In Figure 7 do the authors have isotype control for TNFa because gating seems a bit random so an isotype control graph would help a lot as supplementary information, in order to make the figure more persuasive

To address the concern regarding gating in Figure 7, we have included the FMO showing TNFα as a histogram Supplementary Figure 8gG. These control reaffirm the accuracy and reliability of our gating strategy for TNFα, further supporting the robustness of our data. The corresponding description has been added to the Results section (Page 9, Lines 272-274) as follows:`` We observed an exacerbated TNF-α expression by PD-L1+ neutrophils from MAFLD when compared to control chow animals (Fig. 7A, Fig. 7B, Fig. 7D, and Fig8SG).

(9) Figure 6C IFNg+/+ mice on CHOW +LPS is same as Figure 8E mice chow +LPS but just with different numbers. Can the authors explain this?

Although the data points in Figures 6C and 8E may appear similar, we confirm that they originate from entirely independent experiments and represent distinct datasets. To enhance clarity and avoid any potential confusion, we have adjusted the figure presentation and sizing in the revised manuscript. These changes make it clear that the datasets, while comparable, are derived from separate experimental replicates.

(10) Figure 1E chow B6+LPS is the same as Figure 5D B6+LPS but should they be different since those should be two different experiments?

We confirm that Figures 1E and 5D correspond to data obtained from independent experiments. Although the experimental conditions were similar, each dataset was generated and analyzed separately to ensure the reproducibility and robustness of our results.

Reviewer #2 (Recommendations for the authors):

(1) Why did you look at kidney injury in Figure 1D? I think this should be explained a little.

We assessed kidney injury alongside ALT, a marker of liver damage, because both the liver and kidneys are among the primary organs affected during sepsis and endotoxemia. This rationale has been added to the manuscript (page 5, lines 129–131): “Remarkably, compared to the Chow group, HFCD mice exposed to LPS did not show greater changes in other organs commonly affected by endotoxemia, such as the kidneys (Figure 1D).” By evaluating markers of injury in both organs, we aimed to determine whether our physiopathological condition was liver-specific or indicative of broader systemic injury.

(2) I know Figure 2C isn't your data, but why are there so few NK cells, considering NK cells are a resident liver cell type? Doesn't that also bring into question some of your data if there are so few NK cells? And the IFNG expression (2E) looks to mostly come from T-cells (CD8?).

The data shown in Figure 2C were reanalyzed from a separate NAFLD model based on a 60% high-fat diet. Although this model differs from ours, the observed low number of NK cells is consistent with expectations for animals subjected solely to a hyperlipidic diet, which primarily provides an inflammatory stimulus that promotes recruitment rather than maintaining high baseline NK cell numbers.

In our experimental model, these observations align with published data. Specifically, liver tissue from NAFLD animals typically exhibits low baseline NK cell numbers, but upon LPS challenge, there is a marked increase in NK cell recruitment to the liver. This dynamic illustrates the interplay between dietinduced inflammation and immune cell recruitment in our experimental context and supports the interpretation of our IFNγ data.

(3) In your methods, I think you didn't explain something. You said LPS was administered to 56 week old mice, but that HFCD diet was started in 5-6 week old mice and lasted 2 weeks, then LPS was administered. So LPS administration happened when the mice were 7-8 weeks old, right?

We thank the reviewer for pointing out this inconsistency in our Methods section. The reviewer is correct: the HFCD diet was initiated in 5–6-week-old mice, and LPS was administered after 2 weeks on the diet, such that LPS challenge occurred when the mice were 7–8 weeks old.

We have revised the Methods section (add page 15-16, lines 474–480).  to clarify this timeline and ensure it is accurately described in the manuscript. The corresponding description has been added to the Materials and Methods section (Page 14, Lines 436-442) as follows: “Lipopolysaccharide (LPS; Escherichia coli (O111:B4), L2630, Sigma-Aldrich, St. Louis, MO, USA) was administered intraperitoneally (i.p.; 10 mg/kg) in C57BL/6, CCR2 -/-, IFN-/-, and TNFR1R2 -/- mice. The HFCD was initiated in 5–6 week-old mice, and LPS was administered after 2 weeks on the diet, meaning that LPS administration occurred when the mice were 7–8 weeks old, with body weights ranging from 22 to 26 g. LPS was previously solubilized in sterile saline and frozen at -70°C. The animals were euthanized 6 hours after LPS administration”.

(4) Throughout the manuscript, I would consider changing the term NAFLD to something else. I think HFCD diet is a closer model to NASH, so there needs to be some discussion on that. And the field is changing these terms, so NAFLD is now MASLD and NASH is now MASH.

We appreciate the reviewer’s comment regarding the terminology and disease classification. In our experimental conditions, the animals were subjected to a high-fat, choline-deficient (HFCD) diet for only two weeks, a period considered very early in the progression of diet-induced liver disease. At this stage, histological analysis revealed lipid accumulation in hepatocytes without evidence of hepatocellular injury, inflammation, or fibrosis. Therefore, our model more closely resembles the metabolic-associated fatty liver disease (MAFLD, formerly NAFLD) stage rather than the more advanced metabolic-associated steatohepatitis (MASH, formerly NASH).

Indeed, prolonged exposure to HFCD diets, typically 8 to 16 weeks, is required to induce the inflammatory and fibrotic features characteristic of MASH. Since our objective was to study the initial metabolic and immune alterations preceding overt liver injury, we believe that using the term MAFLD more accurately reflects the pathological stage represented in our model. Accordingly, we have revised the text to align with the updated nomenclature and disease context.

(6) I am concerned about over interpretation of the publicly available RNA-seq data in Figure 2. This data comes from human NAFLD patients with unknown endotoxemia and mouse models using a traditional high-fat diet model. So it is hard to compare these very disparate datasets to yours. Also, if these datasets have elevated IFNG, why does your model require LPS injection?

We thank the reviewer for their thoughtful comments regarding the interpretation of the RNA-seq data presented in Figure 2. We would like to clarify that the human NAFLD datasets referenced in our study do not specifically include patients with endotoxemia; rather, they focus on individuals with NAFLD alone.

Comparing data from human and murine MAFLD models, we observed that NK cells, T cells, and neutrophils are present and contribute to the hepatic inflammatory environment. Our reanalysis indicates that the elevations of IFNγ and TNF in NAFLD are primarily derived from NK cells, T cells, and myeloid cells, respectively.

In our experimental model, LPS administration was used to evaluate whether these immune populations particularly NK cells are further potentiated under a hyperinflammatory state, leading to exacerbated IFNγ production. This approach allows us to determine whether increased IFNγ contributes to worsening outcomes in NAFLD, providing mechanistic insights that cannot be obtained from static human or traditional mouse datasets alone.

(7) The zoom-ins for the histology (for example, Figure 1E) don't look right compared to the dotted square. The shape and area expanded don't match. And the cells in the zoom-in don't look exactly the same either.

We have thoroughly re-examined the histological sections and the corresponding zoom-ins, including the example in Figure 1E. Upon verification, we confirm that the zoom-ins accurately represent the highlighted areas indicated by the dotted squares. The apparent discrepancies in shape or cellular appearance are likely due to minor differences in orientation or cropping during figure preparation. Nevertheless, the content and regions depicted are consistent with the original sections.  

(8) Did the authors measure myeloid infiltration in the CCR2-/- mice? Did you measure Neutrophil infiltration in the TNF-Receptor KO mice?

Analysis of CD45+ cell migration in CCR2 knockout mice, as shown in Supplemental Figure 5C and 5D, demonstrates that the absence of CCR2 does not impair overall leukocyte migration. Similarly, assessment of neutrophil migration in TNF receptor (TNFR1/2) knockout mice, presented in Supplemental Figure 8A, shows that neutrophil trafficking is not affected in these animals. These results indicate that the respective knockouts do not compromise the migration of the analyzed immune populations, supporting the interpretations presented in our study.

(9) Regarding Methods for RNA-seq Analysis. Was the Mitochondrial percentage cutoff 0.8%, because that seems low. And was there not a Padj or FDR cutoff for the differential expression?

The mitochondrial percentage in our scRNA-seq analysis reflects the proportion of mitochondrial gene expression per cell, which serves as a quality control metric. A low mitochondrial gene expression percentage, such as the 0.8% cutoff used here, is indicative of highly viable cells.

For differential gene expression analysis, we employed the FindMarkers function in Seurat with standard parameters: adjusted p-value (Padj) < 0.05 and log2 fold change > 0.25 for upregulated genes, and adjusted p-value < 0.05 with log2 fold change < -0.25 for downregulated genes. These thresholds ensure robust identification of differentially expressed genes while balancing sensitivity and specificity.

(10) Regarding Methods for Flow Cytometry. How were IFNG and TNF staining performed? Was this an intracellular stain? Did you need to block secretion? TNF and IFNG antibodies have the same fluorophore (PE), so were these stainings and analyses performed separately?

Six hours after LPS challenge, non-parenchymal liver cells were isolated using Percoll gradient centrifugation. Because the animals were in a hyperinflammatory state induced by LPS, no in vitro stimulation was performed; all staining was carried out immediately after cell isolation. Detection of IFNγ and TNF was performed via intracellular staining using the Foxp3 staining kit (eBioscience). Due to both antibodies being conjugated to PE, IFN-γ and TNF-α staining and analyses were conducted in separate experiments. These distinct staining protocols and analyses are detailed in Supplemental Figures 10 and 11. The corresponding description has been added to the Materials and Methods section (Page 16, Lines 490-493) as follows: ``As animals were already in a hyperinflammatory state, no additional in vitro stimulation was required. Intracellular detection of IFN-γ and TNF-α was conducted using the Foxp3 staining kit (eBioscience). Since both antibodies were conjugated to PE, staining and analyses were performed in separate experiments``

Reviewer #3 (Recommendations for the authors):

(1) Achieving an NAFLD model/disease is the starting point of this study. I understand that a two-week HFCD diet period was applied due to the decrease in lymphocyte numbers. Was it enough to initiate NAFLD then? Or is it a milder metabolic disease? Which parameters have been evaluated to accept this model as a NAFLD model?

Indeed, the two-week HFCD diet induces an early-stage form of NAFLD, characterized by initial fat accumulation in the liver without significant hepatic injury. While this represents a milder metabolic phenotype, it is sufficient to study the inflammatory and immune responses associated with NAFLD. To validate this model, we assessed multiple parameters: liver weight, blood glucose levels, and collagen deposition. These measurements confirmed the presence of early-stage NAFLD features in the animals, providing a relevant and reliable context for investigating susceptibility to endotoxemia and immune cell dynamics. They are shown in Figure Suplementary 1 and the text was included in the manuscript (Page 5, Lines 116-117): “Mice fed HFCD showed no increase in liver weight and collagen deposition as evidenced by Picrosirius staining (Fig. S1A and Fig. S1C) ”.

(2) It is true that the CD274 gene (encoding PD-L1) and the IFNGR2 gene, corresponding to the IFNγ receptor, are among the upregulated genes when authors analyzed the publicly available RNAseq data but they are not the most significantly elevated genes. What is the reasoning behind this cherrypicking? Why are other high DEGs not analyzed but these two are analyzed?

We highlighted the expression of the IFN-γ receptor (IFNGR2) and CD274 (encoding PD-L1) in the publicly available RNA-seq data to align and corroborate these findings with the key results observed later in our study. To avoid redundancy, we chose to present these genes in the initial figures as they are directly relevant to the subsequent analyses. Regarding the broader analysis of human RNA-seq data, our primary objective was to identify enriched biological processes and pathways, which served as a foundation for the focus and direction of this study.

(3) Figures 3C-3G: I understand that IFNg-/- and NFR1R2a-/- mice are not showing elevated liver damage but it may simply be because of the non-responsiveness to the LPS challenge. I suggest using a different challenge or recovery experiments with the cytokines to show that the challenge is successful and results are caused by NAFLD, truly. The same goes for Figure 6: Looking at Figure 6D one may think that IFNg deficiency alters the LPS response independent of the diet condition (or NAFLD condition).

We appreciate the reviewer’s insightful comment and fully understand the concern regarding the potential non-responsiveness of IFN-γ⁻/⁻ and TNFR1R2a⁻/⁻ mice to the LPS challenge. To address this point and confirm that these knockout animals are indeed responsive to LPS stimulation, we conducted an additional set of ex vivo experiments.

Specifically, WT and cytokine-deficient (IFN-γ⁻/⁻) mice were fed either Chow or HFCD for two weeks, after which spleens were collected, and splenocytes were challenged in vitro with LPS. We then quantified TNF, IFN, and IL-6 production to confirm that these mice are capable of mounting cytokine responses upon LPS stimulation.

Due to current breeding limitations and a temporary issue in colony maintenance of TNF-deficient mice, we were unable to include TNFR1R2a⁻/⁻ animals in this additional experiment. Nevertheless, we prioritized performing the analysis with the available knockout line to avoid leaving this important point unaddressed.

These additional data demonstrate that IFN-γ-deficient mice remain responsive to LPS, reinforcing that the differences observed in vivo are related to the NAFLD condition rather than a lack of LPS responsiveness.

(4) Figure 1 vs Figure 4: Rag-/- mice seem more susceptible to LPS-derived death even after normal conditions. But If I compare the survival data between Figure 1 and Figure 4, Rag-/- HFCD diet mice seem to be doing better than wt mice after LPS treatment. (1 day survival vs 2 days survival). How do you explain these different outcomes?

We thank the reviewer for this insightful question regarding the survival data in Figures 1 and 4. Although there is a one-day difference in survival outcomes, Rag-/- mice consistently exhibit increased susceptibility to LPS-induced mortality can influence the exact survival timing. Nonetheless, across all experiments, Rag-/- mice display a reproducible phenotype of heightened sensitivity to LPS challenge, which is supported by multiple independent observations in our study.

(5) How do you explain Figure 4J in connection to the observation presented with Figure 7: TNFa tissue levels, even though significant, seem very similar between the conditions?

We would like to clarify that the animals in this study are in a metabolic syndrome state, with early-stage NAFLD characterized by hepatic fat accumulation without significant tissue injury, as shown in Figure 1C.

Under these conditions, the LPS challenge triggers an exacerbated inflammatory response, leading to increased secretion of IFN-γ and TNF-α, primarily from NK cells and neutrophils. While TNFα levels may appear visually similar across conditions, the HFCD mice exhibit a heightened predisposition for an amplified immune response compared to chow-fed mice. This difference is consistent with the functional outcomes observed in our study and highlights the diet-specific sensitization of the immune system.

Comitis Capital GmbH (based in Frankfurt am Main) is buying Threema AG (based in CH). Threema has a pretty big footprint in security services, that's driving this more than its use as private messenger app for individual people.

DOI: 10.1016/j.devcel.2025.12.007

Resource: (IMSR Cat# JAX_022071,RRID:IMSR_JAX:022071)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:022071

What is this?

DOI: 10.1016/j.devcel.2025.12.007

Resource: (IMSR Cat# JAX_031670,RRID:IMSR_JAX:031670)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:031670

What is this?

DOI: 10.1016/j.devcel.2025.12.007

Resource: (IMSR Cat# JAX_014143,RRID:IMSR_JAX:014143)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:014143

What is this?

DOI: 10.1016/j.devcel.2025.12.007

Resource: (IMSR Cat# JAX_008462,RRID:IMSR_JAX:008462)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008462

What is this?

DOI: 10.1016/j.devcel.2025.12.007

Resource: (IMSR Cat# JAX_008179,RRID:IMSR_JAX:008179)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008179

What is this?

DOI: 10.1016/j.xpro.2025.104330

Resource: None

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008888

What is this?

eLife Assessment

This work describes the establishment of an image analysis pipeline for signal correction, segmentation and quantitative data analysis of multilayered organoid and tumoroid systems. The revised study is important for the field to address many practical challenges in deep-tissue visualization. The image analysis pipeline is well-designed and compelling.

Reviewer #1 (Public review):

Summary:

The image analysis pipeline is tested in analysing microscopy imaging data of gastruloids of varying sizes, for which an optimised protocol for in toto image acquisition is established based on whole mount sample preparation using an optimal refractive index matched mounting media, opposing dual side imaging with two-photon microscopy for enhaced laser penetration, dual view registration and weighted fusion for improved in toto sample data representation. For enhanced imaging speed in a two-photon microscope, parallel imaging was used and the authors performed spectral unmixing analysis to avoid issues of signal cross-talk.

In the image analysis pipeline image, different pre-treatments are done dependent on the analysis to be performed (for nuclear segmentation - contrast enhancement and normalisation; for quantitative analysis of gene expression - corrections for optical artifacts inducing signal intensity variations). Stardist3D was used for the nuclear segmentation. The study analyses in toto properties of gastruloid nuclear density, patterns of cell division, morphology, deformation and gene expression.

Strengths:

The methods developed are sound, well described and well validated, using a sample challenging for microscopy, gastruloids. Many of the established methods are very useful (e.g. registration, corrections, signal normalisation, lazy loading bioimage visualisation, spectral decomposition analysis), facilitate the development of quantitative research and would be of interest to the wide scientific community.

Comments on revisions:

I am happy with the job the authors have done with the revision. No further comments.

Reviewer #2 (Public review):

Summary:

This study presents an integrated experimental and computational pipeline for high-resolution, quantitative imaging and analysis of gastruloids. The experimental module employs dual-view two-photon spectral imaging combined with optimized clearing and mounting techniques, enabling improved deep-tissue visualization compared with conventional methods. This advanced approach allows comprehensive 3D imaging of whole-mount immunostained gastruloids, capturing both tissue-scale architecture and single-cell-level information.

The computational module encompasses both pre-processing of acquired images and downstream analysis, providing quantitative insights into the structural and molecular characteristics of gastruloids. The pre-processing pipeline, tailored for dual-view two-photon microscopy, includes spectral unmixing of fluorescence signals using depth-dependent spectral profiles, as well as image fusion via rigid 3D transformation based on content-based block-matching algorithms. Nuclei segmentation was performed using a custom-trained StarDist3D model, validated against 2D manual annotations, and achieving an F1 score of 85+/-3% at a 50% intersection-over-union (IoU) threshold. Another custom-trained StarDist3D model enabled accurate detection of proliferating cells and the generation of 3D spatial maps of nuclear density and proliferation probability. Moreover, the pipeline facilitates detailed morphometric analysis of cell density and nuclear deformation, revealing pronounced spatial heterogeneities during early gastruloid morphogenesis.

All computational tools developed in this study are released as open-source, Python-based software.

Strengths:

The authors applied two-photon microscopy to whole-mount deep imaging of gastruloids, achieving in toto visualization at single-cell resolution. By combining spectral imaging with an unmixing algorithm, they successfully separated four fluorescent signals, enabling spatial analysis of gene expression patterns.

The image analysis method for nuclei segmentation was thoroughly benchmarked against existing methods, demonstrating advantages over conventional approaches, and its applicability across diverse datasets was convincingly established. The authors also evaluated the state-of-the-art Cellpose-SAM framework, showing that it performs well on their data and that the authors' preprocessing strategy can further enhance Cellpose-SAM's segmentation performance in deep tissues.<br /> The entire computational workflow, from image pre-processing to segmentation with a custom-trained StarDist3D model and subsequent quantitative analysis, is made available as open-source software. In addition, user-friendly interfaces are provided through the open-source, community-driven napari platform, facilitating interactive exploration and analysis.

Weaknesses:

In my initial review, I noted that the developed image analysis pipeline lacked benchmarking against existing methods and provided only a limited demonstration of its applicability to other datasets. These points have been appropriately addressed in the revised manuscript, and I have no further weaknesses to note.

Appraisal:

The authors set out to establish a quantitative imaging and analysis pipeline for gastruloids using dual-view two-photon microscopy, spectral unmixing, and a custom computational framework for 3D segmentation and gene expression analysis. This aim was compellingly achieved. The integration of experimental and computational modules enables high-resolution in toto imaging and robust quantitative analysis at the single-cell level. The data presented support the authors' conclusions regarding the ability to capture spatial patterns of gene expression and cellular morphology across developmental stages.

Impact and utility:

This work presents a compelling and broadly applicable methodological advance. The approach is particularly impactful for the developmental biology community, as it allows researchers to extract quantitative information from high-resolution images to better understand morphogenetic processes. The data are publicly available on Zenodo, and the software is released on GitHub, making them highly valuable resources for the community. Given that suitable datasets for developing advanced 3D cell segmentation methods remain scarce in biological image analysis, the public release of these data is significant and is expected to stimulate further advances in the development of sophisticated computational approaches.

Comments on revisions:

The authors have addressed the previous revision thoroughly and appropriately. I have no further suggestions or additional recommendations at this time.

Reviewer #3 (Public review):

Summary

The paper presents a imaging and analysis pipeline for whole-mount gastruloid imaging with two-photon microscopy. The presented pipeline includes spectral unmixing, registration, segmentation, and a wavelength-depended intensity normalization step, followed by quantitative analysis of spatial gene expression patterns and nuclear morphometry on a tissue level. The utility of the approach is demonstrated by several experimental findings such as establishing spatial correlations between local nuclear deformation and tissue density changes, as well as radial distribution pattern of mesoderm markers. The pipeline is distributed as a Python package, notebooks and multiple napari plugins.

Strengths

The paper is well-written with detailed methodological descriptions, which I think would make it a valuable reference for researchers performing similar volumetric tissue imaging experiments (gastruloids/organoids). The pipeline itself addresses many practical challenges including resolution loss within tissue, registration of large volumes, nuclear segmentation, and intensity normalization. Especially the intensity decay measurements and wavelength-dependent intensity normalization approach using nuclear (Hoechst) signal as reference is very interesting and should be applicable to other imaging contexts. The morphometric analysis is equally well done with the correlation between nuclear shape deformation and tissue density changes being a interesting finding. The paper is quite thorough in its technical description of the methods (which are a lot) and their experimental validation is appropriate. Finally, the provided code and napari plugins seem to be well done (I installed a selected list of the plugins and they ran without issues) and should be very helpful for the community.

Comments on revisions:

The minor issues that I originally raised in my first review have been fully resolved in the revised version.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:  

Reviewer #1 (Public review):  

Summary:  

The image analysis pipeline is tested in analysing microscopy imaging data of gastruloids of varying sizes, for which an optimised protocol for in toto image acquisition is established based on whole mount sample preparation using an optimal refractive index matched mounting media, opposing dual side imaging with two-photon microscopy for enhanced laser penetration, dual view registration, and weighted fusion for improved in toto sample data representation. For enhanced imaging speed in a two-photon microscope, parallel imaging was used, and the authors performed spectral unmixing analysis to avoid issues of signal cross-talk.  

In the image analysis pipeline, different pre-treatments are done depending on the analysis to be performed (for nuclear segmentation - contrast enhancement and normalisation; for quantitative analysis of gene expression - corrections for optical artifacts inducing signal intensity variations). Stardist3D was used for the nuclear segmentation. The study analyses into properties of gastruloid nuclear density, patterns of cell division, morphology, deformation, and gene expression.  

Strengths:  

The methods developed are sound, well described, and well-validated, using a sample challenging for microscopy, gastruloids. Many of the established methods are very useful (e.g. registration, corrections, signal normalisation, lazy loading bioimage visualisation, spectral decomposition analysis), facilitate the development of quantitative research, and would be of interest to the wider scientific community.

We thank the reviewer for this positive feedback.

Weaknesses:  

A recommendation should be added on when or under which conditions to use this pipeline. 

We thank the reviewer for this valuable feedback, we added the text in the revised version, ines 418 to 474. “In general, the pipeline is applicable to any tissue, but it is particularly useful for large and dense 3D samples—such as organoids, embryos, explants, spheroids, or tumors—that are typically composed of multiple cell layers and have a thickness greater than 50 µm”.

“The processing and analysis pipeline are compatible with any type of 3D imaging data (e.g. confocal, 2 photon, light-sheet, live or fixed)”.

“Spectral unmixing to remove signal cross-talk of multiple fluorescent targets is typically more relevant in two-photon imaging due to the broader excitation spectra of fluorophores compared to single-photon imaging. In confocal or light-sheet microscopy, alternating excitation wavelengths often circumvents the need for unmixing. Spectral decomposition performs even better with true spectral detectors; however, these are usually not non-descanned detectors, which are more appropriate for deep tissue imaging. Our approach demonstrates that simultaneous cross-talk-free four-color two-photon imaging can be achieved in dense 3D specimen with four non-descanned detectors and co-excitation by just two laser lines. Depending on the dispersion in optically dense samples, depth-dependent apparent emission spectra need to be considered”.

“Nuclei segmentation using our trained StarDist3D model is applicable to any system under two conditions: (1) the nuclei exhibit a star-convex shape, as required by the StarDist architecture, and (2) the image resolution is sufficient in XYZ to allow resampling. The exact sampling required is object- and system-dependent, but the goal is to achieve nearly isotropic objects with diameters of approximately 15 pixels while maintaining image quality. In practice, images containing objects that are natively close to or larger than 15 pixels in diameter should segment well after resampling. Conversely, images with objects that are significantly smaller along one or more dimensions will require careful inspection of the segmentation results”.

“Normalization is broadly applicable to multicolor data when at least one channel is expected to be ubiquitously expressed within its domain. Wavelength-dependent correction requires experimental calibration using either an ubiquitous signal at each wavelength. Importantly, this calibration only needs to be performed once for a given set of experimental conditions (e.g., fluorophores, tissue type, mounting medium)”.

“Multi-scale analysis of gene expression and morphometrics is applicable to any 3D multicolor image. This includes both the 3D visualization tools (Napari plugins) and the various analytical plots (e.g., correlation plots, radial analysis). Multi-scale analysis can be performed even with imperfect segmentation, as long as segmentation errors tend to cancel out when averaged locally at the relevant spatial scale. However, systematic errors—such as segmentation uncertainty along the Z-axis due to strong anisotropy—may accumulate and introduce bias in downstream analyses. Caution is advised when analyzing hollow structures (e.g., curved epithelial monolayers with large cavities), as the pipeline was developed primarily for 3D bulk tissues, and appropriate masking of cavities would be needed”.

Reviewer #2 (Public review):  

Summary:  

This study presents an integrated experimental and computational pipeline for high-resolution, quantitative imaging and analysis of gastruloids. The experimental module employs dual-view two-photon spectral imaging combined with optimized clearing and mounting techniques to image whole-mount immunostained gastruloids. This approach enables the acquisition of comprehensive 3D images that capture both tissue-scale and single-cell level information.  

The computational module encompasses both pre-processing of acquired images and downstream analysis, providing quantitative insights into the structural and molecular characteristics of gastruloids. The pre-processing pipeline, tailored for dual-view two-photon microscopy, includes spectral unmixing of fluorescence signals using depth-dependent spectral profiles, as well as image fusion via rigid 3D transformation based on content-based block-matching algorithms. Nuclei segmentation was performed using a custom-trained StarDist3D model, validated against 2D manual annotations, and achieving an F1 score of 85+/-3% at a 50% intersection-over-union (IoU) threshold. Another custom-trained StarDist3D model enabled accurate detection of proliferating cells and the generation of 3D spatial maps of nuclear density and proliferation probability. Moreover, the pipeline facilitates detailed morphometric analysis of cell density and nuclear deformation, revealing pronounced spatial heterogeneities during early gastruloid morphogenesis.  

All computational tools developed in this study are released as open-source, Python-based software.  

Strengths:  

The authors applied two-photon microscopy to whole-mount deep imaging of gastruloids, achieving in toto visualization at single-cell resolution. By combining spectral imaging with an unmixing algorithm, they successfully separated four fluorescent signals, enabling spatial analysis of gene expression patterns.  

The entire computational workflow, from image pre-processing to segmentation with a custom-trained StarDist3D model and subsequent quantitative analysis, is made available as open-source software. In addition, user-friendly interfaces are provided through the open-source, community-driven Napari platform, facilitating interactive exploration and analysis.

We thank the reviewer for this positive feedback.

Weaknesses:  

The computational module appears promising. However, the analysis pipeline has not been validated on datasets beyond those generated by the authors, making it difficult to assess its general applicability.

We agree that applying our analysis pipeline to published datasets—particularly those acquired with different imaging systems—would be valuable. However, only a few high-resolution datasets of large organoid samples are publicly available, and most of these either lack multiple fluorescence channels or represent 3D hollow structures. Our computational pipeline consists of several independent modules: spectral filtering, dual-view registration, local contrast enhancement, 3D nuclei segmentation, image normalization based on a ubiquitous marker, and multiscale analysis of gene expression and morphometrics. We added the following sentences to the Discussion, lines 418 to 474, and completed the discussion on applicability with a table showing the purpose, requirements, applicability and limitations of each step of the processing and analysis pipeline.

“Spectral filtering has already been applied in other systems (e.g. [7] and [8]), but is here extended to account for imaging depth-dependent apparent emission spectra of the different fluorophores. In our pipeline, we provide code to run spectral filtering on multichannel images, integrated in Python. In order to apply the spectral filtering algorithm utilized here, spectral patterns of each fluorophore need to be calibrated as a function of imaging depth, which depend on the specific emission windows and detector settings of the microscope”.

“Image normalization using a wavelength-dependent correction also requires calibration on a given imaging setup to measure the difference in signal decay among the different fluorophores species. To our knowledge, the calibration procedures for spectral-filtering and our image-normalization approach have not been performed previously in 3D samples, which is why validation on published datasets is not readily possible. Nevertheless, they are described in detail in the Methods section, and the code used—from the calibration measurements to the corrected images—is available open-source at the Zenodo link in the manuscript”.

Dual-view registration, local contrast enhancement, and multiscale analysis of gene expression and morphometrics are not limited to organoid data or our specific imaging modalities. To evaluate our 3D nuclei segmentation model, we tested it on diverse systems, including gastruloids stained with the nuclear marker Draq5 from Moos et al. [1]; breast cancer spheroids; primary ductal adenocarcinoma organoids; human colon organoids and HCT116 monolayers from Ong et al. [2]; and zebrafish tissues imaged by confocal microscopy from Li et al [3]. These datasets were acquired using either light-sheet or confocal microscopy, with varying imaging parameters (e.g., objective lens, pixel size, staining method). The results are added in the manuscript, Fig. S9b.

Besides, the nuclei segmentation component lacks benchmarking against existing methods.  

We agree with the reviewer that a benchmark against existing segmentation methods would be very useful. We tried different pre-trained models:

CellPose, which we tested in a previous paper ([4]) and which showed poor performances compared to our trained StarDist3D model.

DeepStar3D ([2]) is only available in the software 3DCellScope. We could not benchmark the model on our data, because the free and accessible version of the software is limited to small datasets. An image of a single whole-mount gastruloid with one channel, having dimensions (347,467,477) was too large to be processed, see screenshot below. The segmentation model could not be extracted from the source code and tested externally because the trained DeepStar3D weights are encrypted.

Author response image 1.

Screenshot of the 3DCellScore software. We could not perform 3D nuclei segmentation of a whole-mount gastruloids because the image size was too large to be processed.

AnyStar ([5]), which is a model trained from the StarDist3D architecture, was not performing well on our data because of the heterogeneous stainings. Basic pre-processing such as median and gaussian filtering did not improve the results and led to wrong segmentation of touching nuclei. AnyStar was demonstrated to segment well colon organoids in Ong et al, 2025 ([2]), but the nuclei were more homogeneously stained. Our Hoechst staining displays bright chromatin spots that are incorrectly labeled as individual nuclei.

Cellos ([6]), another model trained from StarDist3D, was also not performing well. The objects used for training and to validate the results are sparse and not touching, so the predicted segmentation has a lot of false negatives even when lowering the probability threshold to detect more objects. Additionally, the network was trained with an anisotropy of (9,1,1), based on images with low z resolution, so it performed poorly on almost isotropic images. Adapting our images to the network’s anisotropy results in an imprecise segmentation that can not be used to measure 3D nuclei deformations.

We tried both Cellos and AnyStar predictions on a gastruloid image from Fig. S2 of our main manuscript.  The results are added in the manuscript, Fig. S9b. Fig3 displays the results qualitatively compared to our trained model Stardist-tapenade.

Author response image 2.

Qualitative comparison of two published segmentation models versus our model. We show one slice from the XY plane for simplicity. Segmentations are displayed with their contours only. (Top left) Gastruloid stained with Hoechst, image extracted from Fig S2 of our manuscript. (Top right) Same image overlayed with the prediction from the Cellos model, showing many false negatives. (Bottom left) Same image overlayed with the prediction from our Stardist-tapenade model. (Bottom right) Same image overlayed with the prediction from the AnyStar model, false positives are indicated with a red arrow.

CellPose-SAM, which is a recent model developed building on the CellPose framework. The pre-trained model performs well on gastruloids imaged using our pipeline, and performs better than StarDist3D at segmenting elongated objects such as deformed nuclei. The performances are qualitatively compared on Fig. S9a and S10.  We also demonstrate how using local contrast enhancement improves the results of CellPose-SAM (Fig. S10a), showing the versatility of the Tapenade pre-processing module. Tissue-scale, packing-related metrics from Cellpose–SAM labels qualitatively match those from stardist-tapenade as shown Fig.10c and d.

Appraisal:  

The authors set out to establish a quantitative imaging and analysis pipeline for gastruloids using dual-view two-photon microscopy, spectral unmixing, and a custom computational framework for 3D segmentation and gene expression analysis. This aim is largely achieved. The integration of experimental and computational modules enables high-resolution in toto imaging and robust quantitative analysis at the single-cell level. The data presented support the authors' conclusions regarding the ability to capture spatial patterns of gene expression and cellular morphology across developmental stages.  

Impact and utility:  

This work presents a compelling and broadly applicable methodological advance. The approach is particularly impactful for the developmental biology community, as it allows researchers to extract quantitative information from high-resolution images to better understand morphogenetic processes. The data are publicly available on Zenodo, and the software is released on GitHub, making them highly valuable resources for the community.  

We thank the reviewer for these positive feedbacks.

Reviewer #3 (Public review):

Summary  

The paper presents an imaging and analysis pipeline for whole-mount gastruloid imaging with two-photon microscopy. The presented pipeline includes spectral unmixing, registration, segmentation, and a wavelength-dependent intensity normalization step, followed by quantitative analysis of spatial gene expression patterns and nuclear morphometry on a tissue level. The utility of the approach is demonstrated by several experimental findings, such as establishing spatial correlations between local nuclear deformation and tissue density changes, as well as the radial distribution pattern of mesoderm markers. The pipeline is distributed as a Python package, notebooks, and multiple napari plugins.  

Strengths  

The paper is well-written with detailed methodological descriptions, which I think would make it a valuable reference for researchers performing similar volumetric tissue imaging experiments (gastruloids/organoids). The pipeline itself addresses many practical challenges, including resolution loss within tissue, registration of large volumes, nuclear segmentation, and intensity normalization. Especially the intensity decay measurements and wavelength-dependent intensity normalization approach using nuclear (Hoechst) signal as reference are very interesting and should be applicable to other imaging contexts. The morphometric analysis is equally well done, with the correlation between nuclear shape deformation and tissue density changes being an interesting finding. The paper is quite thorough in its technical description of the methods (which are a lot), and their experimental validation is appropriate. Finally, the provided code and napari plugins seem to be well done (I installed a selected list of the plugins and they ran without issues) and should be very helpful for the community.

We thank the reviewer for his positive feedback and appreciation of our work.

Weaknesses  

I don't see any major weaknesses, and I would only have two issues that I think should be addressed in a revision:  

(1) The demonstration notebooks lack accompanying sample datasets, preventing users from running them immediately and limiting the pipeline's accessibility. I would suggest to include (selective) demo data set that can be used to run the notebooks (e.g. for spectral unmixing) and or provide easily accessible demo input sample data for the napari plugins (I saw that there is some sample data for the processing plugin, so this maybe could already be used for the notebooks?).  

We thank the reviewer for this relevant suggestion. The 7 notebooks were updated to automatically download sample tests. The different parts of the pipeline can now be run immediately:

https://github.com/GuignardLab/tapenade/tree/chekcs_on_notebooks/src/tapenade/notebooks

(2) The results for the morphometric analysis (Figure 4) seem to be only shown in lateral (xy) views without the corresponding axial (z) views. I would suggest adding this to the figure and showing the density/strain/angle distributions for those axial views as well.

A morphometric analysis based on the axial views was added as Fig. S6a of the manuscript, complementary to the XY views.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):  

In lines 64 and 65, it is mentioned that confocal and light-sheet microscopy remain limited to samples under 100μm in diameter. I would recommend revising this sentence. In the paper of Moos and colleagues (also cited in this manuscript; PMID: 38509326), gastruloid samples larger than 100μm are imaged in toto with an open-top dual-view and dual-illumination light-sheet microscope, and live cell behaviour is analysed. Another example, if considering also multi-angle systems, is the impressive work of McDole and colleagues (PMID: 30318151), in which one of the authors of this manuscript is a corresponding author. There, multi-angle light sheet microscopy is used for in toto imaging and reconstruction of post-implantation mouse development (samples much larger than 100μm). Some multi-sample imaging strategies have been developed for this type of imaging system, though not to the sample number extent allowed by the Viventis LS2 system or the Bruker TruLive3D imager, which have higher image quality limitations.

We thank the reviewer for this remark. As reported in their paper, Moos et al. used dual-view light-sheet microscopy to image gastruloids, which are particularly dense and challenging tissues, with whole-mount samples of approximately 250 µm in diameter. Nevertheless, their image quality metric (DCT) shows a rapid twofold decrease within 50 µm depth (Extended Fig 5.h), whereas with two-photon microscopy, our image quality metric (FRC-QE) decreases by a factor of two over 150 µm in non-cleared samples (PBS) (see Fig. 2 c). While these two measurements (FRC-QE versus DCT) are not directly comparable, the observed difference reflects the superior depth performance of two-photon microscopy, owing in part to the use of non-descanned detectors. In our case, imaging was performed with Hoechst, a blue fluorophore suboptimal for deep imaging, whereas in the Moos dataset (Draq5, far-red), the configuration was more favorable for imaging in depth  which further supports our conclusion.

In McDole et al, tissues reaching 250µm were imaged from 4 views, but do not reach cellular-scale resolution in deeper layers compatible with cell segmentation to our knowledge.

We corrected the sentence ‘However, light-sheet and confocal imaging approaches remain limited to relatively small organoids typically under 100 micrometers in diameter ‘ by the following (line 64) :

“While advances in light-sheet microscopy have extended imaging depth in organoids, maintaining high image quality throughout thick samples remains challenging. In practice, quantitative analyses are still largely restricted to organoids under roughly 100 µm in diameter”.

It is worth mentioning that two-photon microscopes are much more widely available than light sheet microscopes, and light sheet systems with 2-photon excitation are even less accessible, which makes the described workflow of Gros and colleagues have a wide community interest.  

We thank the reviewer for this remark, and added this suggestion line 74:

“Finally, two-photon microscopes are typically more accessible than light-sheet systems and allow for straightforward sample mounting, as they rely on procedures comparable to standard confocal imaging”.

Reviewer #2 (Recommendations for the authors):  

Suggestions:  

A comparison with established pre-trained models for 3D organoid image segmentation (e.g., Cellos[1], AnyStar[2], and DeepStar3D[3], all based on StarDist3D) would help highlight the advantages of the authors' custom StarDist3D model, which has been specifically optimized for two-photon microscopy images.  

(1)  Cellos: https://doi.org/10.1038/s41467-023-44162-6

(2)  AnyStar: https://doi.org/10.1109/WACV57701.2024.00742

(3)  DeepStar3D: https://doi.org/10.1038/s41592-025-02685-4

We agree with the reviewer that a benchmark against existing segmentation methods is very useful. This is addressed in the revised version, as detailed above (Figure 3).

Recommendations:  

Please clarify the following point. In line 195, the authors state, "This allowed us to detect all mitotic nuclei in whole-mount samples for any stage and size." Does this mean that the custom-trained StarDist3D model can detect 100% of mitotic nuclei? It was not clear from the manuscript, figures, or videos how this was validated. Given the reported performance scores of the StarDist3D model for detecting all nuclei, claiming 100% detection of mitotic nuclei seems surprisingly high.

We thank the reviewer for this comment. As it was detailed in the methods section, the detection score reaches 82%, and only the complete pipeline (detection+minimal manual curation) allows us to detect all mitotic nuclei. To make it clearer, the following precisions were added in the Results section:

”To detect division events, we stained gastruloids with phosphohistone H3 (ph3) and trained a separate custom Stardist3D model using 3D annotations of nuclei expressing ph3 (see Methods III H). This model together allowed us to detect nearly all mitotic nuclei in whole-mount samples for any stage and size (Fig.3f and Suppl.Movie 4), and we used minimal manual curation to correct remaining errors.”

Minor corrections:  

It appears that Figures 4-6 are missing from the submitted version, but they can be found in the manuscript available on bioRxiv.

We thank the reviewer for this remark, this was corrected immediately to add Figures 4 to 6.

In line 185, is the intended phrase "by comparing the 2D predictions and the 2D sliced annotated segments..."? 

To gain some clarity, we replaced the initial sentence:

“The f1 score obtained by comparing the 3D prediction and the 3D ground-truth is well approximated by the f1 score obtained by comparing the 2D annotations and the 2D sliced annotated segments, with at most a 5% difference between the two scores.” by

“The f1 score obtained in 3D (3D prediction compared with the 3D ground-truth) is well approximated by the f1 score obtained in 2D (2D predictions compared with the 2D sliced annotated segments). The difference between the 2 scores was at most 5%.”

Reviewer #3 (Recommendations for the authors):

(1) How is the "local neighborhood volume" defined, and how was it computed?

The reviewer is referring to this paragraph (the term is underscored) :

“To probe quantities related to the tissue structure at multiple scales, we smooth their signal with a Gaussian kernel of width σ, with σ defined as the spatial scale of interest. From the segmented nuclei instances, we compute 3D fields of cell density (number of cells per unit volume), nuclear volume fraction (ratio of nuclear volume to local neighborhood volume), and nuclear volume at multiple scales.”

To improve clarity, the phrasing has been revised: the term local neighborhood volume has been replaced by local averaging volume, and a reference to the Methods section has been added.

From the segmented nuclei instances, we compute 3D fields of cell density (number of cells per unit volume), nuclear volume fraction (ratio of space occupied by nuclear volume within the local averaging volume, as defined in the Methods III I), and nuclear volume at multiple scales.

(2) In the definition of inertia tensor (18), isn't the inner part normally defined in the reversed way (delta_i,j - ...)?

We thank the reviewer for noticing this error, which we fixed in the manuscript.

(3) For intensity normalization, the paper uses the Hoechst signal density as a proxy for a ubiquitous nuclei signal. I would assume that this is problematic, for eg, dividing cells (which would overestimate it). Would using the average Hoechst signal per nucleus mask (as segmentation is available) be a better proxy?

We agree that this idea is appealing if one assumes a clear relationship between nuclear volume and Hoechst intensity. However, since cell and nuclear volumes vary substantially with differentiation state (see Fig. 4), such a normalization approach would introduce additional biases at large spatial scales. We believe that the most robust improvement would instead consist in masking dividing cells during the normalization procedure, as these events could be detected and excluded from the computation.

Nonetheless, we believe the method proposed by the reviewer could prove relevant for other types of data, so we will implement this recommendation in the code available in the Tapenade package.

(4) Figures 4-6 were part of the Supplementary Material, but should be included in the main text?

We thank the reviewer for this remark, this was corrected immediately to add Figures 4-6.

We also noticed a missing reference to Fig. S3 in the main text, so we added lines 302 to 307 to comment on the wavelength-dependency of the normalization method. We improved the description of Fig.6, which lacked clarity (line 316 to 321, line 327).

(1) Moos, F., Suppinger, S., de Medeiros, G., Oost, K.C., Boni, A., Rémy, C., Weevers, S.L., Tsiairis, C., Strnad, P. and Liberali, P., 2024. Open-top multisample dual-view light-sheet microscope for live imaging of large multicellular systems. Nature Methods, 21(5), pp.798-803.

(2) Ong, H. T.; Karatas, E.; Poquillon, T.; Grenci, G.; Furlan, A.; Dilasser, F.; Mohamad Raffi, S. B.; Blanc, D.; Drimaracci, E.; Mikec, D.; Galisot, G.; Johnson, B. A.; Liu, A. Z.; Thiel, C.; Ullrich, O.; OrgaRES Consortium; Racine, V.; Beghin, A. (2025). Digitalized organoids: integrated pipeline for high-speed 3D analysis of organoid structures using multilevel segmentation and cellular topology.  Nature Methods, 22(6), pp.1343-1354

(3) Li, L., Wu, L., Chen, A., Delp, E.J. and Umulis, D.M., 2023. 3D nuclei segmentation for multi-cellular quantification of zebrafish embryos using NISNet3D. Electronic Imaging, 35, pp.1-9.

(4) Vanaret, J., Dupuis, V., Lenne, P. F., Richard, F., Tlili, S., & Roudot, P. (2023). A detector-independent quality score for cell segmentation without ground truth in 3D live fluorescence microscopy. IEEE Journal of Selected Topics in Quantum Electronics, 29(4:Biophotonics), 1-12.

(5) Dey, N., Abulnaga, M., Billot, B., Turk, E. A., Grant, E., Dalca, A. V., & Golland, P. (2024). AnyStar: Domain randomized universal star-convex 3D instance segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 7593-7603).

(6) Mukashyaka, P., Kumar, P., Mellert, D. J., Nicholas, S., Noorbakhsh, J., Brugiolo, M., ... & Chuang, J. H. (2023). High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos. Nature Communications, 14(1), 8406.

(7) Rakhymzhan, A., Leben, R., Zimmermann, H., Günther, R., Mex, P., Reismann, D., ... & Niesner, R. A. (2017). Synergistic strategy for multicolor two-photon microscopy: application to the analysis of germinal center reactions in vivo. Scientific reports, 7(1), 7101.

(8) Dunsing, V., Petrich, A., & Chiantia, S. (2021). Multicolor fluorescence fluctuation spectroscopy in living cells via spectral detection. Elife, 10, e69687.

didn't get this part.

What is the pseudoenergy? It is some effective "energy" (actually in units of power) that is mapped to a "local" temperature. Derivation: for scattering operator (C[f]), energy conservation enforces $$\sum_{\omega,p} \hbar \omega C[f] = 0.$$

The relaxation time replaces the scattering operator with the deviation dependent scattering lifetime $$C[f] = -\frac{f-f^{loc}}{\tau(\omega, p)}.$$

To enforce energy conservation, we require $$\sum_{\omega,p} \hbar \omega \frac{f-f^{loc}}{\tau(\omega,p)} = 0$$.

This motivates the definition of the pseudoenergy \(\tilde{E}\) as the lifetime-weighted moment of the energy $$\tilde{E} \equiv\sum_{\omega,p} \hbar \omega \frac{f}{\tau(\omega,p)}.$$

Overall, we compute the pseudotemperature by calculating the power emitted from the volume by scattering. This is found in two ways, given in eq. 8. The top one says "we have N phonons with energy \(\hbar \omega\) that scatter on avg every \(\tau\) seconds" and the bottom says "the emission of a volume at temperature \(T_{loc}\)" and T is found via equality.

This breakdown of coding into its smallest pieces is helpful because it explains how algorithms make decisions. While this is a fraction of code compared to the algorithms that are used on social media, it makes you question what the code looks like and what the decisions in these algorithms are based on.

eLife Assessment

This important work compares the size of two brain areas, the amygdala and the hippocampus, across 12 species belonging to the Macaca genus. The authors find, using a convincing methodological approach, that amygdala - but not hippocampal - volume varies with social tolerance grade, with high tolerance species showing larger amygdala than low tolerance species of macaques. Interestingly, their findings also suggest an inverted developmental effect, with intolerant species showing an increase in amygdala volume across the lifespan, compared to tolerant species exhibiting the opposite trend. Overall, this paper offers new insights into the neural basis of social and emotional processing.

Reviewer #1 (Public review):

Summary:

This paper investigates the potential link between amygdala volume and social tolerance in multiple macaque species. Through a comparative lens, the authors considered tolerance grade, species, age, sex, and other factors that may contribute to differing brain volumes. They found that amygdala, but not hippocampal, volume differed across tolerance grades such that high-tolerance species showed larger amygdala than low-tolerance species of macaques. They also found that less tolerant species exhibited increases in amygdala volume with age, while more tolerant species showed the opposite. Given their wide range of species with varied biological and ecological factors, the authors' findings provide new, important evidence for changes in amygdala volume in relation to social tolerance grades. Contributions from these findings will greatly benefit future efforts in the field to characterize brain regions critical for social and emotional processing across species.

(1) This study demonstrates a concerted and impressive effort to comparatively examine neuroanatomical contributions to sociality in monkeys. The authors impressively collected samples from 12 macaque species with multiple datapoints across species age, sex, and ecological factors. Species from all four social tolerance grades were present. Further, the age range of the animals is noteworthy, particularly the inclusion of individuals over 20 years old.

(2) This work is the first to report neuroanatomical correlates of social tolerance grade in macaques in one coherent study. Given the prevalence of macaques as a model of social neuroscience, considerations of how socio-cognitive demands are impacted by the amygdala are highly important. The authors' findings will certainly inform future studies on this topic.

(3) The methodology and supplemental figures for acquiring brain MRI images are nicely detailed. Clear information on these parameters is crucial for future comparative interpretations of sociality and brain volume, and the authors do an excellent job of describing this process in full.

(4) The following comments were brought up during the review. In their revision, the authors have sufficiently addressed all of these comments by providing detailed responses and updating their manuscript. First, the revision clarified how much one could draw conclusions about "nature vs. nurture" from this study. Second, the revision also clarified the contributions of very young and very old animals in their correlations. Third, in their revision, the authors expanded on how their results could be interpreted in the context of multiple behavioral traits by Thierry (2021) by providing more detailed descriptions. Finally, during the revision, the authors clarified that both intolerant and tolerant species experience complex socio-cognitive demands and highlighted that socio-cognitive challenges arise across the tolerance spectrum under different behavioral demands.

Reviewer #2 (Public review):

Summary:

This comparative study of macaque species and type of social interaction is both ambitious and inevitably comes with a lot of caveats. The overall conclusion is that more intolerant species have a larger amygdala. There are also opposing development profiles regarding amygdala volume depending on whether it is a tolerant or intolerant species.

To achieve any sort of power they have combined data from 4 centres - that have all used different scanning methods and there are some resolution differences. The authors have also had to group species into 4 classifications - again to assist with any generalisations and power. They have focussed on the volumes of two structures, the amygdala and the hippocampus, which seems appropriate. Neither structure is homogeneous and so it may well be that a targeted focus on specific nuclei or subfields would help (the authors may well do this next) - but as the variables would only increase further along with the number of potential comparisons, alongside small group numbers, it seems only prudent to treat these findings are preliminary. That said, it is highly unlikely that large numbers of macaque brains will become available in the near future.

This introduction is by way of saying that the study achieves what it sets out to do, but there are many reasons to see this study as preliminary. The main message seems to be twofold: 1) that more intolerant species have relatively larger amygdalae, and 2) that with development there is an opposite pattern of volume change (increasing with age in intolerant sp and decreasing with age in tolerant species). Finding 1 is the opposite of that predicted in Table 1 - this is fine, but it should be made clearer in the Discussion that this is the case otherwise the reader may feel confused. As I read it, the authors have switched their prediction in the Discussion, which feels uncomfortable.

It is inevitable that the data in a study of this complexity are all too prone to post hoc considerations, to which the authors indulge. I suspect I would end up doing the same but it feels a bit like 'heads I win, tails you lose'. In the case of Grade 1 species, the individuals have a lot to learn especially if they are not top of the hierarchy, but at the same time there are fewer individuals in the troop, making predictions very tricky. As noted above, I am concerned by the seemingly opposite predictions in Table 1 and those in the Discussion regarding tolerance and amygdala volume. (It may be that the predictions in Table 1 are the opposite to how I read them, in which case the Table and preceding text needs to align.)

Comments on revisions:

I am happy with all of the revisions and the care shown by the authors.

Reviewer #3 (Public review):

Summary:

In this study, the authors were looking at neurocorrelates of behavioural differences within the genus Macaca. To do so, they engaged in real-world dissection of dead animals (unconnected to the present study) coming from a range of different institutions. They subsequently compare different brain areas, here the amygdala and the hippocampus, across species. Crucially, these species have been sorted according to different levels of social tolerance grades (from 1 to 4). 12 species are represented across 42 individuals. The sampling process has weaknesses ("only half" of the species contained by the genus, and Macaca mulatta, the rhesus macaque, representing 13 of the total number of individuals), but also strengths (the species are decently well represented across the 4 grades) for the given purpose and for the amount of work required here. I will not judge the dissection process as I am not a neuroanatomist, and I will assume that the different interventions do not alter volume in any significant ways / or that the different conditions in which the bodies were kept led to the documented differences across species.

There are two main results of the study. First, in line with their predictions, the authors find that more tolerant macaque species have larger amygdala, compared to the hippocampus that remains undifferentiated across species. Second, they also identify developmental effects, although with different trends: in tolerant species, the amygdala relative volume decreases across the lifespan, while in intolerant species, the contrary occurs. The modifications brought up between the two versions of the article have answered my remarks regarding age/grade/brain area differences.

As such, I think the results are holding strong, but maybe more work is needed with respect to interpretation.<br /> Classification of the social grade, as well as the issue of nature vs nurture have been addressed by the authors, I thank them for this.<br /> I still feel the integration of the amygdala as a common cognitive & emotional center could be possibly more pushed in the discussion, although I acknowledge that it would be complicated to do without knowing how the emotional and social lives of these animals impacted the growth of their amygdala...

Strengths:

Methods & breadth of species tested

Weaknesses:

Interpretations, which, although softened, could still be more integrated with the literature on emotion

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public review):

We thank Reviewer #1 for its thoughtful and constructive feedback. We found the suggestions particularly helpful in refining the conceptual framework and clarifying key aspects of our interpretations.

Summary:

This paper investigates the potential link between amygdala volume and social tolerance in multiple macaque species. Through a comparative lens, the authors considered tolerance grade, species, age, sex, and other factors that may contribute to differing brain volumes. They found that amygdala, but not hippocampal, volume differed across tolerance grades, such that hightolerance species showed larger amygdala than low-tolerance species of macaques. They also found that less tolerant species exhibited increases in amygdala volume with age, while more tolerant species showed the opposite. Given their wide range of species with varied biological and ecological factors, the authors' findings provide new evidence for changes in amygdala volume in relation to social tolerance grades. Contributions from these findings will greatly benefit future efforts in the field to characterize brain regions critical for social and emotional processing across species.

Strengths:

(1) This study demonstrates a concerted and impressive effort to comparatively examine neuroanatomical contributions to sociality in monkeys. The authors impressively collected samples from 12 macaque species with multiple datapoints across species age, sex, and ecological factors. Species from all four social tolerance grades were present. Further, the age range of the animals is noteworthy, particularly the inclusion of individuals over 20 years old - an age that is rare in the wild but more common in captive settings. 

(2) This work is the first to report neuroanatomical correlates of social tolerance grade in macaques in one coherent study. Given the prevalence of macaques as a model of social neuroscience, considerations of how socio-cognitive demands are impacted by the amygdala are highly important. The authors' findings will certainly inform future studies on this topic.

(3) The methodology and supplemental figures for acquiring brain MRI images are well detailed. Clear information on these parameters is crucial for future comparative interpretations of sociality and brain volume, and the authors do an excellent job of describing this process in full.

Weaknesses:

(1) The nature vs. nurture distinction is an important one, but it may be difficult to draw conclusions about "nature" in this case, given that only two data points (from grades 3 and 4) come from animals under one year of age (Method Figure 1D). Most brains were collected after substantial social exposure-typically post age 1 or 1.5-so the data may better reflect developmental changes due to early life experience rather than innate wiring. It might be helpful to frame the findings more clearly in terms of how early experiences shape development over time, rather than as a nature vs. nurture dichotomy.

We agree with the reviewer that presenting our findings through a strict nature vs. nurture dichotomy was potentially misleading. We have revised the introduction and the discussion (e.g. lines 85-95 and 363-365) to clarify that we examined how neurodevelopmental trajectories differ across social grades with the caveat of related to the absence of very young individuals in our samples.  We now explicitly mention that our results may reflect both early species-typical biases and experience-dependent maturation.

We positioned our study on social tolerance in a comparative neuroscience framework and introduced a tentative working model that articulates behavioral traits, cognitive dimensions, and their potential subcortical neural substrates

Drawing upon 18 behavioral traits identified in Thierry’s comparative analyses (Thierry, 2021, 2007), we organize these traits into three core dimensions: socio-cognitive demands, behavioral inhibition, and the predictability of the social environment (Table 1). This conceptualization does not aim to redefine social tolerance itself, but rather to provide a structured basis for testing neuroanatomical hypotheses related to social style variability. It echoes recent efforts to bridge behavioral ecology and cognitive neuroscience by linking specific mental abilities – such as executive functions or metacognition – with distinct prefrontal regions shaped by social and ecological pressures (Bouret et al., 2024).

“Cross-fostering experiments (De Waal and Johanowicz, 1993), along with our own results, suggest that social tolerance grades reflect both early, possibly innate predispositions and later environmental shaping”.

(2) It would be valuable to clarify how the older individuals, especially those 20+ years old, may have influenced the observed age-related correlations (e.g., positive in grades 1-2, negative in grades 3-4). Since primates show well-documented signs of aging, some discussion of the potential contribution of advanced age to the results could strengthen the interpretation.

We thank the reviewer for highlighting this important point. In our dataset, younger and older subjects are underrepresented, but they are distributed across all subgroups. Therefore, we do not think that it could drive the interaction effect we are reporting. In our sample, amygdala volume tended to increase with age in intolerant species and decrease in tolerant species. We included a new analysis (Figure 4) that allows providing a clearer assessment of when social grades 1 vs 4 differed in terms of amygdala and hippocampus volume. While our model accounts for age continuously, we agree that age-related variation deserves cautious interpretation and require longitudinal designs in future studies.

We also added the following statements in the discussion (lines 386-391)

“Due to a limited sample size of our study, this crossing trend, already accounted for by our continuous age model, should be further investigated. These results call for cautious interpretation of age-related variation and further emphasize the importance of longitudinal studies integrating both behavioral, cognitive and anatomical data in non-human primates, which would help to better understand the link between social environment and brain development (Song et al., 2021)”.

(3) The authors categorize the behavioral traits previously described in Thierry (2021) into 3 selfdefined cognitive requirements, however, they do not discuss under what conditions specific traits were assigned to categories or justify why these cognitive requirements were chosen. It is not fully clear from Thierry (2021) alone how each trait would align with the authors' categories. Given that these traits/categories are drawn on for their neuroanatomical hypotheses, it is important that the authors clarify this. It would be helpful to include a table with all behavioral traits with their respective categories, and explain their reasoning for selecting each cognitive requirement category.

Thank you for this important suggestion. We have extensively revised the introduction to explain how we derived from the scientific literature the three cognitive dimensions—socio-cognitive demands, behavioral inhibition, and predictability of the social environment—. We now provide a complete overview of the 18 behavioral traits described in Thierry’s framework and their cognitive classification in a dedicated table , along with hypothesized neural correlates. We have also mentioned traits that were not classified in our framework along with short justification of this classification. We believe this addition significantly improves the transparency and intelligibility of our conceptual approach.

“The concept of social tolerance, central to this comparative approach, has sometimes been used in a vague or unidimensional way. As Bernard Thierry (2021) pointed out, the notion was initially constructed around variations in agonistic relationships – dominance, aggressiveness, appeasement or reconciliation behaviors – before being expanded to include affiliative behaviors, allomaternal care or male–male interactions (Thierry, 2021). These traits do not necessarily align along a single hierarchical axis but rather reflect a multidimensional complexity of social style, in which each trait may have co-evolved with others (Thierry, 2021, 2000; Thierry et al., 2004). Moreover, the lack of a standardized scientific definition has sometimes led to labeling species as “tolerant” or “intolerant” without explicit criteria (Gumert and Ho, 2008; Patzelt et al., 2014). These behavioral differences are characterized by different styles of dominance (Balasubramaniam et al., 2012), severity of agonistic interactions (Duboscq et al., 2014), nepotism (Berman and Thierry, 2010; Duboscq et al., 2013; Sueur et al., 2011) and submission signals (De Waal and Luttrell, 1985; Rincon et al., 2023), among the 18 covariant behavioral traits described in Thierry's classification of social tolerance (Thierry, 2021, 2017, 2000)”.

“To ground the investigation of social tolerance in a comparative neuroanatomical framework, we introduce a tentative working model that articulates behavioral traits, cognitive dimensions, and their potential subcortical neural substrates. Drawing upon 18 behavioral traits identified in Thierry’s comparative analyses (Thierry, 2021, 2007), we organized these traits into three core dimensions: socio-cognitive demands, behavioral inhibition, and the predictability of the social environment (Table 1). This conceptualization does not aim to redefine social tolerance itself, but rather to provide a structured basis for testing neuroanatomical hypotheses related to social style variability. It echoes recent efforts to bridge behavioral ecology and cognitive neuroscience by linking specific mental abilities – such as executive functions or metacognition – with distinct prefrontal regions shaped by social and ecological pressures (Bouret et al., 2024; Testard 2022)”.

(4) One of the main distinctions the authors make between high social tolerance species and low tolerance species is the level of complex socio-cognitive demands, with more tolerant species experiencing the highest demands. However, socio-cognitive demands can also be very complex for less tolerant species because they need to strategically balance behaviors in the presence of others. The relationships between socio-cognitive demands and social tolerance grades should be viewed in a more nuanced and context-specific manner. 

We fully agree and we did not mean that intolerant species lives in a ‘simple’ social environment but that the ones of more tolerant species is markedly more demanding. Evidence supporting this statement include their more efficient social networks (Sueur et al., 2011) and more complex communicative skills (e.g. tolerant macaques displayed higher levels of vocal diversity and flexibility than intolerant macaques in social situation with high uncertainty (Rebout et al., 2020).

In the revised version (lines 106-122), we now highlight that socio-cognitive challenges arise across the tolerance spectrum, including in less tolerant species where strategic navigation of rigid hierarchies and risk-prone interactions is required. We hope that this addition offers a more balanced and nuanced framing of socio-cognitive demands across macaque societies

“The first category, socio-cognitive demands, refers to the cognitive resources needed to process, monitor, and flexibly adapt to complex social environments. Linking those parameters to neurological data is at the core of the social brain theory to explain the expansion of the neocortex in primates (Dunbar). Macaques social systems require advanced abilities in social memory, perspective-taking, and partner evaluation (Freeberg et al., 2012). This is particularly true in tolerant species, where the increased frequency and diversity of interactions may amplify the demands on cognitive tracking and flexibility. Tolerant macaque species typically live in larger groups with high interaction frequencies, low nepotism, and a wider range of affiliative and cooperative behaviors, including reconciliation, coalition-building, and signal flexibility (REF). Tolerant macaque species also exhibit a more diverse and flexible vocal and facial repertoire than intolerants ones which may help reduce ambiguity and facilitate coordination in dense social networks (Rincon et al., 2023; Scopa and Palagi, 2016; Rebout 2020). Experimental studies further show that macaques can use facial expressions to anticipate the likely outcomes of social interactions, suggesting a predictive function of facial signals in managing uncertainty (Micheletta et al., 2012; Waller et al., 2016). Even within less tolerant species, like M. mulatta, individual variation in facial expressivity has been linked to increased centrality in social networks and greater group cohesion, pointing to the adaptive value of expressive signaling across social styles (Whitehouse et al., 2024)”.

(5) While the limitations section touches on species-related considerations, the issue of individual variability within species remains important. Given that amygdala volume can be influenced by factors such as social rank and broader life experience, it might be useful to further emphasize that these factors could introduce meaningful variation across individuals. This doesn't detract from the current findings but highlights the importance of considering life history and context when interpreting subcortical volumes-particularly in future studies.

We have now emphasized this point in the limitations section (lines 441-456). While our current dataset does not allow us to fully control for individual-level variables across all collection centers, we recognize that factors such as rank, social exposure, and individual life history may influence subcortical volumes

“Although we explained some interspecies variability, adding subjects to our database will increase statistical power and will help addressing potential confounding factors such as age or sex in future studies. One will benefit from additional information about each subject. While considered in our modelling, the social living and husbandry conditions of the individuals in our dataset remain poorly documented. The living environment has been considered, and the size of social groups for certain individuals, particularly for individuals from the CdP, have been recorded. However, these social characteristics have not been determined for all individuals in the dataset. As previously stated, the social environment has a significant impact on the volumetry of certain regions. Furthermore, there is a lack of data regarding the hierarchy of the subjects under study and the stress they experience in accordance with their hierarchical rank and predictability of social outcomes position (McCowan et al., 2022)”. 

Reviewer #2 (Public review):

We thank Reviewer #2 for its thoughtful remarks and for acknowledging the value of our comparative approach despite its inherent constraints.

Summary:

This comparative study of macaque species and the type of social interaction is both ambitious and inevitably comes with a lot of caveats. The overall conclusion is that more intolerant species have a larger amygdala. There are also opposing development profiles regarding amygdala volume depending on whether it is a tolerant or intolerant species.

To achieve any sort of power, they have combined data from 4 centres, which have all used different scanning methods, and there are some resolution differences. The authors have also had to group species into 4 classifications - again to assist with any generalisations and power. They have focused on the volumes of two structures, the amygdala and the hippocampus, which seems appropriate. Neither structure is homogeneous and so it may well be that a targeted focus on specific nuclei or subfields would help (the authors may well do this next) - but as the variables would only increase further along with the number of potential comparisons, alongside small group numbers, it seems only prudent to treat these findings are preliminary. That said, it is highly unlikely that large numbers of macaque brains will become available in the near future.

This introduction is by way of saying that the study achieves what it sets out to do, but there are many reasons to see this study as preliminary. The main message seems to be twofold: (1) that more intolerant species have relatively larger amygdalae, and (2) that with development, there is an opposite pattern of volume change (increasing with age in intolerant species and decreasing with age in tolerant species). Finding 1 is the opposite of that predicted in Table 1 - this is fine, but it should be made clearer in the Discussion that this is the case, otherwise the reader may feel confused. As I read it, the authors have switched their prediction in the Discussion, which feels uncomfortable. 

We thank the reviewer for this important observation. In the original version, Table 1 presented simplified direct predictions linking social tolerance grades to amygdala and hippocampus volumes. We recognize that this formulation may have created confusion In the revised manuscript, we have thoroughly restructured the table and its accompanying rationale. Table 1 now better reflects our conceptual framework grounded in three cognitive dimensions—sociocognitive demands, behavioral inhibition, and social predictability—each linked to behavioral traits and associated neural hypotheses based on published literature. This updated framework, detailed in lines 144-169 of the introduction, provides a more nuanced basis for interpreting our results and avoids the inconsistencies previously noted. The Discussion was also revised accordingly (lines 329-255) to clarify where our findings diverge from the original predictions and to explore alternative explanations based on social complexity. Rather than directly predicting amygdala size from social tolerance grades, we propose that variation in volume emerges from differing combinations of cognitive pressures across species.

It is inevitable that the data in a study of this complexity are all too prone to post hoc considerations, to which the authors indulge. In the case of Grade 1 species, the individuals have a lot to learn, especially if they are not top of the hierarchy, but at the same time, there are fewer individuals in the troop, making predictions very tricky. As noted above, I am concerned by the seemingly opposite predictions in Table 1 and those in the Discussion regarding tolerance and amygdala volume. (It may be that the predictions in Table 1 are the opposite of how I read them, in which case the Table and preceding text need to align.)

In order to facilitate the interpretation of our Bayesian modelling, we have selected a more focused ROI in our automatic segmentation procedure of the Hippocampus (from Hippocampal Formation to Hippocampus) and have added to the new analysis (Figure 4) that helps to properly test whether the hippocampus significantly differs between species from social grade 1 vs 4. The present analysis found that this is the case in adult monkeys. This is therefore consistent with our hypothesis that amygdala volumes are principally explained by heightened sociocognitive demands in more tolerant species.

We also acknowledge the reviewer’s concerns about the limited generalizability due to our sample. The challenges of comparative neuroimaging in non-human primates—especially when using post-mortem datasets—are substantial. Given the ethical constraints and the rarity of available specimens, increasing the number of individuals or species is not feasible in the short term. However, we have made all data and code publicly available and clearly stated the limitations of our sample in the manuscript. Despite these constraints, we believe our dataset offers an unprecedented comparative perspective, particularly due to the inclusion of rare and tolerant species such as M. tonkeana, M. nigra, and M. thibetana, which have never been included in structural MRI studies before. We hope this effort will serve as a foundation for future collaborative initiatives in primate comparative neuroscience.

Reviewer #3 (Public review):

We thank Reviewer #3 for their thoughtful and detailed review. Their comments helped us refine both the conceptual and interpretative aspects of the manuscript. We respond point by point below.

Summary:

In this study, the authors were looking at neurocorrelates of behavioural differences within the genus Macaca. To do so, they engaged in real-world dissection of dead animals (unconnected to the present study) coming from a range of different institutions. They subsequently compare different brain areas, here the amygdala and the hippocampus, across species. Crucially, these species have been sorted according to different levels of social tolerance grades (from 1 to 4). 12 species are represented across 42 individuals. The sampling process has weaknesses ("only half" of the species contained by the genus, and Macaca mulatta, the rhesus macaque, representing 13 of the total number of individuals), but also strengths (the species are decently well represented across the 4 grades) for the given purpose and for the amount of work required here. I will not judge the dissection process as I am not a neuroanatomist, and I will assume that the different interventions do not alter volume in any significant ways / or that the different conditions in which the bodies were kept led to the documented differences across species. 

25 brains were extracted by the authors themselves who are highly with this procedure. Overall, we believe that dissection protocols did not alter the total brain volume. Despite our expertise, we experienced some difficulties to not damage the cerebellum. Therefore, this region was not included in our analysis. We also noted that this brain region was also damaged or absent from the Prime-DE dataset.

Several protocols were used to prepare and store tissue. It could have impacted the total brain volume.

We agree that differences in tissue preparation and storage could potentially affect total brain volume. Therefore, we explicitly included the main sample preparation variable — whether brains had been previously frozen — as a covariate in our model. This factor did not explain our results. Moreover, Figures 1D and 1I display the frozen status and its correlation with the amygdala and hippocampus ratios, respectively. Figure 2 shows the parameters of the model and the posterior distributions for the frozen status and total brain volume effects.

There are two main results of the study. First, in line with their predictions, the authors find that more tolerant macaque species have larger amygdala, compared to the hippocampus, which remains undifferentiated across species. Second, they also identify developmental effects, although with different trends: in tolerant species, the amygdala relative volume decreases across the lifespan, while in intolerant species, the contrary occurs. The results look quite strong, although the authors could bring up some more clarity in their replies regarding the data they are working with. From one figure to the other, we switch from model-calculated ratio to modelpredicted volume. Note that if one was to sample a brain at age 20 in all the grades according to the model-predicted volumes, it would not seem that the difference for amygdala would differ much across grades, mostly driven with Grade 1 being smaller (in line with the main result), but then with Grade 2 bigger than Grade 3, and then Grade 4 bigger once again, but not that different from Grade 2.

Overall, despite this, I think the results are pretty strong, the correlations are not to be contested, but I also wonder about their real meaning and implications. This can be seen under 3 possible aspects:

(1)  Classification of the social grade

While it may be familiar to readers of Thierry and collaborators, or to researchers of the macaque world, there is no list included of the 18 behavioral traits used to define the three main cognitive requirements (socio-cognitive demands, predictability of the environment, inhibitory control). It would be important to know which of the different traits correspond to what, whether they overlap, and crucially, how they are realized in the 12 study species, as there could be drastic differences from one species to the next. For now, we can only see from Table S1 where the species align to, but it would be a good addition to have them individually matched to, if not the 18 behavioral traits, at least the 3 different broad categories of cognitive requirements.

We fully agree with this observation. In the revised version of the manuscript, we now include a detailed conceptual table listing all 18 behavioral traits from Thierry’s framework. For each trait, we provide its underlying social implications, its associated cognitive dimension (when applicable), and the hypothesized neural correlate. 

While some traits may could have been arguably classified in several cognitive dimensions (e.g. reconciliation rate), we preferred to assign each to a unique dimension for clarity. Additionally, the introduction (lines 95-169 + Table1) now explains how each trait was evaluated based on existing literature and assigned to one of the three proposed cognitive categories: socio-cognitive demands, behavioral inhibition, or social unpredictability. This structure offers a clearer and more transparent basis for the neuroanatomical hypotheses tested in the study.

“Navigating social life in primate societies requires substantial cognitive resources: individuals must not only track multiple relationships, but also regulate their own behavior, anticipate others’ reactions, and adapt flexibly to changing social contexts. Taken advantage of databases of magnetic resonance imaging (MRI) structural scans, we conducted the first comparative study integrating neuroanatomical data and social behavioral data from closely related primate species of the same genus to address the following questions: To what extent can differences in volumes of subcortical brain structures be correlated with varying degrees of social tolerance? Additionally, we explored whether these dispositions reflect primarily innate features, shaped by evolutionary processes, or acquired through socialization within more or less tolerant social environments”.

“The first category, socio-cognitive demands, refers to the cognitive resources needed to process, monitor, and flexibly adapt to complex social environments. Linking those parameters to neurological data is at the core of the social brain theory to explain the expansion of the neocortex in primates (Dunbar). Macaques social systems require advanced abilities in social memory, perspective-taking, and partner evaluation (Freeberg et al., 2012). This is particularly true in tolerant species, where the increased frequency and diversity of interactions may amplify the demands on cognitive tracking and flexibility. Tolerant macaque species typically live in larger groups with high interaction frequencies, low nepotism, and a wider range of affiliative and cooperative behaviors, including reconciliation, coalition-building, and signal flexibility (REF). Tolerant macaque species also exhibit a more diverse and flexible vocal and facial repertoire than intolerants ones which may help reduce ambiguity and facilitate coordination in dense social networks (Rincon et al., 2023; Scopa and Palagi, 2016; Rebout 2020). Experimental studies further show that macaques can use facial expressions to anticipate the likely outcomes of social interactions, suggesting a predictive function of facial signals in managing uncertainty (Micheletta et al., 2012; Waller et al., 2016). Even within less tolerant species, like M. mulatta, individual variation in facial expressivity has been linked to increased centrality in social networks and greater group cohesion, pointing to the adaptive value of expressive signaling across social styles (Whitehouse et al., 2024)”.

“The second category, inhibitory control, includes traits that involve regulating impulsivity, aggression, or inappropriate responses during social interactions. Tolerant macaques have been shown to perform better in tasks requiring behavioral inhibition and also express lower aggression and emotional reactivity in both experimental and natural contexts (Joly et al., 2017; Loyant et al., 2023). These features point to stronger self-regulation capacities in species with egalitarian or less rigid hierarchies. More broadly, inhibition – especially in its strategic form (self-control) – has been proposed to play a key role in the cohesion of stable social groups. Comparative analyses across mammals suggest that this capacity has evolved primarily in anthropoid primates, where social bonds require individuals to suppress immediate impulses in favour of longer-term group stability (Dunbar and Shultz, 2025). This view echoes the conjecture of Passingham and Wise (2012), who proposed that the emergence of prefrontal area BA10 in anthropoids enabled the kind of behavioural flexibility needed to navigate complex social environments (Passingham et al., 2012)”.

“The third category, social environment predictability, reflects how structured and foreseeable social interactions are within a given society. In tolerant species, social interactions are more fluid and less kin-biased, leading to greater contextual variation and role flexibility, which likely imply a sustained level of social awareness. In fact, as suggested by recent research, such social uncertainty and prolonged incentives are reflected by stress-related physiology : tolerant macaques such as M. tonkeana display higher basal cortisol levels, which may be indicative of a chronic mobilization of attentional and regulatory resources to navigate less predictable social environments (Sadoughi et al., 2021)”.

“Each behavioral trait was individually evaluated based on existing empirical literature regarding the types of cognitive operations it likely involves. When a primary cognitive dimension could be identified, the trait was assigned accordingly. However, some behaviors – such as maternal protection, allomaternal care, or delayed male dispersal – do not map neatly onto a single cognitive process. These traits likely emerge from complex configurations of affective and socialmotivational systems, and may be better understood through frameworks such as attachment theory (Suomi, 2008), which emphasizes the integration of social bonding, emotional regulation, and contextual plasticity. While these dimensions fall beyond the scope of the present framework, they offer promising directions for future research, particularly in relation to the hypothalamic and limbic substrates of social and reproductive behavior”.

“Rather than forcing these traits into potentially misleading categories, we chose to leave them unclassified within our current cognitive framework. This decision reflects both a commitment to conceptual clarity and the recognition that some behaviors emerge from a convergence of cognitive demands that cannot be neatly isolated. This tripartite framework, leaving aside reproductive-related traits, provides a structured lens through which to link behavioral diversity to specific cognitive processes and generate neuroanatomical predictions”.

(2) Issue of nature vs nurture

Another way to look at the debate between nature vs nurture is to look at phylogeny. For now, there is no phylogenetic tree that shows where the different grades are realized. For example, it would be illuminating to know whether more related species, independently of grades, have similar amygdala or hippocampus sizes. Then the question will go to the details, and whether the grades are realized in particular phylogenetic subdivisions. This would go in line with the general point of the authors that there could be general species differences.

As pointed out by Thierry and collaborators, the social tolerance concept is already grounded in a phylogenetic framework as social tolerance matches the phylogenetical tree of these macaque species, suggesting a biological ground of these behavioral observations. Given the modest sample size and uneven species representation, we opted not to adopt tools such as Phylogenetic Generalized Least Squares (PGLS) in our analysis. Our primary aim in this study was to explore neuroanatomical variation as a function of social traits, not to perform a phylogenetic comparative analysis per see. That said, we now explicitly acknowledge this limitation in the Discussion and indicate that future work using larger datasets and phylogenetic methods will be essential to disentangle social effects from evolutionary relatedness. We hope that making our dataset openly available will facilitate such futures analyses.

With respect to nurture, it is likely more complicated: one needs to take into account the idiosyncrasies of the life of the individual. For example, some of the cited literature in humans or macaques suggests that the bigger the social network, the bigger the brain structure considered. Right, but this finding is at the individual level with a documented life history. Do we have any of this information for any of the individuals considered (this is likely out of the scope of this paper to look at this, especially for individuals that did not originate from CdP)?

We appreciate this insightful observation. Indeed, findings from studies in humans and nonhuman primates showing associations between brain structure and social network size typically rely on detailed life history and behavioral data at the individual level. Unfortunately, such finegrained information was not consistently available across our entire sample. While some individuals from the Centre de Primatologie (CdP) were housed in known group compositions and social settings, we did not have access to longitudinal social data—such as rank, grooming rates, or network centrality—that would allow for robust individual-level analyses. We now acknowledge this limitation more clearly in the Discussion (lines 436-443), and we fully agree that future work combining neuroimaging with systematic behavioral monitoring will be necessary to explore how species-level effects interact with individual social experience.

(3) Issue of the discussion of the amygdala's function

The entire discussion/goal of the paper, states that the amygdala is connected to social life. Yet, before being a "social center", the amygdala has been connected to the emotional life of humans and non-humans alike. The authors state L333/34 that "These findings challenge conventional expectations of the amygdala's primary involvement in emotional processes and highlight the complexity of the amygdala's role in social cognition". First, there is no dichotomy between social cognition and emotion. Emotion is part of social cognition (unless we and macaques are robots). Second, there is nowhere in the paper a demonstration that the differences highlighted here are connected to social cognition differences per se. For example, the authors have not tested, say, if grade 4 species are more afraid of snakes than grade 1 species. If so, one could predict they would also have a bigger amygdala, and they would probably also find it in the model. My point is not that the authors should try to correlate any kind of potential aspect that has been connected to the amygdala in the literature with their data (see for example the nice review by DomínguezBorràs and Vuilleumier, https://doi.org/10.1016/B978-0-12-823493-8.00015-8), but they should refrain from saying they have challenged a particular aspect if they have not even tested it. I would rather engage the authors to try and discuss the amygdala as a multipurpose center, that includes social cognition and emotion.

We thank the reviewer for this important and nuanced point. We have revised the manuscript to adopt a more cautious and integrative tone regarding the function of the amygdala. In the revised Discussion (lines 341-355), we now explicitly state that the amygdala is involved in a broad range of processes—emotional, social, and affective—and that these domains are deeply intertwined. Rather than proposing a strict dissociation, we now suggest that the amygdala supports integrated socio-emotional functions that are mobilized differently across social tolerance styles. We also cite recent relevant literature (e.g., Domínguez-Borràs & Vuilleumier, 2021) to support this view and have removed any claim suggesting we challenge the emotional function of the amygdala per se. Our aim is to contribute to a richer understanding of how affective and social processes co-construct structural variation in this region.

Strengths:

Methods & breadth of species tested.

Weaknesses:

Interpretation, which can be described as 'oriented' and should rather offer additional views.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Private Comments:

(1) Table 1 should be formatted for clarity i.e., bolded table headers, text realignment, and spacing. It was not clear at first glance how information was organized. It may also be helpful to place behavioral traits as the first column, seeing that these traits feed into the author's defined cognitive requirements.

We have reformatted Table 1 to improve clarity and readability. Behavioral traits now appear in the first column, followed by cognitive dimensions and hypothesized neural correlates. Column headers have been bolded and alignment has been standardized.

(2) Figures could include more detail to help with interpretations. For example, Figure 3 should define values included on the x-axis in the figure caption, and Figure 4 should explain the use of line, light color, and dark color. Figure 1 does not have a y-axis title.

The figures have been revised and legends completed to ensure more clarity.

(3) Please proofread for typos throughout.

The manuscript has been carefully proofread, and all typographical and grammatical errors have been corrected. These changes are visible in the tracked version.

Reviewer #2 (Recommendations for the authors):

Specific comments:

(1) Given all of the variability would it not be a good idea to just compare (eg in the supplemental) the macaque data from just the Strasbourg centre for m mulatta and m toneanna. I appreciate the ns will be lower, but other matters are more standardized.

We fully understand the reviewer’s suggestion to restrict the comparison to data collected at a single site in order to minimize inter-site variability. However, as noted, such an analysis would come at the cost of statistical power, as the number of individuals per species within a single center is small. For example, while M. tonkeana is well represented at the Strasbourg centre, only one individual of M. mulatta is available from the same site. Thus, a restricted comparison would severely limit the interpretability of results, particularly for age-related trajectories. To address variability, we included acquisition site and brain preservation method as covariates or predictors where appropriate, and we have been cautious in our interpretations. We also now emphasize in the Methods and Discussion the value of future datasets with more standardized acquisition protocols across species and centers. We hope that by openly sharing our data and workflow, we can contribute to this broader goal.

(2) I have various minor edits:

(a) L 25 abstract - Specify what is meant by 'opposite trend'; the reader cannot infer what this is.

Modified in line 25-28: “Unexpectedly, tolerant species exhibited a decrease in relative amygdala volume across the lifespan, contrasting with the age-related increase observed in intolerant species—a developmental pattern previously undescribed in primates.”

(b) L67 - The reference 'Manyprimates' needs fixing as it does in the references section.

After double checking, Manyprimates studies are international collaborative efforts that are supposed to be cite this way (https://manyprimates.github.io/#pubs).

(c) L74 - Taking not Taken.

This typo has been corrected.

(d) L129 - It says 'total volume', but this is corrected total volume?

We have clarified in the figures legends that the “total brain volume” used in our analyses excludes the cerebellum and the myelencephalon, as specified in our image preprocessing protocol. This ensures consistency across individuals and institutions.

(e) L138 - Suddenly mentions 'frozen condition' without any prior explanation - this needs explaining in the legend - also L144.

We have added an explanation of the ‘frozen condition’ variable in in the relevant figure legend.

(f) L166 - Results - it would be helpful to remind readers what Grade 1 signifies, ie intolerant species.

We now include a brief reminder in the Results section that Grade 1 corresponds to socially intolerant species, to help readers unfamiliar with the classification (Lines 240-251).

(g)Figure 4 - Provide the ns for each of the 4 grades to help appreciate the meaningfulness of the curves, etc.

The number of subjects has been added to the Figure and a novel analysis helps in the revised ms help to appreciate the meaningfulness of some of these curves.

(h) L235 - 'we had assumed that species of high social tolerance grade would have presented a smaller amygdala in size compared to grade 1'. But surely this is the exact opposite of what is predicted in Table 1 - ie, the authors did not predict this as I read the paper (Unless Table l is misleading/ambiguous and needs clarification).

As discussed in our response to Reviewer #2 and #3, we have restructured both Table 1 and the Discussion to ensure consistency. We now explicitly state that the findings diverge from our initial inhibitory-control-based prediction and propose alternative interpretations based on sociocognitive demands.

(i) L270 - 'This observation' which?? Specify.

We have replaced ‘this observation’ with a precise reference to the observed developmental decrease in amygdala volume in tolerant species.

(j) L327 - 'groundbreaking' is just hype given that there are so many caveats - I personally do not like the word - novel is good enough.

We have replaced the word ‘groundbreaking’ with ‘novel’ to adopt a more measured and appropriate tone in the discussion.

(3) I might add that I am happy with the ethics regarding this study. 

Thanks, we are also happy that we were able to study macaque brains from different species using opportunistic samplings along with already available data. We are collectively making progress on this!

(4) Finally, I should commend the authors on all the additional information that they provide re gender/age/species. Given that there are 2xs are many females as males, it would be good to know if this affects the findings. I am not a primatologist, so I don't know, for example, if the females in Grade 1 monkeys are just as intolerant as the males?

We thank the reviewer for this thoughtful comment. We now explicitly mention the female-biased sex ratio in the Methods section and report in the Results (Figure 2, Figure 3) that sex was included as a covariate in our Bayesian models. While a small effect of sex was found for hippocampal volume, no effect was observed for the amygdala. Given the strong imbalance in our dataset (2:1 female-to-male ratio), we refrained from drawing any conclusion about sex-specific patterns, as these would require larger and more balanced samples. Although we did not test for sex-by-grade interactions, we agree that this question—especially regarding whether females and males express social style differences similarly across grades—represents an important direction for future comparative work.

Reviewer #3 (Recommendations for the authors):

I found the article well-written, and very easy to follow, so I have little ways to propose improvements to the article to the authors, besides addressing the various major points when it comes to interpretation of the data.

One list I found myself wanting was in fact the list of the social tolerance grades, and the process by which they got selected into 3 main bags of socio-cognitive skills. Then it would become interesting to see how each of the 12 species compares within both the 18 grades (maybe once again out of the scope of this paper, there are likely reviews out there that already do that, but then the authors should explicitly mention so in the paper: X, 19XX have compared 15 out of 18 traits in YY number of macaque species); and within the 3 major subcognitive requirements delineated by the authors, maybe as an annex?

We thank the reviewer for this thoughtful suggestion. In the revised manuscript, we now include a detailed table (Table 1) that lists the 18 behavioral traits derived from Thierry’s framework, along with their associated cognitive dimension and hypothesized neuroanatomical correlate. While we did not create a matrix mapping each of the 12 species across all 18 traits due to space and data availability constraints, we agree this is an important direction that should be tackled by primatologist. We now include a sentence (line 87-90) in the manuscript to guide readers to previous comparative reviews (e.g., Thierry, 2000; Thierry et al., 2004, 2021) that document the expression of these traits across macaque species. We also clarify that our three cognitive categories are conceptual tools intended to structure neuroanatomical predictions, and not formal clusters derived from quantitative analyses.

In the annex, it would also be good to have a general summarizing excel/R file for the raw data, with important information like age, sex, and the relevant calculated volumes for each individual. The folders available following the links do not make it an easy task for a reader to find the raw data in one place.

We fully agree with the reviewer on the importance of data accessibility. We have now uploaded an additional supplementary file in .csv format on our OSF repository, which includes individuallevel metadata for all 42 macaques: species, sex, age, social grade, total brain volume, amygdala volume, and hippocampus volume. The link to this file is now explicitly mentioned in the Data Availability section. We hope this will facilitate comparisons with other datasets and improve usability for the community. In addition, we provide in a supplementary table the raw data that were used for our Bayesian modelling (see below).

The availability of the raw data would also clear up one issue, which I believe results from the modelling process: it looks odd on Figure 2, that volume ratios, defined as the given brain area volume divided by the total brain volume, give values above 1 (especially for the hippocampus). As such, the authors should either modify the legend or the figure. In general, it would be nicer to have the "real values" somewhere easily accessible, so that they can be compared more broadly with: 1) other macaques species to address questions relevant to the species; 2) other primates to address other questions that are surely going to arise from this very interesting work!

We thank the reviewer for pointing this out. The ratio values in Figure 1 correspond to the proportion of the regional volume (amygdala or hippocampus) relative to the total brain volume, excluding the cerebellum and myelencephalon. As such, values above 0.01 (i.e., above 1% of the brain volume) are expected for these structures and do not indicate an error. We have updated the figure legend to clarify this point explicitly. In addition, we have now made a cleaned .csv file available via OSF, containing all raw volumetric data and metadata in a format that facilitates cross-species or cross-study comparisons. This replaces the previous folder-based structure, which may have been less accessible.

Typos:

L233: delete 'in'

L430: insert space in 'NMT template(Jung et al., 2021).'

The idea that responsibility becomes separated between programmers, operators, and the bot’s actions is really compelling. If something harmful happens, we can’t easily point to one party. It feels similar to algorithmic decision-making today where there is no single accountable human.

The section on antagonistic bots was especially interesting to me. It’s concerning how bots can create the illusion of mass support or backlash, even when most real users don’t feel that strongly. This makes me think that bots don’t just add noise, but can actually change how people interpret public opinion.

I think the ethics frameworks section could also include justice as fairness. This framework focuses on whether rules are fair to everyone, especially to people who have less power or visibility. In the context of social media, it helps explain why platform rules should protect vulnerable users, not just benefit the majority.

Although Eq. (14) evaluates \(\eta\) using the on-resonance condition \(\omega=\sqrt{\omega_1 \omega_2}\), the fitted amplitude coefficients \((S, A)\) are extracted from Lorentzian fits that include offresonant field points. This is consistent only if those fit coefficients represent the resonant susceptibility prefactors (i.e., are effectively defined at \(B_0\) and independent of detuning), rather than being detuning-dependent quantities.

defined as: "one of the four bodily humors, identified with bile and believed to be associated with a peevish or irascible temperament."

degradation of women, trait of toxic masculinity

The authors suggest using other research methods along with maps to better understand why some communities have limited access to food.

75 years later I was born into this world.I was born on July 8th 2006, I've met a couple of people who have the same birthday as me. It's not as common as I thought. Of course this date means a lot to me, this date also marks the 2,075th Birthday of the legendary City known as france.

Despite the fact that he's not really a respectful individual with a superiority complex; it's still slightly disheartening for them to play such a prank. When you can see how much he adores her by how well he knows even her handwriting despite not being involved.

CE Stoicism acknowledges the idea of individuals composing a societies functions is like cells composing an organism's biological functions. However, CE Stoicism places greater focus on the idea that an individual's mind, a group's culture and society itself are each composed of one or more P-individuals.

The metasystem transition is described in more detail here.

For more about control of associations see here.

CE Stoicism de-emphasizes cybernetic immortality and instead emphasizes the idea of immortality in the form of a communally-created Paskian P-individual (sustained by many M-individuals).

We do not foresee the integration off all humanity, even into a single cultural P-individual. Rather, there are and will be many, and many individuals are and will not affiliate with any. We do not use the "plankton" metaphor.

CEStoicism believes that certain cultural P-individuals with high fitness will be better competitors for survival, although expanding off Earth is not a priority.

aka, a cultural P-individual.

No need to say this, hide the weakness :)