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NO SABO

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• celui de Villeurbanne

eLife Assessment

This important study provides insights into the role of the cerebellum in fear conditioning, addressing a key gap in the literature. The evidence presented in support of the conclusions is solid. This work will be of interest to both the extinction learning and cerebellar research communities.

Reviewer #1 (Public review):

Nio and colleagues address an important question about how the cerebellum and ventral tegmental area (VTA) contribute to extinction learning of conditioned fear associations. This work tackles a critical gap in the existing literature and provides new insights into this question in humans through the use of high-field neuroimaging with robust methodology. The presented results are novel and will broadly interest both the extinction learning and cerebellar research communities. As such, this is a very timely and important contribution.

Strengths:

The core finding - coupling of cerebellum and VTA as a reward-like prediction errors during fear extinction - is novel and addresses a genuine gap in the literature. Also the paradigm spanning several sessions, a well-powered sample, 7T imaging and complementary analytical approaches to target the question is commendable.

Weaknesses:

The authors have satisfactorily addressed the concerns raised in the previous version of the manuscript. Several results, as well as conclusions drawn from them, still rest on trend-level evidence, although the revised presentation of the results now provides a more balanced interpretation of these findings.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Nio and colleagues address an important question about how the cerebellum and ventral tegmental area (VTA) contribute to the extinction learning of conditioned fear associations. This work tackles a critical gap in the existing literature and provides new insights into this question in humans through the use of high-field neuroimaging with robust methodology. The presented results are novel and will broadly interest both the extinction learning and cerebellar research communities. As such, this is a very timely and impactful manuscript. However, there are several points that could be addressed during the review process to strengthen the claims and enhance their value for readers and the broader scientific community.

(1) Reward Interpretation and Skin Conductance Responses (SCR)

A central premise of the manuscript is that 'unexpected omissions of expected aversive events' are rewarding, which plays a critical role in extinction learning. The authors also suggest that the cerebellum is involved in reward processing. However, it is unclear how this conclusion can be directly drawn from their task, which does not explicitly model 'reward.' Instead, the interpretation relies on SCR, which seems more indicative of association or prediction rather than reward per se. Is SCR a valid metric of reward experienced during the extinction of feared associations? Or could these findings reflect processes tied more closely to predictive learning? Please, discuss.

We thank the reviewer for raising this important point. We agree that skin conductance responses (SCRs) do not directly index reward. More generally, SCRs reflect autonomic arousal in response to salient or motivationally significant stimuli and are closely linked to expectancy and contingency awareness. In our study, SCRs served as a read-out of the participants’ expectation of a US, and were used to fit the hyperparameters of a reinforcement-learning-based deep learning model, which then provided per-trial estimates of prediction and prediction error values. These estimates capture predictive learning about the occurrence of the aversive US, rather than reward per se. The interpretation of unexpected US omissions as “reward-like” prediction errors relies on prior literature, particularly rodent studies showing that dopaminergic neurons in the VTA respond to omitted aversive stimuli and drive extinction learning via projections to the nucleus accumbens (Kalisch et al., 2019; Salinas-Hernández et al., 2018, 2023). We therefore interpret our cerebellar activations during unexpected omissions as being compatible with the processing of reward-like prediction errors, while acknowledging that this inference is indirect.

To clarify this reasoning, we made revisions to the Introduction and Discussion to (i) state explicitly that SCRs do not directly measure reward but were incorporated into the reinforcement learning model as an index of autonomic arousal related to US expectancy and predictive learning, and (ii) consistently replace the term “reward prediction error” with “reward-like prediction error” throughout.

(2) Reinforcement Agent and SCR Modeling

The modeling approach with the deep reinforcement agent treats SCR as a personalized expectation of shock for a given trial. However, this interpretation seems misaligned with participants' actual experience - they are aware of the shock but exhibit evolving responses to it over time. Why is this operationalization useful or valid? It would benefit the manuscript to provide a clearer justification for this approach.

This point is well taken. We did not collect trial-by-trial expectancy ratings, as frequent button-box responses would have induced cerebellar activations unrelated to fear (extinction) learning. Subjective expectancy was assessed only at the end of each experimental phase. As frequently done in the human fear conditioning literature, we used trial-by-trial SCR data (Lonsdorf et al., 2017). Although SCRs show correspondence with US expectancy ratings, they are inherently noisy and show substantial variability across trials and participants (Constantinou et al., 2021). Therefore, individual trial-by-trial responses cannot be used to directly infer US predictions. Accordingly, we used group-averaged SCR data to fit model hyperparameters in a grid search across parameter settings. The best-fitting hyperparameters were then applied to 100 randomly initialized agents, and their outputs were averaged to generate trial-wise estimates of predictions and prediction errors. These averaged values were used as parametric modulators in the fMRI analyses. We have revised the Introduction and Methods to make this procedure clearer.

(3) Clarity and Visualization of Results

The results section is challenging to follow, and the visualization and quantification of findings could be significantly improved. Terms like 'trending' appear frequently - what does this mean, and is it worth reporting? Adding clear statistical quantifications alongside additional visualizations (e.g., bar or violin plots of group means within specific subregions within the cerebellum, or grouped mean activity in VTA and DCN) would enhance clarity and allow readers to better assess the distribution and systematicity of effects. Furthermore, the figures are overly complex and difficult to read due to the heavy use of abbreviations. Consider splitting figures by either phase of the experiment or regions, and move some details to the supplemental material for improved readability.

We agree with the reviewer that the clarity of results can be improved and have revised the manuscript accordingly. Specifically:

(1) We use “trend-level” to refer to uncorrected voxelwise t-maps at p < 0.05, and “significant” to refer to TFCE/FWE-corrected effects at p < 0.05. This distinction was not sufficiently clear in the original figures. To address this, uncorrected t-maps are now displayed with a grey striped background frame, and colorbar labels have been enlarged to emphasize whether TFCE/FWE-corrected or uncorrected t-values are shown.

(2) We added a supplementary table (Table S7) reporting group-level summary statistics for all fMRI contrasts presented in the manuscript, including group means, standard deviations, effect sizes (Cohen’s d), and 95% confidence intervals for cerebellar cortex, cerebellar nuclei, and VTA VOIs. We hope that this helps with the interpretation of effect magnitude and variability across fMRI analyses.

(3) To improve readability, we split overly complex figures: Figure 2 now separates CS-related prediction from US-related presentation contrasts (which are now revised Figures 4 and 5), and Figure 3 separates event-based and parametric modulation contrasts (which are now revised Figures 6 and 7).

(4) We also reduced abbreviations in the figures, and provide full definitions and explanations also including the original abbreviations in the main text and figure captions for clarity.

We considered the suggestion to split figures further by region or by phase. However, we believe it is more informative to present the cerebellar cortex, nuclei, and VTA together for each contrast, and to keep all phases side by side, as this allows readers to directly assess commonalities across phases. We therefore chose to keep the same overall structure, but simplified the figures in other ways (e.g. splitting by contrast type) to improve overall readability. We hope that these changes address the reviewer’s concerns by simplifying the presentation, removing abbreviations, and providing clearer quantification of results.

(4) Theoretical Context for Paradigm Phases

The manuscript benefits from the comprehensive experimental paradigm, which includes multiple phases (acquisition, extinction, recall, reacquisition, re-extinction). This design has great potential for providing a more holistic view of conditioned fear learning and extinction. However, the manuscript lacks clarity on what insights can be drawn from these distinct phases. What theoretical framework underpins the different stages, and how should the results be interpreted in this context? At present, the findings seem like a display of similar patterns across phases without sufficient interpretation. Providing a stronger theoretical rationale and reorganizing the results by experimental phase could significantly improve readability and impact.

We thank the reviewer for this constructive suggestion. We would first like to mention that the primary aim of this manuscript is not to analyze differences between phases, but rather to highlight the commonalities. Across different learning contexts, we consistently observed reward-like prediction error-related activations in the cerebellum and VTA. This consistency and connectivity between the cerebellum and VTA, despite phase-to-phase differences, is the most important finding of our study.

We agree, however, that the manuscript did not sufficiently explain how each phase differs conceptually, which is important for readers to understand why the consistency of responses is notable. We therefore expanded the Introduction and Discussion to provide clearer theoretical context for each phase. More specifically, the phases can be understood as follows:

Extinction (day 2): Because acquisition was conducted with a 100% reinforcement rate, unexpected US omissions during initial extinction trials maximize reward-like prediction errors and yield stronger, more uniform expectations across participants compared to a partial reinforcement rate. This phase should therefore provide the clearest opportunity to observe cerebellar-VTA contributions to the processing of reward-like prediction errors.

Recall (day 3): Despite allowing for the consolidation of extinction learning, the recall test often still elicits conditioned fear responses to the CS+, that is, shows spontaneous recovery of the initial fear association (Bouton, 2002). In these trials, the non-occurrence of the US is unexpected. In this context, US omission-related activations reflect reward-like prediction errors during renewed fear responding in the presence of both a fear memory and an extinction memory. This contrasts with extinction training on day 2, where prediction errors arose primarily against the background of the recently acquired fear memory, without a competing extinction memory.

Reacquisition (day 3): Unlike acquisition, reacquisition used a partial reinforcement rate, such that non-reinforced CS+ trials were interspersed between reinforced CS+ trials (similar to the partially reinforced phase used by Ernst et al., 2019). Because reacquisition occurs in the presence of savings, that is, the presence of a previously acquired fear memory, US expectancy increases rapidly following reinforced trials and relearning occurs faster (Bouton, 2004). Importantly, partial reinforcement maintains high US expectancy and therefore allows prediction errors to remain sustained across omission trials (Figure 9).

Reextinction (day 3): Reextinction is an additional extinction phase but without a consolidation interval, and with an already established fear extinction memory. Because reextinction followed the partially reinforced reacquisition phase, prediction errors during early reextinction decayed more slowly than during extinction on day 2 (following the fully reinforced acquisition phase on day 1) (Figure 9). Together, reacquisition and reextinction were designed to maximize the number and persistence of unexpected US omissions, thereby providing additional opportunities to examine reward-like prediction-error signaling.

By clarifying this framework, we aim to show that while the learning context and history differ across phases, the consistent cerebellum-VTA activation and connectivity related to unexpected US omissions underlines the robustness of the effect. We chose not to reorganize the Results by phase, as our central conclusion rests on similarities rather than differences. Instead, we have clarified the theoretical background in the revised manuscript to help readers interpret both the commonalities and the potential sources of variability.

(5) Cerebellum-VTA Connectivity Analysis

The authors argue that the cerebellum modulates VTA activity, yet they perform the PPI analysis in the reverse direction. Why does this make sense? In their DCM analysis, they found a bidirectional relationship (both cerebellum - VTA and VTA-cerebellum), yet the discussion focused on connectivity from the cerebellum to VTA. A more careful interpretation of the connectivity findings would be useful - especially the strong claims in the discussion on the cerebellum providing the reward signal to the VTA should be tempered.

We thank the reviewer for highlighting this issue. In our primary analysis, we used the VTA as the PPI seed and observed trend-level connectivity with the cerebellum. When we reversed the analysis and used the cerebellar volume of interest (VOI) from the conjunction analysis as the seed, effects in the VTA were substantially weaker. We believe this reflects the broad connectivity profile of the cerebellar VOI (i.e., not specific to the VTA) as well as general limitations of PPI in our study, including the small number of unexpected omission trials and the lack of specificity to reward-like prediction errors (e.g., connectivity also appeared during US presentation). For transparency, we now report the cerebellar-seed PPI results in the Supplementary information (Figure S3). Given their limited robustness, we chose not to include the corresponding VTA maps in the main figures.

Finally, we agree that our conclusions regarding cerebellum-VTA interactions should be framed more cautiously. While the DCM analyses support bidirectional connectivity, our original discussion placed disproportionate emphasis on cerebellum-to-VTA influences. We have revised the text to provide a more balanced interpretation that also considers VTA-to-cerebellum connectivity.

Reviewer #2 (Public review):

Summary

Building upon the group's previous work, this study used a 3-day threat acquisition, extinction, recall, reextinction, and reacquisition paradigm with 7T imaging to probe the mechanism by which the cerebellum contributes to fear extinction learning. The authors hypothesize this may be via its connection to the VTA, a known modulator of fear extinction due to its role in reward processing. Using complementary analysis methods, the authors demonstrate that activity with the cerebellum, DNC, and VTA is modulated by predictions about the occurrence of the US, which shows regional specificity. They show trend-level evidence that there is increased functional connectivity between the cerebellum and VTA during all phases of the paradigm with unexpected omissions. They also present a DCM which indicates that the cerebellum could positively modulate VTA activity during extinction learning. This study adds to a growing literature supporting the role of the historically overlooked cerebellum in the control of emotions and suggests that an interaction between the cerebellum and VTA should be considered in the existing model of the fear extinction network.

Strengths

The authors address their research question using a number of complementary methods, including parametric modulation by model-derived expectation parameters, PPI, and DCM, in a logical and easily understood way. I feel the authors provide a balanced interpretation of their findings, presenting numerous interpretations and offering insight with regard to reward vs attention or unsigned prediction errors and the directionality of the interaction they identify. The manuscript is a timely addition to growing literature highlighting the role of the cerebellum in fear conditioning, and emotion generation and regulation more generally.

Weaknesses

Subjective and skin conductance responses do not completely support the success of the learning paradigm. For example, CS+/CS- differentiation in both domains persisted after extinction training. I do not feel that this negates the findings of this manuscript, though it raises questions about the parametric modulators used, and the interpretation of the neural mechanisms proposed if they do not strongly relate to updated subjective appraisals (the goal of extinction therapy). My interpretation of the manuscript suggests there are some key results based upon contrasts that have as few as three events; I am a little unsure about the power and reliability of these effects, though I await author clarification on this matter. There are a number of unaddressed deviations from the pre-registered protocol that I have asked the authors to elaborate upon.

We thank the reviewer for the thoughtful and constructive evaluation of our work. We appreciate that the manuscript and methods were found to be clearly presented, and we welcome the suggestions for clarification and improvement. Below we address the specific concerns regarding extinction learning in behavioral measures, the reliability of event-based contrasts with few trials, and deviations from the preregistration.

Extinction in self-reports and skin conductance responses (SCRs)

The reviewer is correct that CS+/CS- differentiation persisted after extinction. Although there was no differentiation in SCRs at the end of extinction, post-extinction self-reports continued to do so, albeit to a lesser degree, which is in line with previous literature on dissociation of outcome measures during fear conditioning (Lipp et al., 2003). This residual subjective differentiation is also consistent with extinction forming an inhibitory memory trace that suppresses, rather than erases, the original fear association (Bouton, 2002; Milad & Quirk, 2012), and a single extinction session is often insufficient to eliminate differential responding (Craske et al., 2014; Vervliet et al., 2013). However, both measures showed significant effects of extinction learning.

We included additional analyses of self-reports across phases. Importantly, CS+ ratings were significantly reduced during extinction and recall compared to acquisition (all p ≤ 0.001), whereas CS- ratings remained unchanged (all p > 0.532). This pattern demonstrates that the magnitude of the CS+/CS- difference was significantly reduced relative to acquisition, indicating that extinction learning did occur (Doubliez et al., 2025).

For physiological responses, extinction learning was shown in PSRs but not conclusively in SCRs. PSRs showed a significant reduction of CS+ responses across extinction, while CS- responses remained unchanged. SCRs showed a reduction of CS+/CS- differentiation across extinction; however, this effect remained at trend level, as the Stimulus x Time interaction did not reach significance (p = 0.053). This pattern is consistent with early differentiation followed by rapid attenuation under the full reinforcement structure of the paradigm (100% reinforcement during acquisition and 0% during extinction). Under such conditions, participants rapidly learn that the US is no longer delivered during extinction, such that physiological responses are largely confined to the first few trials, leaving limited power to detect extinction effects in noisier measures such as SCRs. To address the lower robustness of SCR effects, as recommended by the reviewer, we therefore included PSRs in the main Results section, which provide converging physiological evidence for extinction learning.

Of note, on day 3, both physiological measures and self-reports again showed CS+/CS- differentiation, consistent with spontaneous recovery, a well-established phenomenon reflecting the persistence of the original fear trace after consolidation (Bouton, 2002; Vervliet et al., 2013).

Taken together, these findings demonstrate that the paradigm successfully induced both acquisition and extinction of conditioned fear, even though residual fear responses persisted.

Reliability of event-based contrasts with three trials

The initial decision to use three events for event-based contrasts was based on SCR and PSR data, which showed that differentiation between CS+ and CS- occurred almost exclusively in the first few trials of extinction and recall. Consistent with the full reinforcement described above, prediction errors were expected to be high in the very first extinction trials, and to decay rapidly. Thus, the usual half-block division (e.g., first eight trials) would have included many trials without meaningful prediction errors.

We acknowledge that contrasts based on three trials provide limited statistical power. To address this concern, we added a supplementary table showing summary statistics for contrast estimates in the cerebellar cortex, cerebellar nuclei, and VTA VOIs across all fMRI analyses (Table S7), including both the event-based and parametric modulation approaches. Importantly, the event-based contrasts showed moderate to strong effects despite being restricted to the first three unexpected omission trials. Moreover, the parametric modulation analyses, which incorporate all available trials, yielded results that were consistent with the three-trial event-based contrasts and with the patterns shown in the main figures. This convergence between event-based and parametric approaches strengthens our confidence that the observed effects are reliable.

Deviations from preregistration

We acknowledge that deviations from the preregistered protocol were not fully documented and have now added this information. The main deviation concerned our event-based analyses: while the preregistration planned early vs. late block comparisons, in practice the rapid decay of SCRs under our 100% and 0% reinforcement rates rendered later trials uninformative for prediction error analyses. We therefore focused on the first three trials, when prediction errors are expected to be present. These behavioral findings are also consistent with Doubliez et al. (2025), who used the same paradigm and observed similar rapid SCR decay. Other deviations, such as not reporting exploratory whole-brain DCM analyses, are now clearly stated for transparency.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor Point - Paradigm Details

Providing additional details about the experimental paradigm in the main text (e.g., the nature of the visual stimuli associated with shocks) would enhance the manuscript's clarity. Some of the information currently in supplementary Figure 5 could be incorporated into the main text to enhance the understanding of the paradigm

We agree that the current structure reduces clarity, as the paradigm is only explained in detail after the results. To improve readability, we have moved parts of Figure 5 (illustrating the paradigm and scanner setup) to the beginning of the manuscript (now revised Figure 1). In addition, information from Figure 5, including details of the visual stimuli, is now added to the Introduction.

Reviewer #2 (Recommendations for the authors):

Methods

Can the authors please clarify what part of the task went into [US post CS+ > no US post CS-] contrast? Is this the time immediately after the CS presentations, when the US has just occurred/not occurred, or rather more like the CS+>CS- contrast except including trials confounded by the US (i.e. [CS+/US > CS -])?

The contrasts are based on an event-related separation of CS and US. The CS was presented for 6 seconds, with its onset modeled in the GLM as a zero-duration event (delta function). The CS offset coincided with either the delivery or omission of the US, which was likewise modeled as a zero-duration event. Thus, CS onset and offset were modeled separately. The no-US events were further distinguished by whether they followed a CS+ or a CS-. Accordingly, we analyzed both CS and US-related contrasts; for example, the CS+ > CS- contrast reflects CS-related differentiation at CS onset (0 s), whereas [US post CS+ > no US post CS-] reflects (no-)US-related activity at CS offset (6 s; US delivered from 5.9-6.0 s). We have added further clarification to the Methods section.

I was a bit unclear on what this sentence of the methods meant "Notably, all single trials comprised CS+ trials, with CS- trials also being modeled as single trials to facilitate paired analysis", does this mean that some contrasts had 6 events in total - e.g. the first 3 unexpected omissions vs 3 x CS-. If so, which CS- were selected for the comparison?

We agree that this sentence was unclear and have revised it. Our intention was to describe that when CS+ trials were modeled as single trials in the GLM (e.g., each CS+ onset and its associated [no-]US event modeled as separate regressors), the CS- trials were modeled in the same way. This ensured that paired analyses would be possible if required.

For reacquisition and reextinction, single-trial modeling was necessary, as the last unexpected omission of reacquisition is also the first unexpected omission of reextinction. Modeling trials separately allows us to examine the first three unexpected US omissions in each phase independently.

The event-based contrasts for unexpected US omissions were defined in line with a previous study of our group. For example, during extinction we contrasted the first three unexpected US omissions following CS+ with all expected omissions following CS- (i.e. [first 3 no US post CS+ > no US post CS-], corresponding to 3 vs. 16 events). The weights of events were automatically scaled by SPM12 so that both sides of the contrast carried equal total weight (e.g. positive events weighted 1/3, negative events weighted -1/16). This procedure matches the approach in Ernst et al. (2019), where in partially reinforced acquisition 6 unexpected omissions after CS+ were contrasted with 16 expected omissions after CS-.

More generally, can the authors please comment on the power and reliability of analyses that include only 3 events in a condition [e.g. the first 3 unexpected omissions]?

It is not clear if the (US post CS+ > no US post CS-) phases were included. In your pre-registration you say "we will use a "no US post CS+ > no US post CS-" fMRI contrast, where "no US post CS+" designates unexpected omission events in early extinction, early recall (depending on behavioral data which might indicate a return of fear) and a volatile phase (where unexpected omissions occur in the first part of the volatile phase, i.e. reacquisition).", but my reading of the manuscript was that it included both early and late "see 1st level analysis = US post CS+, no US post CS+, no US post CS- separately for each phase; 2nd level = contrast included unexpected omission of the US (no US post CS+ > no US post CS-)". Please clarify and if necessary explain the deviation from preregistration.

We agree that this point requires clarification. In the preregistration, we planned to divide phases into early and late blocks (no US post CS+ > no US post CS-). However, as already outlined in our response (Reviewer 2, public review response: Reliability of event-based contrasts with three trials), both our preliminary behavioral data and subsequent modeling analyses indicated that differentiation between CS+ and CS- declined extremely rapidly under the 100% reinforcement schedule, leaving likely little or no prediction error beyond the first few trials. Based on this, we adapted the event-based analyses to focus on the first three unexpected omission trials in extinction, recall, and reextinction, where prediction errors are expected to be present. In reacquisition, only three omission events occur by design (83% reinforcement), so this naturally constrained the analysis to three trials. We now explicitly describe this deviation from the preregistration in the revised manuscript.

As outlined in the same response, we recognize that contrasts based on three trials provide limited statistical power, and addressed this point by providing additional summary VOI statistics of contrast estimates for both event-based and parametric modulation contrasts, which show moderate-to-strong effect sizes and convergence across methods, which we argue supports that using the first three trials is a reliable approach (Reviewer 1, public review response, point (3) Clarity and Visualization of Results).

Finally, with regard to the reviewer’s specific question: yes, US post CS+ > no US post CS- contrasts were examined for acquisition training, primarily to demonstrate US-related activation (see revised Figure 3).

Results

Page 5 + 6: Including the interaction effects for pupil size responses during extinction and reextinction in the SCR section seems unjustified. I appreciate that the SCR data does not significantly support the key claim that extinction learning towards the CS+ occurred, but I do not feel it is acceptable to draw from the other measure for this effect alone. If the PSR measure is of primary/significant importance to support the validity of your paradigm, please consider adding all of these results to the main manuscript.

We agree with this point and have moved the PSR analysis to the main manuscript. In addition, the SCR Results section no longer includes the PSR analyses, and clearly states the absence of a significant Stimulus x Time interaction effect in extinction (p = 0.053). For completeness, we additionally report trend-level post hoc tests showing CS+/CS- differentiation during early extinction but not during late extinction, consistent with an initial differentiation that attenuates across extinction training.

Subjective and (some) skin conductance responses do not completely support the success of the learning paradigm. For example, CS+/CS- differentiation in both subjective domains and SCRs persisted after extinction training. Can the authors comment on how this might influence the interpretation of their results more generally? What does it mean if these expectations do not appropriately translate to updated subjective appraisals in your participants, contrary to the model from which the parametric modulators were derived would predict?

The persistence of CS+/CS- differentiation in self-reports after extinction, and the return of CS+/CS- differentiation in both self-reports and physiological measures during the recall test, is not unexpected. For self-reports administered after extinction, such persistent CS+/CS- differences are commonly observed in the human fear extinction literature (Hermans et al., 2006; see also Lipp et al., 2003), and may reflect that initial extinction learning establishes a new inhibitory association that suppresses, but does not erase, the original fear memory (Bouton, 2002). At recall on day 3, the remaining differentiation in both self-reports and physiological responses is consistent with spontaneous recovery, a well-documented phenomenon in extinction research (Bouton, 2002). As noted earlier (Reviewer 2, public review response: Extinction in self-reports and skin conductance responses (SCRs)), additional analyses showed that ratings were significantly reduced after extinction and recall compared to acquisition. Thus, while residual differentiation in self-reports remained after extinction and recall, its magnitude was diminished, indicating that extinction learning occurred but was incomplete. This pattern is consistent with partial updating of subjective appraisals in accordance with the reinforcement-learning model used to derive the parametric modulators, rather than a failure of updating.

Figures

Figure 1: Please ensure that the summary of your results in the figure legend is consistent with the quantitative results reported. Example 1: "On day 2, there was a loss of differentiation during extinction training.", however, a significant effect of the stimulus, and time remained (but no interaction). Please tone down this interpretation, or make it clearer how the difference in the initial extinction trials was quantified. If the ANOVA-type analysis was only performed in the first half, this was not clear. Example 2: "During initial reacquisition, there were again differential responses to the CS+ and CS-, which decreased in reextinction and the unexpected US phase". I appreciate that you refer to the difference decreasing, rather than disappearing altogether, but the magnitude of this difference is not reported in the manuscript, and there does remain a significant difference in the amplitude.

We thank the reviewer for this helpful feedback. We have revised the figure legends to tone down overly strong statements and ensure that all descriptions are in correspondence with the quantitative results. For clarity, we have also added significance markers for (trend-level) post hoc comparisons (CS+/CS- differentiation within early and late blocks for each phase) to revised Figures 2 and 3 displaying SCRs and PSRs.

Figure 2, 3, 4: I found it quite confusing to have uncorrected and corrected results displayed in the same way in the same figure. E.g. Figure 2A which, as far as I can tell shows trend-level results for the cerebellum, and corrected results for the VTA. For Figures 2 and 3 it was also not immediately clear which colour bar related to which map. Figure 4A appeared to be missing colour bars. I suggest the authors consider (as much as possible) standardising the colour bar scales, such that the maps across figures/sub-plots are more directly comparable, and differentiate more clearly between corrected and uncorrected results. The 3D renders in Figures 2 and 3 are a little hard to see - would it be possible to make it not so transparent?

We use “trend-level” to refer to uncorrected voxelwise t-maps at p < 0.05, and “significant” to refer to TFCE/FWE-corrected effects at p < 0.05. This distinction was not sufficiently clear in the original figures. In the revised figures, uncorrected t-maps are displayed with a grey striped background frame. Colorbar scales were not standardized, as different panels display different statistical quantities (TFCE values versus t-values), and scaling was chosen to visualize variation within each contrast rather than enforce comparability across panels, which would have reduced interpretability. In addition, the missing colorbar in Figure 8A (formerly Figure 4A) has now been added; it matches the colorbar shown in Figure 8B. See also Reviewer 1, public review response, point (3) Clarity and Visualization of Results.

Is it possible to annotate significant effects on Figure 1 and Supplement Figure 1? The use of square markers makes it quite hard to tell the value of each point, which, given the small scale of the y-axis is quite important for interpretation. Could the authors consider remaking these plots with smaller dots?

We have added post hoc significance markers to Figures 2 and 3 displaying SCRs and PSRs to facilitate interpretation. These markers reflect post hoc comparisons of CS+/CS- differentiation within early and late blocks. In cases where the Stimulus x Time interaction was not significant, the corresponding post hoc markers are still shown but are indicated in red to denote their trend-level status. In addition, the plots have been remade with smaller dots to make individual values clearer.

Discussion

The authors state "Because aversive stimulus presentation results in pronounced cerebellar activations, we were unable to separate cerebellar activation related to the unexpected (initial acquisition trials) and the expected (late acquisition trials) presentation of the US." Could the authors compare between early[CS+>CS-] and late[CS+>CS-] acquisition (which I believe were created in the event-based analysis but results not reported), or between the first 3[CS+ with US>CS-] and later [CS+ with US>CS-] to assess this?

In our terminology, the suggested comparisons (early vs. late [CS+ > CS-] or first three vs. last three [CS+ > CS-]) reflect changes in US prediction rather than prediction error. The statement in the Discussion refers specifically to cerebellar activation during US presentation, where distinguishing between expected and unexpected presentations is complicated by the strong cerebellar activation elicited by the electrical US itself. Moreover, when comparing early “unexpected” US presentations with later “expected” ones, the relatively higher activity in early trials could reflect habituation of the US sensation (i.e., non-associative learning) rather than a prediction error, making interpretation difficult.

Because the current manuscript focuses on reward-like prediction errors, we did not report these US prediction or presentation contrasts in detail. In brief, the suggested comparisons of early versus late CS-related differentiation (CS+ > CS-), revealed only limited trend-level activity. In contrast, US-related responses during acquisition showed robust activations in the cerebellar cortex, DCN, and VTA across the acquisition phase. Comparisons between the first three US presentations and later US presentations showed broadly distributed and stronger responses during early acquisition than during later US presentations. This pattern seems to be more consistent with non-associative effects, such as sensory habituation to the electrical stimulation, rather than with prediction-error–related processing. We have therefore not included them in the manuscript, but would be open to providing them in the Supplementary Information if the editor or reviewers consider them essential.

General

In your pre-registered analysis plan you state "we will explore the use of DCM in a larger network that encompasses known constituents of the fear extinction network, in addition to the cerebellum and VTA.". You have plenty of results to discuss in the current manuscript and adding this may complicate the narrative, but that being said, please either perform and include this analysis as you proposed or explicitly mention why this was not completed. You could also consider adding a whole-brain activation map for the key phases of the experiment. Please also double-check other pre-registered points, for example - the sample size justification is also different.

We decided not to include whole-brain DCM analyses in this manuscript and not to report whole-brain activation results extensively, as the study was primarily hypothesis-driven with a focus on cerebellum-VTA interactions. While we recognize that whole-brain analyses are of interest and plan to explore them in future work, they were considered outside the scope of the current paper. This deviation from the preregistration is now explicitly noted in the revised manuscript.

Regarding the sample size justification, the preregistration contained an error: the parameters were reported incorrectly. The correct sample size justification was already provided in the original 2019 grant application and is correctly reported in the current manuscript. The underlying power analysis was the same, but with different alpha levels depending on whether the study involved healthy participants (where larger samples are feasible) or rare patient populations (where stricter alpha levels are not practical). We have clarified this point in the manuscript under deviations from the preregistration.

Additional changes made in manuscript by authors

To provide a complete overview, we also note changes made independently of specific reviewer comments:

Methods

In the computational modeling section, “reextinction” was mistakenly mentioned where “reacquisition phase” was intended (the initial phase of the volatile phase before experience replay). This has been corrected.

The term “trial sequence” is used in computational modeling, whereas counterbalancing in the fear conditioning methods used different terminology. We added a clarifying sentence in the modeling section to make this consistent.

References in the pupil size analysis section (Jentsch et al. 2020; Mathôt et al. 2017) were misplaced and have now been moved earlier in the sentence.

The citation for MRIcroGL software was updated to the current Nature Methods reference.

We added a reference to Doubliez et al. 2025 which used the same three-day paradigm in a behavioral study showing similar physiological responses.

Supplementary information

During revision, we noted that the SCR statistics had been computed on an earlier preprocessed dataset version, whereas the finalized corrected dataset was already used for plotting and for estimating prediction and prediction-error values in the reinforcement-learning model. We therefore recomputed the SCR statistics on the finalized dataset for the sake of consistency; this did not change any main effects, interactions, or conclusions, with the only difference being an exploratory late-acquisition CS+/CS- post hoc shifting from non-significant to p < 0.05 (interaction still non-significant). Updated statistics are reported in the Supplementary information.

Post hoc significant differences in Table S3 are now marked in bold, as the formatting was missing previously.

To align behavioral analyses more closely with the event-based fMRI approach, we additionally examined physiological responses using a first three versus last three trial division within each phase. These analyses yielded patterns consistent with those obtained using the original early/late block division and are reported in the Supplementary Information.

We added a new supplementary figure (Figure S4) showing the location of the cerebellar VOI on a SUIT flatmap and added a corresponding cross-reference in the Methods section (Volumes of interest (VOI) definition)

References

Bouton, M. E. (2002). Context, ambiguity, and unlearning: sources of relapse after behavioral extinction. Biological Psychiatry, 52(10), 976–986. https://doi.org/10.1016/S0006-3223(02)01546-9

Bouton, M. E. (2004). Context and Behavioral Processes in Extinction: Table 1. Learning & Memory, 11(5), 485–494. https://doi.org/10.1101/lm.78804

Constantinou, E., Purves, K. L., McGregor, T., Lester, K. J., Barry, T. J., Treanor, M., Craske, M. G., & Eley, T. C. (2021). Measuring fear: Association among different measures of fear learning. Journal of Behavior Therapy and Experimental Psychiatry, 70(September 2020), 101618. https://doi.org/10.1016/j.jbtep.2020.101618

Craske, M. G., Treanor, M., Conway, C. C., Zbozinek, T., & Vervliet, B. (2014). Maximizing exposure therapy: An inhibitory learning approach. Behaviour Research and Therapy, 58, 10–23. https://doi.org/10.1016/j.brat.2014.04.006

Doubliez, A., Köster, K., Müntefering, L., Nio, E., Diekmann, N., Thieme, A., Albayrak, B., Nicksirat, S. A., Erdlenbruch, F., Batsikadze, G., Ernst, T. M., Cheng, S., Merz, C. J., & Timmann, D. (2025). Dopaminergic drugs modulate fear extinction-related processes in humans, but effects are mild. Brain Communications, 7(5), fcaf333. https://doi.org/10.1093/braincomms/fcaf333

Ernst, T. M., Brol, A. E., Gratz, M., Ritter, C., Bingel, U., Schlamann, M., Maderwald, S., Quick, H. H., Merz, C. J., & Timmann, D. (2019). The cerebellum is involved in processing of predictions and prediction errors in a fear conditioning paradigm. ELife, 8, e46831. https://doi.org/10.7554/eLife.46831

Hermans, D., Craske, M. G., Mineka, S., & Lovibond, P. F. (2006). Extinction in Human Fear Conditioning. Biological Psychiatry, 60(4), 361–368. https://doi.org/10.1016/j.biopsych.2005.10.006

Kalisch, R., Gerlicher, A. M. V., & Duvarci, S. (2019). A Dopaminergic Basis for Fear Extinction. Trends in Cognitive Sciences, 23(4), 274–277. https://doi.org/10.1016/j.tics.2019.01.013

Lipp, O. V., Oughton, N., & LeLievre, J. (2003). Evaluative learning in human Pavlovian conditioning: Extinct, but still there? Learning and Motivation, 34(3), 219–239. https://doi.org/10.1016/S0023-9690(03)00011-0

Lonsdorf, T. B., Menz, M. M., Andreatta, M., Fullana, M. A., Golkar, A., Haaker, J., Heitland, I., Hermann, A., Kuhn, M., Kruse, O., Meir Drexler, S., Meulders, A., Nees, F., Pittig, A., Richter, J., Römer, S., Shiban, Y., Schmitz, A., Straube, B., … Merz, C. J. (2017). Don’t fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear. Neuroscience and Biobehavioral Reviews, 77, 247–285. https://doi.org/10.1016/j.neubiorev.2017.02.026

Milad, M. R., & Quirk, G. J. (2012). Fear Extinction as a Model for Translational Neuroscience: Ten Years of Progress. Annual Review of Psychology, 63(1), 129–151. https://doi.org/10.1146/annurev.psych.121208.131631

Salinas-Hernández, X. I., Vogel, P., Betz, S., Kalisch, R., Sigurdsson, T., & Duvarci, S. (2018). Dopamine neurons drive fear extinction learning by signaling the omission of expected aversive outcomes. ELife, 7, e38818. https://doi.org/10.7554/eLife.38818

Salinas-Hernández, X. I., Zafiri, D., Sigurdsson, T., & Duvarci, S. (2023). Functional architecture of dopamine neurons driving fear extinction learning. Neuron, 111(23), 3854-3870.e5. https://doi.org/10.1016/j.neuron.2023.08.025

Vervliet, B., Craske, M. G., & Hermans, D. (2013). Fear extinction and relapse: State of the art. Annual Review of Clinical Psychology, 9(March 2013), 215–248. https://doi.org/10.1146/annurev-clinpsy-050212-185542

Some of the things people use social media for are: * Keeping up on other people's lives, whether friend or celebrity * Finding places to eat * General entertainment through videos and posts * Communication * Learning new information * Finding locations to visit

All code snippets need to be explained or at least summarized

what is d?

Explain the example below in more detail

I would move this to be the last section and make Environment and Map Representation with Game board, Tile system Grid structure, Saving/loading (JSON) to go right after System Architecture

Not sure this is helpful, instead start with what the system is and what constraints guided design (to flow from related work)

Can you add a summary table here? Something like: | Feature | Visualizers | Game Engines | Robotics Tools | PathMaker | | ------------- | ----------- | ------------ | -------------- | --------- | | Visualization | ✓ | ✓ | ✓ | ✓ | | Benchmarking | ✗ | Partial | ✗ | ✓ | | Custom Maps | Limited | ✓ | Partial | ✓ | | Ease of Use | ✓ | ✗ | ✗ | ✓ |

Strengthen this by specifying what Pathfinder actually does for dynamic environments

what it does and not what it intends to do

So basically just making policies that give privileges for the EV owner's to make more people use EV can actually have a negative affect, both on the environment and the society. The transition should be balanced, and strictly monitored, rgulated.

It is of course a reason for people to move, but I would not agree that it is necessarily a negative one, since it is only natural for the government to react to high demand with tax increasing policy.

I definitely agree with this, and it's coming form personal experience as my family is moving from the center soon.

One of the main causes of the air pollution as well. \

A room that’s usually located next to the outer door of a building.

A westwork is the main entrance for a church. Usually, it is named for it’s westward-facing direction.

I wonder how the different religions were presented to him. I imagine that there would be bias from the people presenting, maybe they tried to change his mind when he was considering one other than Christianity because of the person's presenting biases.

Basically being allowed to use existing diesel gensets for flexibility

En los sistemas financieros, el acceso al crédito suele depender de la presentación de garantías o colaterales. En este contexto, los hombres tienden a tener una mayor disponibilidad de activos que pueden ser utilizados como respaldo, debido a mayores niveles de acumulación de riqueza, trayectoria laboral y productividad histórica. En América Latina y el Caribe, esta diferencia estructural genera una desventaja para las mujeres, quienes, al poseer menos activos, enfrentan mayores dificultades para cumplir con los requisitos exigidos por las entidades financieras. Como resultado, las mujeres tienen una menor probabilidad de acceder al crédito formal.

I'm confused by the switching between 2025-12-11 and 2026-01-11.

How often is this null and why?

eLife Assessment

This valuable study presents a plastic recurrent spiking network model that spontaneously generates repeating neuronal sequences under unstructured inputs. The authors provide solid evidence that, while the global weight distribution stabilizes, individual synaptic connections undergo constant turnover with strength-dependent timescales, supporting sequence generation. However, the study is purely simulation-based and phenomenological, lacking both a mechanistic explanation for sequence emergence and explicit experimental predictions, and robustness to alternative, more biologically realistic plasticity rules remains to be demonstrated. The work will be of interest to theoretical and experimental neuroscientists working on synaptic plasticity and neural sequence generation.

Reviewer #1 (Public review):

Summary:

The aim of this paper is to model the spontaneous emergence of sequences in networks of plastic spiking neurons. By spontaneous, they mean that the inputs have no structure, no sequences, but the network nevertheless generates sequences. To obtain this, they assume several synaptic plasticity and single neuron plasticity rules. The primary findings are that sequences can emerge, that they slowly drift over time, that weights also constantly change over time, but that very strong weights are more stable. The main driver of this result is the plasticity rules assumed.

Strengths:

The paper is based on simulations of a relatively large network of conductance based integrate and fire neurons. There are two different pair-based STDP rules assumed for excitatory-to-excitatory synapses and for inhibitory-to-excitatory synapses. In addition, weights are normalized, and there is an adaptation due to plasticity of the spiking threshold. The network is analyzed via simulations and data processing akin to what would be done for physiological data. The simulations are extensive, and the analysis seems rigorous.

Weaknesses:

There are several fundamental problems with the paper:

(1) The plasticity mechanisms used assumed that pair-based STDP is sufficient to account for synaptic plasticity in vivo. This is unrealistic. Various different papers have shown that pair-based STDP models do not account well for experimental data. If this model is a simulation of the visual cortex (unclear), then firing rates can be sufficiently high, such that firing rates are more important than spike times. We already know that firing rates matter due to the original Markram et al paper from 1997. Even if pair-based STDP is used, we already know from Bi and Poo 1998 that there is a weight dependence of synaptic plasticity such that strong weights potentiate less and decay more. This additional assumption alone might completely change the results in this study. We don't really know how to model realistic synaptic plasticity, but we know pair-based STDP is a bad model. Would these results be robust enough for a change in the learning rule, for example, to triplet-based, calcium-based, or voltage-based? Are the results shown even robust enough to include slight modifications to the learning rule, for example, weight dependence of pair-based STDP?

(2) The first stage of training, in which the network reaches a steady state, is unclear. What type of activity is exhibited in this network? Does most of it arise from the external inputs? What firing rates are obtained? What are the spike statistics? This is important because this activity is responsible for generating the emergent sequences, and also depends (I think) on the plasticity mechanisms. Does the 'spontaneous activity' in the network depend strongly on the external input? Figure 1E is where we see a raster plot, but we see only neurons within a sequence, and it seems neurons within the sequence fire almost only once. Before showing sequences that more general structure of the spiking activity and how it evolves should be explained and quantified.

(3) Do these sequences really emerge without structured inputs? Is there any evidence to suggest that such sequences emerge without a structured input? If yes, please cite it. It makes sense that it would, because the time scale of these sequences is much faster than the sensory or behavioral time scale. However, experimental evidence to support this will make the paper much more interesting.

(4) This paper is a phenomenological paper. It does not really say what these sequences might be good for, except for a cite or two, and it does not model any specific experiment. There is a medium here (a plastic spiking network) which generates a phenomenon (sequences). It also generates other measurable phenomena, such as connectivity motifs. Such motifs have been quantified in animals. It would be natural to compare the motif statistics found here to motifs characterized experimentally. This would make these results more substantial.

(5) There are implicit predictions in the work. For example, about the stability of strong vs. weak efficacies or the stability of different motifs. Such predictions should be made more explicit.

Reviewer #2 (Public review):

Summary:

This paper investigates how a combination of spike-timing-dependent plasticity rules in recurrent spiking networks leads to the spontaneous emergence of repeating neuronal sequences. The authors show that despite the weight distribution reaching a steady state, individual synaptic connections undergo constant turnover with timescales that depend on connection strength. The plasticity rules promote fan-in/out connectivity motifs that appear to support sequence generation.

Strengths:

The question addressed is important and biologically relevant. The most interesting finding of the paper is the coexistence of a stable weight distribution with constant turnover of individual synaptic connections.The simulations seem to be carefully executed.

Weaknesses:

The paper does not make a sufficient attempt to explain why the observed phenomena arise under the specific learning rules employed. There is no theoretical reduction, no analytical argument, and no mechanistic intuition. As it stands, this reads as a descriptive simulation study.

It is never made clear which results reflect robust qualitative phenomena and which are specific to the particular hyperparameter choices of these simulations. Specific percentages and parameter values are reported throughout the main text without justification of their importance or generality.

The finding that sequence composition undergoes continual turnover while the global weight distribution remains stable is interesting, but the authors should more carefully situate this result within the existing theoretical literature on synaptic drift and sequence stability under ongoing plasticity. Several modeling papers have addressed related phenomena, and the novelty of the present contribution relative to this body of work is not clearly established.

Reviewer #3 (Public review):

Summary:

This modelling study connects synaptic plasticity, connectivity motifs, and representational drift. The authors combine excitatory and inhibitory STDP with weight normalization and intrinsic plasticity in a recurrent spiking network of AdEx neurons. This combination generates heavy-tailed synaptic weight distributions and supports repeating spike sequences under both unstructured and structured inputs. While global network statistics stabilize over time, individual synapses continue to change, creating a form of drift. Structured inputs further stabilize sequences, yet the network retains flexibility to learn new patterns.

Strengths:

(1) Multi-scale turnover analysis:

The authors study the evolution of individual synapses, 3-neuron motifs, follower neurons, and entire neuronal sequences, revealing distinct turnover timescales.

(2) Fan-in/out motif analysis:

A specific connectivity motif (fan-in/out) is shown to be over-represented in the network and preferentially stabilised by the plasticity rules compared to other possible motifs. This generates interesting insights and testable predictions.

(3) Connection to representational drift:

The connection of ongoing synaptic plasticity to drift is timely and interesting, reproducing observations of macro-level stability and synapse-level turnover with a relatively simple mechanism.

(4) Rigour and thoroughness:

The overall quality of the numerical experiments performed in this study is high, with extensive supplementary material performing various controls to solidify the claims.

Weaknesses:

(1) Limited connection to network function:

Sequence detection relies on a rather artificial protocol (forced spiking of a single neuron 1,000 times), which I suspect mostly tests whether the lognormal tail of the weight distribution can propagate activity. This risks being circular. I think performing the same sequence analysis on a random network/a network with the same weight distribution but shuffled would help understand what comes from a generic heavy-tailed weight distribution and the particular weights potentiated by the plasticity rules used here.

The network, which would classically be evaluated as a memory network, is not assessed on this aspect. While the authors do not overclaim, this limits the impact.

Relatedly, the relearning experiment (Figure 5G) shows catastrophic forgetting. This is acknowledged in the discussion, but the suggested solutions (alternating patterns, plastic readout) are speculative without supporting simulations. This limits the applicability of the model as a memory model or, more broadly, as a model of a brain region/function.

Additionally, in the sequence learning experiments with structured input, the ability to learn seems tied to the very specific timescale of pattern presentation (~10 ms per pattern, comparable to the STDP kernel time constants), arguably faster than the timescale of external stimuli. The stability of sequences may also owe more to the normalization scheme than to STDP per se.

(2) Novelty claims and positioning within the literature:

On page 16, the authors write: "Our results demonstrate that spiking sequences can be generated in randomly connected networks trained by synaptic plasticity even under unstructured inputs, which supports STDP being the main actor, while stabilizing mechanisms such as weight normalization and intrinsic plasticity play a complementary role." (c1).

Several aspects of this work are less novel than the presentation suggests:

(a) The fact that STDP can create sequence-like dynamics/asymmetric connectivity matrices in recurrent networks has been studied theoretically [1,2] and in simulations [3,4,5]. While [3] is cited, the manuscript underplays the similarity. [4] (uncited) considers e+iSTDP with a different homeostatic term to represent sequential stimuli in large recurrent spiking networks. [5] (uncited) also considers a recurrent spiking network with several STDP-like rules and shows that many combinations can store and recall sequential inputs.

(b) Lognormal weight distributions emerging from STDP-based plasticity and the autonomous emergence of connectivity structures have extensive literature. While many of these articles are already cited in the manuscript, I fail to see what this work brings to this matter compared to existing work (particularly [6]).

(c) Several published works challenge the manuscript's implicit claim (c1) that sequences require their particular combination of rules. Many other plasticity mechanisms can create sequences [3,4,5,7,8,9]. Some interpretations may also need to be dialed down: [10] (uncited) showed that sequences can be stored and retrieved using EI and IE plasticity alone. iSTDP may be doing more computational work than acknowledged, which complicates the interpretation of which mechanisms are truly driving the phenomena.

Overall, most of the relevant work is already cited in the manuscript, but not necessarily acknowledged adequately.

(3) Justification of plasticity model/robustness analysis:

The parameters in Tables 1 and 2 are quite specific without strong justification (for instance, different sparsity values for each connection type and specific normalization factors). Without parameter sweeps, it is difficult to know whether the key findings are robust or overfit to this particular network configuration. Given the number of parameters, exhaustive sweeps are out of question, and the argument made previously would still prevent the rule combination proposed from being considered as more than one possible mechanism for sequence generation among many others. However, this deserves to be acknowledged, and potentially a few sweeps to be run (e.g., over LTP/LTD ratio, normalization threshold, and network size). I don't think that Figure S12, which shows that removing any component of the model causes it to break down in some way, is enough to cover alternative plasticity rules.

A related concern is that the network is small by current standards (1,200E + 240I neurons), especially with sparse connectivity (6-20%). Small networks with few connections are susceptible to synchronization (other studies typically consider networks of at least 10k neurons). The authors should discuss whether the phenomena they observe would persist at larger scales and under more biologically realistic connectivity. Specifically, are the intrinsic and normalization plasticity terms as crucial in this case?

(4) Fan-in/out motif evidence is correlational:

The evidence linking the fan-in/out motif to sequence stability appears to be correlational. Properly establishing causality would require targeted ablations or rewiring of fan-in/out connections. While designing a clean causal intervention may be difficult, the correlational nature of the evidence should be stated explicitly.

Conclusion:

To summarize, the manuscript would benefit from:

(1) Reframing the contribution:

Multi-scale turnover analysis and the discussion around representational drift as the core novelties. I would reposition sequence emergence and lognormal distributions as reproducing known results under a specific plasticity model and analysis method.

(2) Acknowledging that many rule combinations could produce equivalent outcomes, and not suggesting that the combination chosen here is special.

(3) Adding parameter sensitivity analysis or, at a minimum, discussing robustness.

References:

[1] Kempter, Gerstner and van Hemmen, Hebbian learning and spiking neurons, 1999, PRE

[2] Ocker, Litwin-Kumar and Doiron, Self-organization of microcircuits in networks of spiking neurons with plastic synapses, 2015, plos CB<br /> (Theoretical account of STDP in spiking networks and motifs, though it only looks at 2-synapse motifs (not fan-in/fan-out)).

[3] Fiete et al., Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity, 2010, Neuron

[4] Duarte and Morrison, Dynamic stability of sequential stimulus representations in adapting neuronal networks, 2014, Frontiers in Comp Neuro

[5] Confavreux et al., Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning, 2025, bioRxiv

[6] Zheng, Dimitrakakis and Triesch , Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex, 2013, plos CB

[7] Zheng and Triesch, Robust development of synfire chains from multiple plasticity mechanisms, 2014, Front Comp Neuro

[8] Ravid Tannenbaum and Burak, Shaping Neural Circuits by High Order Synaptic Interactions, 2016, plos CB

[9] Bell, Duffy, and Fairhall, Discovering plasticity rules that organize and maintain neural circuits, 2024, NeurIPS

[10] Gong and Brunel, Inhibitory Plasticity Enhances Sequence Storage Capacity and Retrieval Robustness, 2024, bioRxiv

eLife Assessment

In light of the diverse functions associated with the Dorsal Raphe Nucleus across vertebrate species, this important study presents findings on the role of serotonin in promoting behavioral quiescence through the regulation of neuromotor populations. Combining optogenetics with brain-wide activity analyses, the study provides convincing evidence of interest to researchers in neuromodulation and translational medicine fields.

Reviewer #1 (Public review):

The wide-ranging serotonergic projections emerging from the Dorsal Raphe nucleus (DRN) are suggestive of a central role in regulating brain-wide activity and behavioural states. DRN activity has been associated with diverse functions, ranging from mood, motivation and pain regulation to sleep and cognitive flexibility. Its far-reaching connectivity made it challenging to assess the brain-wide effect of its activation, especially during behaviour.

The present study by Qi et al. addresses these challenges by combining state-of-the-art tracking microscopy with the whole-brain accessibility of the larval zebrafish model. To investigate the effect of DRN activation, the authors leveraged the Tg(tph2:ChrimsonR) line to optogenetically activate tph2-positive neurons in the DRN, while monitoring changes in brain-wide activity, locomotion and auditory-stimuli evoked responses.

Optogenetic activation had a suppressing effect on locomotion, which the authors distinguished from inducing sleep by the maintenance of posture and its sleep disturbing effect of nighttime stimulations. Further, the authors report a distinct effect of DRN activation on motor-related, but not auditory-related neuronal subspaces, identified by demixed principal component analysis.

In addition, rather than affecting all motor-correlated neurons similarly, tph2+ DRN-mediated suppression focused on neurons encoding high-amplitude or turning motion.

In summary, the work of Qi et al. provides solid evidence for a predominant role of the DRN in wake-state motor suppression by aptly combining the vast data-acquisition possibilities of the larval zebrafish model with computational methods to extract relevant information.

The brain-wide scope of the analysis is a key strength, reducing bias, confirming the involvement of known motor and auditory regions, and providing a valuable dataset for future analyses.

While the results well support the conclusion of the authors, certain biological and technical aspects demand discussion.

Reviewer #2 (Public review):

Summary:

The authors examine the effects of activating the dorsal raphe nucleus serotonergic system using a combination of calcium imaging and optogenetics in freely moving larval zebrafish. Their findings show that optogenetic stimulation induces a state of behavioral quiescence.

They further investigate whether this state corresponds to sleep or reduced motor activity. Analyses of posture and sleep-related paradigms indicate that serotonergic activation primarily suppresses motor output rather than promoting sleep. Notably, this suppression appears to be bout type-dependent, with stronger effects on neurons associated with larger tail amplitudes and turning angles.

In addition, auditory stimulation experiments reveal no significant impact of serotonin on sound encoding.

Strengths:

The study combines advanced experimental techniques with state-of-the-art analytical methods, enabling precise and compelling insights into the role of serotonergic modulation. The experiments and analyses are well aligned with the questions being addressed, and the results appear robust and reliable.

Moreover, the implementation of experiments that combine calcium imaging and optogenetics in freely moving animals is technically challenging and appears well justified in the context of the research questions.

Weaknesses:

While the analytical techniques employed are sophisticated and appear to be appropriately applied, their presentation makes the manuscript difficult to follow. Although the explanations are provided in the Methods section, including more guidance in the main text, such as how to interpret each analytical approach and what outcomes would be expected under different scenarios, would help readers who are less familiar with these techniques.

Providing this context would better guide the reader in navigating the figures, broaden the accessibility of the work, and ultimately increase its impact.

While the authors discuss different quiescent states mediated by serotonin reported in previous studies, their interpretation is limited to stating that "a common feature shared by these distinct behavioral states is a pronounced reduction in movement," and consequently proposing that activation of dorsal raphe nucleus is not sufficient to specify a particular behavioral state, but rather plays a primary role in driving motor suppression.

In my view, a more thorough attempt to determine whether the observed state corresponds to any of the previously described forms of quiescence, or represents a subset or variant of them, would strengthen the manuscript. This would help better integrate the findings with the existing literature.

For example, given that the authors have access to whole-brain activity data, it would be valuable to examine and discuss whether there are shared patterns of activation with previously reported quiescent states.

The manuscript largely avoids discussing the mechanisms underlying the observed motor suppression. For instance, is this effect driven directly by serotonin release onto target neurons? Is it mediated by glial activity, as suggested in other studies? Are additional neuromodulatory systems being recruited?

While addressing these questions may require substantial further work, potentially beyond the scope of the present study, the availability of whole-brain data provides an opportunity to at least explore or discuss these possibilities. In particular, it would be interesting to examine the recruitment of regions not directly stimulated but known to be associated with other neuromodulatory systems or promoting glial activation (e.g., the locus coeruleus).

Can't recommend enough "The Future in Ruins: Thoughts on the Temporality of Infrastructure" by Akhil Gupta in The Promise of Infrastructure, will be really useful to you I think

A lot of the stuff on this list is a little old. I feel like Cass might have some good insights for more recent work on algorithmic advertising

eLife Assessment

This manuscript presents important findings that challenge traditional models of speech processing by demonstrating that theta-gamma phase-amplitude coupling in the auditory cortex is primarily a stimulus-driven alignment to external acoustic structures rather than an intrinsic neural oscillatory mechanism. The evidence supporting these claims is convincing, grounded in a robust cross-linguistic acoustic analysis and high-fidelity, time-resolved intracranial recordings.

Reviewer #1 (Public review):

Summary:

This article investigates the application of commonly employed analytic methods in electrophysiological neuroscience to the speech envelope taken from 17 different languages' audio corpora. The findings indicate that features observed in speech-brain tracking responses, specifically theta and gamma oscillations, as well as their phase-amplitude coupling, are actually present within the speech envelope itself. This suggests that the neural data recorded in response to speech primarily reflects an evoked response to the temporal statistical properties of the envelope, rather than an inherent neural mechanism. Data from 18 individuals with epilepsy listening to French speech further support this interpretation: theta and gamma oscillations, along with their phase-amplitude coupling, are absent at rest and are linearly driven by the acoustic envelope during speech perception.

Strengths:

I find these results very interesting and convincing, with a strong take-home message: we should exercise caution when interpreting observed theta/gamma activity and the associated phase-amplitude coupling during speech comprehension tasks.

Weaknesses:

I mostly have comments on clarifications regarding the methods, specifically on the criteria for language exclusion, and on the statistical testing and reporting.

(1) Clarification is needed regarding the rationale for the number of languages analysed: initially, 17 languages were considered, six were excluded due to the absence of PAC in the high gamma range, yet the analysis was ultimately conducted on only nine languages, not eleven. Could you please explain this discrepancy?

(2) Considering the six languages that did not exhibit any statistically significant high-frequency PAC, do you have potential reasons for this result? Might it be related to the fundamental frequency (F0) of the speakers' voices? If six languages out of seventeen do not show PAC, can we argue that this feature is universal across languages?

(3) How is inter-subject variability addressed within the SEEG analysis? The authors report the percentage of SEEG independent components showing significant effects in power spectral changes, PAC, and other measures, but it is unclear whether these components are consistent across participants or whether only a few participants drive the effect. It would be helpful to report how many participants are retained for each selection of SEEG-ICs in the article. Currently, the statistical testing of the SEEG-ICs also appears to assume independent samples. It would be helpful to include group-level statistical tests across subjects, for instance by performing mixed-effects models and including participant as a random factor.

Reviewer #2 (Public review):

Summary:

This paper nicely demonstrates that "speech tracking" in the auditory cortex extends all the way up to 100Hz-150Hz. Specifically, the study asks whether the fluctuations in sound amplitude found in speech at various time scales relate to fluctuations found in similar time scales in intracranial recordings in auditory brain areas. First, it analyzes amplitude fluctuations in speech of 17 different languages, and characterizes fluctuations due to syllabic rate (2-6Hz), vocalic features (30-50 Hz), and fundamental frequency (100-150 Hz, in male speakers). It then analyzes whether neural activity occurs while listening to male and female speakers in French. By measuring changes in power spectrum relative to rest, it links the sound amplitude fluctuations to fluctuations in neural activity in the same frequency bands, referring to them as "theta", "low-gamma", and "high-gamma". Using Grange "causality," it clearly shows that the neural fluctuations can be predicted linearly from the sound fluctuations. Using a cross-frequency coupling measure, they further show that, in the neural dynamic, high-gamma fluctuations precede theta fluctuations.

Strengths:

(1) Analysis of neural activity (Figure 2 is a very compelling account of how theta, low, and high gamma observed in neural recordings closely follow the properties of the acoustic speech signal itself.

(2) This includes phase amplitude coupling, a property that I had not previously seen described for the speech signal itself, and is here nicely demonstrated in Figure 1.

(3) The Grange "causality" analysis makes a compelling case that neural fluctuations in these frequency bands are driven by the stimulus itself.

(4) The finding in Figure 4 that female fundamental emerges at half the frequency in the neural activity is, to my knowledge, an entirely novel observation, not just in speech but in amplitude modulated sounds in general. This non-linear phenomenon is very interesting and prompts a host of interesting questions for future research: Does this happen only for voiced speech, does it depend on the harmonic stack of speech, or can it be produced with a single AM frequency? Are there preferred frequencies for this phenomenon?

(5) The cross-frequency coupling measure shows a number of directed effects in the neural signal which seem to counter the predominant view in neuroscience, namely, that the phase of the slower fluctuations "organize" or "drive" the faster fluctuations seen in power, e.g. theta→gamma coupling, which here is seen to be reversed as gamma→ theta coupling, and this is not a property of sound itself. This, too, should lead to a number of follow-up studies (although there are some potential confounds here).

Weaknesses:

(1) The claim that different frequency bands are processed in different locations, referred to in the abstract as "multiplexing" is less well supported. The neural analysis is performed on independent components that are spatially distributed, making this claim less transparent than it could be, with other, more direct ways of treating electrode location, such as bipolar referencing.

(2) The writing in the Introduction and Results section obscures the source of sound amplitude fluctuations at different timescales. Instead, it treats these fluctuations as some sort of discovery. This is strange because the abstract and discussions are fairly accurate on this point - namely, they are all due to well-known properties of speech. The descriptions are accurate, although I would put it slightly differently: fluctuations below 6Hz are due to varying length of sentences and words, 25Hz-50Hz are well-established stationary times of the vocal tract, and 100-150Hz are the vibration of the vocal cords in male speakers.

(3) The problem of guiding the analysis of sound by notions from neural signals is most glaring when they restrict their analysis to less than 150Hz, which leaves out female-voiced speech.

(4) Along with this, there is a heavy emphasis on notions of "rhythms" and "oscillations" when clearly, aside from the vocal cords, there is no evidence for rhythmic fluctuations. Any reasonable definition of a rhythm would need at least 2 or 3 cycles of a repeated pattern. A spectral "peak" for the sound envelope is shown at 5Hz. But this is not indicative of a regular rhythm. Instead, the peak appears to be an artifact of displaying power per octave rather than power spectral density. A peak in a power per octave is not a reliable indicator of a coherent oscillation, and the speech envelope does not exhibit a clear 5Hz rhythm. Unfortunately, prior literature has not been clear on this. It would be more accurate if the word "rhythm" were replaced with "fluctuation" and/or "activity" for the case of speech envelope and neural activity, respectively.

(5) The Introduction also omits the literature on neural responses to amplitude-modulated sounds that go up at least to 200Hz and more. So the findings here on "high-gamma" are well in line with prior literature.

(6) The fact that neural analysis was cut off at 150Hz to me is a missed opportunity to test if neural speech tracking goes all the way up to 200Hz of the typical female fundamental.

(7) The gamma→theta effects reported here could be confounded by a simple longer delay in the analysis of theta. In fact, Figure S5 confirms that delay. It is unclear whether the CFD metric captures anything more than a temporal delay between the two signals. The term "functionally interconnected" in the abstract is a bit of a stretch; it may be essentially delayed correlation.

(8) There is a minor concern with the claim that low-gamma drives theta amplitude. While statistics on this are reported, the corresponding figure may be suggesting an alpha-harmonic instead of theta (Figure 5c).

Reviewer #3 (Public review):

Summary:

This manuscript investigates whether the theta-gamma phase-amplitude coupling in the human auditory cortex serves as an intrinsically generated neural mechanism for hierarchically parsing speech or not. By analyzing speech corpora across 17 languages alongside human intracranial EEG recordings, the authors demonstrate that these nested oscillatory dynamics are actually inherent, robust acoustic properties embedded within the speech envelope itself. Consequently, they claim that rather than generating parsing windows internally, the early auditory cortex acts as a temporal demultiplexer that segregates syllabic, vocalic, and pitch features into distinct, stimulus-driven neural channels. Furthermore, the study presents evidence for a reversed functional directionality wherein fast-varying gamma activity drives the phase alignment of slower theta rhythms, fundamentally reframing auditory PAC as a stimulus-evoked alignment to a highly structured external signal rather than an endogenous cognitive parsing tool.

Strengths:

(1) The authors demonstrated robust theta-gamma acoustic structure across languages. They analyzed the acoustic speech envelope across 17 typologically distinct languages. This establishes that the nested theta-gamma acoustic structure is a universal feature of human speech, rather than an artifact of one language's specific phonology.

(2) The use of time-resolved, high-SNR intracranial recordings is a critical strength of this study. This approach provides the precise spatiotemporal fidelity required to confidently separate and delineate multiplexed high-frequency dynamics, particularly the low- and high-gamma bands, that are essential for accurate speech decoding but are typically attenuated or lost in non-invasive scalp recordings.

(3) The authors move beyond standard correlational PAC metrics by employing a suite of converging analyses, including the isolation of true oscillations from aperiodic noise and the directional index. Together, these metrics demonstrate that auditory PAC is a stimulus-evoked alignment to a highly structured external speech signal, rather than an intrinsically generated top-down parsing mechanism.

Weaknesses:

(1) A major methodological concern is the use of ICA across SEEG electrode shafts to define distinct neural sources (SEEG-ICs). SEEG electrodes traverse complex macroanatomy, including multiple cortical layers, sulcal banks, and white matter. By constructing components derived from weights across the entire electrode, and subsequently localizing each component solely to the contact with the maximal contribution, the authors risk generating biologically implausible signals. Such an approach potentially mixes true localized cortical gray matter activity with deep structure or white matter signals. Given that a central claim of this manuscript is the spatial and functional segregation of theta and gamma neural populations, the authors could consider further validating these core findings (such as the gamma-to-theta directionality) using single-channel or bipolar-referenced data.

(2) Another methodological concern is the use of GC to evaluate the directional causality between speech and neural signal. As noted in Bastos & Schoffelen (2015) and indeed acknowledged by the authors' own citation of Nolte et al. (2010), Granger Causality is highly sensitive to SNR imbalances and filtering artifacts. Given the inherent SNR disparity between a cleanly extracted acoustic envelope and noisy SEEG data, coupled with the known distortions introduced by distinct filtering pipelines (Barnett & Seth, 2011), the GC results may reflect methodological artifacts rather than true physiological driving.

(3) The third concern is the study's exclusive reliance on linear metrics applied to the envelopes of band-filtered speech and neural signals, e.g., linear Granger Causality and cross-correlations. The human auditory system is an inherently non-linear dynamical system. Complex acoustic features, such as rapid spectrotemporal transitions or dynamic pitch trajectories, often drive non-linear neural responses and complex phase-locking behaviors. While the linear models provide strong interpretable results, by restricting their connectivity and directionality metrics to linear autoregressive models, the authors may be missing substantial non-linear interactions, or conversely, forcing a linear fit onto non-linear data, which can distort estimations of causality and temporal lags. The authors should consider explicitly addressing this limitation in their discussion. Ideally, they should validate their core directional claims on a subset of the data using an information-theoretic, non-linear metric (e.g., Transfer Entropy or Mutual Information), or apply linear methods to nonlinearly abstracted features (e.g., phonemic, syllabic, intonational-level features), to ensure their linear assumptions are not masking or misrepresenting the true underlying dynamics.

BUILDING AUGMENTED KNOWLEDGE ARCHITECTURES: REQUIREMENTS FOR COLLABORATION PLATFORMS OF NEXT- GEN CONCEPT ORGANIZATION TOOLS

eLife Assessment

This is a potentially important study comparing infants (8 months) and adults with respect to rhythmic EEG response properties during periodic and aperiodic visual stimulation. The results provide solid evidence for a ~4 Hz EEG response in infants that emerges independently of stimulation frequency. At this stage, additional work will be required to conclusively establish that this theta-band effect reflects genuine neural resonance rather than oculomotor processes.

Reviewer #1 (Public review):

Summary:

The authors report results from an EEG study investigating neural oscillations in 8-month-old infants, as well as an adult control group. Participants were presented with cartoon figures flickering at different frequencies, as well as a broadband condition. While adults showed the well-known dominant response at 10 Hz, infants showed dominance resonance at 4 Hz, irrespective of stimulation frequency. The authors interpret this finding as evidence for the fundamental role of 4 Hz oscillations in early development and discuss two conflicting theories regarding the underlying functionality.

Strengths:

Overall, this is a very well-designed and rigorous study, and the results significantly add to our understanding of a very fundamental aspect of early brain activity. The study is embedded in a coherent theoretical framework, and the authors discuss possible implications and next steps with great clarity.

Weaknesses:

I see relatively few weaknesses in this paper. It does not statistically compare infant and adult responses, which would add to the argument that infant responses actually differ from adult ones, but I don't think this is necessary at this point for the authors' argument.

In contrast, I actually like about the paper that the authors had a very clear vision of what they wanted to look at - 4 Hz oscillation responses in 8-month-olds - and this is exactly what they did. Yes, this does not answer all questions one might have, especially about the function of 4-Hz-oscillations in infants, but it goes a long way in characterising properties in 4 Hz oscillations, which provides the starting point for several potential future lines of research.

Reviewer #2 (Public review):

Summary:

This study combines EEG with frequency-tagging and broadband stimulation paradigms to investigate the developmental precursors of brain rhythms in 8-month-old human infants. The manuscript employs state-of-the-art methods, focusing on theta and alpha rhythms to assess their functional significance in visual information processing.

By evaluating responses to visual stimulation at different frequencies and broadband stimulation presented simultaneously with sounds, the authors report a stimulation frequency-independent response at ~4 Hz. They interpret this as the precursor of the adult alpha rhythm involved in perceptual echo mechanisms. However, I have a number of questions regarding the hypotheses, experimental framework, and analytical approach that need to be addressed before confirming the conclusions.

Strengths:

(1) The analyses are innovative, and the frequency-tagging paradigm is particularly well-suited for studying challenging populations with short protocols.

(2) The sample size is adequate.

Weaknesses:

There is a gap between the hypotheses and the experimental paradigm, as well as between the hypotheses and the analytical choices. These gaps could alter the interpretation of the findings and thus require clarification (or perhaps a reformulation of the theoretical framework).

I am not convinced that the conclusion - that the theta rhythm is the functional precursor of the alpha rhythm in the infant visual system - holds without addressing the following questions.

In brief, my specific concerns are the following:

(1) Gap Between Hypotheses and Experimental Paradigm:

The experimental paradigm involves the simultaneous presentation of sound and image, i.e., cross-modal sensory information, which contrasts with the manuscript's theoretical framework and conclusions, all of which are grounded in visual information processing. Previous work has shown that preverbal infants spontaneously engage in cross-modal associative learning in such audiovisual paradigms (e.g., Kabdebon et al., 2019). This raises the question of whether the paradigm taps into different mechanisms - such as associative learning - rather than those hypothesized, and whether these mechanisms might better explain the observed 4 Hz response. Associative learning mechanisms are particularly relevant to theta rhythm, involving hippocampal learning and the engagement of wider networks, including frontal areas.

Given this cross-modal design, I question whether it might alter the interpretation of the paradigm and the conclusions drawn. The current framing of the manuscript suggests that theta/4 Hz is the functional equivalent of the alpha rhythm for visual processing in the 8-month-old brain. However, the use of multisensory input complicates this conclusion for the visual domain and the parallel to adult mechanisms.

Kabdebon, C., & Dehaene-Lambertz, G. (2019). Symbolic labeling in 5-month-old human infants. Proceedings of the National Academy of Sciences, 116(12), 5805-5810.

(2) Analytical Focus - Gap Between Hypothesis and Analysis Choices:

The link between the literature described in the introduction and the hypothesis of a 4 Hz inherent rhythm in the visual system remains unclear. This puzzles me as to why the analyses focused on 4 Hz and a control band that is not adapted to the infant population. The focus of the analyses on 4 Hz (and the control band analyses) overlooks the critical frequency range (~6-8 Hz), which other studies have suggested may serve as proxies for the adult alpha rhythm. This omission does not align with the hypotheses regarding the role of the alpha rhythm in visual information processing.

The introduction discusses both alpha rhythm and its significance in perceptual echo phenomena, and theta rhythm and its role in mnemonic function, but these remain as separate phenomena. While the paradigm aims to assess perceptual echo phenomena in infants, one would expect the hypothesis to relate to precursors of the alpha rhythm in infancy (slower frequencies, yet related to alpha, ~6 Hz; Stroganova et al., 1999). However, the authors hypothesize that theta rhythm (4 Hz) is a precursor of the alpha rhythm in infancy: "Given the prominence of the theta rhythm in infancy, we expected the presence of a 4 Hz theta response and resonant activity in the infant visual system upon periodic stimulation and broadband visual input, respectively."

Why did the authors not study the 6-9 Hz frequency range, which previous work suggests may serve as a proxy for alpha in infants? Currently, the analyses are restricted to the theta range (i.e., 4 Hz) and a control band (adult-classical alpha range [8-14 Hz]), but [8-14 Hz] is not adapted to the infant population. At this age, prior work has reported ~6 Hz as the age-adapted range corresponding to alpha. It would be more appropriate to investigate this range. I can see some trace of this in Figure 2a, but perhaps this is weaker compared to the 4 Hz stimulation due to the cross-modal nature of the paradigm.

Stroganova, T. A., Orekhova, E. V., & Posikera, I. N. (1999). EEG alpha rhythm in infants. Clinical Neurophysiology, 110(6), 997-1012.

In the adult results, we also see similar ("two types of") responses: the main response at 8 Hz, which to me is the upper band of the theta rhythm (related to cross-modal learning), and traces around 10 Hz, which are more in line with perceptual echo mechanisms. The cited literature in adults (VanRullen & Macdonald, 2012), on which the authors base their framework and analysis, indicates a response at 10 Hz (not 8 Hz). This supports the idea that the 8 Hz response observed in this work might be related to the cross-modal presentation of stimuli. The authors could evaluate this more easily through a control group of adults with an unimodal (visual-only) presentation of stimuli.

(3) Methodological Approach and Clarity:

The methodological approach is not sufficiently detailed, which is crucial for reproducibility and wider contribution, especially given the difficulties in studying infants. Key points requiring clarification include preprocessing, choice of electrode clusters, and statistical details.

Reviewer #3 (Public review):

Summary:

The authors aim to characterize the intrinsic temporal dynamics of the infant visual system by examining how it responds to rhythmic visual stimulation. Using EEG in 8-month-old infants, they present visual stimuli that flicker at different periodic frequencies as well as broadband (aperiodic) luminance sequences to probe resonance properties of the visual system. The central goal is to determine whether the infant brain exhibits a characteristic oscillatory response independent of the external stimulation frequency, analogous to the well-known alpha (~10 Hz) resonance of the adult visual system. The results are then compared with data from a small adult sample to assess whether the dominant processing rhythm of the visual system shifts across development.

Strengths:

This manuscript presents a compelling and carefully executed study with intriguing findings, and I greatly enjoyed reading it. Several strengths deserve particular mention:

(1) Clear and focused research approach. The study addresses a well-defined question regarding the intrinsic rhythmic dynamics of the infant visual system and applies an elegant experimental paradigm to probe these dynamics directly.

(2) Well-designed parametric stimulation paradigm. The use of rhythmic visual stimulation across multiple frequencies (2-30 Hz), combined with broadband stimulation, provides a systematic way to characterize resonance properties of the visual system. This parametric approach allows the authors to clearly visualize the relationship between stimulation frequency and neural response, making the key effects easy to grasp.

(3) Strong statistical power in the infant sample. The relatively large infant sample (N = 42) is a major strength, particularly given the challenges of infant EEG research. This sample size provides sufficient power to support the conclusions about the robustness of the ~4 Hz response in infants.

(4) Converging analytical approaches. The authors combine periodic stimulation analysis with impulse-response-function (IRF) analyses of broadband stimulation, which provides complementary evidence for the presence of a ~4 Hz resonance in the infant visual system. This convergence strengthens the interpretation of the results.

(5) Direct developmental comparison. Although the adult sample is small, including adults in the same paradigm provides a useful benchmark showing the expected alpha-band response (~8-9 Hz), thereby contextualizing the infant findings within a developmental framework.

Weaknesses:

(1) Potential oculomotor contribution to the frontal 4 Hz effect. My main concern relates to the interpretation of the prominent ~4 Hz response in infants, particularly at frontal electrodes. The frequency range is close to what might be expected for oculomotor activity such as microsaccades, and the scalp distribution appears suggestive of such a contribution. Notably, the topography of the 4 Hz response differs substantially from the topography of the harmonic responses (Figure 2B), which show the expected occipital dominance. The latter is more clearly visual, whereas the former is more complex, definitely going beyond visual responses. This should be considered more in the discussion.

(2) Differences in topography between periodic and IRF effects. The spatial distribution of the 4 Hz response during periodic stimulation also appears to differ from the topography of the 4 Hz impulse response function (IRF; Figure 2B vs 3D). The IRF response appears not really "visual" in its spatial distribution, as compared to, e.g. the harmonic responses in 2B. This difference could indicate distinct underlying generators, but the implications of this discrepancy are not discussed in detail.

(3) Strength of the interpretation of neural resonance. Taken together, these observations make it difficult to determine conclusively whether the observed 4 Hz activity reflects genuine neural resonance of the visual system or potentially other processes (e.g., oculomotor dynamics). While the current findings remain interesting under either interpretation, the manuscript tends to favor the neural resonance account quite strongly without fully addressing alternative explanations.

(4) Relation to known developmental shifts in resting-state oscillations. The dominance of lower-frequency rhythms (theta range) in infancy is well documented in the resting-state EEG literature. Although this point is briefly mentioned in the discussion, it would be interesting to relate the current findings more directly to this literature. For example, it would be informative to know whether peak frequencies observed here align with resting-state theta peaks in infants and whether similar spatial distributions are observed.

(5) Limited follow-up of the proposed theoretical accounts. The discussion introduces both mnemonic and inhibition accounts for infant theta activity. However, these frameworks are not fully developed in relation to the present data. In particular, the mnemonic account might generate testable predictions within the current dataset, for example, whether theta responses change over time with repeated stimulus exposure or learning.

(6) Characterization of the adult alpha response. A minor point concerns the characterization of the adult resonance frequency. The manuscript often refers to a 10 Hz alpha resonance, whereas the data presented here show a peak around ~8 Hz (Figure 5A). In that frequency range, that is a lot. Also, there seems to be some variability, such that for the topography, the authors use the "individual alpha frequency". It would be interesting to see the distribution of peak frequencies across participants to appreciate the actual range. Interestingly, the spatial distribution of the alpha response also appears quite similar to the infant 4 Hz effect (Figure 5B) and differs from the harmonic responses, which may deserve further discussion. A comparison with resting-state alpha characteristics could also be informative here (e.g., does the peak IAF during visual stimulation relate to IAF recorded at "rest").

A really interesting text you could use to think through the psychological differences between the aggragated and individualized audience

Really cool framing

I think we read Rey Chow's "Listening after 'Acousmaticity'" in Greg's Sound class which talked about the inner voice operating as a kind of "primitive" figure, could be a good add here

Do you situate this before the introduction of sound in the cinema? What kinds of acousmatic vocal technologies exist before (I'm thinking of the Pythagoras we learned in Greg's Sound class)

Confused by this going on this list and not the prior one

What kinds of epistemic differences emerge from the addition of the z axis?

Both "attend to" and "natures" are a little vague, would love to know what you specifically mean here

More on this!!

Could be worth checking out some of the chapters in Saturation: An Elemental Politics which was edited by Melody. The one I've read is Hydromedia: From Water Literacy to the Ethics of Saturation by Joanna Zylinska which could work here but looking through the TOC I think there's a decent amount you could be interested in

The stills are a really clever way to find another method of communicating the themes and scope of the lists...I might steal it from you

/💻/thinkpad/🧊/me/📓/2026/03/30

dweb.archive

https://bafybeicnylqisska62bkauncgsn4kku6p5pl5jk4oawffcug6rap27wp74.ipfs.dweb.link?filename=(PDF)%20BUILDING%20AUGMENTED%20KNOWLEDGE%20ARCHITECTURES%EF%BC%9A%20REQUIREMENTS%20FOR%20COLLABORATION%20PLATFORMS%20OF%20NEXT-%20GEN%20CONCEPT%20ORGANIZATION%20TOOLS%20(3_30_2026%2011%EF%BC%9A56%EF%BC%9A41%20AM).html

Interesting to see that propher predicts the steep decline after 2026-01-01, which is evidently seasonal. But also nice that it stops after a couple of months.

Can we tune this using cross-validation? https://facebook.github.io/prophet/docs/diagnostics.html?utm_source=chatgpt.com

Is there no way to do this hierarchically (country is group) to get the shrinkage on country estimates?

Interesting... guessing some countries had none? What's that about?

I thought the prior expiration was 120 days. I think it would be helpful to create a table with each of the rules and with columns for: * the day (30/45/90/120) * waldo rule (if any) * transition in visibility, e.g., organic/jobalert --> sponsored * verbal description of the transition in indices, e.g. record in searchablejobs --> no record in searchablejobs * verbal description of the transition of the job status in dradis, e.g., job listed as active --> job listed as inactive (or deleted or whatever)

I think the index availability and employer experience aspects of this are most blurry to me still. In addition to 90 and 120 days, which I'm obviously confused by. :)

I think that it is very impressive that a 15-year-old created this website, even if it was mostly copied from a Japanese bulletin board site. I, at the age of 15, would have come nowhere close to putting something together like this, creating something that many people use.

I think this example shows how “free speech” can be interpreted very differently by different people. While the user wanted a less restrictive space, it also led to the creation of a platform that later became associated with harmful and extreme content. This makes me question whether having fewer rules online always leads to better outcomes.

Social media is a powerful tool that can bring like minded individuals together, but it's disgusting that a neutral and useful tool like social media can be commandeered so easily by the evil people in our world to spread hate, violence, and other unspeakable things. It genuinely stirs disgust and anguish in my heart hearing about the terrible things that these types of people share into the world.

I thought that Qanon was from 4chan. Did it originate on 8chan and then migrate to 4chan later or has it been on 8chan this whole time and I was just wrong?

/💻/thinkpad/🧊/me/📓/2026/03/30

brave.search-does+taoism+individualistic+or_communitarian

Clearly having no heart in relation to her poetry must have something deeper than that going on

Eli loves these drawings like his life depends on them and that makes sense considering they are practically magic

https://indy.peergos.me/0/💬/?actor=gyuri@♖indy&path=/💻/thinkpad/🧊/me/📓/2026/03/31&filename=iindy.0.💬.html&hyp.is=EKMOzizNEfG-TasWK6T6BA

indy 0 chat communication setup

Who Really Controls the Money | Simon Dixon

need a ten year plan

z

Actually, mowing the lawn frequently reduces plant diversity, destroys habitat for insects, birds and small mammals, and can cut pollinator numbers dramatically, so letting the grass grow is far better for biodiversity. :DDDD Debating gas versus electric mowers misses the main ecological issue, because the core problem is intensive mowing itself.

/💻/thinkpad/🧊/me/📓/2026/04/10

google.search?q=worse+is+better+paper

/💻/thinkpad/🧊/me/📓/2026/04/10

search.brave.com/search?q=worse+is+better+paper

I presented my PhD Research Topic at the

First European Conference on the Practical Applications of Lisp in 1990 in the 5th year of my Part time PhD work at Leeds University.

In my ignorant innocence I submitted a paper on my work because I though it would give me a chance to connect to the greats of Lisp.

/ 💻/ thinkpad/ 🧊/ me/ 📓/ 2026/ 04/ 10

ja-exmélet-english

/💻/thinkpad/🧊/me/📓/2026/04/13

dweb.archive-youtube.search?simon dixon why nothing make sense

Desde el punto de vista técnico, implica la integración rigurosa de estructuras formales provenientes de la matemática, criterios de diseño y factibilidad propios de la ingeniería, así como métodos de modelamiento y análisis característicos de las ciencias naturales.

Python es una herramienta que usamos para comunicarnos con la computadora de manera clara y sencilla, para que realice tareas específicas. Ejemplo : Hacer una suma a = 5 y b = 3 resultado = a + b print (resultado) Suma 5 + 3 y muestra 8.

Su idea era ayudar a personas que nunca han programado, así que todo debía ser sencillo y claro desde el inicio.

El modo script en programación, como en Julia, consiste en escribir el código en un archivo (por ejemplo, .jl) y ejecutarlo completo, en lugar de hacerlo línea por línea en el REPL(la consola interactiva), lo cual este enfoque facilita la organización de programas extensos, permite guardarlos y ejecutarlos nuevamente, y resulta más práctico cuando el código se vuelve complejo, en ese sentido el modo interactivo sirve para empezar y probar cosas rápidamente, pero cuando el código crece, es mejor usar el modo script, porque permite organizar y ejecutar programas completos de forma más cómoda.

Elegir nombres representativos para las variables es una estrategia fundamental en la programación, ya que contribuye a crear código más claro, comprensible y fácil de mantener. Por ejemplo, en lugar de usar nombres genéricos como x o dato1, es mucho más útil emplear nombres como edadUsuario, totalVentas o promedioNotas. Estos nombres permiten entender de inmediato qué tipo de información almacena la variable sin necesidad de revisar todo el código.

F I N I S.

Every other line indented and every 5th line starts a new stanza.

Every other line indented and every 5th line starts a new stanza.

Every other line indented and every 5th line starts a new stanza.

Every other line is indented.

this is supposed to say last.

Again, every other line should be indented and every 5th line should start a new stanza.

Every other line should be indented and every 5th line should start a new stanza.

Every other line should be indented

This should start a new stanza.

Every other line should be idented

Amanda gave. There should be a space in between the 2 words.

Every other line should be indented.

It should start a new stanza

Haply should start a new stanza

Watch should start a new stanza

this period shaped islamic art permanently, geometry and calligraphy became standard style

mosque and palace next to each other which shows a unity between religion and government that they are connected.

after the Almoravids nothing really changed in Spain, they mostly just continued previous styles

This isn't true. Most static site generators just have so little regard for UX that that's the only (or only reasonable) way to use them.

Self-modifying static site "generators" (incl. page builders) could and should be an entire thing, but it's the professional-developer class that's opposed to making that offering available to the public.

The general public also doesn't really give a shit about "CMSes". They don't care. They just want to update the website and be able to create new pages. It's the semi-technical folks whose brains have been tainted by déformation professionnelle that insist on CMSes as the (only) solution.

Den Begriff "Public Use File" (PUF) nutzen wir in der GLES eigentlich nicht mehr. Hier handelt es sich um den "Scientific Use File" (SUF)

This is pretty disturbing to read and think about. That are government went about the steps to "integrate' am entire race of people stripping them of what made them unique and for reasons that do not justify the systematic removal of the native man from his historical ties.

This entire paragraph is but one example of the work that is put in to give a possible manipulated view on the reality of a situation. Giving it an amateur camera work like it was taken on the spot instead of deliberately framed and set up tricks the audience to thinking this was a genuine moment of rapport between a native woman and her supervisor.

This raises a question of how this can be more common knowledge among historians. If organizations entire goals was to mischaracterize historical eras how can we trust the information they put out when often times it is the result of propaganda and hidden motives.

I wonder how many historical images have doctored or set up in a way that wasn't true to the situation. How much of historical photos hide the propaganda within them.

Because emotions are natural to man, every person can relate in some way. Those emotions have to be expressed in a clear, understandable way. This is important so that one can apply their own emotion to the author's versatile way of describing emotion, rather than this complicated, obscure, & niche emotion only a few select feel.

Karr does not say that emotion is just enough. When a poem lacks the indication of who is speaking, the poet has failed their job. The emotion is lost among the reader, because it cannot be applied to any voice or person within the poem.

I think this point is interesting, because often in writing, & especially poetry, Authors try to incorporate as many decorations as possible. Oftentimes, that can lead to the business of a poem that complicates rather than expresses. I love Karr's point about how the primary purpose is to invoke emotion, rather than these formal elements orchestrated to ornament the poem, yet often leads the poem to be lost among the readers.

Est ce qu'il y a donc un terme pour parler de l'usurpation d'identité sur Internet/avec l'IA ?

Tu peux mettre un lien vers un article de presse qui en parle

Quote shows how the author really likes cheese, could be good for the TMA

could be a good document to use for the EMA showing how religious people were?

rift of social peace due to religion

I think this point is interesting, because often in writing, & especially poetry, authors trie to sew in & incorporate as many figures of speech as they can. Often times, that can lead to the business of a poem that complicates rather than expresses. I love how Karr holds the invoking of emotion as the higher priority, rather than these formal elements that are so intentionally orchestrated to ornate, yet sometimes it is lost among the readers

eLife Assessment

Shin et al present important new observations regarding novel REM-specific cortical high-frequency oscillations. The evidence demonstrating the presence of a novel rhythm is convincing. However, the data presented is incomplete to demonstrate claims of a) brain-state-specific effects of these events, b) clear structured reactivation, and c) the specific degree of linkage to memory consolidation.

Reviewer #1 (Public review):

Summary and Strengths:

Shin et al deepen our understanding of high-frequency oscillations in the frontal cortex during REM in a manner that sheds important light on the roles of these events. In particular, they reveal that cortical HFOs are modulated by theta oscillations, occur in chains and recruit cortical neuronal activation patterns in a manner that is distinct from other high-frequency events during non-REM or in the hippocampus. They also show that these events occur during increased oscillatory cross-talk between hippocampus and cortex and may protect cortical neurons from downregulation of firing during sleep. Overall, this is important work with several novel observations pointing towards an important role for these events that will become increasingly understood over time.

I also wanted to comment that 2D is a beautiful illustration of separate and essentially exclusive communication channels used during HF events in NREM vs REM. They almost perfectly complement each other's frequencies.

Weaknesses:

I have only one major scientific critique: I believe we need to see quantification of how phasic REM theta waves with versus without HFOs differ. What do REM HFOs add to the "normal" theta oscillation? Without this comparison, it is more difficult to interpret the meaning of these events. Given that HFO chains have IEIs around the time of a theta cycle duration, are the repeating spiking activities stronger during HFO repeats than during adjacent theta waves without HFOs? What percentage of theta waves contain HFOs, and what is the firing rate during those theta waves with vs without HFOs? Is there differential firing rate modulation? The authors may even consider that all REM-HFO-specific quantifications should be shown as differential from phasic theta cycles without HFOs.

As a non-scientific comment on the manuscript itself: unfortunately, the paper is difficult to read and understand at times, requiring great effort by the reader. This is to an extent that communication is hindered. The paper is dense with changing methods, often from panel to panel. Unfortunately, the panel quantifications are not explained in the results section in a manner that readers can understand without going to read the methods, often for each individual panel. These measures should be explained in a way that lets readers understand the conclusions of each panel and what gross calculations were used to reach those. Instead, too much jargon is used rather than clear descriptions of the overall calculations being done for each panel. 


The authors mention in the discussion section that they see increased functional connectivity between mPFC and CA1, but most data suggesting this seems to be based on LFP rather than spiking. Functional connectivity is best defined by spiking-spiking relationships. And these authors have spiking data. So I believe either the descriptive language should be pulled back to something like "oscillatory coupling" or more analyses should be dedicated to showing spike-spike coordination across regions.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate high-frequency oscillations (HFOs) in the prefrontal cortex during REM sleep. They identify a specific pattern where these HFOs occur in "chains" that are phase-locked to theta oscillations, primarily during the "phasic" periods of REM. The study contrasts these events with isolated HFOs and NREM ripples, suggesting a unique role for these chains in coordinating activity between the prefrontal cortex and the hippocampus. Most notably, the authors report that a specific subset of hippocampal cells-those that co-fire with the prefrontal cortex during these HFOs-increase their firing rates over the course of sleep, suggesting a potential mechanism for selective memory consolidation.

Strengths:

The study addresses an under-explored area of sleep physiology: the fine-grained temporal coordination between the cortex and hippocampus during REM sleep. The identification of HFO "chains" and their association with higher theta power provides an interesting framework for understanding how the brain might organize information transfer outside of NREM sleep. The observation that specific hippocampal populations show differential firing rate changes based on their participation in these HFO events is a striking finding that warrants further investigation.

Weaknesses:

The primary weakness of the study lies in the lack of a clear distinction between global brain states and the specific events being analyzed. Because the authors compare HFOs across different sleep stages (NREM, tonic REM, and phasic REM) without sufficient controls, it is difficult to determine if the observed differences are intrinsic to the HFOs themselves or simply a reflection of the different physiological states in which they occur.

Furthermore, the evidence for "structured reactivation" is not yet convincing. The temporal alignment of these reactivation events appears inconsistent, with peaks occurring well before the HFO itself, and the analysis does not sufficiently control for pre-existing cellular assembly strengths. Additionally, some of the sleep architecture presented appears atypical, such as very short REM bouts and direct NREM-to-REM transitions that bypass standard progression, raising questions about the consistency of the sleep detection across animals. Finally, the study does not account for potential confounds like baseline firing rates when interpreting the behavior of "high-cofiring" neurons, which may simply be the most active cells in the population.

Reviewer #3 (Public review):

Summary:

Shin et al. examine hippocampal-prefrontal interactions during sleep using simultaneous CA1 and prefrontal cortex recordings in rats performing a spatial memory task. They identify high-frequency oscillation (HFO) events in PFC during REM sleep that occur in theta-modulated chains and are associated with increased CA1-PFC coherence and sequential, sparse reactivation of cortical ensembles. This pattern contrasts with the synchronous reactivation observed during NREM cortical ripples. Together with a simple cholinergic network model, the authors propose that REM HFO chains represent a distinct mechanism for hippocampal-cortical coordination that complements NREM ripple-mediated processing during sleep.

Strengths:

A major strength of the work is the extensive electrophysiological dataset, which includes simultaneous recordings of large neuronal populations in both hippocampus and prefrontal cortex across behaviour and subsequent sleep. The analyses linking high-frequency events to population dynamics, interregional coherence, and ensemble reactivation are technically sophisticated and provide an incredibly detailed description of REM-associated cortical activity patterns. In particular, the demonstration that REM HFOs occur in chains aligned to theta phase and organise sequential activation of cortical assemblies represents a potentially important advance in understanding the neural structure of REM sleep activity. The integration of experimental data with a computational model further provides a useful framework for interpreting the observed differences between REM and NREM network states in terms of neuromodulatory influences.

Weaknesses:

While overall this study provides a highly valuable body of work, there are two primary limitations, which, if overcome, would provide substantially more significance to the overall characterisation of REM HFOs. Specifically:

(1) Distinction from wake HFOs

The results largely support the authors' claim that REM HFO chains represent a distinct pattern of neural coordination compared to NREM cortical ripples. The analyses consistently show differences between REM and NREM events in terms of neuronal modulation, ensemble structure, and interregional coupling. However, similar high-frequency events during wake are not examined. Since REM sleep shares several network features with wakefulness, including strong theta oscillations, evaluating whether comparable PFC HFOs occur during wake would provide clarity on whether these events are specific to REM sleep (and its associated functions) or represent a more general theta-associated phenomenon.

(2) Link to memory consolidation

The manuscript proposes throughout that REM HFO chains may contribute to memory consolidation by coordinating hippocampal-cortical reactivation, but the evidence for this functional role remains indirect. The authors do highlight this as a limitation of the study - the inability to link their findings to learning - but it is not clear why. Further details of the behaviour results should be included. If no learning occurred across the eight behavioural sessions, this should be reported. If learning did occur, but could not be linked to HFO events, this should also be reported.

Author Response:

Reviewer #1 (Public review):

Summary and Strengths:

Shin et al deepen our understanding of high-frequency oscillations in the frontal cortex during REM in a manner that sheds important light on the roles of these events. In particular, they reveal that cortical HFOs are modulated by theta oscillations, occur in chains and recruit cortical neuronal activation patterns in a manner that is distinct from other high-frequency events during non-REM or in the hippocampus. They also show that these events occur during increased oscillatory cross-talk between hippocampus and cortex and may protect cortical neurons from downregulation of firing during sleep. Overall, this is important work with several novel observations pointing towards an important role for these events that will become increasingly understood over time.

I also wanted to comment that 2D is a beautiful illustration of separate and essentially exclusive communication channels used during HF events in NREM vs REM. They almost perfectly complement each other's frequencies.

We thank the Reviewer for the positive comments and for highlighting the importance of our work, especially the distinct communication patterns during NREM and REM cortical high-frequency events.

Weaknesses:

I have only one major scientific critique: I believe we need to see quantification of how phasic REM theta waves with versus without HFOs differ. What do REM HFOs add to the "normal" theta oscillation? Without this comparison, it is more difficult to interpret the meaning of these events. Given that HFO chains have IEIs around the time of a theta cycle duration, are the repeating spiking activities stronger during HFO repeats than during adjacent theta waves without HFOs?

We agree with the Reviewer that differences in activity during HFOs versus theta in the absence of HFOs is an important comparison to make to determine whether activity during HFOs reflect a unique state of information processing during REM sleep, or is redundant with theta oscillation signatures. We attempt to clarify this point in Figure S4I where we examined PFC population activity during theta periods outside of HFOs. Here, we extracted REM theta periods at least 250 ms away from detected HFOs and split the theta cycles into quartiles based on the theta power at the preferred theta phase bin determined by theta-coupled-HFOs (during that specific sleep session). We expect that using the preferred phase of HFOs is the most accurate choice for this comparison (compared to random phases). Lastly, we aligned PFC population activity to these theta phases and found that even in the highest theta power quartile, theta modulated fluctuations in PFC population activity were absent without HFOs. This indicates that theta-associated HFOs are the primary driver or signature of the observed population activity patterns (Figures 1H, 3F, S4I). An explanation of this procedure can be found in the Methods section under “Control for periods of high theta power”.

Regarding the comment “what REM HFOs add to the "normal" theta oscillation”, we hypothesize that generation of HFOs and associated population activity is the result of theta-mediated input from other brain regions that converge on PFC. It is possible that CA1 is a candidate region, since we observed that theta frequency activity in CA1 leads PFC (Figure 4K, Phase slope index result). Additionally, the high concentration of acetylcholine and the high inhibitory tone in REM sleep is conducive to local suppression in response to external drive, as shown in the model and noted in the Discussion. Thus, we propose that HFOs delineate transient windows where sparse populations of PFC neurons are activated in the backdrop of overall suppression, potentially to link specific ensembles across PFC and other brain areas such as the hippocampus – a phenomenon that differs from baseline theta activity in REM.

To address this point, we will provide additional analyses investigating PFC activity profiles during theta periods adjacent to HFOs. We will also reorganize the results and figures to highlight these important control analyses.

What percentage of theta waves contain HFOs, and what is the firing rate during those theta waves with vs without HFOs? Is there differential firing rate modulation? The authors may even consider that all REM-HFO-specific quantifications should be shown as differential from phasic theta cycles without HFOs.

To address these points, we will perform the requested analyses and explicitly quantify firing rate differences during HFO and non-HFO theta periods for further clarification.

As a non-scientific comment on the manuscript itself: unfortunately, the paper is difficult to read and understand at times, requiring great effort by the reader. This is to an extent that communication is hindered. The paper is dense with changing methods, often from panel to panel. Unfortunately, the panel quantifications are not explained in the results section in a manner that readers can understand without going to read the methods, often for each individual panel. These measures should be explained in a way that lets readers understand the conclusions of each panel and what gross calculations were used to reach those. Instead, too much jargon is used rather than clear descriptions of the overall calculations being done for each panel.

The point is well-taken and we apologize for the dense text and lack of methodological detail in the results section. We agree with the Reviewer that enhancing clarity and adding additional details about the quantitative methods within the main text and figure panels/legends would improve readability and make the manuscript more accessible for a wider audience.

To address this point, we will include important details in the results section and legends to clarify the methods and calculations used. We will also reorganize the manuscript text and reorder some figure panels for readability, and update the Methods section to parallel the Results/Figure order to the extent possible.

The authors mention in the discussion section that they see increased functional connectivity between mPFC and CA1, but most data suggesting this seems to be based on LFP rather than spiking. Functional connectivity is best defined by spiking-spiking relationships. And these authors have spiking data. So I believe either the descriptive language should be pulled back to something like "oscillatory coupling" or more analyses should be dedicated to showing spike-spike coordination across regions.

To address this point, we will temper the claims of functional connectivity and replace all instances with “oscillatory coupling”.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate high-frequency oscillations (HFOs) in the prefrontal cortex during REM sleep. They identify a specific pattern where these HFOs occur in "chains" that are phase-locked to theta oscillations, primarily during the "phasic" periods of REM. The study contrasts these events with isolated HFOs and NREM ripples, suggesting a unique role for these chains in coordinating activity between the prefrontal cortex and the hippocampus. Most notably, the authors report that a specific subset of hippocampal cells-those that co-fire with the prefrontal cortex during these HFOs-increase their firing rates over the course of sleep, suggesting a potential mechanism for selective memory consolidation.

Strengths:

The study addresses an under-explored area of sleep physiology: the fine-grained temporal coordination between the cortex and hippocampus during REM sleep. The identification of HFO "chains" and their association with higher theta power provides an interesting framework for understanding how the brain might organize information transfer outside of NREM sleep. The observation that specific hippocampal populations show differential firing rate changes based on their participation in these HFO events is a striking finding that warrants further investigation.

We thank the Reviewer for finding our work interesting and for the positive comments regarding our manuscript.

Weaknesses:

The primary weakness of the study lies in the lack of a clear distinction between global brain states and the specific events being analyzed. Because the authors compare HFOs across different sleep stages (NREM, tonic REM, and phasic REM) without sufficient controls, it is difficult to determine if the observed differences are intrinsic to the HFOs themselves or simply a reflection of the different physiological states in which they occur.

We appreciate this concern. We do agree that the generation of these ripples/HFOs in NREM and REM sleep are inextricably linked to global brain state (ex. cholinergic tone, as shown in the model), which results in differing patterns of activity across sleep states. However, we also show that activity associated with ripples and HFOs in NREM and REM sleep, respectively, delineate unique periods that underlie intra- and interregional interactions that differ from activity associated with other phenomena, such as spindles or baseline theta periods, in each respective sleep state. Regarding NREM PFC ripples, in our previous publication (Shin and Jadhav 2024), we show that PFC ripples are strongly associated with spindles and slow oscillations, but when PFC activity was assessed by aligning to each of these events separately, we observed significant differences in activity profiles (Shin and Jadhav 2024), indicating that NREM PFC ripples are indeed periods of differential PFC activity during which local reactivation is particularly strong. Similarly, here, in REM sleep, we see that PFC HFOs are strongly coupled with gamma oscillations and that these two frequency bands separately engage PFC neurons (Figures 2C, S3J, differences in phase locking preference of PFC neurons to gamma and HFO). While we observed strong theta modulated neuronal population activity in response to HFOs (Figure 1H), we did not observe the same for gamma events that were uncoupled from HFOs (Figure S3L, right). However, we did observe the population activity suppression when examining gamma events that were coupled with HFOs, but the theta modulated activity was largely absent (Figure S3L, left), indicating that, in terms of higher frequency oscillations, precise alignment to HFOs drives the theta modulated activity. Furthermore, we provide a control for baseline theta periods outside of HFOs to demonstrate that the phasic, theta-modulated activity (Figures 1H, 3F) is due to association with HFOs, and not a common feature during baseline theta activity (Figure S4I). Together, these results demonstrate that the theta modulated, phasic PFC activity that we report is primarily associated with the presence of HFOs.

To address this point, we will provide a more detailed explanation for the theta controls that we performed, and conduct additional analyses to control for different baseline periods during REM sleep, similar to the response to Reviewer 1’s first comment.

Furthermore, the evidence for "structured reactivation" is not yet convincing. The temporal alignment of these reactivation events appears inconsistent, with peaks occurring well before the HFO itself, and the analysis does not sufficiently control for pre-existing cellular assembly strengths.

We thank the Reviewer for raising these important points. Regarding the temporal alignment of assemblies during REM HFOs, since gamma activity is linked to and precedes HFO activity in REM (Figure S3F,G), we posit that assembly activation preceding HFO alignment may be gamma frequency driven. Indeed, we do observe gamma-associated peaks in PFC population activity temporally adjacent to the start of HFO chains in REM (Figure S5F), which we propose is driving the assembly activation.

Related to our response to Reviewer 1, the hypothesis that we have regarding this finding is that theta-mediated input to PFC, possibly from several brain areas including the hippocampus, converges and elicits cross-frequency activity spanning gamma and HFO bands. We hypothesize that these gamma and HFO oscillations work in concert to evoke the structured reactivation.

Furthermore, as the Reviewer accurately points out, we are not able to determine whether the assembly patterns active during the REM HFOs pre-existed prior to their assessment during sleep. Since there was not enough REM sleep during the earlier sleep epochs, we were not able to investigate assembly activation patterns during REM in the first pre-task sleep session prior to W-Track exposure.

To address these points, we will provide additional support for our claims, add clarification to major points, and expand on the methods used to assess structured reactivation. We will also analyze the spatial rate maps of assemblies during behavior on the W-Track and attempt to link these representations to assembly activity during REM HFOs. If sufficient controls cannot be provided, we will temper the claims of “reactivation” and replace all mentions with assembly “activation”.

Additionally, some of the sleep architecture presented appears atypical, such as very short REM bouts and direct NREM-to-REM transitions that bypass standard progression, raising questions about the consistency of the sleep detection across animals.

The reviewer is presumably referring to the hypnograms in Figure S1H. In Figure S1H, we presented concatenated hypnograms across all 9 sleep sessions, regardless of whether they were included for analysis. Furthermore, these hypnograms illustrate the output of just the sleep scoring algorithm and do not take into account the secondary, manual inspection that is performed to confirm sleep epoch inclusion. Individual epoch sleep state plots (e.g. Figure S1B) were visually inspected to confirm robust increases in theta-to-delta ratio detected in the absence of movement – epochs where microarousals or persistent subthreshold fluctuations in animal movement induced noisy TD ratio increases, and thus inaccurate REM designation, were excluded. We also want to note that omitting the edge cases, which is a minor part of the REM sleep data, does not change any results.

Another consideration is that these animals were running a strenuous learning task that required repeated traversal of multiple maze arms over multiple behavioral session, which likely increased sleep pressure and thus may have altered sleep state dynamics in a subset of animals (Leemburg et al. 2010; Yang et al. 2012).

To address these points, we will provide updated hypnograms that explicitly highlight the epochs used in analysis to resolve ambiguities. We will also further demonstrate that our procedure for sleep state designation is accurate and consistent across animals with supporting materials, including additional sleep stage classification examples, and REM-specific sleep examples marking tonic and phasic REM.

Finally, the study does not account for potential confounds like baseline firing rates when interpreting the behavior of "high-cofiring" neurons, which may simply be the most active cells in the population.

When we compared low and high cofiring neurons in CA1, we did indeed compare baseline firing rates between the two groups and found no differences. We compared both mean firing rates across entire sleep sessions as well as mean firing rates restricted to REM sleep (Figure S7A). We apologize that this important control was not emphasized more clearly.

To address this point, we will explicitly reference this figure in the main text as a standalone point.

Reviewer #3 (Public review):

Summary:

Shin et al. examine hippocampal-prefrontal interactions during sleep using simultaneous CA1 and prefrontal cortex recordings in rats performing a spatial memory task. They identify high-frequency oscillation (HFO) events in PFC during REM sleep that occur in theta-modulated chains and are associated with increased CA1-PFC coherence and sequential, sparse reactivation of cortical ensembles. This pattern contrasts with the synchronous reactivation observed during NREM cortical ripples. Together with a simple cholinergic network model, the authors propose that REM HFO chains represent a distinct mechanism for hippocampal-cortical coordination that complements NREM ripple-mediated processing during sleep.

Strengths:

A major strength of the work is the extensive electrophysiological dataset, which includes simultaneous recordings of large neuronal populations in both hippocampus and prefrontal cortex across behaviour and subsequent sleep. The analyses linking high-frequency events to population dynamics, interregional coherence, and ensemble reactivation are technically sophisticated and provide an incredibly detailed description of REM-associated cortical activity patterns. In particular, the demonstration that REM HFOs occur in chains aligned to theta phase and organise sequential activation of cortical assemblies represents a potentially important advance in understanding the neural structure of REM sleep activity. The integration of experimental data with a computational model further provides a useful framework for interpreting the observed differences between REM and NREM network states in terms of neuromodulatory influences.

We thank the Reviewer for finding our work important and for the positive comments regarding the manuscript.

Weaknesses:

While overall this study provides a highly valuable body of work, there are two primary limitations, which, if overcome, would provide substantially more significance to the overall characterisation of REM HFOs. Specifically:

(1) Distinction from wake HFOs

The results largely support the authors' claim that REM HFO chains represent a distinct pattern of neural coordination compared to NREM cortical ripples. The analyses consistently show differences between REM and NREM events in terms of neuronal modulation, ensemble structure, and interregional coupling. However, similar high-frequency events during wake are not examined. Since REM sleep shares several network features with wakefulness, including strong theta oscillations, evaluating whether comparable PFC HFOs occur during wake would provide clarity on whether these events are specific to REM sleep (and its associated functions) or represent a more general theta-associated phenomenon.

We thank the Reviewer for this suggestion. Indeed, this is an important comparison to make, since electrophysiological patterns of activity are similar across wake and REM sleep states.

To address this point, we will detect and analyze HFOs during running behavior on the W-Track to determine if they elicit similar, phasic population responses in PFC.

(2) Link to memory consolidation

The manuscript proposes throughout that REM HFO chains may contribute to memory consolidation by coordinating hippocampal-cortical reactivation, but the evidence for this functional role remains indirect. The authors do highlight this as a limitation of the study - the inability to link their findings to learning - but it is not clear why. Further details of the behaviour results should be included. If no learning occurred across the eight behavioural sessions, this should be reported. If learning did occur, but could not be linked to HFO events, this should also be reported.

This point is well-taken and we will reduce emphasis on memory consolidation in the manuscript. We do want to note that the primary focus here was to investigate new cortical-hippocampal activity patterns during sleep states that are established to be important for memory consolidation, in this case, REM sleep. Indeed, several major discoveries of reactivation and cortical-hippocampal physiological patterns in rodent sleep and wake states thought to be important for memory consolidation were initially reported without a link to memory consolidation, e.g., NREM hippocampal reactivation and replay (Wilson and McNaughton 1994; Lee and Wilson 2002), cortical – hippocampal activity coordination in slow-wave sleep (Siapas and Wilson 1998; Ji and Wilson 2007), waking replay in hippocampus (Foster and Wilson 2006; Karlsson and Frank 2009), etc. As Reviewer 1 noted, we expect that an important role for these novel events reported here will become increasingly understood over time.

The connection between learning and REM HFO activity is a line of investigation that we find very interesting. However, due to the experimental design and the rapid pace at which the animals learn this task (Shin, Tang, and Jadhav 2019), we were not able to robustly relate REM HFO activity to learning. Firstly, with our threshold criteria for REM sleep detection (>10 s) as well as a total REM sleep duration criterion for sessions, most of the sleep epochs included for analysis came from the later sessions when REM sleep was more abundant (Figure SF,G). Consequently, many of the sleep sessions following the earlier behavioral/learning sessions were excluded. Making a claim about the contribution of REM HFOs to the learning process requires the inclusion of REM sleep periods after each behavior session to examine incremental changes in response to learning. Furthermore, a comparison of these REM sleep periods to pre-task REM sleep (pre-task sleep session #1 prior to task exposure) is important to demonstrate that any changes are dependent on experience. However, we were unable to make this comparison due to lack of REM sleep in pre-task sleep session #1. It is likely that an investigation of the role of these novel events in memory consolidation may require rodent task designs that are known to require REM sleep, such as inference tasks (Abdou et al. 2024; Ellenbogen et al. 2007), motor learning (Nitsche et al. 2010), or emotional memory (van der Helm and Walker 2011; Cairney et al. 2015).

To address this point, we will reinforce this as a limitation of our study, reduce emphasis on memory consolidation, and further clarify that we were not able to link REM HFO activity to learning. We will also include additional details about the behavioral results.

References

Abdou, K., M. Nomoto, M. H. Aly, A. Z. Ibrahim, K. Choko, R. Okubo-Suzuki, S. I. Muramatsu, and K. Inokuchi. 2024. 'Prefrontal coding of learned and inferred knowledge during REM and NREM sleep', Nat Commun, 15: 4566.

Cairney, S. A., S. J. Durrant, R. Power, and P. A. Lewis. 2015. 'Complementary roles of slow-wave sleep and rapid eye movement sleep in emotional memory consolidation', Cereb Cortex, 25: 1565–75.

Ellenbogen, J. M., P. T. Hu, J. D. Payne, D. Titone, and M. P. Walker. 2007. 'Human relational memory requires time and sleep', Proc Natl Acad Sci U S A, 104: 7723–8.

Foster, D. J., and M. A. Wilson. 2006. 'Reverse replay of behavioural sequences in hippocampal place cells during the awake state', Nature, 440: 680–3.

Ji, D., and M. A. Wilson. 2007. 'Coordinated memory replay in the visual cortex and hippocampus during sleep', Nat Neurosci, 10: 100–7.

Karlsson, M. P., and L. M. Frank. 2009. 'Awake replay of remote experiences in the hippocampus', Nat Neurosci, 12: 913–8.

Lee, A. K., and M. A. Wilson. 2002. 'Memory of sequential experience in the hippocampus during slow wave sleep', Neuron, 36: 1183–94.

Leemburg, S., V. V. Vyazovskiy, U. Olcese, C. L. Bassetti, G. Tononi, and C. Cirelli. 2010. 'Sleep homeostasis in the rat is preserved during chronic sleep restriction', Proc Natl Acad Sci U S A, 107: 15939–44.

Nitsche, M. A., M. Jakoubkova, N. Thirugnanasambandam, L. Schmalfuss, S. Hullemann, K. Sonka, W. Paulus, C. Trenkwalder, and S. Happe. 2010. 'Contribution of the premotor cortex to consolidation of motor sequence learning in humans during sleep', J Neurophysiol, 104: 2603–14.

Shin, J. D., and S. P. Jadhav. 2024. 'Prefrontal cortical ripples mediate top-down suppression of hippocampal reactivation during sleep memory consolidation', Curr Biol, 34: 2801–11 e9.

Shin, J. D., W. Tang, and S. P. Jadhav. 2019. 'Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and Memory-Guided Decision Making', Neuron, 104: 1110–25 e7.

Siapas, A. G., and M. A. Wilson. 1998. 'Coordinated interactions between hippocampal ripples and cortical spindles during slow-wave sleep', Neuron, 21: 1123–8.

van der Helm, E., and M. P. Walker. 2011. 'Sleep and Emotional Memory Processing', Sleep Med Clin, 6: 31–43.

Wilson, M. A., and B. L. McNaughton. 1994. 'Reactivation of hippocampal ensemble memories during sleep', Science, 265: 676–9.

Yang, S. R., H. Sun, Z. L. Huang, M. H. Yao, and W. M. Qu. 2012. 'Repeated sleep restriction in adolescent rats altered sleep patterns and impaired spatial learning/memory ability', Sleep, 35: 849–59.

According to this trusted government source, the Federal Trade Commission has accused the social media giant Facebook of using shady practices, such as acquiring or destroying its competitors instead of innovating as a way to maintain its monopoly in the industry. One detail I noticed within the source is that the FTC's complaint mentions that Facebook first supported 3rd party developers but then decided to restrict them to prevent any potential competition in the case that any of them could start their own similar project that could become a competition to Facebook itself.

I think sites like yours are largely responsible for the spread of certain ideas. It's so easy to find other people who agree with you. It makes everything seem normal or acceptable. It's also harder to discern what is good information and what isn't because it's all mixed together. I think this is why some more extreme ideas seem to spread so quickly.

I didn’t think it would be back that far. I always thought it was a mostly a modern day thing having to do with social media. I mean, I guess the actual problem isn’t new, it’s just now it spreads a lot faster. I guess social media just makes it easier for people to actually see and buy in to this.

The reading shows that affordance is the key to for true robotic autonomy because without it, robots are just precisely repeating what they're programmed to do. However, in order for it to operate in an unpredictable way, it has to follows three steps of recognize if an object is interactice, find the right grip/learn how to grab the object, and connect that grip to a specific goal/learn how to use the object. This hammer problem is the perfect example for teaching robots common sense through sight and experience.

In today’s era of misinformation, I had never thought of conspiracies as an everyday occurrence, but according to Cheatham’s article, they were a common tool used by 19th-century American political parties to gain voter support. They used these conspiracy theories to gain popularity; however, these conspiracy theories contributed to an erosion of faith in our democracy. What really stands out to me is that all of this was accomplished using nothing other than newspapers and "word-of-mouth" (social networks). This phenomenon shows that the actual mechanism behind spreading conspiracies has nothing to do with what you are using as your medium; rather, it is simply for political advantage. And if anything social media has made it easier and less expensive.

The Web 2.0 article explains how the internet shifted from mostly static pages in Web 1.0 to more interactive platforms where users create and share content. What stood out to me is how this change is what influenced and made modern social media possible. Platforms like X (Twitter) or Instagram depend on user participation, not just information being posted by a few sources. I believe it shows why social media today is faster-moving and more dynamic compared to other forms of media.

One detail from the Smithsonian article that stood out to me is how conspiracy theories were already common in the 1800s, especially during elections when political parties were forming. The article explains that rumors about secret alliances and hidden plots became more common as more people gained the right to vote. This surprised me because it shows that misinformation and distrust in politics are not new problems. It also makes me think about how social media today might just amplify patterns that have existed for a long time.

The fact that even a once giant at the time myspace was laying off this many shows how communication transform over time. Once our needs/demands shift from generation to generation, and tools get stronger and stronger, we need new systems to keep up. Myspace could have been the victim of this, and I see current giants in communication like email taking a similar drop off (though maybe not AS much). Specifically from the article, myspace got rid of 47% of their workforce, which is astounding for a once communication giant.

I am surprised to see someone who invented such a big money making feature be apologetic for the ethical consequences of what he made. I feel like big tech companies never take responsibility for stuff like that, so it's refreshing to see this.

Including the segment from Drunk History regarding John Adams and Thomas Jefferson as part of a lesson about social media history is quite fascinating. I am reminded that political antagonism and the use of media as a tool of the trade did not begin with modern day social media sites, but Adams and Jefferson used papers and pamphlets for their own political gain as well.

Futaba Channel (also called 2chan) was the Japanese equivalent to 4chan and inspired the creation of its American counterpart.

In this article Aza Raskin expresses his regrets about creating the technology that allows social media users to consume content endlessly without the use of a next button. The article mentions that the creation of this feature can be linked to the increasing rates of social media addiction and that social media addiction may be correlated to increases in depression. Due to the evident negative side effects of this feature, Aza Raskin expresses his deep regret of creating it.

Briefing : Feuille de route pour l'autonomie et l'inclusion des enfants en situation de handicap

Résumé exécutif

Ce document synthétise les orientations stratégiques et les mesures concrètes présentées par la ministre déléguée chargée de l'Autonomie et des Personnes handicapées lors de son audition devant la délégation aux droits des enfants.

L'action gouvernementale repose sur un changement de paradigme : passer d'une logique de soins où la personne doit s'adapter au système, à une société pleinement inclusive qui s'adapte à la diversité des besoins.

Les points clés incluent le déploiement du plan « 50 000 solutions » pour répondre à la saturation des structures (notamment pour les enfants en situation de « double vulnérabilité » relevant de la protection de l'enfance), la réforme de l'école inclusive combinant scolarisation ordinaire et dispositifs spécialisés, et une simplification drastique des démarches MDPH.

Un engagement fort a également été pris pour rendre les recommandations de la Haute Autorité de Santé (HAS) opposables dans le secteur médico-social afin d'éradiquer les pratiques obsolètes, notamment concernant l'autisme.

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1. Philosophie et cadre d'action politique

La politique nationale est guidée par les lois de 1975 et 2005, ainsi que par la Convention des Nations Unies relative aux droits des personnes handicapées.

• Citoyenneté pleine et entière : L'objectif est de garantir que chaque enfant puisse s'épanouir en tant que citoyen, sans être réduit à un « objet de soin ».

• Adaptation de la société : Le gouvernement prône une société qui se rend accessible à tous, plutôt que de contraindre les individus à entrer dans des cadres préétablis.

• Parcours de vie : L'accompagnement est structuré de la naissance (détection et diagnostic) jusqu'à l'âge adulte (emploi et autonomie).

2. Petite enfance et diagnostic précoce

L'intervention précoce est jugée cruciale pour éviter les ruptures de parcours et l'épuisement parental.

• Repérage et guidance : Mise en place de programmes de guidance parentale et intégration du repérage des troubles du neurodéveloppement (TND) dans le nouveau carnet de santé.

• Parcours de soins : Structuration de services de repérage précoce (0-6 ans pour tout handicap, jusqu'à 11 ans pour les TND, et 19 ans pour la rééducation).

• Accueil en crèche :

  • Le « bonus inclusion » de la CAF a été un succès mais doit être réévalué. Actuellement basé sur le nombre d'enfants, il ne reflète pas toujours l'intensité de la prise en charge (nombre d'heures ou troubles importants).

• Une attention particulière est portée aux expérimentations de « crèches inclusives » nécessitant des financements plus adaptés.

3. L'école inclusive et l'accompagnement scolaire

Le gouvernement rejette le « tout inclusion » au profit d'une approche flexible et modulable.

• Dispositifs spécialisés au sein de l'école : Développement des unités d'enseignement (UEMA pour l'autisme, UEA) et des classes externalisées (IME, ITEP).

Ces structures permettent une présence en milieu scolaire ordinaire pour ceux qui ne peuvent pas suivre une classe classique.

• Transformation médico-sociale : Création de passerelles entre les établissements spécialisés et les écoles de quartier pour garantir des liens constants entre les enfants.

• Moyens humains : Les effectifs d'AESH (Accompagnants des élèves en situation de handicap) ont triplé depuis 2017, devenant le deuxième métier de l'Éducation nationale.

L'objectif est d'atteindre un nombre d'AESH à la hauteur des besoins réels pour la rentrée 2025.

4. Capacité d'accueil et le plan « 50 000 solutions »

Face à la saturation des Instituts Médico-Éducatifs (IME) et au maintien de 6 000 à 7 000 adultes en structures pour enfants (amendement Creton), le gouvernement déploie des moyens massifs.

• Trajectoire financière : 15 000 solutions ont été créées en 2025, avec un objectif de 22 000 d'ici fin 2026.

• Double vulnérabilité : Un effort prioritaire est porté sur les enfants relevant à la fois du handicap et de la protection de l'enfance (ASE).

Ces solutions, bien que coûteuses et complexes (prise en charge 24h/24, 365 jours par an), sont une priorité de la loi de financement de la sécurité sociale.

• La situation en Belgique : Un moratoire est en place pour stopper l'envoi d'enfants vers des établissements belges par manque de places en France.

Le gouvernement travaille à rapatrier ces enfants lorsque cela est possible et souhaité par les familles.

5. Protection et lutte contre les violences

Les enfants handicapés sont trois à quatre fois plus exposés aux risques d'agressions sexuelles et de maltraitances.

• Contrôles et honorabilité : Renforcement des contrôles dans les établissements avec hébergement et accélération de la vérification de l'honorabilité des professionnels.

• Communication Alternative Améliorée (CAA) : Déploiement d'outils (pictogrammes, etc.) pour permettre aux enfants non-verbaux d'exprimer leurs besoins et de dénoncer des violences, y compris devant la justice.

• Éducation : Réflexion sur l'intégration des programmes EVARS (Éducation à la vie affective, relationnelle et sexuelle) dans le secteur médico-social.

6. Simplification administrative et MDPH

Les délais de traitement et la complexité des dossiers MDPH sont identifiés comme une source majeure de précarité pour les familles.

• Tour de France des solutions : Une initiative pour identifier les bonnes pratiques locales et harmoniser les délais.

• Mesures de simplification :

  • Alignement de la durée des droits sur les cycles scolaires pour éviter les renouvellements annuels.

• Automatisation et usage de l'intelligence artificielle pour simplifier les formulaires de demande.

• Renforcement de la continuité des droits pour éviter les ruptures financières (notamment pour l'AEEH).

• Mise en place de rendez-vous physiques pour les premiers demandeurs.

7. Vers une excellence des pratiques médico-sociales

Un point de tension majeur soulevé par les parlementaires concerne le retard de la France dans l'application des connaissances scientifiques, notamment sur l'autisme.

• Opposabilité des recommandations de la HAS : La ministre s'est engagée à 200 % pour intégrer dans la future loi l'opposabilité des recommandations de la Haute Autorité de Santé.

L'objectif est de contraindre les établissements à abandonner les pratiques obsolètes (comme certaines approches psychanalytiques) au profit de méthodes éprouvées.

• Transformation de l'offre : Encourager le médico-social « hors les murs » pour intervenir directement dans les lieux de vie (famille, clubs sportifs, culture).

8. Accessibilité universelle : Sport, Culture et Mobilité

• Sport : Objectif de 4 000 clubs inclusifs (3 000 déjà identifiés) et généralisation des 30 minutes d'activité physique adaptée en ESMS.

• Mobilité : Depuis le 1er décembre, les fauteuils roulants (y compris sportifs) sont pris en charge à 100 %, sans avance de frais pour les familles.

• Culture : Accessibilité renforcée du Pass Culture pour les jeunes en situation de handicap.

Citations clés

« Ce n'est pas aux personnes de s'adapter à rentrer dans les cases qu'on pourrait leur construire... mais bien une société qui elle-même s'adapte. »

« Le "tout inclusion" n'est pas la solution ; les classes spécialisées au sein de nos écoles sont une bonne solution pour ceux qui ne peuvent pas être dans une classe ordinaire. »

« Je suis tout à fait favorable à ce que les directives de la Haute Autorité de Santé soient opposables... je vous suis à 200 % là-dessus. »

I agree that the primary purpose of social media is to connect different users together, whether it be through text or audio. In recent times, however, I have been quite surprised to see that there are different and newer forms of social media for more unique purposes, such as for romantic interests and to sell used goods. These types of social media platforms do more than just connect people. This is done not only for social networking but for a more specific and unique purpose. Additionally, their way of connecting people is not just through the traditional method of text and audio but also through newer ways and technology, such as AI.

Reading about Affordances remind me of the design of social platforms that we use everyday. For example, Instagram stories don't have any buttons that show "back" or "next"; however, even new users are aware that tapping the right side of the screen will lead us to the next stories. There is no button, but it feels so natural that we don't even think of it as using an interface. This shows how affordances are used in social platform screen design, as well as how they are manipulating our thumbs into moving and using the functions naturally.

I have personal experience with this design feature of social media applications. For instance, I primarily use TikTok and Instagram, and I have noticed that there is no endpoint when scrolling on them. When I use these applications, I find myself using them for longer than intended because I am constantly fed more content.

Useful to sense check out own repos

I think this part is interesting because it shows how blogs started as very simple personal tools, almost like online diaries. It’s surprising how something so basic later became a foundation for modern social media. Today, platforms like Instagram or Twitter feel very advanced, but they actually come from the same idea of regularly sharing updates and interacting with others.

El nacimiento de España no es Alfonso VI. Además Hernán Cortés en 1520 nombra al territorio conquistado 'Nueva España' y no 'Nueva Castilla' como podría haber sido si era conquista sólo de Castilla (como se dice en p. 32)

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The study 'Excess Met1-ubiquitination leads to solid aggregate formation' by Kaypee et. al suggests a previously unrecognised role for the E3 ligase HOIL-1 in clearing protein aggregates via autophagy (e.g. aggrephagy). In their model, toxic protein aggregates in cells are modified with ubiquitin chains, including M1-linked Ub-chains catalysed by LUBAC (of which HOIL-1 is a component). The HOIL-1 ubiquitin signal is posited to induce trafficking of aggregates to lysosomes for subsequent clearance. However, when HOIL-1 is inactive (catalytic C460A mutation), the pathway is interrupted. As a result, protein aggregates fail to clear, they increase in size and shift their biophysical properties from liquid-like to more rigid, insoluble aggregates. The authors explain their observations by an increasing amount of M1-linked chains on protein aggregates, which occur as a result of 'unrestrained HOIP activity' due to HOIL-1 inactivity (based on previous work). Increasing amounts of M1-chains are posited to promote aggregate formation, aggregate growth, and prevent clearance.

The major claims made in this manuscript are the following:

1. Following the induction of protein aggregate formation (e.g. alpha-synuclein, tau, beta-amyloid, p62 bodies), cells that express catalytically inactive HOIL-1 fail to clear protein aggregates and end up with more, larger and more rigid protein aggregates compared to cells that express WT HOIL-1.
2. The observations made in 1. are due to disruptions in late-stage autophagic flux. While aggregates in cells that express WT HOIL-1 co-localise with autophagy and lysosomal markers, aggregates in cells that express mutant HOIL-1 show autophagic, but not lysosomal markers.
3. An increase in M1-chains (either due to HOIL-1 inactivity or due to OTULIN knockout) is believed to be the cause for claims 1. and 2.

Main methodologies used:

The authors use two cellular systems. The first one is SH-SY5Y cells in which either WT or mutant HOIL-1 are transiently overexpressed (via the pcDNA3.1 plasmid), and physiologically important aggregates (Tau, Abeta, asyn) are induced. The second cellular system is MEF cells in which either WT or mutant HOIL-1 are endogenously expressed; in these cells aggregates are formed crudely through disruption of ribosomal translation. It is questionable if both systems can be compared. Aggregate formation is mainly monitored and quantified via fluorescent microscopy in both fixed and live cells, or via sucrose gradient fractionation to separate soluble and insoluble (=aggregate) fractions. The rigidity of protein aggregates is analysed in cells via FRAP, size and circularity measurements and 1,6-HD treatment, or in vitro after aggregate formation assays via size and circularity measurements. The observations are on the whole interesting, though the authors fail to discuss their data in light of previously published work. For example, HOIL-1 KO and KI animals were shown to feature polyglucososan bodies in brain, which is not mentioned. Also, McCrory et al on HOIL-1 chain types is not cited but seems relevant (Figure S4). Yet, the manuscript reports a number of interesting findings, more or less coherently, most useful for scientists embedded in current ubiquitin, autophagy, and LLPS fields. These reviewers believe that this manuscript will make a lot of sense in due course, and be well cited for a first description of the role of HOIL-1 in cellular quality control processes. A number of improvements seem required to consolidate the findings, and improve readability and impact.

Major:

1. Figure 1A-C: The authors transiently overexpress either WT HOIL-1L or catalytically inactive (C460A) HOIL-1 in SH-SY5Y cells, then induce and compare the formation of protein aggregates (alpha-synuclein, tau, amyloid-beta) in those cells over 72 h. More cells with aggregates were found in cells that overexpressed mutant HOIL-1L. While these findings are interesting, the cellular system used is artificial due to the transient overexpression of HOIL-1 (in presence of endogenous HOIL-1). Crucial controls are missing:

a. Adding a condition in which no protein is overexpressed, for example via an empty pcDNA3.1 or GFP only vector. This would help ruling out secondary effects due to the transient overexpression. It would also allow to monitor whether the same amount of aggregates form in the empty ctr compared to when WT HOIL-1 is overexpressed.

b. Figure legends and raw data points (?) in graphs do not match. The graphs show dubious statistics from 2-3 grey dots, while the figure legend refers to n=100 cells etc. This needs to be fixed.

c. Showing Western Blots of HOIL-1, to better understand the levels of endogenous HOIL-1 vs overexpressed HOIL-1 in these cells, and to compare overexpression levels between WT and mutant HOIL-1.

d. The study would also improve by western blotting and IF staining for other LUBAC components such as HOIP and SHARPIN. Do alpha-synuclein aggregates in both WT and mutant conditions co-localise with the other LUBAC components, and are there any differences between WT and mutants. This would further help strengthening the claims made in Figure S1A: '...suggesting that LUBAC is recruited to or retained within α-Synuclein aggregates.' And in the discussion: 'we found that LUBAC components were sequestered in aggregates, as evidenced by microscopy and gradient fractionation of soluble and insoluble proteins, confirming the direct involvement of LUBAC in aggregate processing.' 2. Figure 2A-F: The authors change to a genetic-derived system (comparing endogenously expressed HOIL-1 WT with mutant HOIL-1 based on MEF cells from their mouse models). However, they use puromycin to produce aggregates from random protein homeostasis defects, which yes leads to aggregates, but is not as nice as the induced generation of neiurodegeneration-relevant aggregates. It was observed that after 2 h of puromycin treatment, cells accumulate p62-positive protein aggregates, and in during recovery (2 h washout), the aggregates in the HOIL1 mutant cells outgrow the aggregates in the WT HOIL1.

a. However, the authors claim that: 'While Hoil-1+/+ MEFs efficiently cleared puromycin-induced p62 bodies,...', which is not supported by the data shown here. When comparing WT in panel C with WT in panel E, it becomes evident that the average number of p62 puncta before and after recovery is the same (around 5 puncta/cell in both pre and post washout conditions). A similar observation can be made for the mutant (around 12 puncta/cell in both pre and post washout conditions). Can the authors please amend their claims, or comment and perform a direct statistical comparison between the pre and post recovery conditions to test for clearance of p62 puncta in the WT after puromycin washout.

b. The authors state that: 'These findings indicate that although HOIL-1 catalytic activity is dispensable for the initial formation of puromycin-induced aggregates, it is essential for their subsequent clearance.'

As long as clearance of p62 bodies in the WT is not clearly shown, the second part of this sentence should be amended/removed.

c. The experiments shown would improve by adding a t = 0 condition. How many p62 granules are present before puromycin treatment? Is there already a basal difference between WT and mutant HOIL-1L cells? 3. Figure S1A: The authors claim that other LUBAC components co-localise to protein aggregates, based on sucrose gradient fractionation and the presence of the respective proteins in the insoluble fractions. Could the authors perform IF and stain for other LUBAC components (SHARPIN and/or HOIP) in their MEF cell system to directly validate this claim? 4. Figure 3G-H: The authors created a GFP-mCherry-p62 reporter system in both their WT and mutant HOIL-1 MEF cells and performed live cell imaging following puromycin treatment, which allows monitoring of both aggregate formation and loss of GFP signal due to the acidic lysosomal localisation. Excitingly, the ratio of GFP/mCherry in the later timepoints is reduced in the WT compared to mutant HOIL-1, indicating that HOIL-1 activity is required to traffic p62 bodies to lysosomes.

a. In panel G, a surprisingly large amount of p62 granules are present at t0, which (according to the relevant method section) is the time of puromycin treatment. This observation can be made for both WT and mutant cells. After 80 min of puromycin treatment in the WT, the majority of these puncta are cleared. Can the authors please comment on this high amount of p62 granules at t0 (before the effects of puromycin? And also on the observation that after 80 min there are now less granules than before puromycin? In case that t0 indicates the time of puromycin washout rather than puromycin addition, could this please be clarified in the methods or figure legend?

b. Panel H would improve by adding the quantifications for t=0 (or ideally for all the time points).

c. Fig S3C-D: Same comments as before but for GFP-mCherry-LC3. 5. Fig. S4B and Fig 4A-B: The authors state that circular aggregates are more soluble and have more LLP characteristics, whereas non-circular aggregates are less soluble and have more aggregate-like characteristics. However, the aggregates shown in Fig. S4B are un-circular but easily dissolve in response to 1,6-HD treatment, which seems contradictory. On the other hand, the aggregates shown in Fig 4B in HOIL-1 mutant cells appear much rounder than the ones in S4B, but do not dissolve in response to 1,6-HD treatment. Can the authors please comment on these discrepancies? 6. Fig. 4E-G. Here the authors suddenly switch to an in vitro aggregate-formation assay using mCherry-p62. In-vitro M1-chain reactions with either WT LUBAC or LUBAC with mutant HOIL-1L, together with the respective M1-chain reaction product, are added. This is not clear from the figure, and a schematic, as well as a gel (Coomassie) should be included to show component purity and indicate the biochemical in vitro nature of the experiment. It is good to have this breadth of methods, but does not help in the presentation if all figures look alike.

a. The key difference to the cellular situation is p62 aggregates are not directly ubiquitinated here, and instead ubiquitin chains are (non-covalently) added to samples. Can the authors please make this important difference clearer in their text? Why not directly ubiquitinate mCherry-p62 via LUBAC (WT vs mutant HOIL-1) and then perform an aggregation assay on the reaction product?

b. Can the authors please clarify whether the reaction was inactivated prior to addition to the aggregate-formation assay? If not, the enzymes might still be active at the point of aggregate formation, and the observed effects might be influenced by enzymatic activities and not only the presence of different M1-chain architectures. 7. Fig. 5B-C: The M1-specific DUB OTULIN is knocked down (again, cells) to increase the overall amount of M1-linked Ub-chains present in cells. P62 aggregate formation is induced and the authors claim that the increase in M1-chains influences aggregate size. This claim would be strengthened if it was directly shown that M1-chains form on p62 aggregates in this assay, for example via IF using an M1-antibody (and potentially a total ubiquitin antibody). This would also enable to directly compare the abundance of M1-chains between conditions (ctr vs Otulin, WT vs HOIL-1L mutant).

Minor:

1. Figure 1 D: The authors state that 'Notably, HOIL-1 C460A was detected within these structures, as demonstrated by its colocalization with tau aggregates' and show a co-localisation comparison between WT and mutant HOIL-1L. This sentence implies that WT HOIL-1 was not detected in aggregates, however the chosen image of the WT cells does not show any obvious tau aggregate, even though aggregates were induced in this condition according to 1A-C. The better comparison would be to pick an image that includes a tau aggregate. Moreover, this experiment would benefit from quantification, calculating the percentage of total aggregates that co-localise with WT HOIL-1L vs with mutant HOIL-1L.
2. Figure S1A: The authors state that 'Cells expressing HOIL-1 C460A displayed a pronounced accumulation of high-molecular-weight α-Synuclein species in the insoluble pellet fraction.' While the difference seen by Western Blot is apparent and seems to match Fig 1 A-C, it is not very strong. Moreover, the relevant comparison (WT vs mutant) is made between two different blots/membranes, and it is difficult to assess equal input solely based on the TCL lane. This experiment could be improved by normalising samples (for example via BCA) and by loading and imaging the two conditions (or at least the TCL and pellet samples) on the same membrane. The authors also state that: 'LUBAC subunits, HOIP, HOIL-1, and SHARPIN, were also enriched in this fraction, suggesting that LUBAC is recruited to or retained within α-Synuclein aggregates.' Both Sharpin and HOIL-1 seem to be present in similar levels in the pellet fractions of WT and mutant HOIL-1. Overall HOIP levels seem to be significantly increased in the mutant over the WT (see TCL lane), and to a similar level in the pellet fraction. It would be great if the authors could include these observations in their interpretation.
3. Figure 2G: Similar to before, this experiment would improve if the authors could find a way to normalise samples between conditions prior to sucrose gradient fractionation or have the most relevant samples on the same blot. It is challenging to properly interpret the results while the bands in the total cell lysate (TCL) lane do not have similar intensities between samples. A blot in which only the TCL and the pellet samples of all conditions were loaded onto the same gel would solve this, allowing for a better comparison between conditions. Based on what is shown in panel G, the authors should amend their claim: 'Consistent with microscopic observations, denser fractions from Hoil-1C458A/C458A MEFs contained increased signals of p62 specifically during the recovery phase (Figure 2G).' It is not apparent that more p62 is present in the insoluble fractions of mutant HOIL-1 cells after puromycin treatment. The band intensities look very similar (This is different for the recovery condition, which shows a strong difference, as stated).
4. Figure S2: Similar to before, the authors induce protein aggregate formation and compare cells endogenously expressing WT vs mutant HOIL-1L. The size of aggregates increases in mutant cells under proteotoxic stress. What happens to the number of aggregates per cell in these conditions? Does it also increase, or is it just the size?
5. Fig. S4A: Here the authors analyse the circularity of p62 aggregates in HOIL-1L mutant cells after recovery from puromycin treatment. This experiment would improve if the same analysis could be performed for the WT cells and for the pre-recovery condition (under the condition that large enough granules are present), allowing to make a comparison between WT and mutant, as well as between pre- and post-recovery.
6. Fig. 4H-J: The authors use a p62 mutant that is known for its enhanced ubiquitin affinity, repeat Fig. 4E-G and state: 'These aberrant condensates were similar to those observed in a reaction using wild-type p62.' Can the authors please comment on what they conclude from this similarity and why this experiment was performed? A quantitative comparison (condensate size and circularity) between WT p62 and mutant p62 may further be useful here.
7. Fig. 5A: Here the authors pulldown M1-linked Ub-chains in WT vs mutant HOIL-1 cells, with or without puromycin-induced aggregate formation. More M1-chains are observed in mutant HOIL-1 cells under puromycin treatment, but the difference is very subtle. The conclusions drawn from this experiment could be strengthened by including alternative methods, for example (if available) Ub-AQUA to measure the abundance of M1-chains, or using the M1-antibody for IF analysis.
8. Fig. S5B: The described differences between puromycin treatment and untreated conditions are extremely subtle on the anti-Ub blot, and absent in the anti-Met1 blot. I recommend that the authors remove this sentence, based on the shown data: 'we observed a modest increase in the signal of Met1-linked ubiquitin chains after puromycin treatment'.
9. Fig. 5F-G: The authors went back to their more-artificial system from Fig 1, in which HOIL-1L was transiently overexpressed (WT or inactive mutant) in SH-SY5Y cells, alongside alpha-synuclein aggregate formation.

a. The claims made from this experiment would be stronger in the other cell system. Could OTULIN be transiently overexpressed in the MEF cells, to monitor the effect of aggregate formation and clearance. Again, staining aggregates with the M1-antibody would improve this experiment.

b. The authors claim that HOIL-1 activity fine-tunes the function of HOIP within LUBAC (from discussion: 'This regulatory mechanism ensures that in the presence of functional HOIL-1, the overall quantity and potentially the architecture of Met1-linked ubiquitin chains are tightly controlled'). What is the quantity and architecture of M1-chains catalysed by LUBAC when HOIL-1 is very highly abundant, as it would be the case in this cellular overexpression system? 10. Sentence structure: 'A comprehensive understanding of how Met1-linked ubiquitination, particularly through intricate regulation by Linear Ubiquitin Chain Assembly Complex (LUBAC) components, such as HOIL-1, influences aggregate dynamics and clearance; therefore, it is crucial to develop targeted therapeutic strategies against neurodegenerative proteinopathies'.

Significance

The conclusions drawn from this study are very intriguing and give LUBAC (and HOIL-1) a so far unrecognised role in the clearance of protein aggregates, which are a hallmark of several neurodegenerative diseases for which there are currently no cures. Some of the findings described in this manuscript have the potential to be of very high impact and interest to a broader community, in particular researchers interested in protein homeostasis, autophagy, ubiquitin biology and neurodegeneration. In fact, those findings might even expand to autophagic pathways that target other cargo than protein aggregates. Both the novelty aspect and the potential for translational/therapeutic applications comprise the major strength of this manuscript. However, multiple of the presented experiments are currently lacking crucial controls, show weak effect sizes or were performed in artificial settings that likely do not represent relevant in vivo conditions, overall weakening or not fully supporting the claims made. Consequently, further experiments, data re-analyses and validations were recommended to fully support all the claims made here.

This review was written from the perspective of a researcher in the ubiquitin field.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors show that the branching activity of the E3 ligase HOIL-1, a component of the LUBAC complex, contributes to the autophagic clearance of p62 bodies and protein aggregates. This activity is attributed to enhanced linear, unbranched ubiquitin chain formation by the second E3 ligase of LUBAC, called HOIP. The model systems employed are cell lines including MEFs expressing a catalytically dead version of HOIL-1. In addition, the authors perform in vitro reconstitution experiments with purified ubiquitin chains, the LUBAC complex and p62. The main message is that solid p62 bodies are poor substrates for autophagy and that linear, non-branched ubiquitin chains promote solidification. The mechanism remains unclear and some of the effects sizes are rather modest.

Major comments:

The key observations mentioned above are convincingly shown. Since the authors don't claim any detailed molecular mechanisms, the number of conclusions in this study are limited.

Overall, the authors are quite careful regarding their conclusion, and therefore the ones that are made in this manuscript are generally well supported. The data regarding the clearance of the p62 bodies presented in Figure 3 should be backed up with additional data. The authors could add a macroautophagy inhibitors such as VPS34 IN1 and/or perform the clearance experiments in a ATG KO/KD cell line to corroborate the contribution of macroautophagy to the clearance. In addition, a proteasome inhibitor should be used for comparison.

The expertise and resources for the experiments mentioned above are expected to be well within the authors' capacity and should be doable within a few weeks.

Some of the effects sizes (e.g. Fig. 5 and S5) are very small and it is possible that some of them are below statistical significance if the number of replicates are increased.

Minor comments:

Figure 1D should be quantified, for example using PCC, Pearson correlation coefficient. Figure S1 should be quantified. Figure S3: It should be explained how the region for the profiles are shown were selected.

It is suggested to include a scheme of the LUBAC complex and its E3 ligase activities in Figure 1A. This will make it more accessible for readers, who are not so familiar with this complex, in particular as HOIP and HOIL can be easily confused. The authors may also want to clarify this in the abstract.

Significance

As mentioned in the summary. The authors report the observation that an excess of linear ubiquitin chains produced by HOIP in the absence of HOIL-1 activity results in the solidification of p62 bodies and reduced clearance by autophagy. This observation is novel and will be interesting for the proteostasis field.

This reviewer is expert in autophagy and protein degradation, but less so in the LUBAC complex.

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Referee #1

Evidence, reproducibility and clarity

Background

GWAS analyses carried out several years ago identified over 40 genetic loci associated with increased risk of developing Alzheimers and other types of dementia. They included loci encoding the E3 ubiquitin ligase HOIL-1 and the protein SHARPIN, two of the three components of the Linear Ubiquitin Assembly Complex (LUBAC) (Bellenguez et al, 2022, cited in the paper). The third component of LUBAC, HOIP, is the only E3 ubiquitin ligase known to catalyse the formation of Met1-linked ubiquitin (also known as linear ubiquitin). HOIL-1 is one of the few E3 ubiquitin ligases that attaches ubiquitin to serine and threonine residues in proteins forming ester bonds (Kelsall et al. 2019) and has been reported to restrict the HOIP-catalysed formation of Met1-linked ubiquitin (Kelsall et al. 2019; Fuseya et al. 2020; Rodriguez Carvajal et al. 2021).

Summary of Paper's findings:

In this study the authors report that HOIL-1 catalytic activity prevents neurodegenerative protein aggregation (synuclein, tau, A) in the human SH-SY5Y neuroblastoma cell line or in mouse embryonic fibroblasts (MEFs) expressing a catalytically inactive mutant of HOIL-1. They argue that this is achieved by maintaining the dynamic, liquid-like properties of protein condensates through regulation of Met1-linked ubiquitin chain levels, thereby facilitating efficient clearance via the aggrephagy pathway. They report that loss of HOIL-1 activity leads to excess Met1-ubiquitylation that drives the transition to rigid, solid-like aggregates resistant to autophagic degradation. In support of this conclusion, they also report that the siRNA knock-down of Otulin, a deubiquitylase that hydrolyses Met1-linked ubiquitin specifically, produces the same effect . The reframing of HOIL-1 as a key factor for fine-tuning ubiquitylation to maintain cellular protein homeostasis is an interesting development and the paper is generally well-written, focused and concise. Further work is required however, to fully convince these reviewers that the effects observed are entirely attributable to excess Met1-linked ubiquitylation, as claimed.

Major comments:

1. The causal link between elevated Met1-linked chains and solid-like aggregates in cells is the central claim of the paper. Throughout the study the authors use inactive HOIL-1 to enhance aggregate formation, which they attribute to increased Met1-linked ubiquitylation, something observed by themselves and others previously (Kelsall et al. 2019; Fuseya et al. 2020; Rodriguez Carvajal et al. 2021). However, the immunoblot for Met1-linked ubiquitin (Fig 5A) is not very convincing. In addition, the authors have not excluded the possibility that the loss of HOIL-1 enzyme activity has other effects on ubiquitylation, such as a change in the architecture of the ubiquitin chains caused by the absence of HOIL-1 catalysed formation of oxyester linkages. Many/most ubiquitin chains formed in cells contain more than one ubiquitin linkage type. It is therefore important for the authors to perform immunoblots for other ubiquitin linkage types, such as Lys63-linked ubiquitin, and to include these results in Fig 5.
2. The reviewers also think that the authors' claims that the transition of condensate property is linked to elevated Met1-linked ubiquitin chains would be strengthened by performing the biophysical assays (FRAP and 1,6-hexanediol resistance) after Otulin knockdown/knockout (and ideally also with Otulin rescue). This will provide direct biophysical evidence linking Met-1 linked chain elevation to condensate liquidity and 1,6-HD sensitivity.
3. The authors have not shown any evidence that Met1-linked chains are more enriched at the sites of protein aggregation. Would the authors be able to demonstrate direct spatial colocalization of Met1-Ub with the analysed aggregates?
4. Do the authors know if the effects that they are seeing are general effects on autophagy? For example, is starvation-induced autophagy similarly impaired in the cells studied? A simple flux-style experiment looking at LC3-II levels and p62 with starvations vs puromycin (-/+ bafilomycin) would be informative here.

Minor comments:

1. The authors show that loss of HOIL-1 catalytic activity causes p62 bodies to transition from dynamic liquid-like states to rigid solid-like states and claim this as a more general effect on protein aggregates. But the study does not directly demonstrate a liquid-to-solid transition for the disease-relevant α-synuclein, tau, or Aβ aggregates, limiting the generalisation of the claim beyond p62 bodies. Perhaps the authors should modify the text to better reflect this (or, even better, consider treating α-synuclein/tau/Aβ aggregates with 1,6-hexanediol to measure the response). [optional]
2. Given that the blots presented in Fig S1A appear to come from different membranes, and high-molecular-weight species of α-synuclein seem to exist in the insoluble pellet fraction of both WT and C460A expressing cells, the reviewers would caution against concluding anything about differences, which can only be assessed if the samples are run side-by-side on the same gel.
3. The Methods section says that two different total ubiquitin antibodies were purchased, but which one was used in Figure 5 and other figures are not stated. Please clarify.
4. On page 10 ABIN1 is mentioned but it is not mentioned that it is the protein product of the TNIP1 gene that is mentioned in the Introduction. This will confuse to many readers.
5. 1st paragraph of Discussion line 5 from bottom:- change "oof" to "of".

Significance

Prior research has established that the components of LUBAC are recruited to, and are components of, protein aggregates. A link between LUBAC and selective autophagy has also been established previously. The significance of this paper is that it identifies the catalytic function of HOIL-1 as a brake on the activity of LUBAC in proteostasis. The reviewer and co-reviewer are not experts in autophagy or aggregate formation in dementia but, if those reviewers who are find the data presented in these areas to be convincing, then this paper may be the first to suggest a molecular mechanism by which polymorphism/mutation of HOIL-1 leads to increased formation of the aggregates observed in Alzheimer's and other dementias. The results presented in the paper also suggested that initial autophagosome recruitment to aggregates is intact but subsequent late-stage autophagy is impaired. Hence, the study begins to identify the specific step that fails. However, as the authors themselves acknowledge, validation of these potentially exciting findings using in vivo models of neurodegeneration should be the aim of future studies. The paper combines the molecular dissection of ubiquitin and autophagy pathways to understand the causes of neurodegenerative disease. The paper will therefore be of interest to a broad audience, encompassing both the basic research and clinical research communities.

Reviewers field of expertise: Biochemists and cell biologists with an interest in ubiquitin and cell signalling.

Tu peux mettre des exemples de changements que les usagers peuvent adopter. Tu peux t'appuyer sur la partie du cours d'identité numérique "la réappropriation des traces" (la désaffection, la médiation, l'adoption)

Please could we update a link to the published version of this article? https://bmjopen.bmj.com/content/16/3/e103753.full

Many thanks

Update: This preprint has now been published in a peer-reviewed journal. The final published version is available at: https://doi.org/10.1002/pds.70369 We recommend citing the published version rather than this preprint.

eLife Assessment

This study is useful and unique, since hagfish brains are of phylogenetic importance and can reveal features ancestral to all vertebrates. The manuscript is, however, incomplete and would benefit from contextualization with the current literature; comparisons with the recent amphioxus study are suggested, plus an increased focus on the specific, unique features of the hagfish brain. One significant concern is the apparent absence of Datx2 expression, given that the riboprobe was synthesized from cDNA derived from whole-brain RNA extracts. Ideally, the authors should identify a tissue in which Datx2 is known to be strongly expressed and then apply the probe as a positive control.

Reviewer #1 (Public review):

Summary:

The manuscript presents a three-dimensional and molecular atlas of the adult hagfish brain to investigate the evolutionary origin and early diversification of vertebrate brain organization. Using whole-brain tissue clearing, light-sheet microscopy, and computational reconstruction, the authors generate a high-resolution 3D anatomical model of the hagfish brain. They complement this structural analysis with gene-expression profiling of neurotransmitter systems and receptors, including glutamatergic, GABAergic, cholinergic, serotonergic, and dopaminergic markers.

Strengths:

Together, the work aims to establish a modern neuroanatomical reference for the hagfish. Given the phylogenetic importance of hagfish as one of two extant species of cyclostomes (the other being lamprey), and the fact that the hagfish brain has barely been studied in contrast to the lamprey, the atlas provides a foundational resource and should be of interest to evolutionary and comparative neurobiology.

Weaknesses:

However, there are several places where both data presentation and the narrative can be improved and clarified, and particularly some of the homology and evolutionary claims seem to be superlative and need to be toned down. I present more detailed comments below:

(1) The authors spend too much effort trying to convince readers of the monophyly of hagfish and lamprey to stress its importance for evolutionary comparisons. This is now well accepted; instead, there could be more details on some of the specific, unique features of the hagfish brain relevant to a comparative atlas. For instance, the unusual fusion of the telencephalon anteriorly with the olfactory bulb and posteriorly with the diencephalon (Wicht and Northcutt, 1992), the degenerate visual system, the absence of the pineal gland, and the oculomotor system can be discussed in reference to the generated atlas and examined marker expression in related structures and their possible identity.

(2) The assertion that the MGE is absent in the lamprey is incorrect based on Sugahara et al. (2016; 2017), who identified lamprey paralogues of Nkx2.1/2.4 that are expressed in the ventral subpallium. This should be corrected.

(3) The major contribution of this study, in my mind, is the "three-dimensional atlas" of the hagfish brain. However, the atlas itself is not presented; A video of the 3D reconstructed Nissl-stained hagfish brain would be an important data resource and should be added. Annotations of forebrain, midbrain and hindbrain regions and constituent major structures can also be illustrated, which will be a useful resource.

(4) In the pallium, there seems to be an inner GABAergic cell layer and inner and outer glutamatergic cell layers, as noticed in lampreys (Suryanarayana et al., 2017). What are the overall proportions of glutamatergic and GABA neurons? In the images, it does seem that vGlut neurons are present in both P2 and P4, while there appear to be more GAD neurons in P4.

(5) As a general comment, homology claims should be toned down throughout the manuscript. This would at least require some connectivity data or transcriptomic analysis for any possible suggestions; the current data, with few markers, are insufficient for any reasonable comparisons.

(6) Expression of Pax6 and AChE is not sufficient to suggest a cerebellum-like structure. While it is true that embryonic Pax6 expression in the rhombic lip of the hagfish embryo is more comparable to other vertebrates than lamprey, and the presence of a rudimentary cerebellum-like structure would be of great interest, the evidence is too limited for such claims and should be toned down.

(7) Again, expression of Tbr1 and GAD1 in NCvl neurons does not suggest that these could be hippocampal neurons. One would at least need to rule out expression of prethalamic markers and demonstrate the presence of pallial markers through transcriptomic data (as in Lamanna et al., 2023).

(8) Presence of GABAergic neurons in the striatum - is there any data on expression of dopamine receptors, particularly given the seeming loss of the D2 receptor subtype in the hagfish?

Reviewer #2 (Public review):

Summary:

The work of Harada and collaborators fills an important gap in our knowledge of neuronal identities in the adult hagfish brain. There is essentially no modern, cell-type-level characterisation of neuronal identity in the hagfish brain yet. Existing data are limited to classical neuroanatomy (e.g. Nieuwenhuys) and sparse transmitter/gene-expression studies, mostly in embryos (e.g. work from the Kuratani lab). This study reveals a very broad peculiar pattern of dopaminergic identities and a strikingly unusual pattern of serotonergic transmission, with serotonergic cell bodies present in the telencephalon, which is uncommon for vertebrates and contrasts with previous reports (e.g., Kadota, 1991).

Strengths:

The three-dimensional reconstruction of the brain, including the ventricular system, is novel and very useful. Most of the neurotransmitter identity patterns presented here have not been previously described, and those that were published earlier, such as the serotonergic system (e.g. Kadota, Nieuwenhuys, Wicht), are old and would clearly benefit from re-evaluation using more modern approaches.

Weaknesses:

Neurotransmitter identities are highly relevant for interpreting the possible presence of LGE/MGE territories in hagfish (e.g. GABAergic patterns), for characterising the raphe nuclei (e.g. serotonergic system), and for refining our understanding of the central prosencephalic complex in relation to other vertebrate brain architectures. However, the authors do not address these points and overlook recent evidence from the amphioxus brain that could help interpret their results in an evolutionary context. Overall, the results are insufficiently discussed in relation to the current state of the art.

The study would clearly benefit from complementary gene expression profiling to place these neurotransmitter patterns within a broader framework of brain partitions, to enable more direct comparisons with other vertebrates, and, importantly, to interpret them in relation to the prosomeric model. Furthermore, the work lacks appropriate controls for the in situ hybridization experiments; Datx2 does not show any expression, so there is currently no evidence that this probe is functional. Including such controls would also strengthen the overall description of the dopaminergic system, especially given that the expression patterns of the different genes analysed appear very diffuse and somewhat random.

The way in which Standage reconsiders this point is significant. It seems that what many regard as "the ideal model for reliable news "the media landscape of the middle of the last century is a one-time event and not the norm. In fact, since time began, humans have habituated communicating through forms of decentralized and thus often very noisy methods of communication (graffiti, handwritten reproductions, and blogs). This brings up another difficult question: are people who lament the loss of unified public discourse because social media "destroyed it" grieving for something that was never truly beneficial or simply grieving over the short period during which a few gatekeepers agreed on things?

I think this part shows that social media behavior hasn't really changed, only the speed and scale have. People were already sharing rumors and sharing updates before the internet, just through either pamphlets or graffiti. But today, we often blame social media for misinformation, but these patterns have always existed, just in a different form. I think the main difference is that it can spread much faster and reach a wider audience.

A gut feeling, instinct

수정 가능한 post/update 게시판 용 기능ㅇ ㅣ있어야

mcp로 붙이면 서비스로 엮을 수 있을 것인가

테스트입니다

Note d'Information : Priorités de la Protection de l’Enfance et Justice des Mineurs

Synthèse de l'Exécutif

Ce document synthétise les orientations stratégiques et les réformes engagées par le ministère de la Justice pour renforcer la protection de l’enfance et moderniser la justice des mineurs.

Les points clés incluent :

• Urgence et Rapidité : Réduction des délais de jugement (passés de 18 mois à 8,7 mois en quatre ans) et création d'une ordonnance de protection provisoire permettant au procureur de statuer en 72 heures.

• Refonte du Placement : Fermeture des Centres Éducatifs Fermés (CEF) publics au profit des Unités de Placement de la Jeunesse et de l'Éducation (UJPE), mettant l'accent sur la continuité pédagogique (52 semaines/an).

• Moyens Humains Massifs : Création de 1 600 postes au ministère de la Justice, dont 50 nouveaux cabinets de juges des enfants en deux ans et 70 postes à la Protection Judiciaire de la Jeunesse (PJJ).

• Évolutions Législatives : Soutien à l'imprescriptibilité des crimes sexuels sur mineurs, à la présence obligatoire de l'avocat pour l'enfant, et volonté de réformer l'« excuse de minorité » pour les crimes les plus graves.

• Protection contre les Fléaux Modernes : Lutte contre la prostitution des mineurs (6 prostituées sur 10 sont mineures), interdiction des téléphones portables en centres de placement, et encadrement du protoxyde d'azote.

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1. Renforcement de la Protection des Enfants Victimes

Urgence Judiciaire et Mesures de Sûreté

L'accent est mis sur la nécessité d'une justice qui s'adapte au rythme de l'enfant.

• Ordonnance de protection provisoire : Un nouveau dispositif permet au procureur d'agir en 72 heures pour protéger immédiatement un mineur, avec des interdictions de contact et l'attribution provisoire du logement au parent protecteur.

Le juge dispose ensuite de 8 jours pour être saisi et de 15 jours pour statuer.

• Loi du 18 mars 2024 : Prévoit le retrait automatique de l'autorité parentale pour les parents condamnés pour crime ou violence sexuelle sur leur enfant, ainsi que l'élargissement de la suspension de l'exercice de cette autorité dès la mise en examen.

Accompagnement et Droits des Mineurs

• Avocat pour l'enfant : Soutien à la présence obligatoire d'un avocat en assistance éducative.

Une expérimentation avec les barreaux est envisagée avant une généralisation législative.

• Unités d'Accueil Pédiatrique (UAPED) : Déploiement en cours sur tout le territoire pour améliorer le recueil de la parole et le soin des victimes.

• Chiens d'assistance judiciaire : Passage de 10 à une trentaine de chiens actuellement, avec un objectif de 100 chiens (un par département) d'ici un à deux ans pour apaiser les enfants lors des procédures.

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2. Réforme de la Justice Pénale des Mineurs

Équilibre entre Sanction et Éducation

La doctrine ministérielle refuse l'opposition entre ces deux concepts.

• La sanction comme acte éducatif : « La sanction fait partie de l'éducation. La sanction toute seule n'est pas un but en soi [...] et une éducation sans aucun interdit mène au n'importe quoi. »

• Efficacité du Code de la Justice Pénale des Mineurs (CJPM) : Les délais entre les faits et la sanction ont été divisés par deux en quatre ans (8,7 mois en 2024 contre 18 mois en 2020).

Transformation des Structures de Placement

Le constat sur les Centres Éducatifs Fermés (CEF) est jugé sévère : coût élevé (30 à 50 % de plus), taux de fugue identique aux centres classiques, et déshérence éducative (seulement 5 à 10 heures de cours par semaine).

• Création des UJPE : Ces nouvelles unités fusionnent les anciens foyers et les CEF pour garantir un parcours de reconstruction pédagogique.

• Recrutement de professeurs techniques : Réouverture d'un concours pour 40 professeurs dépendant directement du ministère de la Justice afin d'assurer 26 heures de cours par semaine, 52 semaines sur 52, y compris durant les vacances scolaires.

• Santé et Addictions : Recrutement de 60 infirmiers pour pallier les carences de soins psychiatriques et de prise en charge des addictions dans les centres de placement.

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3. Moyens et Organisation de la Justice

Augmentation des Effectifs

Le budget de la Justice permet une hausse inédite des moyens humains :

• Magistrature : Création de 50 cabinets de juges des enfants supplémentaires en deux ans (notamment à Bobigny, Cambrai, Alès).

Actuellement, certains cabinets gèrent entre 400 et 500 dossiers.

• PJJ : Recréation de 70 postes, permettant de renforcer les effectifs là où ils baissaient depuis 20 ans (ex: Marseille, Île-de-France).

• Milieu Ouvert : Réaffectation de 150 éducateurs vers le milieu ouvert pour ramener la charge de travail à environ 23 dossiers par agent (contre 25 auparavant).

Unité de Commandement

Le système actuel est jugé trop fragmenté (plusieurs ministères concernés, compétences partagées avec les départements pour l'ASE).

Une volonté de meilleure coordination, voire d'unité de responsabilité, est exprimée.

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4. Enjeux de Société et Nouvelles Menaces

Violences Sexuelles et Imprescriptibilité

• Fin de la prescription : Avis favorable pour l'imprescriptibilité des crimes sexuels sur mineurs, ainsi que pour les crimes de sang (assassinats).

• Prostitution des mineurs : Un constat alarmant montre que 60 % des prostituées en France sont mineures.

Des unités dédiées au sein de la PJJ sont opérationnelles depuis trois mois pour lutter contre ce fléau et les réseaux de proxénétisme.

Sécurité Numérique et Addictions

• Interdiction des téléphones : La nouvelle circulaire de politique éducative et pénale impose l'interdiction des téléphones portables dans les chambres des centres de placement pour protéger les mineurs des prédations numériques (trafiquants, proxénètes).

• Protoxyde d'azote : Soutien à la pénalisation du transport et de l'achat en ligne (en dehors du cadre médical), alors que les intoxications ont triplé entre 2020 et 2023.

Débats sur la Responsabilité Pénale

• Excuse de minorité : Position favorable à la fin de l'automatisme de l'atténuation de peine pour les crimes les plus graves (assassinats, tortures) commis par des mineurs de 13 à 15 ans.

Cela nécessiterait une évolution constitutionnelle tout en préservant la spécialisation du jugement des mineurs.

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5. Données Clés et Statistiques

| Indicateur | Donnée Source | | --- | --- | | Délai moyen de jugement (2020) | 18 mois | | Délai moyen de jugement (2024) | 8,7 mois | | Dossiers par cabinet de juge des enfants | 400 à 500 (moyenne) | | Proportion de mineurs parmi les prostitués | 60 % | | Nombre de mineurs à l'ASE | 400 000 (dont 200 000 placés) | | Heures de cours en CEF | < 10h/semaine (contre 26h en milieu classique) | | Placements chez des tiers de confiance | < 9 % (19 000 jeunes) |

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Citations Marquantes

« L'enfant ne vit pas au rythme d'un dossier administratif ou d'un dossier judiciaire. [...] 4 mois pour un mineur c'est une vie. »

« Nous devrions pouvoir en grande partie avoir honte de la façon dont on traite une partie de ces enfants notamment à l'aide sociale à l'enfance. »

« Le placement doit protéger et pas rendre encore plus vulnérable. »

« La sanction fait partie de l'éducation. [...] Une éducation sans jamais aucun interdit mène au n'importe quoi. »

eLife Assessment

This important work outlines why commonly applied performance metrics in predictive modelling do not accurately reflect translational potential using the example of psychiatric care; it provides a web-based tool to contextualize effect sizes in psychiatry with respect to reliability and base rates, and to calculate the real-world utility of prediction models under different scenarios. The evidence supporting the conclusions is convincing, incorporating established psychometric principles that will be of use for multiple fields, along with transparent quantitative logic and example applications. The manuscript would benefit from further details about how the tool can be optimally applied and how the resulting outputs should be interpreted. The work will be of broad interest to both clinical experts and scientists in biomedicine and the life sciences.

Reviewer #1 (Public review):

Summary:

The manuscript provides a well‑argued discussion of the misalignment between common predictive performance evaluations reported in the literature and actually measuring clinical utility in the context of predictive psychiatry. Specifically, the authors discuss measurement reliability and prevalence as two neglected factors which can substantially inflate the assessment of model performance for clinical practice. To mitigate this, the authors offer a concrete framework and an accompanying web tool, with which to adjust performance metrics and additional predictive‑value and decision‑analytic measures.

Strengths:

The manuscript speaks convincingly about the risk of face validity and the practical irrelevance of seemingly promising predictive models in psychiatry. The authors outline how predictive performance estimations often fail to generalize to clinical contexts and thereby potentially mislead scientific efforts. In the face of ubiquitous biomarker models and incremental improvements in the literature, the reader is reminded that, irrespective of the glory of the proposed model, low reliability of clinical measurements fundamentally affects (and limits) both effect sizes and predictive performance ("garbage in, garbage out"), and that neglecting this can ultimately lead to misinformed decisions in the treatment of individual patients. The provision of an online tool with a user‑friendly interface and clearly worked examples is a major practical asset that will facilitate the adoption of the proposed framework beyond quantitative methodologists.

Weaknesses:

While the outlined issues highlight important aspects in the translational gap, the suggested solutions remain somewhat theoretical. For example, the use of prevalence might not reflect what a model would see in practice, assuming that population prevalence and the composition of actual clinical cohorts are aligned. Accounting for who presents to care, and under which referral or triage patterns, is a crucial determinant of effective base rates. While the authors do acknowledge the importance of using base rates from the target population, these nuances could be emphasized more prominently at the points where practical recommendations are made. Relatedly, the analytical context and the methodological assumptions are not clearly specified. Many arguments and demonstrations are derived in univariate, group‑comparison settings and then discussed in a way that can be read as broadly applicable.

Reviewer #2 (Public review):

Summary and strengths:

The authors present a description of their online tool to estimate real-world performance of predictive models. The authors bring together different calculations to make better-informed implementation choices. It is a very nice tool to go from effect sizes to base rates to decision curve analysis. The paper describes the background and use of the tool with examples and seems like an extended version of their online how-to. The methods themselves are not new, but I think the tool will be valuable for researchers from different fields. Tools already exist for the conversion of effect sizes (my current favorite is https://www.escal.site/), but I haven't seen measurement noise being incorporated previously. The main benefit is the evaluation of performance under different real-world scenarios. Code is available on GitHub, and the manuscript is well-written.

Weaknesses:

While comprehensive explanation and examples are important for correct use of the tool, I don't really see the added value above their online how-to guide, as the software itself has already been published (Karvelis, P. and Diaconescu, A. O. (2025b). E2p simulator: An interactive tool for estimating real world predictive utility of research findings. Journal of Open Source Software, 10(114):8334.)

Reviewer #3 (Public review):

Summary:

This important work provides a web-based tool to contextualize effect sizes in psychiatry with respect to reliability and base rates (collectively referred to as predictive utility analysis). The methods for the tool incorporate established psychometric principles that I think are of use for multiple fields in this seemingly easy-to-use tool. I agree with the critical importance of this tool and the methodological points made in this manuscript. Enthusiasm for the manuscript is weakened by a lack of clarity on the formulation of the paper and stated goals of the examples used, with the inferences and impact on clinical decision making from various parameterizations via this tool left open-ended.

Strengths:

This paper presents a well-considered and, what I think will be highly useful, web-based tool to contextualize effect sizes with respect to reliability and base rates. As the authors rightly point out, such a tool could be used in conjunction with widespread analytic power analysis tools in study planning. The paper also well contexualizes the need for such a tool in the relatively recent history of concerns of power, reliability, and inference in psychiatry specifically, and more general meta-scientific debates in psychology and neuroscience.

Weaknesses:

My primary feedback on this manuscript is the lack of clarity in what the paper itself, specifically, separate from the tool, is hoping to achieve. There is a central, but unresolved, tension in whether the reader is supposed to:

(1) focus on the specifics of the examples used and whether to reevaluate the substantive claims from the studies, (2) buy in to how various reliability and base rate parameters impact modeling outcomes, (3) receive an introduction to the tool itself.

In my estimation, the largest contribution to the field here is in (2) and (3), but currently much of the real estate of the paper is dedicated to several examples of (1). While these specific examples may be illustrative to some degree, I think given the number and brevity of such, they are unlikely to incidentally achieve points (2) and (3) above. Specific examples include the assertion of kappas for DSM diagnoses, without much nuance (e.g., see https://psycnet.apa.org/buy/2015-27500-001). Given the relatively limited space given to this example, however, it's hard to be entirely certain what the reviewer should take away.

A second point of concern is where this tool would be situated in the research pipeline. I agree with the authors that this tool could be used in ways that parallel power analysis. With that in mind, it seems the most common use of this tool for an individual investigator is likely to be in a priori study planning. In contrast, and with my point above in mind, the use of the tool for existing results is likely best done with multiple estimates of effect sizes, reliability, and base rates, as is common in meta-analysis or consensus reviews. Nevertheless, there is no real example or guidance around how this influences new study planning.

A third point is that more nuance would be useful in the introduction about the current state of psychiatry research. For example, I share many of the authors' concerns about reliability, power, reproducibility, and barriers to translation. That said, it is the case that while effect sizes should be considered considerably more, they are widely considered in psychiatry research via the common place of meta-analysis and other data pooling approaches. Another such example that the authors state in the context of reliability: "However, this [reliability] attenuation is rarely accounted for in routine analyses in psychiatry". This is true in practice, but somewhat misleading insofar as the method by which to do this remains unclear. For example, should we all report disattenuated associations, assuming there is no error and everything is perfectly reliable? This, of course, would be unrealistic to expect zero error. That we can achieve this with the new tool is clear, but the nuance of how and under what circumstances it should be done is not clear, and such nuance should be better reflected in the framing of the problem. That is, there is also a lack of clarity on what ought to be best practices and field-wide goals, rather than simply the lack of an ability to model these factors.

Minor point

For conceptual clarity, it would benefit the manuscript to at least briefly mention the role of validity in translational importance. Of course, the current psychometric issues of reliability, base rate, power, etc are critical, but it should at least be mentioned, given the potential wide audience of this manuscript, validity is important as well. For example, highly reliable measures may not be valid indicators of underlying disease etiology (e.g., fMRI head motion is a highly reliable trait-level feature, but typically not considered an important predictor or consequence of mental health worth investing translational resources in). Relatedly, confounding as a general topic would be useful to mention just briefly, to help with the spirit of considering underlying issues in translation.

Es muy importante la similitud del diseño de programas y el proceso creativo ya que al tenerlo en cuenta para otras actividades fuera de la programación podría generar una forma más sencilla de planificar una tarea y así mismo resolver un problema. Por ejemplo al momento de dibujar un comic es importante como el primer paso, definir el qué y el cómo formular prototipos y ejemplos ayuda a empezar de mejor manera. Así podría seguir con cada paso pero el punto es la forma tan similar que se puede usar este diseño de procesos para abordar retos de una manera más organizada y sobre todo algo más sencilla.

Esta afirmación aplica totalmente para mí, mi forma de programar en lo poco que sé, es afinar detalles línea por línea hasta que todo salga bien o medio salga, eso revisando guías, video entre otras ayudas que puedan ser útiles para el problema presente en el momento. Mencionarlo es relevante para todas las personas que así como yo también programan o hacen otras cosas de la misma manera sin ser consientes de otras mejores y diferentes formas de abordar los problemas y sobre todo pensar fuera de la caja, en este contexto de clase funciona para la programación pero la idea sería aplicarlo en otros contextos de ser posible.

Peut-être aussi (et surtout ? ) un biais de classe sociale / un biais classiste ?

Vraiment ? Classique en quel sens ? Peut-être qu’il faut distinguer le champ de l'histoire des idées (telle qu'on l'enseigne au lycée par exemple ou à l'université) et la philosophie comme un champ académique.

Or la philosophie comme discipline - surtout lorsqu'elle est contemporaine - s’intéresse presque toujours aux études empiriques qui portent sur son objet. Exemple : si vous faites de la philosophie de la musique, vous allez aller lire des choses sur l’histoire de la musique, sur la musicologie, etc… Si vous faites de la philosophie politique, vous allez lire des chercheurs en sciences politiques, des sociologues, etc. Donc quand on ouvre un bouquin de philosophie politique, on a dans la bibliographie ce genre de sources aussi. Il me semble que c'est assez rare les philosophes qui développent des réflexion «a priori». En général, les philosophes s’appuient sur ce que les sciences sociales ont écrit sur leur objet. Donc à mes yeux, ce reproche, qui certes est fréquent dans les écrits sur la philosophie de terrain, est en fait un mauvais procès…

Oui, et il aurait aussi été intéressant d’intégrer ce critère dans votre typologie : quels types de philo de terrain implique des recommandations ou non ?

Peut-être même «au-delà», non ? (puisqu’elle appelle à la «dédisciplinarisation de la philosophie» !)

Une fois de plus : Ne faut-il tout de même pas se demander s’ils y parviennent ? :)

Une fois de plus : pourquoi ne pas mobiliser les savoirs empiriques des sciences sociales sur son objet ? Cela permettrait une philosophie tout aussi inductive et pas surplombante… (et dès lors il n'y a pas nécessairement besoin d’ aller sur le terrain pour ça ;) )

Uniquement issu de ce dialogue ? Quid des données issues des sciences sociales sur leur objet ?

Donc la recherche se fait uniquement à destination des personnes enquêtées ?

Peut-être faudrait-il que vous disiez clairement en introduction que vous ne jugerez pas ici des pratiques effectives mais simplement que vous théorisez le point de vue que ces philosophes portent sur leur travail ? Car je trouve troublant de ne pas connaître votre avis sur la question de savoir si ces philosophies "font vraiment ce qu'ils disent faire"... Par exemple, on pourrait estimer que la méthode déployée par Vollaire a tous les traits de l’ethnographie, même elle en fait - par la suite - un usage philosophique.

Ah oui ? … Il me semble pourtant que Vollaire est vraiment plus bas car elle dit véritablement se situer à égalité sur le plan du savoir avec les acteurs.

One thing that stood out to me is how lists and dictionaries are used to model social media relationships, like users and who they follow. It’s interesting that something as complex as online networks is built from simple structures in code. This connects to earlier course ideas about how platforms shape interactions. It also makes me wonder how much these technical choices influence what users see online, since the way data is organized could affect visibility, recommendations, and even social behavior.

I know this may sound silly, but I had not known about artificial pollination! I wanted to share a video I watched about it (https://youtu.be/FAZ82x-Tmw0?si=UqFizxbBXoWz8vWF) and in the video Dr. Broussard mentions that artificial pollination has been happening for over 50 years! I was surprised hearing that as I had thought it was something that existed very recently.

Hypothesis: If pollinators are gone and there is a lack of pollen for the ecosystem to grow how can we substitute pollen. Why don’t we use spores to further assist the pollen reduction to help slow down the rapid decline in the ecosystems.

This was making me wonder where the future of communication is going, and how it could change from generation to generation. I find it interesting that we don't truly use email until we properly start work etc. From my own experience (though biased), it feels like email is still widely utilized but other forms of communication like direct messaging (on a platform like teams) is taking over. Could we start seeing the end of email (atleast the start of the dropoff of its usage)? I think maybe as more generations grow up utilizing direct messaging.

This shows how email was one of the most significant moments in the evolution of communication. The ability to transport information digitally increases the convenience factor. This reminded me of how e-mail wasn't always a public tool; in the 1960s-1970s, it was a private tool used by researchers in the U.S. military.

Treating AI as a "writing partner" or coach" rather than a replacement for the writer original thought.

Relates to first annotation.

1st part of highlighted text. apart of next page annotation.

If Ai makes writing too easy we lose out on the part that teaches us how to think. Ai should assist not bypass.

Ai handles the "boring work" of generating basics ideas, allowing the teacher or student to focus on the higher level work of seeing which idea is actually the best.

2nd part of highlighted text

The Student used Ai to "flood her with ideas". the student changed the characters and plot mantaining true authorship.

When using Ai for Grammar or editing, the writer's job is to ensure the corrected version doesn't change their meaning. The writer is the final judge of Intention.

Ai dose not think, it just predicts patterns. this explains why it can be wrong even when it sounds right. This is also Called a Bullshitter in philosophical terms.

the actual thinking and judgement comes from one, no matter what the Ai says.

Never ask an Ai what it thinks, it doesn't have opinions.

The goals is not to get rid of Ai but to make it in a way where we can protect peoples creativity and diversity.

Every Ai prompt has a real world cost in energy and water.

Treat Ai like a source. If you didn't write it use quotes.

While Ai can help people, It will also erase voices by forcing everyone to sound the same.

Ai is better at looking at the big picture.

Ai will smooth out a unique voice to make it sound like "everyone else". If i use Ai for style I risk losing my personal voice.

You are responsible for every word in your paper even if Ai suggested it. Reviewing is not optional its an academic requirement.

"Hallucination" is a misleading word, it sounds human. In reality, the AI is just making a statistical prediction that happened to be wrong.

Ai doesn't process meaning, only math. This is why it can't really produce fluent sentences.