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eLife Assessment

This valuable work identifies a subpopulation of neurons in the larval zebrafish pallium that responds differentially to varying threat levels, potentially mediating the categorization of negative valence. The evidence supporting these claims is solid; however, the study would be strengthened by more sophisticated analyses of functional imaging results, behavioral confirmation of stimulus valence, and further evidence linking the functionally distinct clusters to their molecular identity. This work will be of interest to systems neuroscientists investigating the circuit-level encoding of emotion and defensive behavior.

Reviewer #1 (Public review):

Summary:

This study presents a map of neurons responding to aversive stimuli in zebrafish and suggests that the regions containing these neurons are homologous to mammalian brain areas involved in aversive processing. Specifically, this study found that neurons in a part of the pallium, the homolog of the amygdala, responded vigorously to strongly noxious and fully looming stimuli, but not to the milder cues. In contrast, neurons in another part of the pallium responded to all of these stimuli. The findings provide valuable insights into the neural mechanisms underlying negative-valence computation in zebrafish.

Strengths:

This study performed whole-brain functional imaging using two-photon light-sheet microscopy and identified the activity of individual neurons in awake zebrafish. This technique is highly valuable and will be broadly applicable to future studies aimed at elucidating the neural mechanisms underlying zebrafish behavior at single-neuron resolution.

Weaknesses:

Although this study reports neuronal responses to aversive stimuli, it did not directly assess how aversive these stimuli were for zebrafish. In general, studies of this kind quantify the aversiveness of test stimuli by measuring behavioral indices such as avoidance or escape responses. The present study states that "neurons responded vigorously to strongly noxious and fully looming stimuli, but not to milder cues." However, the authors did not provide behavioral evidence demonstrating that the stimuli were indeed aversive or that the so-called milder cues were perceived as less aversive by the animals. Without a behavioral measure of aversiveness, it is difficult to determine whether the reported neural responses reflect negative-valence processing, rather than general sensory salience or stimulus intensity.

Reviewer #2 (Public review):

Summary:

The authors aim to map neurons encoding negative valence at the whole-brain scale in larval zebrafish. Using two-photon light-sheet imaging combined with various aversive stimuli, they visualize and quantify stimulus-evoked neural responses, identify the anatomical locations of responsive neurons, and explore the possibility of genetically accessing Rl neurons that respond preferentially to strongly noxious stimuli.

Strengths:

The major strength of this study lies in its use of two-photon light-sheet imaging, which provides a system-level characterization of neuronal response to aversive stimuli. The authors systematically compare multiple classes of aversive stimuli (heat, electric shock, looming, etc.), showing that strongly threatening stimuli converge on a compact neuronal population in the Rl, supporting the robustness of the finding. Finally, the identification of Tiam2a expression in these neurons provides a potential genetic handle for future functional studies.

Weaknesses:

The main weakness of the study is the lack of causal evidence supporting the functional role of the identified neurons. Without optogenetic, chemogenetic, or ablation experiments, it is difficult to determine whether these neurons are required for or sufficient to encode negative valence. In addition, the study does not include positive-valence or neutral stimuli controls, making it difficult to distinguish whether the observed neural responses reflect valence per se or more general downstream response such as motor output. Finally, the lack of behavioral readouts limits the ability to directly link the identified neural populations to defensive behaviors.

Reviewer #3 (Public review):

Overview and Strengths:

Accurate evaluation of threat levels allows animals to determine whether to escape. The precise mechanism underlying threat evaluation remains unclear. Smith et al. identified a cluster of neurons in the zebrafish rostrolateral dorsal pallium (Rl) that respond differentially to varying levels of negative-valence stimuli.

This work leverages the small size and optical transparency of the larval zebrafish, using two-photon selective plane illumination microscopy to assay the response of pallial neurons to various negative-valence stimuli. Interestingly, unlike the ventromedial pallium and habenula, which responded to all stimuli tested, neurons in the Rl were activated by a selection of stimuli representing relatively higher levels of threats. By leveraging a zebrafish brain atlas, the authors identified a transgenic line labeling a tiam2a+ cluster of neurons that appears to be the activated population in the Rl. Together, these results demonstrate a subpopulation of pallial neurons that likely categorizes the strength of negative valence in larval zebrafish.

The primary conclusions of this work are well supported by the data. The identification of a neuronal cluster that may underlie the categorization of threat-associated sensory stimuli is significant. Furthermore, this study generates a high-quality functional imaging dataset using cutting-edge microscopy, setting the foundation for understanding the neuronal encoding of emotions in zebrafish.

Results from this work set the stage to answer further exciting questions: How do tiam2a+ Rl neurons modulate the activity of the hindbrain escape circuit? What is the functional role of the Rl population inhibited by threat stimuli? Computationally, how does Rl integrate sensory signals and classify threat levels? How does the activity of Rl change in the context of habituation and conditioning? Future work may use more nuanced stimuli and combine new genetic tools, behavioral recording, and circuit-level analysis to systematically reveal how emotions modulate defensive behaviors.

Weaknesses:

The impact of this work could be further enhanced by incorporating more sophisticated data analysis and by more clearly anchoring the findings within the known framework of zebrafish defensive behavior.

(1) The authors performed statistical analyses across six ROIs per experiment in Figures 1E/J, 3E/J, and 6B/D/F. This increases the probability of Type I errors. Applying multiple comparison corrections would mitigate this concern. Given that most stimuli (except for the "IR heating") are non-directional, the authors may consider first testing for the response symmetry following each stimulus and then combining ROIs from the two hemispheres to calculate a single averaged measurement per region per fish for comparisons of regional dF/F.

(2) I found the topographical mapping of activated and inhibited ROIs very informative. There appear to be two subpopulations of Rl: a posterior-medial population often activated by negative valence stimuli, and an anterior-lateral population that is frequently inhibited. I wonder if it is possible to decode the valence or category of a stimulus based on the topography and response profiles of these neurons? These results would provide additional evidence for the Rl's roles of threat evaluation.

(3) Findings in this paper, especially differential responses of the Rl to full and partial looming, deserve an expanded discussion. The authors should better anchor these findings to established literature to emphasize their significance in the Discussion. For example, how might this potential categorization mechanism contribute to, or differ from, the mechanisms underlying habituation (Fotowat & Engert, 2023, eLife); what are the possible connections between the pallium and the hindbrain escape circuits that could relay these Rl signals (Kunst et al., 2019, Curr Biol)?

(4) The authors make conservative claims associating the tiam2a+ cluster with Rl neurons activated by noxious stimuli, and their data support this conclusion. However, this link could be further strengthened by testing whether the tiam2a+ cluster shows differential responses to full vs partial looming. This could be achieved by performing pERK staining following the stimulus paradigm. While future tools may allow for direct functional imaging of this population, I believe such experiments are beyond the scope of this paper.

(5) Figure 1E/J, Figure 3E/J: Please clarify whether the dashed red vertical lines indicate the onset or the offset of the stimuli. Additionally, different time windows were used for AUC calculations across these experiments; the authors should provide a rationale for these varying windows in the Results or Methods.

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Reply to the reviewers

Reviewer 1 1. The code used for simulations is available on a public repository, but it does not directly ensure that results are reproducible. To do so would require a clear step-by-step guide referring the user to the specific pieces of code which have been used for the results and figures presented in the paper. At the moment, I could not find any such guide and the large number of scripts, executables and jupyter notebooks are not clearly linked to the paper's contents

We agree that the code should be as accessible as possible for reproducing the results. We have updated the public repository (linked given in the 'data and code availability' section of the manuscript, lines 350-352) to include the SLURM job scripts used to run the evolutionary simulations and analyses, together with an overview of which scripts and notebooks were used for creating the figures.

2. The methods themselves involve a number of arbitrary choices. Though this is understandable given the nature of the work, one aspect in particular that would deserve better clarity is the modeling of gene network dynamics. The stochastic model (l.516 & following) involves a nesting of "Hill-like" terms (those in Eqs. (7) and (11)) which is unusual and given without justification. There should be some explanation of how this approach relates to standard approaches such as those reviewed e.g. in: Bintu et al. Current opinion in genetics & development 15.2 (2005): 116-124.

We agree that the formulation of the developmental model requires clearer justification and contextualisation. We have added a citation to situate our implementation within existing modelling frameworks, and a brief explanation of the choice for Hill equations in the Methods section (lines 577-579).

1. It is also unclear at the moment how exactly the GRN dynamics is used; are time-stepping algorithms used until the system reaches a stationary regime? If so, how is stationarity assessed? This needs to be explained both in the main text and in the methods. The table of parameters suggests that there was a cut-off time, but there is no explanation whatsoever about the state of the dynamics at this time.

We have revised the main text to briefly explain how the developmental dynamics are implemented (lines 88-90) and expanded the Methods section (Gene expression and regulation in the developmental model) to describe the integration procedure in detail (lines 617-620).

The GRN dynamics are modelled as stochastic differential equations (SDEs), which are numerically integrated for a fixed developmental duration of T_D = 140 hours, regardless of whether a stationary state is reached.

Instead, stationarity is indirectly favored by the fitness function. Fitness is calculated as the time average of the phenotype (protein states) over a window at the end of development (Equation 23 in the Methods). As a result, GRNs that exhibit large fluctuations or ongoing transient dynamics during this evaluation window tend to have lower fitness (and in turn, reproduction rate) than GRNs that have stabilised their expression patterns. We now mention this in the model introduction of the results section (lines 98-99).

As a result of this, we observe that the vast majority of evolved GRNs reach a stable gene expression state by the end of development (aside from small fluctuations as expected from the SDEs).

1. Related to the previous point, the table of parameters (Table S1) is provided without any explanation; through what process (exploratory, literature review, trial and error...) where the values selected? As there been any type of sensitivity analysis?

We have clarified in the revised manuscript how each group of parameters was chosen (lines 618-620 and 744-746). In brief:

Developmental time parameters (e.g., integration time, diffusion coefficient) were set to roughly match the developmental window of H. trionum from stage 0 to stage 2 (~150 hours; Riglet et al. 2024), during which pre-patterning is established. Molecular concentrations are expressed in arbitrary units Evolutionary parameters (e.g., mutation rates) are based on previous published work using this modeling framework and were slightly adjusted during an initial exploratory phase to ensure stable evolutionary dynamics. We have added citations for this. We have not performed a full global sensitivity analysis across all parameters. Such an analysis would be computationally expensive given the cost of running evolutionary simulations and the difficulty of assessing parameter effects in this multi-scale system. Importantly, the core GRN parameters (expression rates, interaction topology, and interaction strengths) are evolvable rather than fixed. We have conducted sensitivity analyses at the level of individual evolved GRNs, but a systematic analysis is beyond the scope of this paper.

Minor Comments

1. The fitness function used in simulations specifically encodes the desired pattern, with two zones having differential gene expression. This allows the artificial selection to evolve towards such patterns, as expected, but it is not entirely clear how this relates to natural selection itself. At the very start of the paper, the authors briefly review some possible sources of selective pressure for flowers to exhibit patterns such as bullseye, among others. None of the selective factors would likely act on the plants as a direct incentive for two regions, as specified in the cost function. Instead, one may expect a more high level criterion, such as "conspicuousness" for a pollinator, for instance. This is admittedly not naturally represented as a fitness function, but the choice of this function definitely influences the outcomes of a simulation. Some further numerical experiments may allow to demonstrate that the exact cost function is not critical for the findings of the paper, but I understand they would likely be computationally costly, to the point of unfeasibility. This limitation should be mentioned at least.

We agree that natural selection acts on higher-level criteria such as pollinator attraction or conspicuousness rather than a predefined measure like "two distinct regions." However, our goal in this study is specifically to understand how the bullseye pattern in particular is produced, motivated by comparison to Hibiscus and other angiosperms where this pattern has documented adaptive relevance. The fitness function was therefore designed to ensure this particular pattern evolved, which results in evolving between-level novelty rather than constructive novelty (as defined in Colizzi et al., Essays Biochem 2022: of interest here is the evolved dynamics of development, not the resulting pattern). In this way, the fitness function serves as a proxy for selection on floral patterning. We have clarified this rationale more explicitly in the Results section (lines 97-98).

The choice of fitness function does influence simulation outcomes. Within the scope of selecting for a bullseye pattern, we previously ran simulations where bullseye size was fixed rather than dynamic, and boundary cell types still evolved in those cases. This suggests our findings are robust across variations of the bullseye fitness function. Of course, selecting for a more abstract ecological criterion such as "conspicuousness" rather than a distinct spatial pattern would affect outcomes more substantially. However, translating such high-level criterion into a quantitative fitness function is a non-trivial challenge and outside the scope of this study. We have added a note on this point in the Methods section on the fitness function (lines 687-691).

1. The number of genes used in the simulations is very small in comparison to real organisms. This is clearly justified by the complexity of the work, but one wonders if simulations could be made more efficient by using a much simplified approach for the gene network dynamics. At the time scales of interest, it seems that the use of SDEs and the numerical intricacies they require might be an unnecessary burden. Have the authors considered a much simpler approach, for instance based on Boolean models? Since the study only uses static tissues, all the GRN dynamics could be by-passed, determining steady states very quickly and using them to determine fitness. If this saved significant computational time, this would allow a more comprehensive survey of the "purely genetic" part of the model.

While the number of genes may indeed be indeed small compared to real organisms, our simulations should be viewed as operating on subnetworks that form part of a much larger developmental GRN. This is a common approach in modelling the evolution of developmental processes, which we now highlight in the methods section. Furthermore, we find that the functional part of the GRN (which we identify by pruning away the redundant genes and interactions) always uses only a subset of the gene types, showing that we provide sufficient degrees of freedom for the evolutionary process to find a solution. We now also make note of this in a new figure (Figure S12) where we explain the pruning algorithm.

We agree that simplified representations of the GRN, such as Boolean models or direct steady-state mappings, could substantially reduce computational cost. However, the use of stochastic differential equations (SDEs) in the present study is deliberate. Continuous, stochastic GRN dynamics allow us to capture key features that would be difficult or impossible to represent in Boolean or purely steady-state frameworks. In particular, they enable (i) gradual spatial distributions of morphogens, which are central to pattern formation, (ii) explicit treatment of gene expression noise, and (iii) consider and analyse the developmental dynamics in detail.

Finally, in response to Reviewer 2's comment 1, we show all evolved networks (Figure S3 & S4) and perform a GRN motif comparison between noisy and deterministic simulations (Figure S15) to provide more information about the genetic part of the model.

_Reviewer 2_

1. There is a major missed opportunity to analyze the evolved networks. Only one of the 30 GRNs is analyzed in figure 4. Please add further analysis of the GRNs from all the populations. Within a population after 30K generations, how much variation is there in the GRNs of individuals? How similar are the optimal fitness evolved GRNs across all 35 populations? Are there common motifs across networks? Is there always an antagonism between proximal and distal proteins somewhere in the network? A lot of previous work on GRNs has established the function of common motifs, and these should be analyzed. Please provide all 30 gene regulatory networks in the supplement.

We have substantially expanded the analysis of evolved networks across all populations. Specifically, we now (i) provide two supplementary figures showing the final pruned GRNs from all 35 simulations (Figures S3 & S4), and (ii) quantify motif frequencies across all evolved networks and compare motif distributions between GRNs evolved with and without molecular noise (Figure S15). This new analysis is summarised in a dedicated Results paragraph where we identify regulatory asymmetries and condition-dependent differences in feedback architecture, including changes in abundance of mutual inhibition and positive autoregulation (lines 233-239).

We find that, while the evolved maximum fitnesses are very similar across simulations (Fig. 2Ai), the networks are highly variable. Nevertheless, the motif analysis shows some trends that differ between the noise and no-noise simulations, such as a bias towards mutual inhibition between PROX and DIST in the no-noise compared to the noise simulations.

As to the variation within a population: we find that at any timepoint, all individuals are descended from a common ancestor that lived on average ~600 generations back, meaning that they form a single (quasi)species. We therefore analyse a single, highly fit individual at the last timepoint.

1. The purpose and significance of examining the evolutionary lineage is not clear. Please explain your logic. This is most important for Figure 5 where it becomes clear that the boundary cells are often formed transiently in the evolution of the GRN. If this boundary cell type does not persist, how can it help the petal generate a bullseye. What happens after the boundary cell type is lost? Has the GRN evolved into a more stable place where it no longer needs the boundary? In several instances it looks like they come and go many times. Please explain how these transient boundary cells in the evolutionary lineage can make a difference. This point also comes up in lines 113-115 "For each simulation, we traced back the ancestral lineage of the final fittest individual and sampled 12 of its ancestors at evenly spaced generational intervals, performing this analysis on each sampled ancestor." I could understand if the boundary cell type were developmentally transient, but I have a hard time what its significance is since it is evolutionarily transient.

The persistence of the boundary cell type over evolutionary time is used as a signal for its functional role in establishing the bullseye pattern. We observe that mostly two extremes occur: boundary cell types can be conserved over long evolutionary periods, or they can be highly transient. In our simulations, boundary cell types that are functionally important tend to persist, whereas the ones that are not involved in producing the bullseye pattern appear only transiently. The fact that both cases can occur suggests that boundary cell types are a "free" or easily accessible feature during the evolution of this patterning system: they can arise repeatedly without being strictly required, but may nonetheless become functionalised under certain evolutionary trajectories (see also our discussion of the Mimulus leaf stripe). We have added more explanation on the logic of examining the evolutionary lineage at the beginning of the results section related to Figure 5 (lines 205-209 and caption of Figure 5).

To further clarify this point, we have added a supplementary figure (Figure S16) focusing on a deterministic simulation with a highly evolutionarily transient boundary cell type. By identifying the GRN mutations associated with the (re-)appearance of the boundary, we show that the patterning mechanism producing the bullseye slowly mutates while preserving the bullseye, while the mutational neighbourhood of the GRN contains diverse mutations that generate boundary cell types. In this case, boundary cells arise independently through distinct mutations rather than repeated rediscovery of a single change, explaining both their frequent appearance and their lack of long-term evolutionary stability.

1. It is worth saying more about how the 9 lineages without a boundary cell types manage to make a robust bull's eye pattern because this is also interesting.

This is indeed a good idea, we have carried out an analysis similar to that in Figure 4 for a GRN from a lineage without a boundary cell type and included it as a supplementary figure (Figure S11).

4. How were 12 proteins chosen for the network, as opposed to 6 or 20 for instance? In the network pruning, it seems like fewer proteins are required. How many proteins are required to produce a bulls eye pattern?

This choice is indeed somewhat arbitrary. We settled on 12 gene types to provide enough degrees of freedom while also keeping the evolutionary simulations computationally feasible. In practice, we find that pruned GRNs typically only use a subset of the 12 gene types, suggesting that the system has enough degrees of freedom to produce the bullseye pattern. For example, the smallest networks that evolved (after pruning) have 5 genes in the deterministic model and 7 in the noisy model.

To clarify this choice, we now added a brief mention of these considerations to the relevant methods section (lines 641-643).

Minor Comments

1. The title needs to be changed to include computational modeling or simulation because otherwise the current version of the title implies that these boundary cell types are found in plant species evolution.

We agree and have renamed the paper "Computational Model of Flower Pattern Evolution Predicts Spontaneous Emergence of Boundary Cell Types Across Petal Epidermis."

1. Line 103 - 106 "We found that over a third of all simulations evolved a bullseye size of approximately 50% of the petal's central height (Figure 2A.ii). This indicates a tendency for simulations to converge toward these proportions, possibly due to the interaction between the patterning signal distribution and the tissue geometry." The phrasing here is confusing. Which proportions does "these proportions" refer to? Presumably, 50% from the preceding sentence. But the second proportion is not clear from the text. Maybe it is the peak at approximately 65% seen in the graph. Please clarify in the text.

The 50% figure refers to the bin with the highest peak in Figure 2A.ii, reflecting a bias toward certain bullseye proportions rather than a uniform distribution across all possible sizes. We have rewritten the sentence to clarify this (lines 109-112): "This indicates a tendency for simulations to converge towards certain proportions more than others, possibly due to the interaction between the patterning signal distribution and the tissue geometry"

1. Line 118 "To further explore cell identity in the third cluster, we analysed the gene expression profiles of the three identified cell types." It is not clear what the third cluster refers to. The previous sentence mentions 9 lineages without boundary cell types. So, a transition here back to lineages with boundary cell types, would help here.

We agree and have improved the phrasing here by referencing back to the lineages with boundary cell types (lines 124-125):

"Focusing on the majority of lineages in which this third boundary cell type arose, we analysed the gene expression profiles of the three identified cell types."

1. Figures 3C-D, it would help to label these volcano plots proximal versus boundary and distal versus boundary. Although they do fit your color scheme and legend for the color scheme, it is important to specify it explicitly.

We have added labels inside the volcano plots in Figure 3C-D to clarify proximal versus boundary and distal versus boundary.

1. On Figure 4A it would help to label which gene is Prox and Dist. I assume they are the purple and yellow genes, but it would be easier if they were labeled.

We have added labels in Figure 4A here to clarify.

6. Line 185-186 "Gene 5 delays and spatially restricts the expression of gene 10, ensuring the symmetric development of the pattern." This statement needs to be supported by showing a time series simulation-movie or timepoints-revealing this timing aspect of Gene 5.

We agree with the reviewer that this is currently lacking a clear visualisation and thank them for pointing this out. To address this, we have updated Figure 4 to include the temporal expression of genes 5 and 10 in the wild type and mutant for cells along the left-right axis in the proximal bullseye region. We have also included the following extra details in the results text (lines 194-199):

** Decreasing the spatial range of gene 5's regulatory influence by turning it into a TF resulted in a delay in its inhibition of gene 10 and reduced its self-activation range, explaining the smaller bullseye. In this mutant, expression of gene 5 is progressively delayed in cells located further from the origin of the patterning signal, and is ultimately absent on the right side of the proximal region of the bullseye (Figure 4C.ii). As a consequence, gene 10 becomes expressed in the right region, resulting in DIST identity instead of PROX, and leading to an asymmetric bullseye pattern.

Reviewer 3

1. How are the cell types defined from the simulations? Are they attractors of the dynamics of the corresponding proteins? And how are they computationally defined? Please provide more details about how the HBSCAN was used. In Figure S5, simulations #6 and #8 appear to have a 4th cell type (coloured in green), but the authors do not mention this result in the text. If cell types are defined by gene expression profiles, then the number of cell types will be dependent on the kind of clustering performed. Clarifying the definition of cell types will help resolve this issue.

We thank the reviewer for raising this point and agree that the definition of cell types in our simulation results requires clearer explanation.

The concept of cell type / cell identity is a complex theme which is still yielding interesting debate and discussion in the literature (see for instance Rafelski and Theriot, 2024). In our simulations, cell types are defined based on gene expression profiles rather than being explicitly identified as mathematical attractors of the underlying dynamical system. Operationally, we perform dimensionality reduction (UMAP) followed by clustering (using HDBSCAN) on the gene expression profiles across cells. This clustering serves as an initial, automated indication of distinct expression states across the petal.

We recognise that the clustering results depend on the chosen dimensionality reduction and clustering method, as well as their parameterisation. For example, clustering applied to a smooth gradient (e.g., arising from diffusion alone) can artificially partition continuous variation into multiple discrete groups. For this reason, we do not rely solely on the clustering output: we use it as a first-pass classification and then manually verify the resulting groups by manually inspecting their gene expression profiles across the petal. This additional step ensures that identified "cell types" correspond to distinct expression states rather than arbitrary thresholds along a gradient. We have clarified both the computational procedure (dimensionality reduction + HDBSCAN clustering + manual verification) and the conceptual definition of cell types in the Methods section (lines 748-753).

Regarding Figure S5, the fourth cell type (shown in green) in simulations #6 and #8 is indeed a distinct gene expression profile. We do occasionally observe the evolution of more and different cell types, this second boundary cell type being one of them, but also for example a salt-and-pepper type cell type (not shown). These cell types are however usually very transient and infrequent.

* Rafelski, S.M. and Theriot, J.A., 2024. Establishing a conceptual framework for holistic cell states and state transitions. Cell, 187(11), pp.2633-2651.*

2. In relation to the previous question, are the phenotypes used in the evolutionary simulations' steady states of the underlying dynamics?

As clarified in response to Reviewer 1's comment 3, we do not explicitly require or enforce that phenotypes correspond to steady states of the underlying GRN dynamics. The developmental dynamics are always simulated for a fixed duration, and the fitness of a GRN is defined as the time-averaged gene expression pattern over a window at the end of this (lines 88-90) and Methods (lines 617-620).

Because fitness is computed from this late-stage average, selection favors GRNs that produce consistent and stable expression patterns during that window. Networks that remain in strong transient or oscillatory regimes during this phase are typically penalised through reduced fitness.

Therefore, while steady states are not imposed as a constraint, selection strongly favors solutions that are effectively stationary by the end of development. Indeed, inspection of the evolved GRNs shows that they converge to stable expression states.

1. In Figure 3A it seems there are probably two cell types in the boundary region, is that right? Or are the elongated purple and elongated white cells basically the same cell type? Please clarify. If there are two, why did the authors choose to do the transcriptome analysis of the boundary region as one region, and not two subregions, to capture the two cell types?

Correct, there are two different boundary cell types at the mature stage 5 petal: flat, elongated purple cells (lower boundary), and flat, elongated cream cells (upper boundary). However, the transcriptome data comes from an earlier stage (stage 2), where the boundary cells have not yet developed their characteristic shape and texture and the petal only comprises visibly pigmented (proximal) and non-pigmented (distal) cells. The morphological differences that distinguish the two boundary cell types at stage 5 are not yet apparent, hence we can only treat the boundary as one region at this stage, defined as the transition zone between pigmented and unpigmented cells

We have made this distinction clearer in the figure caption of the Stage 2 petal (Figure 3B).

1. I appreciate the explanation of the GRN pruning in the methods, but could the authors illustrate the network pruning process with an example and show that it works in this example?

We have added a supplementary figure (Figure S12) depicting the pruning process for a GRN which keeps its boundary cell type during pruning and one for a GRN which loses its boundary cell type after pruning.

1. From the methodological perspective, I suggest further clarifying what is new from this study and what is not. For instance, is the GRN pruning idea new or has it done before? The authors could consider reducing the formalities in the methods of the main text when they are not needed or when they are not new, to facilitate the readability of what is really important and novel in this work, and what is not. E.g., it is not really needed to mathematically define a Voronoi tessellation in the main methods section; this could be simplified or moved to a supplementary methods section.

We agree that the distinction between methodological novelty and established components of the framework should be made clearer. We have therefore streamlined the description of non-novel methods and added appropriate citations to prior work where relevant, for example in the section on pruning.

1. I believe the diffusion term used in Eqs. 14 and 17 does not conserve the total number of protein molecules; could the authors verify that? An example of a correct passive transport term for cell i of protein concentration p_i would be the sum of (p_j-p_i) for all j-cell neighbours, normalized by the area of cell i, or the formulation by Sukumar and Bolander (2003). This is especially important when noise is added, as the non-conservation of the number of proteins can lead to unwanted instabilities. Likely, these effects do not invalidate the results of the paper, but the authors should clarify the reason for their choice or double-check the conclusions using a correct, mass-conserved diffusion term.

Thank you for pointing this out, this is indeed an error in our mathematical description. We double-checked our implementation, and confirmed our implementation correctly normalises by the area of cell i. We have a unit test which tests for mass conservation (https://gitlab.developers.cam.ac.uk/slcu/teamrv/evo-framework/-/blob/paper-2024-stoch-sims/tests/petal_test.cc?ref_type=tags#L66), which also confirms that our implementation is correct and this is only an error in the mathematical description in the paper. We have updated the equations to correctly reflect the implementation.

1. It is important to facilitate the reproducibility of the results whenever possible, especially given that the computational framework used in this work has great value. I truly appreciate that the authors uploaded the code to a Gitlab. Please add further information in the readme file to facilitate reproducing the results, beyond the information regarding the code installation, whenever possible.

We thank the reviewer for emphasising the importance of reproducibility. As noted in our response to Reviewer 1's comment 1, we have improved the structure and documentation of the public repository to facilitate reproduction of the results, including the SLURM scripts used for the evolutionary simulations and documenting code used for analysis and creating figures.

Minor comments

1. What is the reasoning behind the choice of the number of protein species? Why 12? Would the same results hold with a smaller number of proteins? As I imagine that the more species one considers, the more chances one has to get the desired phenotypes (or any desired phenotype for that matter). I could imagine that with 12 or more proteins, one could get more than 3 cell types (as defined by the clustering of their expression profiles). Is there something inherent in the creation of a boundary that leads to only 1 additional cell type and not more? Further simulations would be ideal to address this point, but otherwise, please comment on that if possible.

As noted in our response to Reviewer 2's comment 4, the choice of 12 protein species is to some extent arbitrary. We selected this number as a compromise between providing sufficient degrees of freedom and maintaining computational feasibility of the evolutionary simulations. In a recently published manuscript from our team (van der Jagt et al., 2026), we tested the impact of reducing the number of genes and showed that important evolutionary dynamics are by and large the same.

Regarding the possibility of obtaining more than three cell types: while rare, we do observe the emergence of additional cell types in simulation #6 and #8 in Figure S9. A larger number of proteins could in principle support more combinations of expression patterns, but the number of stable cell types that emerge is strongly determined by the fitness function and by the spatial structure of the task (i.e., generating two pre-specified domains). That is, the emergence of a single additional boundary cell type is driven primarily by the developmental and selective constraints, rather than being directly limited by the number of proteins in our simulations.

van der Jagt, Pjotr L., Steven Oud, and Renske MA Vroomans. "System drift in the evolution of plant meristem development." PLOS Genetics 22.4 (2026): e1012089.

2. What is the fundamental difference between Gene profiles I and II in generating cell types? If a cell type is defined by the specific expression of certain genes, then are not Gene Profiles I and II just different sides of the same coin? For instance, Gene profile I is characterized by the expression of a single gene at the boundary. Why do their simulations they do not obtain patterns where 2 genes are expressed in the boundary? Or 3? Or is there a fundamental difference in how these are generated, like the boundary being a stripe of a Turing pattern, or something similar? This also links with the work of Ding et al. and Lu et al.-which the authors mention in the introduction- where they propose that self-organized (Turing) patterns can explain anthocyanin patterning in petals. Could the authors clarify these points and maybe contextualize these results with previous works on petal patterns?

The fundamental difference between the two gene profiles lies in how the boundary cell type is generated. In gene profile II, genes expressed in the boundary are also expressed in the proximal region, but some genes expressed proximally are not present in the boundary. The boundary cell type therefore emerges as the intersection of two differently-sized proximal bullseyes (Fig. 2B.ii). In gene profile I, by contrast, genes are more expressed in the boundary than anywhere else, producing a central striped expression pattern. While gene profile I can arise from profile II (Fig. S10), we also find cases where mechanism I appears independently, without mechanism II being present (Fig. S9; Simulation #25). This shows the two mechanisms are genuinely distinct, and we therefore treat them separately.

Profile I includes infrequent cases where several genes are preferentially expressed at the boundary (see for example simulation #23 in Figure S9). As for why we rarely observe two or more genes uniquely expressed in the boundary, we are not sure, however we suspect this may relate to the limited number of distinct gene types available in our model, which constrains how many genes can play a flexible, boundary-specific role.

Regarding the link to Turing patterns and the work of Ding et al. and Lu et al.: our model addresses the pre-patterning mechanism upstream of anthocyanin patterning, which subdivides the petal into distinct spatial regions. Based on evidence from Hibiscus, this pre-patterning is thought to be initiated by an asymmetric signal. The problem we investigate is therefore how an existing asymmetric signal is converted into a bullseye pattern, which is fundamentally different from Turing-type symmetry breaking from a uniform state. Our work thus complements Ding et al. and Lu et al. by addressing the upstream question of how the spatial regions that constrain these self-organised patterns to specific petal domains are first established. We have added a discussion of this connection in the Discussion section (lines 301-306).

1. In relation to the previous point regarding the mechanisms underlying boundary formation, the authors could consider whether the theoretical works by the J. Sharpe lab on stripe formation might be relevant to cite (e.g., Cotterell and Sharpe 2010 or Jimenez et al 2015)

We agree that they are relevant and have added a section about theoretical work on stripe formation as part of the discussion on novel phenotypes (lines 305-310).

1. If possible, it would be ideal to have at least one video/animation of both the dynamics of each phenotype and the evolution of the phenotypes as their fitness increases, to see the evolutionary trajectories and test whether similar phenotypes can be achieved through different trajectories.

We thank the reviewer for the suggestion, since the temporal dynamics can indeed be informative. We have added two supplementary videos (Video S1 & S2) illustrating the developmental dynamics of two GRNs: one that generates a boundary cell type via gene profile I, and one via gene profile II. These videos provide a clearer view of the developmental model's dynamics, and how boundary cell types emerge dynamically during development. References to these videos have been added to the main text immediately after introducing the two gene profiles.

In addition, we have added two supplementary figures containing evolutionary trajectories: one tracing an individual's evolutionary trajectory including detailed changes in fitness and gene expression over time (Figure S8), and one showing the evolution of PROX and DIST expression during the early adaptive phase across the first 10 simulations (Figure S6).

1. In the Discussion, I believe that the emergence of the novel cell type would benefit from stronger contextualization within known evo-devo frameworks. In particular, the authors describe that a new cell type emerges as a byproduct of the selection of a higher-order developmental process-the bullseye pattern with a clearly defined boundary-rather than through direct selection of the cell type itself. I am confident the authors know these phenomena have been discussed under the term spandrels (Gould & Lewontin, 1979), and have been the subject of extensive study and debate. While identifying traits as spandrels is complicated-largely because in practice we lack reliable frameworks to distinguish them from actual adaptations-the work presented here provides a plausible mechanism of how such features could arise. To me, this fact alone is interesting, as not many works (as far as I know) have addressed this problem explicitly. Maybe the authors want to emphasize this fact as a novelty of their approach. To be clear, I am not suggesting that the authors should adopt a specific terminology; rather, I believe that explicitly invoking the concept of spandrel would resonate with readers familiar with the foundations of evo-devo and would strengthen the main message of the paper.

We thank the reviewer for this great suggestion. We have added a reference to Gould & Lewontin's seminal paper in our discussion, placing our findings in the context of spandrels (lines 320-323).

• 1. *Some additional considerations related to figures

Please change colours in the figures to be colour-blind whenever possible The stripes in the striped purple cell shown in Fig. 3A are not seen unless one zooms in on it; would it be possible to represent this differently? In Fig. 5 Aii and Bii, it would be easier for the reader to connect with the statements in the main text if the x-axis is x 1000 or x100 instead of x500 Perhaps clarify panel captions of Fig. panels 3C and 3D. Probably I am missing something basic, but I was also wondering how their numbers are connected to the numbers in the panel of Fig. 3F. Why does Fig. 3F have three subpanels? Is it because of different expression levels? Please clarify.

We thank the reviewer for bringing this up. On revisiting our figures, we noticed some hard-to-distinguish colours for the common red-green colorblindness (deuteranopia). We have improved this by changing the reds closer to magenta, making the figures more accessible. We increased the size of the cartoon cell in Figure 3A and increased the contrast of the colours used to indicate the stripes. We have changed this to read x1000 to improve clarity. We have added the following text to the caption of Fig 3E, page 6, to clear this up: The number in the intersection indicates genes enriched in the boundary compared to both proximal and distal regions.

The numbers within each non-overlapping portion of the circles indicate genes enriched in the boundary relative to only one region (proximal or distal), minus those shared in the intersection.

Yes indeed, they represent different order of magnitudes in expression (high, medium, and low, respectively). We have clarified this in the caption of Figure 3F.

1. Could the authors clarify the choice of using the Stratonovich approach in the stochastic simulations?

We decided on the Stratonovich interpretation, as it is the interpretation that is most natural when comparing with the deterministic model, where we "turned off" the noise. With the Stratonovich interpretation, we can get a deterministic system by simply dropping the noise terms. Had we chosen the Ito interpretation, this same approach would require changing the dynamics of the deterministic system by including a noise-induced bias in the drift term.

1. Note equations are referred to in the text as Eq. S (...) whereas they are not supplementary equations

Thanks for pointing this out, we have fixed this in the revised manuscript.

1. The code is very large (more than 1GB), and I believe much of the space is used by Voronoi tessellations. If the authors have the time and have the scripts generating the Voronoi tessellations, the authors could add them to the repository and ensure that these tessellations are generated during the simulations whenever needed (but I am aware that code organization takes time). I would recommend having the code also in a repository with a DOI (e.g., Zenodo or OSF).

We have significantly reduced the repository size by removing some Voronoi tessellations that are not used in this work, and have created a DOI for the code (line 352).

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Oud et al. explores the evolution of a developmental mechanism generating bullseye patterns in petals using evolutionary simulations of gene regulatory networks and transcriptomics data. The authors provide a plausible mechanism of how a novel cell type can emerge as a byproduct of selecting for a higher-order process-in this case, the establishment of a bullseye pattern with two clearly delineated regions. Moreover, the authors show that the emergence of the new cell type persists longer in their evolutionary simulations when the system is noisy, suggesting a functional role of the cell type in buffering developmental variability. The approach is very impressive, bridging in silico-generated GRNs that model a patterning process and evolve over generations, and in turn, combining them with transcriptome analysis experiments. However, precisely due to the complexity of the work done, I would like the authors to clarify and/or address key elements of the methodology, especially those related to the assumptions regarding the modelling approach and their implications for the validity of the results, as well as from the analysis.

Major comments:

1. There are some aspects to clarify; some are mentioned here, but others are mentioned in minor points.

1.1. How are the cell types defined from the simulations? Are they attractors of the dynamics of the corresponding proteins? And how are they computationally defined? Please provide more details about how the HBSCAN was used. In Figure S5, simulations #6 and #8 appear to have a 4th cell type (coloured in green), but the authors do not mention this result in the text. If cell types are defined by gene expression profiles, then the number of cell types will be dependent on the kind of clustering performed. Clarifying the definition of cell types will help resolve this issue.

1.2. In relation to the previous question, are the phenotypes used in the evolutionary simulations' steady states of the underlying dynamics?

1.3. In Figure 3A it seems there are probably two cell types in the boundary region, is that right? Or are the elongated purple and elongated white cells basically the same cell type? Please clarify. If there are two, why did the authors choose to do the transcriptome analysis of the boundary region as one region, and not two subregions, to capture the two cell types?

1.4. I appreciate the explanation of the GRN pruning in the methods, but could the authors illustrate the network pruning process with an example and show that it works in this example?

1.5. From the methodological perspective, I suggest further clarifying what is new from this study and what is not. For instance, is the GRN pruning idea new or has it done before? The authors could consider reducing the formalities in the methods of the main text when they are not needed or when they are not new, to facilitate the readability of what is really important and novel in this work, and what is not. E.g., it is not really needed to mathematically define a Voronoi tessellation in the main methods section; this could be simplified or moved to a supplementary methods section. 2. I believe the diffusion term used in Eqs. 14 and 17 does not conserve the total number of protein molecules; could the authors verify that? An example of a correct passive transport term for cell i of protein concentration p_i would be the sum of (p_j-p_i) for all j-cell neighbours, normalized by the area of cell i, or the formulation by Sukumar and Bolander (2003). This is especially important when noise is added, as the non-conservation of the number of proteins can lead to unwanted instabilities. Likely, these effects do not invalidate the results of the paper, but the authors should clarify the reason for their choice or double-check the conclusions using a correct, mass-conserved diffusion term. 3. It is important to facilitate the reproducibility of the results whenever possible, especially given that the computational framework used in this work has great value. I truly appreciate that the authors uploaded the code to a Gitlab. Please add further information in the readme file to facilitate reproducing the results, beyond the information regarding the code installation, whenever possible.

Minor comments:

1. What is the reasoning behind the choice of the number of protein species? Why 12? Would the same results hold with a smaller number of proteins? As I imagine that the more species one considers, the more chances one has to get the desired phenotypes (or any desired phenotype for that matter). I could imagine that with 12 or more proteins, one could get more than 3 cell types (as defined by the clustering of their expression profiles). Is there something inherent in the creation of a boundary that leads to only 1 additional cell type and not more? Further simulations would be ideal to address this point, but otherwise, please comment on that if possible.
2. What is the fundamental difference between Gene profiles I and II in generating cell types? If a cell type is defined by the specific expression of certain genes, then are not Gene Profiles I and II just different sides of the same coin? For instance, Gene profile I is characterized by the expression of a single gene at the boundary. Why do their simulations they do not obtain patterns where 2 genes are expressed in the boundary? Or 3? Or is there a fundamental difference in how these are generated, like the boundary being a stripe of a Turing pattern, or something similar? This also links with the work of Ding et al. and Lu et al.-which the authors mention in the introduction- where they propose that self-organized (Turing) patterns can explain anthocyanin patterning in petals. Could the authors clarify these points and maybe contextualize these results with previous works on petal patterns?
3. In relation to the previous point regarding the mechanisms underlying boundary formation, the authors could consider whether the theoretical works by the J. Sharpe lab on stripe formation might be relevant to cite (e.g., Cotterell and Sharpe 2010 or Jimenez et al 2015)
4. If possible, it would be ideal to have at least one video/animation of both the dynamics of each phenotype and the evolution of the phenotypes as their fitness increases, to see the evolutionary trajectories and test whether similar phenotypes can be achieved through different trajectories.
5. In the Discussion, I believe that the emergence of the novel cell type would benefit from stronger contextualization within known evo-devo frameworks. In particular, the authors describe that a new cell type emerges as a byproduct of the selection of a higher-order developmental process-the bullseye pattern with a clearly defined boundary-rather than through direct selection of the cell type itself. I am confident the authors know these phenomena have been discussed under the term spandrels (Gould & Lewontin, 1979), and have been the subject of extensive study and debate. While identifying traits as spandrels is complicated-largely because in practice we lack reliable frameworks to distinguish them from actual adaptations-the work presented here provides a plausible mechanism of how such features could arise. To me, this fact alone is interesting, as not many works (as far as I know) have addressed this problem explicitly. Maybe the authors want to emphasize this fact as a novelty of their approach. To be clear, I am not suggesting that the authors should adopt a specific terminology; rather, I believe that explicitly invoking the concept of spandrel would resonate with readers familiar with the foundations of evo-devo and would strengthen the main message of the paper.
6. Some additional considerations related to figures:

9.1. Please change colours in the figures to be colour-blind whenever possible.

9.2. The stripes in the striped purple cell shown in Fig. 3A are not seen unless one zooms in on it; would it be possible to represent this differently?

9.3. In Fig. 5 Aii and Bii, it would be easier for the reader to connect with the statements in the main text if the x-axis is x 1000 or x100 instead of x500

9.4. Perhaps clarify panel captions of Fig. panels 3C and 3D. Probably I am missing something basic, but I was also wondering how their numbers are connected to the numbers in the panel of Fig. 3F.

9.5. Why does Fig. 3F have three subpanels? Is it because of different expression levels? Please clarify. 10. Could the authors clarify the choice of using the Stratonovich approach in the stochastic simulations? 11. Note equations are referred to in the text as Eq. S (...) whereas they are not supplementary equations. 12. The code is very large (more than 1GB), and I believe much of the space is used by Voronoi tessellations. If the authors have the time and have the scripts generating the Voronoi tessellations, the authors could add them to the repository and ensure that these tessellations are generated during the simulations whenever needed (but I am aware that code organization takes time). I would recommend having the code also in a repository with a DOI (e.g., Zenodo or OSF).

Referee cross-commenting

The comments by other referees are complementary to mine; there are some common aspects with my comments and other important points to look into.

Significance

This study provides a plausible explanation of how new cell types can emerge as byproducts of the selection of other processes. This is an important advance in understanding the mechanisms underlying the origin of evolutionary novelties, particularly from the point of view of morphogenesis and patterning, rather than from a more traditional, strictly gene-centric views which focus on changes in specific loci, gene duplications, or neofunctionalization. By highlighting evolutionary novelty as a consequence of higher-order constraints, this work broadens the frameworks through which cellular diversity can be understood.

I believe most of the limitations of the study are conceptual and regarding improving clarity rather than methodological. For instance, the definition of what a cell type is remains, in my opinion, somewhat vague, especially if the clustering has been performed with only 12 genes. However, I am aware of the conceptual difficulty in defining cell types in general. In addition, the emergence of only a single additional cell type, rather than multiple types, might be a consequence of the limited number of proteins considered. Aside from these issues, the methodology is sound and provides a useful framework for exploring the origin of novel cell types.

I see this work as being of substantial interest to researchers concerned with the conceptual foundations of evo-devo, particularly those interested in the origins of novelty and in the role of constraints in shaping such novelty. It should also be relevant to studying morphogenesis from a dynamical systems perspective. Finally, this work will be of interest to those investigating the ecological roles of petal patterns, especially in relation to their roles in attracting pollinators or protecting reproductive organs from environmental factors.

Overall, I think this work represents a very valuable contribution to the evo-devo community, providing conceptual advances into our understanding of the emergence of novelty, as well as providing a complex computational framework addressing cellular patterning in evolving GRNs.

Field of expertise: developmental biology, nonlinear dynamics, pattern formation, evo-devo.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "Recurrent emergence of boundary cell types during evolution of floral bullseye patterns" Oud et al use computational modeling to determine how gene regulatory networks can set up the prepattern for a bullseye pigmentation. They use a modeling template that is similar to the hibiscus petal primordium, create a gene regulatory network composed of the interaction of cell autonomous transcription factors, transcription factors that can diffuse from one cell to another, and cell-cell communication signals. Each simulation started from a diffusing signal from the base and all other genes with no expression. Such a signal diffusing from the base of the organ has been hypothesized many times in plant morphogenesis, so this is plausible. They started 35 populations with initially random GRNs and let them evolve for 30,000 generations selecting for simulated petals with higher bullseye fitness in each generation. All 35 generated bullseyes. The authors used a UMAP dimensionality reduction similar to single cell RNA-seq to identify different cell types in the models. I have not seen this analysis applied to modeling before, and I thought this approach was innovative. Interestingly 26 out of the 35 initiated a boundary cell type to help in the robust establishment of the symmetric bullseye, whereas 9 did not. There are two major ways these boundary cell types is established: (1) boundary specific gene expression and (2) two nested proximal genes with one extending beyond the other. Then the authors examine real hibiscus petals and identify boundary cells, which express 30 boundary specific genes. The authors then examine one of the GRNs from one of their populations and find that gene 5 is crucial for setting up the boundary. Finally, the analyze over evolutionary time in each population and see that these boundary cells come and go in the lineages, but they have a longer persistence time when there is noise in the modeling, suggesting that they add robustness to the generation of the bullseye.

Major comments:

There is a major missed opportunity to analyze the evolved networks. Only one of the 30 GRNs is analyzed in figure 4. Please add further analysis of the GRNs from all the populations. Within a population after 30K generations, how much variation is there in the GRNs of individuals? How similar are the optimal fitness evolved GRNs across all 35 populations? Are there common motifs across networks? Is there always an antagonism between proximal and distal proteins somewhere in the network? A lot of previous work on GRNs has established the function of common motifs, and these should be analyzed. Please provide all 30 gene regulatory networks in the supplement.

The purpose and significance of examining the evolutionary lineage is not clear. Please explain your logic. This is most important for Figure 5 where it becomes clear that the boundary cells are often formed transiently in the evolution of the GRN. If this boundary cell type does not persist, how can it help the petal generate a bullseye. What happens after the boundary cell type is lost? Has the GRN evolved into a more stable place where it no longer needs the boundary? In several instances it looks like they come and go many times. Please explain how these transient boundary cells in the evolutionary lineage can make a difference. This point also comes up in lines 113-115 "For each simulation, we traced back the ancestral lineage of the final fittest individual and sampled 12 of its ancestors at evenly spaced generational intervals, performing this analysis on each sampled ancestor." I could understand if the boundary cell type were developmentally transient, but I have a hard time what its significance is since it is evolutionarily transient.

It is worth saying more about how the 9 lineages without a boundary cell types manage to make a robust bull's eye pattern because this is also interesting.

How were 12 proteins chosen for the network, as opposed to 6 or 20 for instance? In the network pruning, it seems like fewer proteins are required. How many proteins are required to produce a bulls eye pattern?

Minor comments:

The title needs to be changed to include computational modeling or simulation because otherwise the current version of the title implies that these boundary cell types are found in plant species evolution.

Line 103 - 106 "We found that over a third of all simulations evolved a bullseye size of approximately 50% of the petal's central height (Figure 2A.ii). This indicates a tendency for simulations to converge toward these proportions, possibly due to the interaction between the patterning signal distribution and the tissue geometry." The phrasing here is confusing. Which proportions does "these proportions" refer to? Presumably, 50% from the preceding sentence. But the second proportion is not clear from the text. Maybe it is the peak at approximately 65% seen in the graph. Please clarify in the text.

Line 118 "To further explore cell identity in the third cluster, we analysed the gene expression profiles of the three identified cell types." It is not clear what the third cluster refers to. The previous sentence mentions 9 lineages without boundary cell types. So, a transition here back to lineages with boundary cell types, would help here.

Figures 3C-D, it would help to label these volcano plots proximal versus boundary and distal versus boundary. Although they do fit your color scheme and legend for the color scheme, it is important to specify it explicitly.

On Figure 4A it would help to label which gene is Prox and Dist. I assume they are the purple and yellow genes, but it would be easier if they were labeled.

Line 185-186 "Gene 5 delays and spatially restricts the expression of gene 10, ensuring the symmetric development of the pattern." This statement needs to be supported by showing a time series simulation-movie or timepoints-revealing this timing aspect of Gene 5.

Referee cross-commenting

I agree with all reviews, which are aligned.

Significance

How pigment patterns in petals are established is an important and fascinating question, that sheds light on broader issues of how tissues are pre-patterned. Previous studies focus on the reaction diffusion gene regulatory networks that create beautiful petal pigment spot patterns. This paper fills the gap in addressing how a prepattern is established to create a simple proximal distal bullseye pigment pattern. Overall, the use of modeling in this study raises several novel and exciting hypotheses for how a pre-pattern can be established during development. One limitation of the study as acknowledged by the authors is that the actual petal grows, whereas the model does not. Although growth is likely to make an interesting contribution to the pattern, I agree that it is beyond the scope of this manuscript. Modeling papers are always challenging to write clearly, and I point out some areas where clarifications are needed below. The figures illustrate the results well.<br /> This paper will be of interest to developmental biologists, gene regulatory network afficionados and computational biologists.

My expertise is in plant morphogenesis and patterning as well as computational modeling.

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Referee #1

Evidence, reproducibility and clarity

The manuscript presents the findings of a computational investigation, whereby populations of artificial "genomes" and their products are evolved algorithmically. They are subjected to a fitness constraint defined in terms of a spatial expression pattern on a petal shaped template. The specific focus of this work is the formation of two-pigment patterns on flower petals, which give rise to "bullseye" patterned flowers. A computational survey suggests that besides the two main genetic identities which are strictly required to form such patterns, a third population is likely to emerge, as a marker located at the interface between the two main identities. This prediction is then tested by dissecting petals of Hibiscus trionum and performing an mRNA-seq survey. The resulting data set is consistent with the simulations, with a population of genes specifically expressed at the boundary between the two main regions. The paper then discusses a number of hypotheses on the evolution of underlying gene regulatory networks, testing them computationally. In particular, by comparing simulations with and without stochastic terms in the dynamics of gene regulation/expression, it is suggested that the 3rd identity is contributing to robustness of the pattern in the face of noise. Overall the main text is clear and makes an interesting case.

Major comments:

1. The code used for simulations is available on a public repository, but it does not directly ensure that results are reproducible. To do so would require a clear step-by-step guide referring the user to the specific pieces of code which have been used for the results and figures presented in the paper. At the moment, I could not find any such guide and the large number of scripts, executables and jupyter notebooks are not clearly linked to the paper's contents.

2. The methods themselves involve a number of arbitrary choices. Though this is understandable given the nature of the work, one aspect in particular that would deserve better clarity is the modeling of gene network dynamics. The stochastic model (l.516 & following) involves a nesting of "Hill-like" terms (those in Eqs. (7) and (11)) which is unusual and given without justification. There should be some explanation of how this approach relates to standard approaches such as those reviewed e.g. in: Bintu et al. Current opinion in genetics & development 15.2 (2005): 116-124.

3. It is also unclear at the moment how exactly the GRN dynamics is used; are time-stepping algorithms used until the system reaches a stationary regime? If so, how is stationarity assessed? This needs to be explained both in the main text and in the methods. The table of parameters suggests that there was a cut-off time, but there is no explanation whatsoever about the state of the dynamics at this time.

4. Related to the previous point, the table of parameters (Table S1) is provided without any explanation; through what process (exploratory, literature review, trial and error...) where the values selected? As there been any type of sensitivity analysis?

Minor comment:

1. The fitness function used in simulations specifically encodes the desired pattern, with two zones having differential gene expression. This allows the artificial selection to evolve towards such patterns, as expected, but it is not entirely clear how this relates to natural selection itself. At the very start of the paper, the authors briefly review some possible sources of selective pressure for flowers to exhibit patterns such as bullseye, among others. None of the selective factors would likely act on the plants as a direct incentive for two regions, as specified in the cost function. Instead, one may expect a more high level criterion, such as "conspicuousness" for a pollinator, for instance. This is admittedly not naturally represented as a fitness function, but the choice of this function definitely influences the outcomes of a simulation. Some further numerical experiments may allow to demonstrate that the exact cost function is not critical for the findings of the paper, but I understand they would likely be computationally costly, to the point of unfeasibility. This limitation should be mentioned at least.
2. [optional suggestion] The number of genes used in the simulations is very small in comparison to real organisms. This is clearly justified by the complexity of the work, but one wonders if simulations could be made more efficient by using a much simplified approach for the gene network dynamics. At the time scales of interest, it seems that the use of SDEs and the numerical intricacies they require might be an unnecessary burden. Have the authors considered a much simpler approach, for instance based on Boolean models? Since the study only uses static tissues, all the GRN dynamics could be by-passed, determining steady states very quickly and using them to determine fitness. If this saved significant computational time, this would allow a more comprehensive survey of the "purely genetic" part of the model.

Referee cross-commenting

I agree with both other reviewers. As mentioned by them, our reviews bring complementary suggestions, while being overall in good agreement.

Significance

Reviewer's expertise: mathematical modeling, mathematical biology.

This paper is mostly a conceptual study, in which the majority of results are based on computer simulations. The findings are biologically interesting, but it is hard to prove these evolutionary claims through physical experiments. The complexity of the simulations requires a large number of technical assumptions and parameter choices, which overall make it very difficult to assess how plausible these simulations are, compared to the natural processes they are meant to represent. All the findings are well-argued and provide an overall convincing case, but it is by design impossible to fully assess experimentally. As such, this work will be mostly valuable to theoretical biologists, computational modelers, and researchers interested in "artificial life" and gene evolution.

Tadaaa.

This is interesting and might be good to have a specific example. I think the key point here is that power structures are, indeed, analysed in their own historical context. Anachronistic readings of the past, even when they are seemingly feminist, are also not helpful.

This partly answers my question what exactly the limitations are, but it would still be great to specify more what people do when they are not doing feminist DH. How does the absence of a certain awareness typically translate into research practices?

I find these two sentences rather vague. What exactly are the hindrances in adaptation? Is there a tendency only to go for "low hanging fruit" in DH? And if so, why?

not a crisis?

statistics for young people - there was friction between generations throughout - a commonality throughout history. However, did this neccisarily mean there was a crisis of social peace? While some periods are more peaceful than others, to argue that there was a 'crisis' throughout the entirety of the period may be hyperbolic

As in the beginning of our period, young people remained a key source of disruption to social peace and spearheaded protest groups.

disruption of social peace as people disagreed

peaceful protest doesn't sound like a crisis of social peace to me?

Très bonne ouverture qui élargit la réflexion

Métaphore pertinente pour illustrer le propos

Concept intéressant mais pourrait être défini plus clairement

Accroche très efficace qui capte l’attention et introduit bien le sujet

§ 12. A garantia de execução de que trata o § 11 deste artigo aplica-se também às programações incluídas por todas as emendas de iniciativa de bancada de parlamentares de Estado ou do Distrito Federal, no montante de até 1% (um por cento) da receita corrente líquida realizada no exercício anterior. (Redação dada pela Emenda Constitucional nº 100, de 2019) (Produção de efeito) (Vide) (Vide) (Vide ADI 7697)

Question pertinente qui ouvre bien la réflexion

Analyse pertinente et bien reliée aux sciences cognitives

Concept bien mobilisé et clairement expliqué

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Action建议：评估持续学习技术时，关注模型在长任务序列中学习新事物的能力。这种评估标准更接近实际应用需求。

Action建议：明确定义持续学习为突破当前技术可行边界的努力集合。这种定义有助于确定研究方向和评估进展。

Action建议：接受新技术初期表现不佳但最终超越的规律。这种预期管理有助于持续学习技术的研发决策和资源分配。

Action建议：采用任务完成边界作为衡量模型进步的北极星指标。这种量化方法有助于评估持续学习技术的实际效果和进展。

Action建议：避免将持续学习视为独立方法的集合，而应关注其统一目标。这种方法论转变能减少概念混淆，提高研究效率。

Arrow vs Line Perspective

Action建议：将持续学习视为推动任务边界的集体努力，而非离散方法集合。这种视角帮助理解其方向性和系统性本质。

heap 想扩展，但：

👉 中间空间不够 👉 或被别的 segment 挡住

那怎么办？

可能结果： ❌ 扩展失败 ❌ 必须搬整个段（非常昂贵）

也就是加权限位

一个 CPU 拥有多个 segment 的 base/bounds 寄存器对（每个 segment 一对），执行时根据访问类型选择对应的那一对使用

比如说取指令自动会去code space

虚拟地址高位来表示segment ID

分给某个进程的那块连续区域内部，有很多没用上的空间。

👉 bounds 表示的是“大小（size）”，不是位置（position）

👉 检查的时候：

检查的是：虚拟地址 VA < bounds

👉 计算物理地址才用：

PA = base + VA

一种通用寄存器

CPU 是执行计算的地方 DRAM 是主内存（存数据）

打印的是虚拟地址 main 函数地址 code（代码段） malloc(...) 返回值 堆内存地址 heap 参数是申请的大小(byte) &x 变量地址 stack

✔ Big Endian（大端） 高位字节（Most Significant Byte）放在低地址 看起来和我们写数字顺序一样 ✔ Little Endian（小端） 低位字节（Least Significant Byte）放在低地址 “反着放”

up to ep. 42

quitar todo esto y poner "respuestas"

Por lo tanto, es ideal para combinarlo con intervenciones tácticas.

eLife Assessment

This important study uses an optimized IOR-Stroop fMRI paradigm to dissociate integration and segregation processes and to show that attentional orienting modulates conflict processing at both the semantic and response levels. The evidence is compelling, supporting the integration-segregation theory of exogenous attention in inhibition of return while also deepening our understanding of how attentional orienting shapes downstream cognitive processing. The work will therefore be of broad interest to researchers in attention and cognitive control.

Reviewer #1 (Public review):

Summary:

This study makes a significant and timely contribution to the field of attention research. By providing the first direct neuroimaging evidence for the integration-segregation theory of exogenous attention, it fills a critical gap in our understanding of the neural mechanisms underlying inhibition of return (IOR). The authors employ a carefully optimized cue-target paradigm combined with fMRI to elegantly dissociate the neural substrates of cue-target integration from those of segregation, thereby offering compelling support for the integration-segregation account. Beyond validating a key theoretical hypothesis, the study also uncovers an interaction between spatial orienting and cognitive conflict processing, suggesting that exogenous attention modulate conflict processing at both semantic and response levels. This finding shed new light on the neural mechanisms that connect exogenous attentional orienting with cognitive control.

Strengths:

The experimental design is rigorous, the analyses are thorough, and the interpretation is well grounded in the literature. The manuscript is clearly written, logically structured, and addresses a theoretically important question. Overall, this is an excellent, high-impact study that advances both theoretical and neural models of attention.

Comments on revisions:

I appreciate the authors' thorough and thoughtful revisions, which have successfully addressed all of my prior concerns.

Reviewer #2 (Public review):

This study provides neuroimaging evidence supporting the integration-segregation theory of inhibition of return (IOR), a widely studied attentional phenomenon. It also explores the neural interactions between IOR and cognitive conflict, demonstrating that conflict processing is potentially modulated by attentional orienting.

The integration-segregation theory was investigated using a sophisticated, well-executed experimental task that accounted for cognitive conflict processing, which is phenomenologically related to IOR but is non-spatial. The behavioral and neuroimaging data were carefully analyzed.

The authors have thoughtfully addressed all my previous concerns. By demonstrating how attentional orienting can modulate neural processing of cognitive conflict, this study helps to advance a more unified and mechanistic understanding of the cognitive and neural processes that govern our visual perception and response selection.

Reviewer #3 (Public review):

Summary:

This study provides direct neuroimaging evidence relevant to the integration-segregation theory of exogenous attention-a framework that has shaped behavioral research for more than two decades but has lacked clear neural validation. By combining an inhibition-of-return (IOR) paradigm with a modified Stroop task in an optimized event-related fMRI design, the authors examine how attentional integration and segregation processes are implemented at the neural level and how these processes interact with semantic and response conflicts. The central goal is to map the distinct neural substrates associated with integration and segregation and to clarify how IOR influences conflict processing in the brain.

Strengths:

The study is well-motivated, addressing a theoretically important gap in the attention literature by directly testing a long-standing behavioral framework with neuroimaging methods. The experimental approach is creative: integrating IOR with a Stroop manipulation expands the theoretical relevance of the paradigm, and the use of a genetic-algorithm-optimized fMRI design ensures high efficiency. Methodologically, the study is rigorous, with appropriate preprocessing, modeling, and converging analyses across multiple contrasts. The results are theoretically coherent, demonstrating plausible dissociations between integration-related activity in the fronto-parietal attention network (e.g., FEF, IPS, TPJ, dACC) and segregation-related activity in medial temporal regions (e.g., PHG, STG). Importantly, the findings provide much-needed neural support for the integration-segregation framework and clarify how IOR modulates conflict processing.

Revisions and Evaluation:

The authors have responded thoroughly and convincingly to the concerns raised in the previous round of review. In particular, issues related to the interpretation of dACC activity, the functional characterization of PHG and STG, and reporting clarity have been carefully addressed. The manuscript has been improved in terms of transparency, consistency of reporting, and overall readability.

As a result, I no longer see any major weaknesses. The study is now clearly presented, methodologically sound, and theoretically informative. It makes a valuable contribution to the literature on attention and cognitive control.

Comments on revisions:

I appreciate the authors' efforts in addressing the previous comments. They have responded thoroughly to the concerns raised in the prior round of review. The work is well executed and makes a meaningful contribution to the field.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This important study provides the first direct neuroimaging evidence for the integration segregation theory of exogenous attention underlying inhibition of return, using an optimized IOR-Stroop fMRI paradigm to dissociate integration and segregation processes and to demonstrate that attentional orienting modulates semantic- and response-level conflict processing. Although the empirical evidence is compelling, clearer justification of the experimental logic, more cautious framing of behavioral and regional interpretations, and greater transparency in reporting and presentation are needed to strengthen the conclusions. The work will be of broad interest to researchers investigating visual attention, perception, cognitive control, and conflict processing.

We appreciate the positive reception to our manuscript. In the revised manuscript, we have further clarified the logic underlying the task design, adopted a more cautious tone in interpreting the behavioral and neuroimaging results, and enhanced the transparency of reporting and presentation.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study makes a significant and timely contribution to the field of attention research. By providing the first direct neuroimaging evidence for the integration-segregation theory of exogenous attention, it fills a critical gap in our understanding of the neural mechanisms underlying inhibition of return (IOR). The authors employ a carefully optimized cue-target paradigm combined with fMRI to elegantly dissociate the neural substrates of cue-target integration from those of segregation, thereby offering compelling support for the integration-segregation account. Beyond validating a key theoretical hypothesis, the study also uncovers an interaction between spatial orienting and cognitive conflict processing, suggesting that exogenous attention modulates conflict processing at both semantic and response levels. This finding shed new light on the neural mechanisms that connect exogenous attentional orienting with cognitive control.

Strengths:

The experimental design is rigorous, the analyses are thorough, and the interpretation is well grounded in the literature. The manuscript is clearly written, logically structured, and addresses a theoretically important question. Overall, this is an excellent, high-impact study that advances both theoretical and neural models of attention.

Weaknesses:

While this study addresses an important theoretical question and presents compelling neuroimaging findings, a few additional details would help improve clarity and interpretation. Specifically, more information could be provided regarding the experimental conditions (SI and RI), the justification for the criteria used for excluding behavioral trials, and how the null condition was incorporated into the analyses. In addition, given the non-significant interaction effect in the behavioral results, the claim that the behavioral data "clearly isolated" distinct semantic and response conflict effects should be phrased more cautiously.

We thank the reviewer for these helpful comments. In the revised manuscript, we have provided additional clarification regarding the SI and RI conditions (page 29), expanded the justification for the behavioral trial exclusion criteria (page 32), and clarified how the null condition was modeled and incorporated into the analyses (page 29). In addition, we have revised the description of the behavioral results to adopt more cautious wording, particularly given the absence of a significant interaction effect. For detailed responses to these specific points, please refer to the "Recommendations for the Authors" section below.

Reviewer #2 (Public review):

Summary:

This study provides evidence for the integration-segregation theory of an attentional effect, widely cited as inhibition of return (IOR), from a neuroimaging perspective, and explores neural interactions between IOR and cognitive conflict, showing that conflict processing is potentially modulated by attentional orienting.

Strengths:

The integration-segregation theory was examined in a sophisticated experimental task that also accounted for cognitive conflict processing, which is phenomenologically related to IOR but "non-spatial" by nature. This study was carefully designed and executed. The behavioral and neuroimaging data were carefully analyzed and largely well presented.

Weaknesses:

The rationale for the experimental design was not clearly explained in the manuscript; more specifically, why the current ER-fMRI study would disentangle integration and segregation processes was not explained. The introduction of "cognitive conflict" into the present study was not well reasoned for a non-expert reader to follow.

We thank the reviewer for raising these important points. In the revised manuscript, we have further clarified the rationale of the experimental design and the motivation for introducing cognitive conflict.

First, we clarified that previous neuroimaging studies relied primarily on SOA-based contrasts, which capture the temporal dynamics of attentional orienting but do not directly distinguish the functional processes of integration and segregation. We therefore established the direct comparison between cued and uncued targets in the long SOA as the critical test required by the theory, as these conditions are hypothesized to engage integration and segregation processes, respectively (pages 6-7, “The Challenge of Neural Verification”). Crucially, to successfully implement this comparison, we highlighted the specific methodological advantage of our study: the use of a Genetic Algorithm (GA) to optimize the stimulus sequence. We explained how this design maximizes statistical power specifically for contrast detection (i.e., cued vs. uncued) while maintaining high estimation efficiency, thereby directly overcoming the power constraints that had likely obscured these subtle neural signatures in prior ER-fMRI work (pages 7-8).

Second, we clarified that the manipulation of cognitive conflict was introduced with the additional aim of examining IOR expression mechanisms, specifically investigating how spatial attention modulates ongoing cognitive processing after target onset, rather than the generation of IOR itself. We have now provided a clearer rationale for embedding a modified Stroop task within the cue-target paradigm, and explained how this design allows us to dissociate semantic and response conflicts while avoiding methodological confounds present in previous studies (page 8).

The presentation of the results can be further improved, especially the neuroimaging results. For instance, Figure 4 is challenging to interpret. If "deactivation" (or a reduction in activation) is regarded as a neural signature of IOR, this should be clearly stated in the manuscript.

We thank the reviewer for pointing out the interpretational challenges in Figure 4. To address this, we have revised Figure 4 and provided a clearer and more precise interpretation of these interaction effects in the manuscript.

First, we have added explicit panel titles to Figure 4 (page 17). Panel A is now clearly labeled as the “Effect of IOR on Semantic Conflict”, while Panel B is labeled as the “Effect of IOR on Response Conflict”. We hope this visual labeling helps readers clearly identify the IOR modulation effects specific to each conflict type.

Second, we have revised the figure caption to explicitly define the interaction contrasts used to quantify these modulations, providing specific formulas (e.g., [UncuedRI – Uncued-SI] > [Cued-RI – Cued-SI] for response conflict) to ensure transparency.

Finally, regarding the reviewer’s comment on “deactivation”, we realized that our original figure terminology (e.g., “IOR effect under...”) might have caused confusion by mixing the interaction effect with the IOR effect itself. We have clarified that Figure 4 specifically illustrates the “Effect of IOR on the Semantic Conflict and the Response Conflict” (i.e., interaction effect between IOR and cognitive conflict). To interpret this interaction, we further examined the simple effects of conflict under each cueing condition. Specifically, we analyzed the neural signatures of semantic conflict (SI minus NE) and response conflict (RI minus SI) separately for the cued and uncued targets. Importantly, regarding the nature of the IOR effect itself (as displayed in Figure 3, page 14), it is not simply a uniform deactivation. Instead, by directly comparing the cued and uncued conditions for the neutral words, we observed neural changes in two directions: some specific regions exhibited an increased activation (Cued > Uncued), while others showed a reduced activation (Uncued > Cued). These differential patterns involved distinct brain networks and corresponded to the distinct integration and segregation mechanisms, respectively, rather than a global loss of activation (pages 20-21).

Reviewer #3 (Public review):

Summary:

This study aims to provide the first direct neuroimaging evidence relevant to the integration-segregation theory of exogenous attention - a framework that has shaped behavioral research for more than two decades but has lacked clear neural validation. By combining an inhibition-of-return (IOR) paradigm with a modified Stroop task in an optimized event-related fMRI design, the authors examine how attentional integration and segregation processes are implemented at the neural level and how these processes interact with semantic and response conflicts. The central goal is to map the distinct neural substrates associated with integration and segregation and to clarify how IOR influences conflict processing in the brain.

Strengths:

The study is well-motivated, addressing a theoretically important gap in the attention literature by directly testing a long-standing behavioral framework with neuroimaging methods. The experimental approach is creative: integrating IOR with a Stroop manipulation expands the theoretical relevance of the paradigm, and the use of a genetic algorithm-optimized fMRI design ensures high efficiency. Methodologically, the study is sound, with rigorous preprocessing, appropriate modeling, and analyses that converge across multiple contrasts. The results are theoretically coherent, demonstrating plausible dissociations between integration-related activity in the fronto-parietal attention network (FEF, IPS, TPJ, dACC) and segregation-related activity in medial temporal regions (PHG, STG). The findings advance the field by supplying much-needed neural evidence for the integration-segregation framework and by clarifying how IOR modulates conflict processing.

Weaknesses:

Some interpretive aspects would benefit from clarification, particularly regarding the dual roles ascribed to dACC activation and the circumstances under which PHG and STG are treated as a single versus separate functional clusters. Reporting conventions are occasionally inconsistent (e.g., statistical formatting, abbreviation definitions), which may hinder readability. More detailed reporting of sample characteristics, exclusion criteria, and data-quality metrics-especially regarding the global-variance threshold-would improve transparency and reproducibility. Finally, some limitations of the study, including potential constraints on generalization, are not explicitly acknowledged and should be articulated to provide a more balanced interpretation.

We thank the reviewer for the positive and constructive assessment of our study. In response to the concerns raised, we have carefully revised the manuscript and addressed all points in detail below. In brief, we have clarified key interpretation issues in the Discussion section, including the complementary roles of dACC activation and the distinction between statistical clustering and functional interpretation of PHG and STG activations (pages 20-21). We have also improved transparency and reporting throughout the manuscript by providing more detailed sample characteristics, clarifying exclusion criteria and global variance computation, adding illustrative supplementary figures, and standardizing statistical reporting and abbreviations (pages 28, 33). Finally, we have added a concise paragraph on limitations of the study to provide a more balanced interpretation of the findings (pages 26-27). Detailed, point-by-point responses to all specific comments are provided below (see the “Recommendations for the authors” Section).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Specific comments:

(1) The figure caption contains an unclear sentence (lines 195-196): "The target was a 450-ms colored Chinese character presented 600 ms after the fixation cue onset at the two target locations with equal probabilities." This description is ambiguous and should be revised for clarity.

Thanks for pointing this out. In the revised manuscript, we have rephrased the figure caption to improve clarity as follows (pages 9-10):

“Each trial started with a 150-ms non-informative cue presented at one of the two peripheral boxes. After a 150-ms interstimulus interval (ISI), a 150-ms fixation cue was presented at the central fixation box. Following a further 450-ms ISI, the target, a colored Chinese character, appeared at one of the two target locations with equal probabilities and remained on the screen for 450 ms. The trial ended with a variable intertrial interval (ITI) of 850, 1050, 1250, or 1450 ms (with equal probabilities).”

(2) Please provide a more detailed and clearer description of the SI and RI experimental conditions in the Methods section.

Thanks for this helpful suggestion. We have revised the Methods section to provide a more detailed description of the SI and RI conditions. Specifically, we have further described the stimulus-response mapping and clarified how the SI and RI conditions are defined based on whether the ink color and the character meaning fell into the same or different response categories under this mapping. In addition, we have added a clarification in the Methods section to make it clearer that the SI trials involved semantic conflict without response conflict, whereas RI trials involve both semantic and response conflicts (page 29).

(3) As the data were collected across two research centers, please clarify the number of participants enrolled at each site.

Thanks for this suggestion. We have now explicitly stated in the Apparatus and Data Acquisition section that 16 participants were enrolled at each site. The revised text reads (page 31)：

“The imaging data were acquired at two research sites following comparable protocols, with equal numbers of participants scanned at each site (n = 16 per site).”

(4) In the behavioral data analysis, please provide the rationale or justification for the criteria used to exclude trials.

Thanks for this comment. In the revised manuscript (page 32), we have clarified that reaction times (RTs) shorter than 150 ms were excluded as anticipatory responses, and RTs longer than 1,300 ms were excluded to limit the influence of unusually slow responses. These exclusion criteria are commonly adopted in RT research and were applied consistently across all conditions (Ratcliff, 1993; Whelan, 2008).

(5) Given that the behavioral interaction effect was not statistically significant, the conclusion on lines 236-237, "These data clearly isolated the two distinct conflict effects in the Stroop effect, namely the semantic conflict (SI-NE difference) and the response conflict (RI-SI difference)" appears overstated and should be softened accordingly.

We thank the reviewer for this important comment. We have clarified that our original statement was intended to highlight the successful isolation of conflict types based on the significant main effects of congruency (validating the task design), rather than implying a significant interaction effect. However, we agree that the original phrasing appeared unclear in this context. We have therefore revised the sentence to adopt a more cautious tone in the revised manuscript (page 12):

“These data demonstrated typical Stroop interference effects (Veen & Carter, 2005) in both the semantic (SI-NE difference) and response conflicts (RI-SI difference).”

(6) The statement on lines 281-282, "Although the IOR effect showed no effect on either the semantic conflict difference (SI-NE) or the response conflict difference (RI-SI) in the behavioral performance" lacks supporting statistical evidence. Please report the relevant test statistics.

We appreciate the reviewer’s careful reading and note that the relevant statistical evidence was missing from the original manuscript. This has now been added in the revised version. Specifically, we examined the interactions between cue validity and semantic conflict (SI vs. NE) as well as between cue validity and response conflict (RI vs. SI). Neither interaction was significant (see revised Results for full statistics on page 12), supporting our original statement that cue validity did not modulate either conflict component in behavioral performance.

(7) The manuscript mentions that a null condition (with no Chinese character presented) was included to increase statistical power for detecting differences across conditions. However, it is unclear how this null condition was actually used in the data analyses. Please clarify the role of the null condition in both the behavioral and neuroimaging analyses.

Thanks for this comment. We regret that this was not sufficiently clear in the original manuscript. The null condition was included for neuroimaging purposes and was not used in the behavioral analyses, as no response was required in these trials. In the fMRI analyses, null trials served as the implicit baseline and were not modeled as regressors of interest. Task-related activities for all experimental conditions were therefore estimated relative to this null baseline, facilitating estimations of task-related responses in randomized event-related designs (Burock et al., 1998; Friston et al., 1999; Liu, 2004). We have clarified this point in the revised manuscript (page 29).

References

Burock, M. A., Buckner, R. L., Woldorff, M. G., Rosen, B. R., & Dale, A. M. (1998). Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. NeuroReport, 9(16), 3735-3739. https://doi.org/10.1097/00001756-199811160-00030

Friston, K. J., Zarahn, E., Josephs, O., Henson, R. N. A., & Dale, A. M. (1999). Stochastic designs in event-related fMRI. NeuroImage, 10(5), 607-619. https://doi.org/10.1006/nimg.1999.0498

Liu, T. T. (2004). Efficiency, power, and entropy in event-related fMRI with multiple trial types: Part II: design of experiments. NeuroImage, 21(1), 401-413. https://doi.org/10.1016/j.neuroimage.2003.09.031

Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114(3), 510-532. https://doi.org/10.1037/0033-2909.114.3.510

Whelan, R. (2008). Effective analysis of reaction time data. The Psychological Record, 58(3), 475-482. https://doi.org/10.1007/BF03395630

Reviewer #2 (Recommendations for the authors):

(1) The paper is a bit too lengthy, with a lot of information that is hard for non-experts to grasp.

We thank the reviewer for this comment. We realized that the Introduction was the most challenging section for general readers. In the revision, we refined the text in the Introduction for a better structure and more reader-friendly wording to improve readability. In addition, following the reviewer’s suggestion (Recommendation 4 below), we have added short subsection titles to the Introduction, Results, and Discussion sections to better organize the content and highlight the main ideas. We hope these revisions make the manuscript more accessible and easier for a broader audience to follow.

(2) Please double-check the stats, as some of the results presented in the main text do not align well with the figures. Take Figure 2 as an example.

We appreciate the reviewer’s concern and have double-checked all statistics. All the results are consistent between the figures and the main text. Take Figure 2 as an example (page 12), the perceived discrepancy probably was caused by the fact that the descriptive values reported in the main text are marginal means for the main effects (i.e., the overall average of one factor, collapsed over the other factor), whereas Figure 2 shows the mean for each Congruency × Cue Validity condition (i.e., simple effect).

(3) The reasoning that the neuroimaging findings support the dissociation between integration and segregation needs to be improved.

We thank the reviewer for this important comment. In the revised Discussion (pages 1921), we have strengthened the reasoning linking our neuroimaging findings to the dissociation between the integration and segregation processes. Specifically, we make it clear how the distinct activation patterns observed for the cued and uncued targets map onto the different functional demands proposed by the integration-segregation theory. The cued targets were theorized to recruit the frontoparietal attentional control networks, consistent with the re-engagement of an existing object file (integration). On the other hand, the uncued targets should engage the medial temporal and temporal association regions responsible for novelty detection and episodic encoding, consistent with the creation of a new object file (segregation). We hope the reviewer finds that the revision offers a clearer explanation of how the observed neural patterns are consistent with a dissociation between the integration and segregation processes.

(4) Please use short section titles to organize the introduction, results, and discussion sections. For instance, the discussion section is a long chunk of text (almost 9 pages) and is pretty dense, making it hard to quickly grasp the ideas the authors want to convey.

Thanks for this helpful suggestion. Following the reviewer’s recommendation, we have now added short subsection titles to the Introduction and Discussion sections to improve structure and readability. For the Results section, we have maintained and further refined the existing subheadings to ensure consistent organization.

Reviewer #3 (Recommendations for the authors):

I found this manuscript to be a timely and substantive contribution to the study of attention and cognitive neuroscience. To my knowledge, it provides the first direct neuroimaging evidence relevant to the integration-segregation theory of exogenous attention, a framework that has been influential in behavioral work for more than two decades but has lacked clear neural support. The study is conceptually well motivated, methodologically solid, and generally clearly reported. The findings differentiate neural substrates associated with integration and segregation processes and further show how inhibition of return (IOR) interacts with semantic and response conflicts at the neural level.

The manuscript is well organized, the writing is mostly clear, and the progression from theory to hypotheses and methods is easy to follow. The combination of IOR with a modified Stroop paradigm is a clever choice that extends the theoretical scope of exogenous attention research. The use of an optimized event-related fMRI design based on a genetic algorithm is also a strength and reflects careful attention to design efficiency.

The main results are internally consistent and theoretically meaningful. Integration related activity in the fronto-parietal attention network (including FEF, IPS, TPJ, and dACC) and segregation-related activity in medial temporal areas (PHG and STG) it well with the proposed framework, and the pattern of activations is coherent across analyses.

Overall, I think this is a carefully executed study that offers much-needed neural evidence bearing on the integration-segregation theory of exogenous attention. I would recommend the following revisions.

Suggestions:

(1) In the Discussion (pp. ~17-18), dACC activation is described both in terms of general cognitive control demands and as reflecting a possible inhibitory bias toward the cued direction. It would help the reader if you could briefly indicate whether you see these as complementary (e.g., dual roles within the same region) or as more competing interpretations.

We thank the reviewer for this helpful comment. We have clarified in the revised manuscript that dACC exerts general cognitive control demands and biasing against the cued direction are complementary rather than competing interpretations. Specifically, we described how the dACC is involved in both the cognitive control required for target integration and the inhibitory bias toward the cued location, thereby highlighting its dual roles within the same region. The revised section reads as follows (page 20):

“Furthermore, the observed increase in the left dACC activity under the cued relative to the uncued condition likely reflected the engagement of cognitive control mechanisms (Botvinick et al., 2004; Chung et al., 2024; Mayer et al., 2012; Veen & Carter, 2005), particularly in resolving the conflict between the task-driven requirement of target integration and the reduced accessibility of the cue-initiated representation. In this context, the heightened activation of dACC may also reflect its role in fulfilling the inhibitory bias toward the cued location (Mayer et al., 2004) and discouraging inefficient integration attempts at a location marked as less relevant.”

(2) In the Discussion, you could consider adding a short paragraph explicitly acknowledging a few limitations and how they might constrain generalization of the findings. A concise reflection of this kind would give a more balanced picture without undermining the main conclusions.

We appreciate this helpful suggestion. In the revised manuscript, we have added a concise paragraph explicitly addressing a key limitation of the present study (pages 26-27). Specifically, we acknowledge that the absence of behavioral interactions alongside clear neural effects requires cautious interpretation. We discussed how this dissociation may reflect differences in measurement sensitivity between behavioral and neural indices, consistent with prior findings (Chen et al., 2006; Wilkinson & Halligan, 2004). We also note that the use of a GA-optimized sequence, while improving statistical efficiency, may have introduced unintended regularities in event order that could influence behavioral strategies.

(3) Since the dataset is hosted on GitHub, adding a short note in the Data Availability section about whether the repository will also include analysis scripts or future replication data would further enhance transparency and long-term usefulness.

Thanks for this helpful suggestion. We have revised the Data Availability section (page 35) to clarify that the GitHub repository contains the processed data used in the final analyses. Analysis scripts and additional materials for replication are available from the authors upon reasonable request.

(4) In the Results section, the formatting of statistics is not fully consistent. For example, some reports use spaces around symbols (e.g., "η² = 0.301") whereas others do not (e.g., "p< .001"). It would be good to standardize this (e.g., "p < .001", "η² = .30") across the manuscript.

Done as suggested.

(5) A few abbreviations appear before they are defined-for instance, SPC (superior parietal cortex) shows up in the Results (response conflict section) before the full name is given. Ensuring that each abbreviation is defined at first mention would help readers who may be less familiar with all of the regional acronyms.

Thanks for this comment. We have conducted a thorough check of the manuscript and ensured that all abbreviations are defined upon their first occurrence.

(6) The text sometimes refers to "PHG/STG" as a combined cluster, while at other points, PHG and STG are described separately. It would be useful to clarify under what circumstances they are treated as a single functional cluster versus distinct regions of interest, and to keep the nomenclature as consistent as possible between the main text and the tables.

Thanks for raising this point. In the revised manuscript, we have clarified this issue by distinguishing between statistical clustering and functional interpretation. In the whole brain analysis, activations in the left hemisphere formed a single continuous cluster spanning the PHG and STG; therefore, this cluster is labeled as “PHG/STG” in Table 1. We have explicitly noted the continuous nature of this cluster in the Results section (page 15) to ensure clarity:

“Notably, in the left hemisphere, these activations formed a continuous cluster spanning both regions (labeled as PHG/STG in Table 1).”

(7) It would be helpful to provide a bit more detail about the sample characteristics (e.g., age range, handedness, and inclusion/exclusion criteria) and to state explicitly how many participants, if any, were excluded from the analyses and for what reasons. This would help readers better evaluate data quality and generalizability.

Thanks for this helpful suggestion. We have revised the Participants section (page 28) to provide the full details regarding our sample:

“32 healthy participants with normal or corrected-to-normal vision and normal color vision were recruited. All participants were right-handed and reported no history of neurological or psychiatric disorders. Data from three participants were excluded due to excessive head movements and high global variances (see fMRI Data Analysis), leaving 29 participants for analysis (18 female, 11 male; aged 18-30 years, M = 22.69, SD = 2.58).”

Furthermore, we have provided a clearer description of the exclusion criteria in the Data Analysis section (pages 33-34) as follows:

“Runs with motions exceeding one voxel length in any direction were excluded (resulting in the exclusion of two runs) …Runs with global variance equal to or over 0.1% were excluded, resulting in the exclusion of eight runs (see Supplementary Information for details). Ultimately, three participants were excluded because neither run met the quality criteria. All remaining participants retained both runs, except for three individuals who each contributed only one valid run.”

(8) Given that participants were excluded based on global variance exceeding 0.1%, it would be very informative to include, in the Supplementary Materials, an illustrative figure showing the signal time series (or global signal variance over time) for excluded participants.

We appreciate this valuable suggestion. In the revised Supplementary Materials, we have included a new figure (Figure S2) that plots the global signal time series for the excluded runs to illustrate the signal patterns that led to their exclusion based on global variance.

(9) Relatedly, it may help to more explicitly describe how global variance was computed (e.g., over which time window, after which preprocessing steps, and whether it was calculated on whole-brain signal or within specific masks). A concise clarification would make the exclusion criterion easier to interpret.

Thanks for this helpful suggestion. We have now clarified in the manuscript how global variance was computed (page 33) and have also provided a more detailed description of the computation procedure in the Supplementary Materials (page 4). Specifically, after the standard preprocessing (slice timing correction, 3D motion correction, spatial smoothing, linear trend removal, and high-pass temporal filtering), the global signal was computed for each run as the mean signal across voxels with intensity values greater than 100 in each volume. Global variance was then quantified as the temporal variance of this run-wise global-signal time course across all volumes, providing a quality-control index of signal stability.

(10) Rather than only reporting a single overall exclusion rate (e.g., 5.52% of total trials), it would be informative to break this down by source, reporting separately the proportion of trials excluded as RT outliers and the proportion excluded due to response errors. This would further improve transparency regarding the behavioral preprocessing pipeline.

Thanks for this helpful suggestion. We have now broken down the overall exclusion rate by source in the revised manuscript. Specifically, we reported that 4.29% of trials were excluded due to incorrect responses, and 1.24% of trials were excluded as RT outliers (page 32).

References

Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: an update. Trends in Cognitive Sciences, 8(12), 539-546. https://doi.org/10.1016/j.tics.2004.10.003

Chen, Q., Wei, P., & Zhou, X. (2006). Distinct neural correlates for resolving stroop conflict at inhibited and noninhibited locations in inhibition of return. Journal Of Cognitive Neuroscience, 18(11), 1937-1946. https://doi.org/10.1162/jocn.2006.18.11.1937

Chung, R. S., Cavaleri, J., Sundaram, S., Gilbert, Z. D., Del Campo-Vera, R. M., Leonor, A., Tang, A. M., Chen, K.-H., Sebastian, R., Shao, A., Kammen, A., Tabarsi, E., Gogia, A. S., Mason, X., Heck, C., Liu, C. Y., Kellis, S. S., & Lee, B. (2024). Understanding the human conflict processing network: A review of the literature on direct neural recordings during performance of a modified stroop task. Neuroscience Research, 206, 1-19. https://doi.org/10.1016/j.neures.2024.03.006

Mayer, A. R., Seidenberg, M., Dorflinger, J. M., & Rao, S. M. (2004). An event-related fMRI study of exogenous orienting: supporting evidence for the cortical basis of inhibition of return? Journal Of Cognitive Neuroscience, 16(7), 1262-1271. https://doi.org/10.1162/0898929041920531

Mayer, A. R., Teshiba, T. M., Franco, A. R., Ling, J., Shane, M. S., Stephen, J. M., & Jung, R. E. (2012). Modeling conflict and error in the medial frontal cortex. Human Brain Mapping, 33(12), 2843-2855. https://doi.org/10.1002/hbm.21405

Veen, V. V., & Carter, C. S. (2005). Separating semantic conflict and response conflict in the Stroop task: A functional MRI study. Neuro Image, 27(3), 497-504. https://doi.org/10.1016/j.neuroimage.2005.04.042

Wilkinson, D., & Halligan, P. (2004). The relevance of behavioural measures for functional imaging studies of cognition. Nature Reviews Neuroscience, 5(1), 67-73. https://doi.org/10.1038/nrn1302

密钥安全被忽视 用户常将敏感凭证存储在Claude配置中，迁移过程中存在安全风险，需要特殊处理。

Claude数据类型超预期 用户数据复杂度远超简单对话，包含大量配置和状态信息，增加了迁移的技术难度和价值。

中心化迁移更高效 避免多平台间重复迁移，通过neuDrive作为统一枢纽，大幅降低迁移复杂度和维护成本。

用户数据风险被低估 Claude用户资产价值远超预期，但官方缺乏备份机制，数据安全完全依赖单一平台稳定性。

这段描述精确地定义了套利者的行为模式，突显了其完全信息和无限资本的优势。它强调了套利者如何利用过时的报价，以及为什么做市商需要管理报价的时效性以避免被套利。

这一技术细节揭示了初始条件和创建合约的成本结构。它强调了初始资本管理和对冲成本的重要性，这是构建有效做市策略的基础考虑因素。

这段描述精确地定义了竞争对手的行为模式，强调了其静态特性。它突显了竞争对手的局限性：不重新居中，不适应市场条件，这为适应性策略提供了明确的竞争优势来源。

这一观察强调了跳跃事件在模拟中的重要性。它指出即使在默认设置下，跳跃也是显著的信息冲击，而非微小波动。这突显了策略需要能够检测和响应这些离散信息事件的能力。

这一技术性解释揭示了模拟环境的关键特征：扩散是固定的，而跳跃参数的随机变化创造了不同的市场环境。这强调了策略需要适应不同跳跃特性的重要性，而不仅仅是处理随机波动。

这句话精准地捕捉了做市商面临的核心困境：必须在价格变动前报价，从而面临逆向选择风险。这一洞见揭示了预测市场挑战的本质结构，以及为什么适应性策略如此重要。

这一简洁对比揭示了做市商面临的双面性：从零售交易中获利，却遭受套利者的损失。它清晰地区分了两种交易对手及其对策略的影响，强调了识别和管理不同类型订单流的重要性。

这句话精炼地概括了整个预测市场挑战的核心策略思想。静态竞争对手的局限性（不重新锚定公平价值，不反应跳跃）为适应性策略创造了机会，强调了在市场中灵活调整的重要性。

I agree with this statement. Any presentation that I've sat in where the presenter is reading directly off the slide makes me assume they put it together the night before. I know this is usually the case of some people are nervous so the rather look at the screen then the audience. I can't help but think if they put the information together they should know a good jest of it.

This is a useful tip because I never thought of presentations like this. I thought the idea of slideshow was to grab the audience attention completely and have myself fall into the background. The slide is simply supposed to enforce the information I'm saying, sort of like a reminder to help keep the audience on track.

This is an interesting section. I never thought of preparing the audience for the conclusion of my presentation. I always felt with the flow of things the audience should know it's coming to an end, or a simple "The End" slide was enough. Seeing a lack of clear signal will leave them confuse makes me realize the importance of a clear end.

通过环境变量进行配置管理的做法提供了灵活性和安全性，但同时也提出了一个值得思考的问题：在AI安全平台中，如何平衡配置的灵活性与安全性？敏感信息如API密钥的环境变量管理可能需要额外的安全层。

使用Terraform和Helm进行云基础设施部署体现了现代DevOps实践在AI安全平台中的应用。这种基础设施即代码(IaC)方法确保了部署的可重复性和一致性，同时支持阿里云等特定云平台，显示了平台对生产环境的适应性。

简化的启动流程展示了容器化部署的优势，使用Docker Compose一键启动所有服务，大大降低了部署复杂度。这种设计反映了现代AI平台开发的一个重要趋势：简化环境配置，使研究人员能够快速开始工作，而不是陷入环境设置的困境。

模型抽象层的统一接口设计体现了对多模型支持的战略考虑，同时整合了安全操作工具。这种设计使平台能够灵活适应不同模型，同时保持安全操作的一致性。成本和token追踪功能反映了AI使用中的经济考量，这在企业级应用中至关重要。

这个功能模块代表了AI安全研究的前沿方向，不仅关注当前能力，还追踪能力演化和效率变化。计算'能力倍增时间'特别值得关注，这可能揭示AI安全能力发展的加速趋势，对预测未来安全挑战具有重要意义。

这种三级警报系统展示了AI安全监控的精细化程度，将代理行为分为不同风险级别，从完全禁止到仅记录跟踪。这种分类方法反映了AI安全中'双重用途'挑战的复杂性，即同一技术既可用于防御也可用于攻击。

这段陈述定义了Aegis Core的核心功能，它不仅仅是一个工具，而是一个完整的生态系统，用于管理AI安全代理并监控其行为。这种架构反映了当前AI安全研究的一个重要趋势：从静态防御转向动态监控和适应。

eLife Assessment

This important study offers insights into the anatomical and physiological features of cold-selective lamina I spinal projection neurons. The evidence supporting the authors' claims is convincing, although including a larger sample size and more quantification would have strengthened the study, and the claims of monosynaptic connectivity would benefit from further experimental evidence. The work will interest those in the field of somatosensory biology, especially researchers studying spinal cord dorsal horn circuits and projection neuron cell types

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers.]

Summary:

Spinal projection neurons in the anterolateral tract transmit diverse somatosensory signals to the brain, including touch, temperature, itch, and pain. This group of spinal projection neurons is heterogeneous in their molecular identities, projection targets in the brain, and response properties. While most anterolateral tract projection neurons are multimodal (responding to more than one somatosensory modality), it has been shown that cold-selective projection neurons exist in lamina I of the spinal cord dorsal horn. Using a combination of anatomical and physiological approaches, the authors discovered that the cold-selective lamina I projection neurons are heavily innervated by Trpm8+ sensory neuron axons, with calb1+ spinal projection neurons primarily capturing these cold-selective lamina I projection neurons. These neurons project to specific brain targets, including the PBNrel and cPAG. This study adds to the ongoing effort in the field to identify and characterize spinal projection neuron subtypes, their physiology, and functions.

Strengths:

(1) The combination of anatomical and physiological analyses is powerful and offers a comprehensive understanding of the cold-selective lamina I projection neurons in the spinal cord dorsal horn. For example, the authors used detailed anatomical methods, including EM imaging of Trpm8+ axon terminals contacting the Phox2a+ lamina I projection neurons. Additionally, they recorded stimulus-evoked activity in Trpm8-recipient neurons, carefully selected by visual confirmation of tdTomato and GFP juxtaposition, which is technically challenging.

(2) This study identifies, for the first time, a molecular marker (calb1) that labels cold-selective lamina I projection neurons. Although calb1+ projection neurons are not entirely specific to cold-selective neurons, using an intersectional strategy combined with other genes enriched in this ALS group or cold-induced FosTRAP may further enhance specificity in the future.

(3) This study shows that cold-selective lamina I projection neurons specifically innervate certain brain targets of the anterolateral tract, including the NTS, PBNrel, and cPAG. This connectivity provides insights into the role of these neurons in cold sensation, which will be an exciting area for future research.

Weaknesses:

(1) The sample size for the ex vivo electrophysiology conducted on the calb1+ lamina I projection neurons (Figure 5) is limited to a total of six recorded neurons. Given the difficulty and complexity of the preparation, this is understandable. Notably, since approximately 87% of lamina I projection neurons heavily innervated by Trpm8+ terminals are calb1+, these six recordings of such neurons in Figure 4E could also be calb1+.

Reviewer #2 (Public review):

Summary:

In this study, the authors took advantage of a semi-intact ex vivo somatosensory preparation that includes hindlimb skin to characterize the response of projection neurons in the dorsal horn of the spinal cord to peripheral stimulation, including cold thermal stimuli. The main aim was to characterize the connectivity between peripheral afferents expressing the cold sensing receptor TRPM8 and a set of genetically tagged neurons of the anterolateral system (ALS). These ALS neurons expressed high levels of the calcium binding protein calbindin 1.

In addition, combining different viral tracing methods, the authors could identify the anatomical targets of this specific subset of projection neurons within the brainstem and diencephalon.

Strengths:

The use of a relatively new (seldom used previously) transgenic line to label TRPM8-expressing afferents, combined with the genetic characterization of a previously identified subset of projections neurons add specificity to the characterization. The transgenic line appears to capture well the subpopulation of Trpm8-expressing neurons.

In addition, the use of electron microscopy techniques makes the interpretation of the structural contacts more compelling

The writing is clear and the presentation of findings follows a logical flow.

Overall, this study provides solid, novel information about the brain circuits involved in cold thermosensation.

Weaknesses:

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recordedd neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors acknowledge that, technically, this is a very difficult preparation with very low yield as far as obtaining successful recordings. Moreover, the tissue needs to be maintained at room temperature which is obviously not ideal when characterizing cold thermoreceptors due to the unavoidable effects of low temperature on cold-activated receptors.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of previous transcriptomic analysis of ALS neurons, the authors identify calbindin as a marker for cold activatetd lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex-vivo physiology, optogenetics and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data, neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

The main limitation remains the relatively small number of neurons that could be recorded electrophysiologically. While understandable given the complexity of the preparation, this necessarily limits generalization.

Author response:

The following is the authors’ response to the previous reviews

Public reviews:

Reviewer #1 (Public review):

The sample size for the ex vivo electrophysiology conducted on the calb1+ lamina I projection neurons (Figure 5) is limited to a total of six recorded neurons. Given the difficulty and complexity of the preparation, this is understandable. Notably, since approximately 87% of lamina I projection neurons heavily innervated by Trpm8+ terminals are calb1+, these six recordings of such neurons in Figure 4E could also be calb1+.

As noted in our initial resubmission, we fully accept that the sample size is limited. We have already toned down statements related to this, to say that our findings “strongly suggest” that the cells with dense Trpm8 input are cold-selective (both in the Abstract and Results)

Reviewer #2 (Public review):

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recorded neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors acknowledge that, technically, this is a very difficult preparation with very low yield as far as obtaining successful recordings. Moreover, the tissue needs to be maintained at room temperature which is obviously not ideal when characterizing cold thermoreceptors due to the unavoidable effects of low temperature on cold-activated receptors.

Please see our response to Reviewer #1 (Public review):

Reviewer #3 (Public review):

The main limitation remains the relatively small number of neurons that could be recorded electrophysiologically. While understandable given the complexity of the preparation, this necessarily limits generalization.

Again, please see our response to Reviewer #1 (Public review):

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Line 609. The authors used the Trpm8Flp;RCE:FRT;Ai9 mice in some electrophysiological experiments. What is the function of the Ai9 allele (a Cre-dependent reporter) in this cross? Should not be a Cre line as well?

One of the mice used for electrophysiological experiments was Trpm8Flp;RCE:FRT;Ai9, and this animal received an injection of AAV encoding Cre into the caudal ventrolateral medulla, resulting in tdTomato expression in spinal projection neurons. This part of the Methods was inadvertently omitted from the resubmitted version (see next point). This has been corrected, and in addition, this information is shown in the cartoon in Fig 4A and is explained in the figure legend.

(2) Line 860. Phrase is incomplete

We apologise for this – 3 lines from the original version had been deleted inadvertently. This has now been corrected.

(3) Line 103 "These results are therefore consistent with the transcriptomic findings described above (36,37)."

I would revise the references used to support this claim. Reference 37 is a transcriptomic atlas of the brain. I could not find TRPM8 expression data in DRG in this reference.

Figure S4 of reference 37 deals with the mouse peripheral nervous system and describes Trpm8 classes of primary afferent. More detail on these cells (including expression of VGLUT3, Tac1, Calca and Trpv1) can be found in the associated website: mousebrain.org/adolescent/genesearch.html. We have therefore left this reference as it is.

(4) Line 242. "neurons with dense Trpm8 input had significantly lower sEPSC frequencies compared to those that lacked dense Trpm8 input".

This is an interesting paradox because cold thermoreceptors (i.e. the presumed direct monosynaptic input to these projection neurons) are known to be spontaneously active at physiological skin temperatures. This is well characterized in trigeminal corneal endings (DOI: 10.1038/nm.2264). In fact, the decrease in this spontaneous activity can be used by mice to faithfully detect warm stimuli (DOI: 10.1016/j.neuron.2020.02.035). This reviewer likes to remark that this low spontaneous frequency may be due to the non-physiological temperature of this preparations, leading to partial adaptation/desensitization of the afferents. Perhaps, it also influences the amplitude (e.g. release probability) of EPSPs (I do not expect you to do anything about my remark).

These are interesting points, but we do not feel that we can add anything here.

(5) Figure 3A. It would be useful to include orientation references (dorso-ventral, mediolateral) in the images. Same comment applies to Figure 5C.

Since these are horizontal sections, the axes are medio-lateral and rostro-caudal. Corresponding orientation markers have been added to both figures.

(6) Figure 3F. If I understood correctly, the light pulse used for optogenetic activation is delivered directly through the objective used for recording the cell. Thus, the distance between pre and postsynaptic neuron should be minimal. That being the case, I do not understand how a monosynaptic input can have a delay of 5 or 7 ms. Am I missing something?

The relatively long duration of latency is likely to reflect a slow rise time of depolarisation in the Trpm8 terminals, so that although channels will open very rapidly, there is a delay until the boutons reach action potential threshold. Hachisuka et al (2016) recorded from Nts^Cre;Ai32 mice (i.e. coding for channelrhodopsin) and found typical latencies of >5 ms (Fig 5E in that paper). We believe that this delay is exacerbated by the low levels of expression of ChR2 that we were able to achieve with the neonatal i.p. injection approach. We have provided a brief explanation for this, and cited the reference in the Results section (lines 197-198).

(7) Figures 4E/H. To be meaningful, the pie charts should include the n (total number of neurons). See, for example figure 5J.

Numbers have been added to the pie charts.

Sudan

eLife Assessment

This manuscript presents a valuable analysis of how locomotion modulates the activity of different subtypes of cortical neurons in the mouse primary visual cortex, showing that locomotion more strongly increases responses in sensitizing than in depressing excitatory cells. This data is then used to constrain a model of the responses. While the data are very interesting, the analyses remain incomplete, in particular due to concerns surrounding the modelling.

Reviewer #1 (Public review):

In this manuscript, Hinojosa and colleagues analysed the changes in V1 visual responses induced by locomotion in head-fixed mice using two-photon calcium imaging. The authors observe that locomotion strongly increases the visual responses of V1 excitatory neurons that exhibit sensitizing responses to visual stimuli. Also, there is an increased response in VIP interneurons, and to a lesser extent, PV interneurons and SST interneurons (non-significant). The authors used a model fitted with data presented in the manuscript, as well as previous knowledge on cortical connectivity among different neuron types. The model suggests that the major component of the increased responses during locomotion is an increase in excitatory drive from external inputs (feedforward, feedback and modulatory), most importantly onto VIP interneurons and excitatory neurons. However, the excitatory drive of local excitatory neurons onto other surrounding excitatory and inhibitory cells is reduced.

The manuscript is well presented and represents a valuable analysis of how locomotion modulates the activity of different subtypes of cortical neurons. However, major issues should be addressed to strengthen the results.

Major issues:

(1) Speed and mismatch between locomotion and visual stimulation.

The authors do not clearly describe the definition of locomotion versus the resting state. The speed should, by itself, have an impact on neuronal responses, especially at the onset of locomotion. Several published studies show that the mismatch between a visual stimulus and the speed of the animal induces specific responses in V1, both in excitatory and subtypes of inhibitory neurons. The authors should address these points upfront in the manuscript, since it is likely a major variable explaining their results

(2) Use of deconvolution with MLSpike.

Some results (Figure 2) exclusively depend on the deconvolution of calcium signals into spikes (since the initial peak is not seen in calcium transients). The authors should validate this result either with electrophysiological recordings or with the use of another deconvolution method (e.g. CASCADE), emphasising the limitations of this approach and the limitations of the time resolution of calcium imaging.

(3) The manuscript is centred around a specific increase in visual responses in sensitizing neurons during locomotion, both in the fraction of responsive neurons and response magnitudes.

It is hard to tell whether this difference is due to a greater scaling effect of locomotion, a difference in responses during the resting state, or both. The manuscript should further explore and discuss the differences in responses between sensitizing and depressing neurons, both during the resting state and locomotion. Adding metrics and direct comparisons of the magnitudes of fast responses, slow responses, and time integrals between sensitizing and depressing neurons in resting and locomotion states would help to clarify this. Same for fractions of responsive neurons of each type in each condition. E.g., the slow phase is harder to judge from the plots, but the DeltaF/F integral shown in Figure 1G seems to suggest the difference in response magnitude between sensitizing and depressing neurons is largest in locomotion state, rather than resting state. How do these integrals look for inferred firing rates shown in Figure 2?

(4) There is something counterintuitive about how the changes in inhibition onto sensitizing and depressing neurons during locomotion explain the reported activity changes.

Sensitizers receive reduced SST input and increased PV input during locomotion. If SSTs depress and PVs sensitize (and this is the main reason why sensitizers, which receive dominant input from SSTs sensitize, and vice-versa), how is it possible that this switch does not alter the sensitizing or depressing nature of these neurons' responses in locomotion? Are these changes insufficient to flip the dominant SST-PV drive? Figure 6D-E seems to show there is a flip, at least for sensitizers. How do authors explain this? Do authors think this is related to the narrowing of the adaptive index distribution shown in Figure 1C?

(5) Presentation of the experimental data and the model.

The manuscript introduces the results of interneuron recordings during the description of the model. Similarly, the results of optogenetic manipulations are presented inside the model's description. It would be clearer to present all experimental data first and introduce the model later, fitting it to all experimental evidence previously presented.

Reviewer #2 (Public review):

This is an interesting paper with important results. The authors, working in V1, have previously, in a 2022 paper, defined sensitizing and depressing excitatory (E) cells as those whose response increases or decreases, respectively, across the 10 seconds of showing a drifting grating stimulus. They showed that sensitizing E cells are dominantly inhibited by SST inhibitory cells, which are dominantly depressing, and that depressing E cells are dominantly inhibited by PV inhibitory cells, which are very largely sensitizing. It's been well established that locomotion greatly increases E-cell firing rates in V1 compared to rest, but much remains to be worked out as to the mechanism. Here, they find that locomotion increases the responses of the sensitizing E cells much more than depressing cells. They develop a model of changes in synaptic weights between rest and locomotion to account for the changes. One reason that sensitizers are increased more by locomotion than depressors is that PV cells, which more strongly inhibit depressors, have increased firing for locomotion, whereas SST cells, which more strongly inhibit sensitizers, don't change their firing rates with locomotion. However, in the mode,l a complex array of postulated changes in connection strengths is also involved.

I have, though, a number of concerns: with the model, with the lack of proper discussion of connection to some previous works, and with an overall unclear and confusing presentation and certain controls that should be done.

In the model, they postulate that synapses within the 6-cell-type network - sensitizing, intermediate, and depressing E cells, and PV, SST, and VIP I cells - and from three sources of external input to each of the six types all change between rest and locomotion (except that connections between the E cells don't depend on their types). There are a lot of degrees of freedom, and this makes interpretation of the results difficult. I would have liked to have seen more efforts to constrain the degrees of freedom. For example, there seems to be very little difference between the three E cell types in any of the three types of external input received. Why not constrain them all to get the same external input and see if it significantly affects model fit? Or what if synapses from the three types of external input are left unchanged, and only change their strengths between rest and locomotion? How well could this do? During optimization, why not constrain the changes between rest and locomotion, for example, by putting an L1 penalty on the changes or the relative changes, trying to force them to be sparse, and see whether there are roughly equally good fits? And then, if the main changes are in a small set of synapses, can the authors isolate changes to that small set and do roughly equally well? What about looking at the principal components of the weight changes across models, to isolate patterns of change that are most important?

In terms of comparing to previous works, when optogenetic manipulations of SST and PV are done to test various hypotheses, I would like to see some discussion of what is already known from the authors' 2022 paper and what they are adding or testing that wasn't known or tested from that paper. And Dipoppa et al (2018) also found weight changes to account for the difference between rest and locomotion. They were looking at a fixed point of responses of neurons across retinotopic space to stimuli of various sizes with only one E-cell type, whereas they are accounting for trajectories across time considering 3 E-cell subtypes but without variation in stimuli or retinotopic position of neurons, so the efforts are somewhat different, but still, it would be good to see a bit more discussion of what is in agreement or in contradiction in the conclusions.

In terms of presentation and controls, I have many concerns, which include:

(1) The main result is that sensitizers increase their responses with locomotion ~2X (for dF/F) or about 3.5X (for spikes) more than depressors. But there are other differences between sensitizers and depressors, for example sensitizers have smaller initial stimulus responses at rest, and depressors have larger. What if cells were divided into tertiles by initial stimulus response at rest? Would the authors see the same differences in the effects of locomotion? If so, can they establish whether the difference is really attached to the adaptation properties rather than to, for example, the initial responses, for example, by comparing the regression of response increase against AI vs the regression of response increase against initial resting response? And there might be other controls to be done for other features in which sensitizers and depressors differ.

(2) Lines 103 and following: the authors refer to a "second notable change" which is the narrower distribution of adaptive effects, but I think this is trivial. The adaptive index is AI=(R1-R2)/(R1+R2), where R1 is response 0.5-2.5s after stimulus onset and R2 over 8-10s. But if the change is additive, as suggested by the dF/F figures (and I believe the distributions of AI here are based on dF/F measurements) -- adding the same constant to R1 and R2 will shrink |AI| without changing the sign of AI. So this would seem to just be a signature of a change that is primarily additive rather than multiplicative.

Also, if the authors do decide that they are going to focus on spikes after showing the raw dF/F, then this analysis should be repeated for spikes.

(3) Figure 2, F is supposed to be D minus E, but it doesn't look like it. For example, the initial response under locomotion is very similar in sensitizers and depressors, so the initial difference in F should be small, but it's not; and at rest, depressors initially have larger responses than sensitizers, whereas later depressors have smaller responses than sensitizers, yet the difference at rest is positive at all times. Something seems wrong here.

Reviewer #3 (Public review):

This study aimed to understand the depressing and sensitizing effects of adaptation in mice visual cortex during different behavioral states: locomotion and stationary. There is an impressive characterisation of the responses in different cortical cell types and with different optogenetic manipulations to the inhibitory populations. These form a very interesting dataset to understand the effects of the state on the circuits and gain insight into the mechanisms. This data is then used to constrain a model of the responses. Unfortunately, the model appears to be too flexible, and it was difficult to interpret the insights gained from the different model fits.

Strengths:

The data is impressive. There is a characterisation of responses of PCs and VIP, SST and PV interneurons. Additionally, there is the characterisation of some responses to specific optogenetic manipulations, VIP inactivation, SST or PV activation or inactivation. These data will help develop a good insight into the system. The principle of using the optigenetic manipulations to constrain model parameters is very interesting.

Weaknesses:

Many of the analyses have some concerns in the methodology used, which we list in detail below. Further, the model used to gain insight into the mechanism appears overly complicated and seems hard to gain clear insights from.

Major concerns:

(1) Key concern is the usage of dF/F signals for all analyses, especially when comparing responses.

1a) Figure 1G: Comparison of sensitisers and depressors. It is important to consider what the baseline rates are when making these comparisons, especially when comparing the degree of effects between different cell types. For example, if baseline rates for sensitizers were overall higher, it would mean the difference in gain of response would be lower, and could affect the results in the opposite direction of what is claimed. One option to account for this would be to z-score the overall responses, using the same normalization for locomotion and rest. We also suggest plotting differences in sensitisers, intermediates, and depressors as a function of firing rate. Matching for firing rate across each PC categorization and calculating delta AI for each matched firing rate bin.

1b) Figure 2A-F: The above is an even more significant issue when it comes to estimating spiking rates. The methods do not state how dF/F is calculated. If these are based on using the pre-stim as the reference, the algorithms for spike rate used might not be appropriate if this were used. Using pre-stimulus referencing could result in the estimate going into the wrong range in the calculation of the spike rate.

1c) In both cases above, it could be a problem if baseline firing rates are different between cell types, or states (locomotion/stationary). The latter is established to have effects on many cell types measured, and so needs to be accounted for very carefully.

1d) It would be informative to see per-neuron comparison for adaptive indices during rest and locomotion states. This could be visualized using a scatter plot with AI-rest vs. AI-locomotion for Figures 1D- 1F and 2J- 2L.

1e) Are neurons more strongly modulated between locomotion and rest, also more likely to experience a shift in AI indices (i.e. delta AI). Is there a correlation between the change in firing rate between behavioral states and Delta AI (Loco-Rest)? If so, is this present for all neuron subtypes (e.g. VIP, SST, and PV)?

1f) Optogenetic inhibition of VIP neurons on average abolished the slow depressive effects of adaptation in SST (Figure 3). The strength and prevalence of this effect are unclear. Perhaps one can perform a bootstrap control and opto AI indices and calculate whether AI was significantly reduced following optogenetics inhibition, and if so, on average, how likely was this to occur for the recorded SST neurons? This is important in knowing that the average effects (Figure 3D) aren't driven by a portion of SST neurons, especially as this is later used to confirm the region of parameter space and affects the subsequent results in Figure 4.

(2) Statistics for the effects. There is a mention of Liner mixed models, but no information is given on the actual models being used and tested. This is particularly for the case of Figure 1G, where there is a composition of effect sizes between different populations. What precise significance test is being used? Are the stats on paired cells when considering locomotion and rest?

(3) Model parameters: It is acknowledged that there is a large range of parameters that can model the responses effectively, up to 11% of initial conditions. At 9000 initial conditions, this is around 1000. The parameter estimates are then considered as the mean of each parameter. This seems like a strange choice for a few different reasons:

3a) A mean solution might not be one of the solutions. Let's say the parameters range over a large dimensional space. They could occupy non-overlapping / discontinuous subspaces. In that case, the mean parameters do not necessarily fall within the solution subspaces. Therefore, this reduction to means might not be valid.

3b) Compare distributions rather than means. There are multiple distributions of parameters between conditions. All stats should be on the comparison of distributions rather than just the means.

(4) Visualizing weight matrices: It is very challenging to interpret the weight matrices. Furthermore, it appears that the stationary and locomotion conditions fit independently, and given the large parameter spaces, it is even harder to interpret. Can the fitting instead be done by fitting on one and using those at the initial conditions for the other state? Figure 7 shows an initiative cartoon, but it is not clear how the matrices in Figures 5 and 6 lead to the summary shown in Figure 7. It is also not clear why the connections between inhibitory neurons are not shown in Figure 7. One option is to perhaps run some kind of dimensionality deduction on the parameter space to better interpret the data. When showing deltaWeights, was the model initialised with 'Rest' weights and allowed to change? It is not obvious what the difference is between 'relative change in connection weights' and 'relative change in synaptic weights'.This needs to be clarified.

4a) Model parameters were reduced differently for locomotion and rest (Figure 4). We suggest evaluating the results for locomotion and rest using the same chi-square value of 3 for both behavioral states (at least in controls).

Author response:

Public Reviews:

Reviewer #1 (Public review):

(1) Speed and mismatch between locomotion and visual stimulation.

The authors do not clearly describe the definition of locomotion versus the resting state. The speed should, by itself, have an impact on neuronal responses, especially at the onset of locomotion. Several published studies show that the mismatch between a visual stimulus and the speed of the animal induces specific responses in V1, both in excitatory and subtypes of inhibitory neurons. The authors should address these points upfront in the manuscript, since it is likely a major variable explaining their results.

We will clarify in the methods that a trial was considered as locomotion when an animal ran at a minimum of 3 cm/s for at least 80% of the 10 s stimulus presentation, and was considered rest when running under 3 cm/s during the same fraction of time. Trials with abrupt changes from locomotion to rest were rare and excluded following these criteria.

Locomotion speed and visuomotor mismatch can influence neuronal responses in V1 but in the large majority of our trials mice either run continuously at a stable speed or remained still

i.e locomotion onsets or offsets did not occur (see Hinojosa et al. 2026 for example running traces). Furthermore, sensitizing and depressing neurons were typically recorded simultaneously within the same field of view, experiencing identical locomotor behaviour. For these reasons, we think it is unlikely that differences in speed or mismatch alone can account for the different increase in amplitude observed between depressors and sensitizers.

To directly address this point and further explore the role of speed on V1 neurons, we will quantify the relationship between running speed and amplitude increase in both PCs and interneurons, and include these analyses in the revised version of the manuscript.

(2) Use of deconvolution with MLSpike.

Some results (Figure 2) exclusively depend on the deconvolution of calcium signals into spikes (since the initial peak is not seen in calcium transients). The authors should validate this result either with electrophysiological recordings or with the use of another deconvolution method (e.g CASCADE), emphasising the limitations of this approach and the limitations of the time resolution of calcium imaging.

A similar initial increase in amplitude followed by fast depression has been observed previously with electrophysiological recordings in V1 (Chance et al., 1998; Jin & Glickfeld, 2020; Varela et al., 1997). We will further validate our results using an alternative spike inference method like CASCADE (Rupprecht et al., 2021), as well as expanding on the limitations of our approach.

(3) The manuscript is centred around a specific increase in visual responses in sensitizing neurons during locomotion, both in the fraction of responsive neurons and response magnitudes.

It is hard to tell whether this difference is due to a greater scaling effect of locomotion, a difference in responses during the resting state, or both. The manuscript should further explore and discuss the differences in responses between sensitizing and depressing neurons, both during the resting state and locomotion. Adding metrics and direct comparisons of the magnitudes of fast responses, slow responses, and time integrals between sensitizing and depressing neurons in resting and locomotion states would help to clarify this. Same for fractions of responsive neurons of each type in each condition. E.g., the slow phase is harder to judge from the plots, but the DeltaF/F integral shown in Figure 1G seems to suggest the difference in response magnitude between sensitizing and depressing neurons is largest in locomotion state, rather than resting state. How do these integrals look for inferred firing rates shown in Figure 2?

We will further explore the response dynamics of adaptive types within the locomotion and resting state, highlighting the differences between calcium signals and inferred spikes. We will then include our findings in the new version.

(4) There is something counterintuitive about how the changes in inhibition onto sensitizing and depressing neurons during locomotion explain the reported activity changes.

Sensitizers receive reduced SST input and increased PV input during locomotion. If SSTs depress and PVs sensitize (and this is the main reason why sensitizers, which receive dominant input from SSTs sensitize, and vice-versa), how is it possible that this switch does not alter the sensitizing or depressing nature of these neurons' responses in locomotion? Are these changes insufficient to flip the dominant SST-PV drive? Figure 6D-E seems to show there is a flip, at least for sensitizers. How do authors explain this? Do authors think this is related to the narrowing of the adaptive index distribution shown in Figure 1C?

This result is only counterintuitive if we consider exclusively the internal connections within V1. The PV:SST ratio changes from 0.9 during rest, dominated by SST induced sensitization, to 1.2, dominated by PV depression. Although adaptation is strongly driven by the opposing inhibition of PV and SST in PCs during locomotion, its origin is more easily explained by an external input (SS) that targets VIPs, PVs and PCs. As a result, when locomotion increases the drive coming from SS input, it injects a source of sensitization that partly balances the decrease in PV:SST ratio, preventing a switch in their adaptive properties which, although reduced, remain sensitizing. We will include these calculations in the revised version.

(5) Presentation of the experimental data and the model.

The manuscript introduces the results of interneuron recordings during the description of the model. Similarly, the results of optogenetic manipulations are presented inside the model's description. It would be clearer to present all experimental data first and introduce the model later, fitting it to all experimental evidence previously presented.

We understand that a clear separation between experimental and modelling results is often preferred in papers that combine these approaches but in our case modelling and experimental data are highly interdependent and we believe that an overlapping presentation make it easier for the reader to appreciate the links. One example is Fig. 2G-L that shows experimental results validating a key feature of the model - the use of average response dynamics for each population of interneuron. Similarly, the results in Fig. 3 validate the use of the VIP response dynamics as the template for the slow modulatory input to layer 2/3. Then the results of optogenetic experiments in Fig. 4 are used to narrow down fits to the model. For these reasons, we have chosen to present experimental results and the model in this more integrated manner.

Reviewer #2 (Public review):

In the model, they postulate that synapses within the 6-cell-type network - sensitizing, intermediate, and depressing E cells, and PV, SST, and VIP I cells - and from three sources of external input to each of the six types all change between rest and locomotion (except that connections between the E cells don't depend on their types). There are a lot of degrees of freedom, and this makes interpretation of the results difficult. I would have liked to have seen more efforts to constrain the degrees of freedom. For example, there seems to be very little difference between the three E cell types in any of the three types of external input received. Why not constrain them all to get the same external input and see if it significantly affects model fit? Or what if synapses from the three types of external input are left unchanged, and only change their strengths between rest and locomotion? How well could this do? During optimization, why not constrain the changes between rest and locomotion, for example, by putting an L1 penalty on the changes or the relative changes, trying to force them to be sparse, and see whether there are roughly equally good fits? And then, if the main changes are in a small set of synapses, can the authors isolate changes to that small set and do roughly equally well? What about looking at the principal components of the weight changes across models, to isolate patterns of change that are most important?

To reduce the number of degrees of freedom and ease interpretation we did limit the model fitting for adaptive subtypes by fixing the PC-PC (𝑤_{𝑃𝐶_𝑃𝐶}) and restricting the external inputs weights (𝑤_{𝐹𝐹_𝑃𝐶}, 𝑤_{𝑆𝑆_𝑃𝐶}, 𝑤_{𝐹𝐵_𝑃𝐶}) to changes of ± 10 %. We will explicitly explain these constrains in the methods and discuss its limitations.

We thank the reviewer for their suggestions of testing different conditions to find those providing the best fit for sensitizing and depressing PCs. We tried an approach similar to that described by Dipoppa et al. 2018 by using the locomotion weights as initial conditions for the rest traces and introducing penalties at later stages. However, the local optimization algorithms failed to reach distant regions of parameter space containing minimum solutions for the rest condition. We finally opted for repeating the same process of initial condition searching for locomotion and rest, making the L1 penalty approach impracticable in our case. We believe this approach is effective because it has both allowed us to describe circuit changes during internal-state transitions (the present paper) and, more recently, it has made a series of predictions about different learning states that have been confirmed by optogenetic tests (Hinojosa et al., 2026). We will nevertheless explore this and other of the reviewer suggestions to further optimize the fitting in the revised manuscript.

In terms of comparing to previous works, when optogenetic manipulations of SST and PV are done to test various hypotheses, I would like to see some discussion of what is already known from the authors' 2022 paper and what they are adding or testing that wasn't known or tested from that paper. And Dipoppa et al (2018) also found weight changes to account for the difference between rest and locomotion. They were looking at a fixed point of responses of neurons across retinotopic space to stimuli of various sizes with only one E-cell type, whereas they are accounting for trajectories across time considering 3 E-cell subtypes but without variation in stimuli or retinotopic position of neurons, so the efforts are somewhat different, but still, it would be good to see a bit more discussion of what is in agreement or in contradiction in the conclusions.

Thanks for this prompt. We will add further discussion of this work in light of the Heintz et al. (2022) and Dipoppa et al. (2018) papers.

(1) The main result is that sensitizers increase their responses with locomotion ~2X (for dF/F) or about 3.5X (for spikes) more than depressors. But there are other differences between sensitizers and depressors, for example sensitizers have smaller initial stimulus responses at rest, and depressors have larger. What if cells were divided into tertiles by initial stimulus response at rest? Would the authors see the same differences in the effects of locomotion? If so, can they establish whether the difference is really attached to the adaptation properties rather than to, for example, the initial responses, for example, by comparing the regression of response increase against AI vs the regression of response increase against initial resting response? And there might be other controls to be done for other features in which sensitizers and depressors differ.

We will explore the possibility that initial response influences the increase in amplitude. Preliminary data suggest that initial amplitude is higher in depressors than in sensitizers.

(2) Lines 103 and following: the authors refer to a "second notable change" which is the narrower distribution of adaptive effects, but I think this is trivial. The adaptive index is AI=(R1-R2)/(R1+R2), where R1 is response 0.5-2.5s after stimulus onset and R2 over 8-10s. But if the change is additive, as suggested by the dF/F figures (and I believe the distributions of AI here are based on dF/F measurements) -- adding the same constant to R1 and R2 will shrink |AI| without changing the sign of AI. So this would seem to just be a signature of a change that is primarily additive rather than multiplicative.

Also, if the authors do decide that they are going to focus on spikes after showing the raw dF/F, then this analysis should be repeated for spikes.

We agree with the reviewer and will change the text accordingly to highlight the additive nature of the change in amplitude. We will also show the analysis with spikes (this shows similar results as the calcium data).

(3) Figure 2, F is supposed to be D minus E, but it doesn't look like it. For example, the initial response under locomotion is very similar in sensitizers and depressors, so the initial difference in F should be small, but it's not; and at rest, depressors initially have larger responses than sensitizers, whereas later depressors have smaller responses than sensitizers, yet the difference at rest is positive at all times. Something seems wrong here.

We apologize for the confusion this has caused. Figure 2F does not represent the difference between sensitizing and depressing PCs from panels D and E. Instead, it shows the time-varying difference between locomotion and rest states of sensitizers (blue, in figure 2D) and depressors (green, in figure 2E). Thus, panel F shows within-population modulation by behavioural state, rather than differences between sensitizing and depressing neurons. We will amend the figure legend and main text to explain this point and avoid misinterpretation.

Reviewer #3 (Public review):

(1) Key concern is the usage of dF/F signals for all analyses, especially when comparing responses.

(1a) Figure 1G: Comparison of sensitisers and depressors. It is important to consider what the baseline rates are when making these comparisons, especially when comparing the degree of effects between different cell types. For example, if baseline rates for sensitizers were overall higher, it would mean the difference in gain of response would be lower, and could affect the results in the opposite direction of what is claimed. One option to account for this would be to z-score the overall responses, using the same normalization for locomotion and rest. We also suggest plotting differences in sensitisers, intermediates, and depressors as a function of firing rate. Matching for firing rate across each PC categorization and calculating delta AI for each matched firing rate bin.

(1b) Figure 2A-F: The above is an even more significant issue when it comes to estimating spiking rates. The methods do not state how dF/F is calculated. If these are based on using the pre-stim as the reference, the algorithms for spike rate used might not be appropriate if this were used. Using pre-stimulus referencing could result in the estimate going into the wrong range in the calculation of the spike rate.

(1c) In both cases above, it could be a problem if baseline firing rates are different between cell types, or states (locomotion/stationary). The latter is established to have effects on many cell types measured, and so needs to be account ted for very carefully.

The DF/F0 trace was calculated using the mode of the whole trace as F0. While this approach is less sensitive to biases than subtracting the pre-stimulus, it does not consider noise levels like the z-score suggested by the reviewer. We will, therefore, normalize the calcium traces to z-score to further account for changes in the baseline. Spike inference using MLSpike, however, explicitly models baseline noise and subtracts its effect from that of the spikes calculated from the calcium signal (Deneux et al., 2016). This transformation preserved the difference in amplitude triggered by locomotion between depressing and sensitizing PCs while revealing their similar baseline activity (see Figs. 2D,E and F). These results indicate that the distinct changes in response amplitude between sensitizing and depressing PCs during locomotion are not driven by baseline differences. We will add this explanation to the methods section.

We will also plot the changes in activity with locomotion across cell types as a function of firing rate and add these results to the revised manuscript.

(1d) It would be informative to see per-neuron comparison for adaptive indices during rest and locomotion states. This could be visualized using a scatter plot with AI-rest vs. AI-locomotion for Figures 1D- 1F and 2J- 2L.

(1e) Are neurons more strongly modulated between locomotion and rest, also more likely to experience a shift in AI indices (i.e. delta AI). Is there a correlation between the change in firing rate between behavioral states and Delta AI (Loco-Rest)? If so, is this present for all neuron subtypes (e.g. VIP, SST, and PV)?

Sorting was carried out separately on locomotion and rest data sets to capture the adaptive properties of the network under each condition. When assessing the change in adaptive index in individual cells there was a weak but significant correlation (r = 0.10, p<0.05), probably due to trial to trial stochasticity in the network which has been shown to be present in V1 (Carandini, 2004; Lee et al., 2010). Although adaptation profiles of individual PCs are not fully conserved across rest and locomotion, the observed overlap exceeds that expected by chance, suggesting that stochastic fluctuations modulate an underlying, stable circuit organization. Despite including the stochastic component of the responses, the conclusions hold: sensitizers undergo a larger gain modulation than that of depressors. We will include this analysis and the correlation between change in firing rate and Delta AI in the revised version of the paper.

(1f) Optogenetic inhibition of VIP neurons on average abolished the slow depressive effects of adaptation in SST (Figure 3). The strength and prevalence of this effect are unclear. Perhaps one can perform a bootstrap control and opto AI indices and calculate whether AI was significantly reduced following optogenetics inhibition, and if so, on average, how likely was this to occur for the recorded SST neurons? This is important in knowing that the average effects (Figure 3D) aren't driven by a portion of SST neurons, especially as this is later used to confirm the region of parameter space and affects the subsequent results in Figure 4.

The strength and prevalence of the effect are reflected in the distribution of AI changes across SST neurons, which is centred at AI = -0.3 ± 0.3, indicating a consistent reduction in AI across the population instead of being driven by a small portion of SST neurons. To further clarify this, we will report the proportion of SST neurons showing a reduction in AI and include statistical analyses on the changes.

(2) Statistics for the effects. There is a mention of Liner mixed models, but no information is given on the actual models being used and tested. This is particularly for the case of Figure 1G, where there is a composition of effect sizes between different populations. What precise significance test is being used? Are the stats on paired cells when considering locomotion and rest?

We used Linear mixed models to test for statistical significance between different conditions composed of hundreds of cells from several mice, i.e. nested analysis (cells nested within mice; see (Judd et al., 2017)). For analyses such as Fig. 1G, we considered locomotion state, adaptive type and their interaction (loco’adap) as fixed effects and mouse number as the random effect. The p-values depicted in the legend indicates the interaction between locomotion and adaptive type, i.e. the increase in amplitude during locomotion is significantly different in sensitizers compared to depressors with p < 0.0001. We will revise the method section and figure legends to explicitly describe the model and statistical test used.

(3) Model parameters: It is acknowledged that there is a large range of parameters that can model the responses effectively, up to 11% of initial conditions. At 9000 initial conditions, this is around 1000. The parameter estimates are then considered as the mean of each parameter. This seems like a strange choice for a few different reasons:

(3a) A mean solution might not be one of the solutions. Let's say the parameters range over a large dimensional space. They could occupy non-overlapping / discontinuous subspaces. In that case, the mean parameters do not necessarily fall within the solution subspaces. Therefore, this reduction to means might not be valid.

(3b) Compare distributions rather than means. There are multiple distributions of parameters between conditions. All stats should be on the comparison of distributions rather than just the means.

To test for the presence of subsets of solutions grouped around different parameter values we plotted the distribution of each parameter across all the good solutions found. Most of the weights were a gaussian distribution centred around the mean and, most importantly, none of them had two peaks. Furthermore, after computing the mean weight values we plotted the solutions given by them in the model, and it rendered a good fit as shown in the figures. We will include those distributions in the new version and base the overall comparison on these distributions.

(4) Visualizing weight matrices: It is very challenging to interpret the weight matrices. Furthermore, it appears that the stationary and locomotion conditions fit independently, and given the large parameter spaces, it is even harder to interpret. Can the fitting instead be done by fitting on one and using those at the initial conditions for the other state? Figure 7 shows an initiative cartoon, but it is not clear how the matrices in Figures 5 and 6 lead to the summary shown in Figure 7. It is also not clear why the connections between inhibitory neurons are not shown in Figure 7. One option is to perhaps run some kind of dimensionality deduction on the parameter space to better interpret the data. When showing deltaWeights, was the model initialised with 'Rest' weights and allowed to change? It is not obvious what the difference is between 'relative change in connection weights' and 'relative change in synaptic weights'.This needs to be clarified.

Thanks for raising this concern. We will firstly try to make the weight matrices clearer to interpret.

Regarding the fitting of rest and locomotion conditions, we fitted the locomotion traces first and used those solutions as initial conditions for the rest traces. However, this rendered no good solutions as minimums in the parameter space were too far from the initial starting points. We opted, therefore, for repeating the same process of initial condition searching for locomotion and rest. This approach is less biased in satisfying our aim of finding solutions that fit the data and can explain their dynamics, which are different for each condition. We believe this approach is effective, as not only has it allowed us to describe circuit changes during internal-state transitions but has also made a series of predictions under different learning states that were confirmed by optogenetic tests (Hinojosa et al., 2026).

We simplified Fig. 7 for clarity but we will make it more accurate and explain it more in detail in the legend, including connections between interneurons.

Interpreting high-dimensional parameter spaces can be challenging. In this study, we focused on low-dimensional summaries of the parameter space (e.g., average connection weights and their distributions across populations), which revealed consistent and interpretable differences between sensitizing and depressing neurons. Importantly, our conclusions do not rely on individual parameter values, but rather on systematic differences across populations that are robust across solutions. Additionally, we ran clustering analysis and found that there is no parameter that can be removed. We focused, therefore, on the larger and more robust differences. We will explore additional dimensionality reduction approaches and include these results if they provide further insight beyond the current analyses.

Finally, the change in weights was calculated with equation 4, in which the weight from locomotion and rest, obtained through independent fits, were used to calculate the relative change from rest to locomotion. These were either connection weights (equation 2) which consider the strength of the connection between cell j and i, or synaptic weights (equation 3) which express the weight of individual synapses by dividing connection weights by the number of presynaptic cells and probability of connection. This distinction arises because we used average traces from all the neurons imaged to fit the model, requiring considering the number of cells to know the strength of individual synapses. We will add this explanation in the results and methods sections.

(4a) Model parameters were reduced differently for locomotion and rest (Figure 4). We suggest evaluating the results for locomotion and rest using the same chi-square value of 3 for both behavioral states (at least in controls).

Thank you for this prompt, this is an important point that we tried to resolve during our analysis. We used the reduced chi-square () to evaluate model fits within locomotion and rest condition independently. As defined in equation 12, reduced chi-square is inversely proportional to the standard error of the data which is higher in the rest dataset. As a consequence, setting the same threshold across conditions would not correspond to an equivalent goodness-of-fit criterion, and would impose a disproportionately strict constraint on the condition with lower variability, where deviations between model and data are more heavily penalized. For this reason, we used condition specific thresholds to ensure comparable fit quality relative to the noise level in each condition. In addition, to enable direct comparison across conditions independent of their noise levels, we used the RMSE as a complementary metric.

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Varela, J. A., Sen, K., Gibson, J., Fost, J., Abbott, L. F., & Nelson, S. B. (1997). A Quantitative Description of Short-Term Plasticity at Excitatory Synapses in Layer 2/3 of Rat Primary Visual Cortex. The Journal of Neuroscience, 17(20), 7926–7940. https://doi.org/10.1523/JNEUROSCI.17-20-07926.1997

Intención e INTUICIÓN también podría decir. Hace poco leía en "Arte y Cosmotécnica" de Hui sobre la intuición, que es un término que usamos mucho en Nodo Común y el Club Manhattan, y creo que hay que usarlo más. Debe estar más presente porque es la base de nuestras creaciones desalgoritmizadas.

Jaap Koek die onderdook bij mijn pake en beppe / Stepelerveld. 6-12-1926 Boekelo / 19-2-2003 Enschede Ovl 2003. Bij de crematie van pake feb 1993, spraken zijn kleinkinderen mij aan dat hun opa bij mijn opa was ondergedoken. Wij wisten nergens van.

The correct order is: Sookyung Kim; Chang Gi Park; Mi-So Shim. This will be changed in the next revision and the final manuscript.

DOI: 10.64898/2026.04.11.717899

Resource: Akoya Biosciences Vectra 3 Automated Quantitative Pathology Imaging System (RRID:SCR_025828)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_025828

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 11966, RRID:AB_2797783)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2797783

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 24930, RRID:AB_2798889)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2798889

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 9661, RRID:AB_2341188)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2341188

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 29047, RRID:AB_2773013)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2773013

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 5483, RRID:AB_10693472)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_10693472

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (KCLB Cat# 80103, RRID:CVCL_0434)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0434

What is this?

DOI: 10.64898/2026.04.11.717899

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.64898/2026.04.10.717727

Resource: (IMSR Cat# JAX_021875,RRID:IMSR_JAX:021875)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:021875

What is this?

DOI: 10.64898/2026.04.10.717727

Resource: RRID:IMSR_JAX:036761

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:036761

What is this?

DOI: 10.64898/2026.04.10.717727

Resource: RRID:IMSR_JAX:000664

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.64898/2026.04.10.717727

Resource: (IMSR Cat# JAX_033951,RRID:IMSR_JAX:033951)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:033951

What is this?

DOI: 10.64898/2026.04.10.717727

Resource: (IMSR Cat# JAX_028581,RRID:IMSR_JAX:028581)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:028581

What is this?

DOI: 10.64898/2026.04.09.717595

Resource: (ATCC Cat# PTA-6671, RRID:CVCL_3755)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_3755

What is this?

DOI: 10.64898/2026.04.09.717595

Resource: (NIH-ARP Cat# 177-450, RRID:CVCL_0367)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0367

What is this?

DOI: 10.64898/2026.04.09.717400

Resource: Cytek SpectroFlo (RRID:SCR_025494)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_025494

What is this?

DOI: 10.64898/2026.04.09.717005

Resource: RRID:IMSR_JAX:021807

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:021807

What is this?

DOI: 10.64898/2026.04.09.717005

Resource: RRID:Addgene_28047

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_28047

What is this?

DOI: 10.64898/2026.04.09.717005

Resource: RRID:Addgene_21691

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_21691

What is this?

DOI: 10.64898/2026.04.09.715318

Resource: (IMSR Cat# JAX_030212,RRID:IMSR_JAX:030212)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:030212

What is this?

DOI: 10.64898/2026.04.09.715318

Resource: (IMSR Cat# JAX_029591,RRID:IMSR_JAX:029591)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:029591

What is this?

DOI: 10.64898/2026.04.08.717226

Resource: RRID:Addgene_87306

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_87306

What is this?

DOI: 10.64898/2026.04.08.717226

Resource: RRID:Addgene_105558

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_105558

What is this?

DOI: 10.64898/2026.04.08.717226

Resource: (IMSR Cat# JAX_013044,RRID:IMSR_JAX:013044)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:013044

What is this?

DOI: 10.64898/2026.04.08.717226

Resource: RRID:Addgene_179459

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_179459

What is this?

DOI: 10.64898/2026.03.24.714080

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.64898/2026.03.24.714080

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.64898/2026.03.16.711738

Resource: (Cell Signaling Technology Cat# 2535, RRID:AB_331250)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_331250

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (ATCC Cat# TIB-71, RRID:CVCL_0493)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0493

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (ATCC Cat# CRL-2593, RRID:CVCL_5440)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_5440

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: RRID:CVCL_B221

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_B221

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (ATCC Cat# HTB-21, RRID:CVCL_1115)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1115

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (ATCC Cat# CRL-2327, RRID:CVCL_1252)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1252

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (RRID:CVCL_0617)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0617

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (IMSR Cat# JAX_005557,RRID:IMSR_JAX:005557)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:005557

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: University of Colorado Anschutz Medical Campus Cancer Center Cell Technologies Shared Resource Core Facility (RRID:SCR_021982)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_021982

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: University of Colorado Anschutz Medical Campus Cancer Center Flow Cytometry Shared Resource Core Facility (RRID:SCR_022035)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_022035

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: University of Colorado Anschutz Medical Campus Cancer Center Cell Technologies Shared Resource Core Facility (RRID:SCR_021982)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_021982

What is this?

DOI: 10.64898/2026.03.13.711653

Resource: (ECACC Cat# 88113004, RRID:CVCL_1661)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1661

What is this?

DOI: 10.64898/2026.03.13.710942

Resource: RRID:BDSC_8283

Curator: @maulamb

SciCrunch record: RRID:BDSC_8283

What is this?

DOI: 10.64898/2026.03.13.710942

Resource: RRID:BDSC_64879

Curator: @maulamb

SciCrunch record: RRID:BDSC_64879

What is this?

DOI: 10.64898/2026.01.01.25343252

Resource: PhysioNet (RRID:SCR_007345)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_007345

What is this?

DOI: 10.3390/membranes16030093

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.3390/jox16020066

Resource: (RCB Cat# RCB0988, RRID:CVCL_0025)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0025

What is this?

DOI: 10.3390/cells15060549

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_001905

What is this?

DOI: 10.3390/cells15060549

Resource: Bio Rad ChemiDoc MP Imaging System (RRID:SCR_019037)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_019037

What is this?

DOI: 10.3390/cells15060549

Resource: (Vector Laboratories Cat# PI-1000, RRID:AB_2336198)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2336198

What is this?

DOI: 10.3390/cells15060549

Resource: (Vector Laboratories Cat# PI-2000, RRID:AB_2336177)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2336177

What is this?

DOI: 10.3390/biom16040570

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.3389/finsc.2026.1666444

Resource: RRID:BDSC_23292

Curator: @maulamb

SciCrunch record: RRID:BDSC_23292

What is this?

DOI: 10.1371/journal.pone.0346783

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1186/s12951-026-04235-2

Resource: (JCRB Cat# IFO50409, RRID:CVCL_0462)

Curator: @evieth

SciCrunch record: RRID:CVCL_0462

What is this?

DOI: 10.1186/s12943-026-02637-2

Resource: (ECACC Cat# 88062428, RRID:CVCL_0131)

Curator: @evieth

SciCrunch record: RRID:CVCL_0131

What is this?

DOI: 10.1158/2767-9764.CRC-25-0634

Resource: VARSCAN (RRID:SCR_006849)

Curator: @evieth

SciCrunch record: RRID:SCR_006849

What is this?

DOI: 10.1158/1541-7786.MCR-25-1153

Resource: (Cell Signaling Technology Cat# 5246, RRID:AB_10694683)

Curator: @evieth

SciCrunch record: RRID:AB_10694683

What is this?

DOI: 10.1158/1541-7786.MCR-25-1153

Resource: (Cell Signaling Technology Cat# 2820, RRID:AB_2177962)

Curator: @evieth

SciCrunch record: RRID:AB_2177962

What is this?

DOI: 10.1158/1541-7786.MCR-25-1153

Resource: (Cell Signaling Technology Cat# 13202, RRID:AB_2687461)

Curator: @evieth

SciCrunch record: RRID:AB_2687461

What is this?

DOI: 10.1158/1078-0432.CCR-25-4110

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @evieth

SciCrunch record: RRID:CVCL_0105

What is this?

DOI: 10.1158/1078-0432.CCR-25-4110

Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)

Curator: @evieth

SciCrunch record: RRID:CVCL_0035

What is this?

DOI: 10.1158/1078-0432.CCR-25-4110

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @evieth

SciCrunch record: RRID:CVCL_0105

What is this?

DOI: 10.1158/1078-0432.CCR-25-3315

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @evieth

SciCrunch record: RRID:SCR_001905

What is this?

DOI: 10.1158/1078-0432.CCR-24-2335

Resource: (BioLegend Cat# 980908, RRID:AB_2888883)

Curator: @evieth

SciCrunch record: RRID:AB_2888883

What is this?

DOI: 10.1158/1078-0432.CCR-24-2335

Resource: RRID:AB_3101749

Curator: @evieth

SciCrunch record: RRID:AB_3101749

What is this?

DOI: 10.1158/0008-5472.CAN-25-2630

Resource: (ECACC Cat# 93121055, RRID:CVCL_1299)

Curator: @evieth

SciCrunch record: RRID:CVCL_1299

What is this?

DOI: 10.1158/0008-5472.CAN-25-2630

Resource: (ATCC Cat# CRL-2172, RRID:CVCL_1723)

Curator: @evieth

SciCrunch record: RRID:CVCL_1723

What is this?

DOI: 10.1158/0008-5472.CAN-25-2630

Resource: (ATCC Cat# CRL-1997, RRID:CVCL_0313)

Curator: @evieth

SciCrunch record: RRID:CVCL_0313

What is this?

DOI: 10.1158/0008-5472.CAN-25-2630

Resource: (BCRC Cat# 60495, RRID:CVCL_3517)

Curator: @evieth

SciCrunch record: RRID:CVCL_3517

What is this?

DOI: 10.1158/0008-5472.CAN-25-2630

Resource: (JCRB Cat# JCRB1072, RRID:CVCL_3178)

Curator: @evieth

SciCrunch record: RRID:CVCL_3178

What is this?

DOI: 10.18699/vjgb-26-14

Resource: RRID:BDSC_4264

Curator: @maulamb

SciCrunch record: RRID:BDSC_4264

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (KCB Cat# KCB 90029YJ, RRID:CVCL_0004)

Curator: @evieth

SciCrunch record: RRID:CVCL_0004

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (RRID:CVCL_4972)

Curator: @evieth

SciCrunch record: RRID:CVCL_4972

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (DSMZ Cat# ACC-582, RRID:CVCL_1844)

Curator: @evieth

SciCrunch record: RRID:CVCL_1844

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (RRID:CVCL_1511)

Curator: @evieth

SciCrunch record: RRID:CVCL_1511

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (CLS Cat# 300436/p606_HGC-27, RRID:CVCL_1279)

Curator: @evieth

SciCrunch record: RRID:CVCL_1279

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (KCB Cat# KCB 200970YJ, RRID:CVCL_0336)

Curator: @evieth

SciCrunch record: RRID:CVCL_0336

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (NCBI_Iran Cat# C520, RRID:CVCL_1824)

Curator: @evieth

SciCrunch record: RRID:CVCL_1824

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (NCI-DTP Cat# NCI-H1299, RRID:CVCL_0060)

Curator: @evieth

SciCrunch record: RRID:CVCL_0060

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

Curator: @evieth

SciCrunch record: RRID:CVCL_0031

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (RRID:CVCL_0042)

Curator: @evieth

SciCrunch record: RRID:CVCL_0042

What is this?

DOI: 10.1158/0008-5472.CAN-25-0733

Resource: (RRID:CVCL_0291)

Curator: @evieth

SciCrunch record: RRID:CVCL_0291

What is this?