Pallium tracks valence-specific chemosensory amplification of eye-body coordination in larval zebrafish

  1. Samuel KH Sy
  2. Danny CW Chan
  3. Jenny J Zhang
  4. Jing Lyu
  5. Crystal Feng
  6. Kui Wang
  7. Vincent CT Mok
  8. Kenneth KY Wong
  9. Yu Mu
  10. Yu Hu
  11. Ho Ko

  • Curated by eLife

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    eLife Assessment

    Using their unique Fish-On-Chips optofluidics platform, the authors make three important findings: the presence of precise coupling between saccades and tail flips can be used to discriminate between turning or gliding behaviours; aversive and appetitive chemosensory cues differentially modulate these behaviours; transformation from cue valence to behaviour is encoded by the pallium. The evidence supporting these findings is solid. The work advances our understanding of the ancient interplay between chemosensation and motor output through the modulation of eye-body coordination.

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Abstract

Coordinated eye-body movements are essential for adaptive behavior, yet little is known about how multisensory input, particularly chemosensory cues, shapes this coordination. Using our enhanced Fish-on-Chips optofluidic platform, we uncovered complex dynamics in how larval zebrafish coordinate saccadic eye movements with tail flips. Under baseline conditions, spontaneous tail flips dynamically align with saccades in frequency and direction for coordinated turns. Chemosensory valence further modulates this coordination: death-associated cues intensify both the strength and relative frequency of coupled saccade-tail flips during turns, whereas food-related cues promote forward gliding without altering saccade coupling. Concurrent brain-wide neuronal imaging reveals that the zebrafish pallium represents the mapping between aversive valence and enhanced saccade-tail coordination, with stronger coupling associated with higher pallium activation. These findings uncover the neural representations and behavioral strategies by which chemosensory inputs of different valences are associated with amplified eye-body coordination and adaptive locomotion in a developing vertebrate, offering insights into principles of sensory-motor integration.

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  1. eLife

    eLife Assessment

    Using their unique Fish-On-Chips optofluidics platform, the authors make three important findings: the presence of precise coupling between saccades and tail flips can be used to discriminate between turning or gliding behaviours; aversive and appetitive chemosensory cues differentially modulate these behaviours; transformation from cue valence to behaviour is encoded by the pallium. The evidence supporting these findings is solid. The work advances our understanding of the ancient interplay between chemosensation and motor output through the modulation of eye-body coordination.

  2. eLife

    Reviewer #1 (Public review):

    This study was designed to manipulate and analyze the effects of chemosensory cues on visuomotor control. They approach this by analyzing how eye-body coordination and brain wide activity are altered with specific chemosensation in larval zebrafish. After analyzing the dynamics of coupled saccade-tail coordination sequences - directionally linked and typically coupled to body turns - the authors investigated the effects of sensory cues shown to be either aversive or appetitive on freely swimming zebrafish on the eye-body coordination. Aversive chemicals lead to an increase of saccade-tail sequences in both number and dynamics, seemingly facilitating behaviors like escape. Brain-wide imaging led the authors to neurons in the telencephalic pallium as a target to study eye-body coordination. Pallium neuron activity correlated with both aversive chemicals and coupled saccade-tail movements.

    Comments on revised version.

    The revised manuscript addresses my previous concerns.

  3. eLife

    Reviewer #3 (Public review):

    The manuscript investigates the coupling of saccadic eye-movements (S) with directed tail flips (T), and the remarkable discovery is that tail flips that are preceded by a conjugate sacced (S-T) can be credibly classified as specific "volitional" turns that are distinguished from the standard tail movements that seem to be more of a spontaneous and "impulsive" nature.

    They show that 'turning intent', as indicated by a small increase in S, is elevated by aversive odors, while 'gliding intent', as indicated by a decrease in S and an increase in undulation cycles, is elevated by appetitive odors.

    This is a very important finding, which is backed up by a thorough behavioral analysis, and the identification of neural populations in the pallium and sub-pallium that clearly distinguish between these kinds of turns is very promising. Here they identify neuronal populations that are preferentially active during - and predictive of - coupled (S-T) versus isolated (T) tail flips.

    Especially the fact that S-T turns (but not T turns) can be predicted already by pre-event, ramping, pallial activity is intriguing.

    The authors then go on and demonstrate that the frequency of (S-T) turns is modulated in fish exposed to appetitive or aversive odors.
    Specifically, they quantify the aversiveness and appetitive-ness of several odors in a free swimming assay, they select a couple of these odors based on their valence and they demonstrate that these odors induce moderate modulation in the frequency of eye saccades (S) and tail flips (T) and (S-T) turns.

    The study is rigorous and thorough and the findings are informative and novel.

    In important controls they confirm that brain wide imaging can distinguish between appetitive and aversive context, and they show that pallial activation by aversive odors is consistent with neural activity in the rhombencephalon that correlates with turning activity, whereas sub-pallial activation by appetitive odors correlates with rhombencephalic activity related to gliding.

    It's all very good.

    Comments on revised version.

    The authors have addressed all my concerns.

  4. eLife

    Author response:

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

    We were appreciative of the quality of the work presented in this manuscript and had some points that the authors could address in a revision:

    (1) The pallium interpretation: The data clearly show that pallial activity encodes chemosensory context and predicts upcoming S-T turns. At the same time, in the absence of perturbations, one cannot conclude that the pallium is necessary for transforming chemosensory input into motor output. To align the text with the current evidence, we recommend reframing the pallium's role as correlative/predictive (and avoiding terms like "key region," "hub," or "transformation" that imply causality). The authors could also add a brief limitations note on causality.

    (2) Statistics: There were a few points related to the statistics that needed clarification/tightening. We felt that the animal-level replication was limited and with mixed ages, and the data related to events/neurons were treated as independent observations. It was unclear if the hierarchical structure of the data (events/neurons in different fish) was taken into account, how age differences were handled, and what the exclusion criteria were for 'robustly behaving fish'. We recommend reanalysing the data with fish as the unit of inference (or use clustered/mixed models with fish-level random effects), reporting effect sizes with CIs, and clarifying age (5-6 dpf) handling and any exclusion criteria.

    (3) Figure clarity/data reduction: Some of the figures were crowded, obscuring the main message. To make the main effects easy to see, we recommend that the authors aggregate across doses and move detailed, condition-by-condition results to tables/SI. We also recommend that they include per-fish distributions where helpful, standardize axes/legends, and define onset metrics in captions.

    Summary of Revision

    We thank the reviewers and editors for their overall positive evaluations and encouraging remarks, as well as for their suggestions and constructive criticisms. We have performed additional experiments and analyses, and have substantially revised the manuscript based on the reviewers’ comments and in places where we felt additional clarity was needed. Major additions and changes include:

    - We reframed the pallium’s role as correlative/predictive and avoided all the terms (e.g., “key region”, “hub”, or “transformation”) that imply causality throughout the manuscript. In Discussion, we added a brief limitation note on causality, and incorporated five recent independent OB/pallium-related studies to better contextualize our neuronal findings.

    - To improve animal-level replication, we performed new partially immobilized behavioral experiments in the microfluidic chip (now n = 8 larvae in total; i.e., increased from 5), and reanalyzed the data using linear mixed-effects models with larva as a random intercept (Figures 2, 3, Supplementary Figures 3, 5). For the presentation, IQRs are used to visualize the spontaneous structure of S-T events (Figures 2d, e, i, j, Supplementary Figures 3e, f), the freely swimming plots (Supplementary Figure 4), and chemical-elicited bout kinematic changes (Figures 3e, f, Supplementary Figures 5g, h), whereas 95% CIs are used to visualize time-dependent chemical-elicited rate changes across larvae (Figures 3b–d) and in regression plots (Figures 3g, h). We also included per-larva distributions (Figures 1e, 2b, c, f, 3b–f, Supplementary Figures 3b, c, 5a–h). These additions strengthen the behavioral findings and their presentation. For neuronal analyses (Figures 4, 5, Supplementary Figures 7–11), statistical tests were performed at the individual-larva level, as explicitly clarified in the corresponding figure legends and Methods.

    - We rearranged the figures to highlight findings, simplified analyses and their presentation (Figures 1–3, Supplementary Figures 1, 3–6). We now present the most representative concentration for the valence determination assay in Figure 1c, while leaving the rest in Supplementary Figure 1c. We added a new PCA analysis to present the valence-dependent relationship of temporal proximity and turn magnitude in chemical-evoked S-T events (Figures 3g, h, Supplementary Figure 6). We also added within-trial circular saccade-time shuffle analyses to test whether increased aversive saccade occurrence (Figure 3b) could explain the structured temporal and directional properties of chemical-evoked S-T events (Supplementary Figures 5i, j). In addition, we clarified the handling of larval age and explicitly stated in Methods that there were no additional exclusion criteria (page 19, paragraph 2). We also defined onset metrics and referenced the illustrative schematic (Figure 2a) in captions (Figure 5, Supplementary Figure 10).

    Public review:

    Reviewer #1 (Public review):

    Summary:

    This study was designed to manipulate and analyze the effects of chemosensory cues on visuomotor control. They approach this by analyzing how eye-body coordination and brain-wide activity are altered with specific chemosensation in larval zebrafish. After analyzing the dynamics of coupled saccade-tail coordination sequences - directionally linked and typically coupled to body turns - the authors investigated the effects of sensory cues shown to be either aversive or appetitive on freely swimming zebrafish on the eye-body coordination. Aversive chemicals lead to an increase in saccade-tail sequences in both number and dynamics, seemingly facilitating behaviors like escape. Brain-wide imaging led the authors to neurons in the telencephalic pallium as a target to study eye-body coordination. Pallium neuron activity correlated with both aversive chemicals and coupled saccade-tail movements.

    Recommendations for improvement are minimal. So much of the data is ultimately tabular, and the figures are an impenetrable wall of datapoints. 1c is an excellent example: three concentrations are presented, but it's rare for the three averages to trend appropriately. The key point, which is that aversive odors are repulsive and attractive odors (sometimes) attractive just gets lost in showing the three concentrations individually; it also makes direct comparisons impossible. There are similar challenges abound in the violin plots in 4e-4h, the error bars on the "fits" in 4i-4m, and so on. We recommend selecting an illustrative subset of data to present to permit interpretation and putting the rest in a supplemental table. (Presenting) less is more (effective).

    We are very grateful to the reviewer for their positive assessment and important comments. We have revised the figures to improve clarity and interpretability in line with this suggestion. Specifically, for the valence determination assay, we now present only the most representative one in Figure 1c, while the remaining concentrations are moved to Supplementary Figure 1c. In addition, the new PCA analysis reduced multiple regression plots into summary plots to show the valence-dependent relationship of temporal proximity and turn magnitude in chemical-evoked S-T events (Figures 3g, h, Supplementary Figure 6). For improved presentations, we selected a subset of the most important panels for the behavioral findings (Figures 2, 3, Supplementary Figures 3–6), added the per-larva data points and larva-level summary statistics (Figures 1e, 2b, c, f, 3b–f, Supplementary Figures 3b, c, 5a–h), and adjusted the optical transparency of the error bands (Figures 3b–h, Supplementary Figures 5g, h).

    Reviewer #2 (Public review):

    Summary:

    The manuscript by Sy SKH. et al. on pallium encoded chemosensory impact of eye-body coordination describes how the valence of chemosensory stimuli can affect the coordination of eye saccades with tail flips. They show that aversive valence stimuli can increase both the strength and frequency of tail flips through a pallium-mediated circuit.

    Overall, the manuscript is well-written and easy to follow, although the figures are quite dense, the methodology is mostly sound, and the improvement to the fish on chips system is very interesting. The methods description is thorough and welcome, making the experiments clear. The limited number of animals, and the spread between 5 and 6dpf is a concern as most of the statistics seem to have been done on the individual events, and not the number of biological samples.

    The initial behavioural experiments are very promising. However, the conclusions surrounding the role of the pallium are a lot more speculative and not supported by the results.

    We thank the reviewer for their positive assessment of our manuscript, including the clarity of the writing, methodological description, improvements to the Fish‑on‑Chips system, and very promising behavioral experiments. We also appreciate the important concerns raised about the limited number of animals and the spread between 5 and 6 dpf, as well as the strength of our original phrasing regarding the role of the pallium. As described in the above Summary of Revision, we have substantially addressed these points through additional behavioral experiments and refined statistical analyses, a more cautious framing of our neuronal findings throughout the manuscript and contextualization of our findings in Discussion, and clarifications in the revised manuscript, including the explicit specification of age and statistics in Methods (page 19, paragraph 2). In addition, we now clarify that 5 and 6 dpf larvae were intermixed across all stimulus conditions in a within‑subject design, so that age is not confounded with chemosensory valence and condition effects are estimated within animals rather than across different groups of larvae (Methods, page 18, paragraph 2).

    Comments:

    (1) The fish on chips 2.0 methods show a lot of promise for future studies of chemosensory stimuli, combined with whole-brain imaging. This will provide new avenues of research for zebrafish neuroscientists.

    We thank the reviewer for the positive assessment of the Fish‑on‑Chips 2.0 methods and their potential for future chemosensory whole‑brain imaging studies.

    (2) Chemosensory cues would have a very different timing than visual cues; timing is very important for multisensory integration. How do the authors suggest those are integrated? How would they differentiate between an integration of various cues or a different arousal state, as they describe in the introduction?

    We thank the reviewer for these important comments. In Discussion, we now explicitly consider how visual and chemosensory cues with different temporal dynamics might be integrated. We propose that visual cues may provide rapid guidance for individual movements, whereas chemosensory input may bias the selection and kinematic expression of broader orientation and gaze-shift strategies, including updating gaze after larger reorientations (page 10, paragraph 4).

    We also clarify the alternative explanation of altered arousal. The chemosensory effects were largely confined to the 10-s stimulus window and returned to baseline thereafter, which is more consistent with a rapid cue-locked adjustment rather than a sustained global state change. However, this temporal profile does not exclude a transient arousal-related contribution. We therefore state that our data establish an association between chemosensory context and enhanced eye–body coordination, while distinguishing a specific integration mechanism from transient arousal will require further experiments (page 11, paragraph 1).

    (3) Studies have looked at chemosensation in Drosophila, including multisensory integration, which should be discussed by the authors (see the work of Mark Frye, amongst others).

    We thank the reviewer for pointing out the importance of prior work on chemosensation and multisensory integration in Drosophila. In Discussion, we now relate our findings to studies showing that visual and olfactory cues jointly shape steering and odor‑tracking in flies, including work from the Frye lab, and highlight that reorientation and active visual sampling are common multisensory strategies in small animals (page 10, paragraph 4).

    (4) In the brain imaging methods, there is a mention of robustly behaving larvae. Does that mean that an exclusion criterion was used to select only 5 larvae? If so, this should be stated clearly. The authors also do not mention how they avoid the switch to a passive state that one of the coauthors has observed in closed closed-loop setup. The authors should comment on this point.

    We thank the reviewer for this clarification request. The mention of “robustly behaving” larvae was not intended to introduce any additional exclusion criterion beyond the leveling requirement already described in Methods. In the revised manuscript, we now state explicitly that there were no further exclusions of animals beyond this positioning criterion, so that the basis for the final sample size is clear (page 19, paragraph 2). We also clarify in Methods that appetitive and aversive cues were presented in brief (10-s), balanced, pseudo‑random trials, rather than as a continuous aversive exposure (page 18, paragraph 2).

    (5) Were the statistics in Figure 2 done with an n of 5, or do they assume that each tail flip and saccade is an independent event? I would imagine the latter would have inflated p-values and should be avoided.

    We thank the reviewer for highlighting this important issue. In the original analyses for Figures 2, 3, Supplementary Figures 3, 5, individual tail flips and saccades were treated as independent observations, which could underestimate variability across larvae. In the revised manuscript, we have addressed this in two ways. First, we collected data from three additional larvae and now base our analyses on eight larvae in total. Second, we re‑analyzed these data using linear mixed‑effects models including larva as a random intercept, so that multiple events from the same larva are explicitly modeled as non‑independent. The mixed‑effects analyses yields qualitatively similar conclusions to our original analysis, while avoiding inflated p‑values due to event‑level pseudoreplication. We have updated Methods and legends (Figures 2, 3, Supplementary Figures 3, 5) to describe this model specification and to clarify that larva identity was included as a random effect to account for the non-independence of multiple behavioral events recorded from the same larva.

    (7) Page 7: Why do the authors think that the cumulative effect of these minor differences could lead to very different behavioural goals? Especially when comparing to actual startle responses, which are extremely strong and stereotypical. How do their observations compare to the thermosensory navigation of larval zebrafish observed by Martin Haesemeyer, for example, or the work of the RoLi lab?

    We thank the reviewer for this important point. While the observed changes in individual motor parameters are modest compared to the large and stereotyped responses seen in startle behaviors, our interpretation is based on the principle that larval zebrafish navigation is governed by the accumulation of small, probabilistic changes in bout statistics rather than single, large motor events. This has been well demonstrated in thermosensory navigation, where subtle biases are sufficient to drive reliable temperature preference over time (Haesemeyer et al.). We have now clarified this comparison in the manuscript (page 7, paragraph 2) to better present how the cumulative effects of these small changes can give rise to distinct behavioral outcomes such as approach or avoidance.

    (8) Page 8: Figure 5, I am confused by the y-axis of g, in e and f, the values are capped at 2, whereas in g they go up to 6, with apparently a number of cells whose preference is out of the y-axis limit (especially in Q2). Having the number of cells in each quadrant would also help to assess if indeed there is some preference in the pallium towards Q1.

    We thank the reviewer for highlighting this potential source of confusion regarding Figure 4g. The difference in the y-axis caps between Figures 4f, g arises because these panels display related but distinct quantities. Figure 4f shows regional mean valence preference, averaged across ROIs and across larvae, which lies within a relatively narrow range and is therefore naturally confined to approximately −2 to 2. In contrast, Figure 4g plots the valence preference of individual motor- and valence‑encoding ROIs, which spans a broader dynamic range; for this reason, the y-axis in Figure 4g extends up to ±6. We have also made this distinction more explicit in the Figure 4 legend.

    To address the concern that a substantial number of cells might lie outside the visible axis limits in Figure 4g, we have now quantified the fraction of ROIs whose motor or valence preference values exceed the plotted range. We find that only 0.47% of ROIs have values beyond the chosen axis limits on either axis. These ROIs are plotted at the respective axis limits (i.e. values are clipped to the shown range) and therefore remain visible in the scatter plot. Thus, the plot is representative of virtually all sensorimotor‑tuned neurons included in this analysis.

    In addition, in line with the reviewer’s suggestion, we have clarified the distribution of cells across quadrants by modifying Figure 4g. Specifically, we have added a lower subpanel that shows a grouped bar plot of the proportion of ROIs in each sensorimotor quadrant (I–IV) for pallial versus non‑pallial brain regions. To make the underlying numbers explicit, the corresponding absolute ROI counts for pallial and non‑pallial regions in each quadrant are now reported in the Figure 4 legend. These additions demonstrate that pallial ROIs are strongly enriched in quadrant I relative to other quadrants and to non‑pallial regions, and they directly address the reviewer’s request for the number of cells in each quadrant.

    (9) Figure 6: How is the onset of neuronal activity determined compared to the motor stimulus? Looking at Supplementary Figure 8, it is quite unclear how the pallium is different from the OB or subpallium. The label of onset delay is also confusing in this figure.

    In our analysis, neuronal activity is aligned to tail flip onset for each S-T or T event (dotted vertical lines in neuronal traces in Figures 5a, b, Supplementary Figures 8a, b, 9–11). At each of the two time points preceding the motor event, we compare the fluorescence signal of the motor‑encoding ROIs to their pre‑event baseline; activity exceeding baseline by ≥ 2.5 S.D. is considered significantly elevated (Z‑test with Benjamini–Hochberg correction; see Methods and Figure 5 legend). In Supplementary Figure 9, pallial motor‑encoding neurons exhibit earlier statistically significant elevations relative to tail flip onset during aversive, cue‑associated S-T events, whereas OB and subpallial neurons primarily encode chemosensory input and valence and do not show the same pattern of early significant activation.

    Additionally, we have clarified in the legends of Figure 5 and Supplementary Figure 10 that ‘saccade–tail flip onset delay’ is defined as the time difference between tail flip onset and saccade onset within an S-T event (tail flip onset minus saccade onset), and we indicate how this metric is used to group “more-coupled” versus “less-coupled” S-T events. We note that this “saccade–tail flip onset delay” captures how tightly the eye and body components of S-T events are temporally coupled, and thus serves as a quantitative link between motor coordination (Figures 3g, h, Supplementary Figure 6) and the pallial premotor activation observed during aversive cues (Figure 5a, Supplementary Figure 9).

    We hope these clarifications resolve the ambiguity the reviewer noted.

    (10) Page 9: I do not think that the small differences observed in the pallium are as clear-cut as the authors make them out to be, or that they provide such strong evidence of their importance. As there are no interventions showing any causality in the presence of these pallium responses and the sensorimotor responses, these could represent different arousal states rather than any integration of sensory information.

    We thank the reviewer for this thoughtful comment. We agree that, without targeted perturbations, our pallium signals cannot be interpreted as causal for the sensorimotor responses, and we have accordingly reframed them throughout the manuscript (in Title, Abstract, Results and Discussion) as correlative/predictive rather than causal, with an explicit limitation note in Discussion. In addition, we added five related recent studies in Discussion to contextualize our pallial findings. While the pallial differences are moderate in magnitude, they are quantified at the larval level and consistently show a relative bias toward S-T events together with aversive valence, distinguishing the pallium from other regions. Please see also the response to comment 2 regarding the altered arousal point.

    Reviewer #3 (Public review):

    The manuscript investigates the coupling of saccadic eye movements (S) with directed tail flips (T). The remarkable discovery is that tail flips that are preceded by a conjugate sacced (S-T) can be credibly classified as specific "volitional" turns that are distinguished from the standard tail movements that seem to be more of a spontaneous and "impulsive" nature.

    They show that 'turning intent', as indicated by a small increase in S, is elevated by aversive odors, while 'gliding intent', as indicated by a decrease in S and an increase in undulation cycles, is elevated by appetitive odors.

    This is a very important finding, which is backed up by a thorough behavioral analysis, and the identification of neural populations in the pallium and sub-pallium that clearly distinguish between these kinds of turns is very promising. Here they identify neuronal populations that are preferentially active during - and predictive of - coupled (S-T) versus isolated (T) tail flips.

    Especially the fact that S-T turns (but not T turns) can be predicted already by pre-event, ramping, pallial activity is intriguing.

    The authors then go on and demonstrate that the frequency of (S-T) turns is modulated in fish exposed to appetitive or aversive odors.

    Specifically, they quantify the aversiveness and appetitive-ness of several odors in a free swimming assay. They select a couple of these odors based on their valence, and they demonstrate that these odors induce moderate modulation in the frequency of eye saccades (S) and tail flips (T) and (S-T) turns.

    The study is rigorous and thorough, and the findings are informative and novel.

    In important controls, they confirm that brain-wide imaging can distinguish between appetitive and aversive contexts, and they show that pallial activation by aversive odors is consistent with neural activity in the rhombencephalon that correlates with turning activity, whereas sub-pallial activation by appetitive odors correlates with rhombencephalic activity related to gliding.

    Overall, this manuscript is very good.

    We are very grateful to the reviewer for their positive and thoughtful assessment of our work. We are pleased that the distinction between saccade‑coupled (S‑T) turns and independent tail flips, their modulation by appetitive versus aversive odors, and the identification of pallial populations predictive of these events are viewed as rigorous, informative, and novel.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the authors):

    Full disclosure: We reviewed the manuscript previously for Cell Reports, and we're happy to see that it is much improved. As such, our comments are pretty minimal.

    We thank the reviewer for revisiting the manuscript and for their positive assessment of the revisions. In the present revision, we also performed within-trial saccade-time shuffle analyses to directly examine whether the increased occurrence of aversive saccades could account for the temporal and directional organization of S-T events (Supplementary Figures 5i, j). These analyses showed that altered saccade occurrence alone cannot account for this organization.

    Reviewer #2 (Recommendations for the authors):

    (1) The introduction would benefit from more discussion of the existing disruption of coordination beyond Parkinson. Especially in terms of the circuits involved, as these have been well-studied, and it would provide a better understanding of the system as studied in the manuscript.

    We thank the reviewer for this suggestion. In Introduction, we now place Parkinson’s disease within the broader context of movement disorders that disrupt eye–body coordination and expand the circuit‑level discussion accordingly, emphasizing the shared involvement of cortical eye fields, superior colliculus, basal ganglia, cerebellum, and brainstem locomotor centers (page 2, paragraph 2).

    (2) "For example, a sudden surge of noxious chemical signals can trigger fleeing" - in what model and condition, what type of escape? There are a number of options to discuss here.

    We thank the reviewer for this helpful suggestion. In response, we have revised the sentence to include a concrete and well-characterized example from teleost fish (page 3, paragraph 1). Specifically, we now refer to the response to injury-released alarm substances (“Schreckstoff”), which reliably elicit rapid escape bouts. This provides a clear example of the model system, stimulus condition (alarm cue), and behavioral output (escape locomotion).

    (3) The introduction mostly mentions the methodological advantages of zebrafish, but the authors should discuss the relevance of the model to the research question. Especially with regard to the differences in chemosensory spread underwater and in the air.

    We thank the reviewer for this suggestion. We have revised the Introduction to better highlight the biological relevance of zebrafish beyond their technical advantages, emphasizing that larval zebrafish naturally use waterborne chemosensory cues to guide locomotion (page 3, paragraph 2). We also address differences between chemosensory signal propagation in water and air, noting that despite these physical differences, animals in both environments must integrate chemical cues with other sensory inputs to select appropriate behavioral responses. We believe this revision clarifies the relevance of the model to our research question.

    (4) The GitHub is missing some of the registration code used for calcium imaging. It would be useful to make sure that all the code mentioned in the manuscript is shared.

    We thank the reviewer for this thoughtful comment and for drawing attention to code availability. The calcium-imaging processing pipeline in this study used established, publicly available tools, including NoRMCorre for motion correction, as cited and described in Methods. In addition, we used custom MATLAB scripts to implement and link the affine and non-rigid registration workflow for aligning anatomical stacks to the Z-Brain Atlas. These custom scripts, together with the analysis code, have now been uploaded to the GitHub repository.

    (5) Figure 4: In subpanel c, it seems that the blank stimulus has a similar, but opposed, effect to aversive stimuli. Would it be possible to see the full 30s pre-stimulus to judge how variable the baseline is?

    We thank the reviewer for this helpful comment. In the analyses, the first 10s of each behavioral recording are discarded to avoid transient responses to the onset of light‑sheet illumination, and we normalize the time courses to the mean pre‑stimulus activity in the 10s immediately preceding stimulus onset. In the extended 40s plot provided in Figure 3b, we additionally display 10s of earlier baseline and, for visualization, normalize the traces to the mean activity in these first two 5s bins, so that baseline fluctuations across blank, appetitive, and aversive conditions can be directly compared. All conditions show modest pre-stimulus fluctuations, but the largest and most sustained increase is observed for the aversive condition and is confined to the stimulus window, whereas blank and appetitive traces remain comparatively close to baseline throughout. Thus, although some baseline variability is present, the strong aversive effect remains stimulus-locked and is not explained by pre-stimulus drift in the blank condition.

    Because this extended plot was used only to assess baseline variability in response to the reviewer’s question and does not alter the quantitative analyses or figure panels in the manuscript, we have included it in the rebuttal rather than in the revised manuscript.

    (6) Figure 5: I assume the legend is flipped between subpanels a and b? Subpanel d is used to show that the pallium has a preference for S-T events, but the result is not convincing; the motor preference is still below zero, very similar to PO, pTec or even Rh3. Furthermore, I assume the statistics have been done on all the neurons across the 5 fish, not treating them as individual samples.

    We thank the reviewer for their important comments. The legend on Figure 4 has been corrected so that the labels for subpanels a and b match the plotted data. Although the pallial motor preference is below zero, indicating a net bias toward independent tail flips, we note that the pallium is nonetheless the least T‑preferring region and shows the strongest relative preference for S-T events when motor preference is compared across all brain regions. We have clarified this relative, contrast-based interpretation in the revised manuscript. Importantly, motor‑preference statistics are computed at the individual-larva level (n = 5), averaging neurons within each region for each larva and then testing across larvae rather than treating individual neurons as independent samples. This approach is now described more clearly in Methods, and the Figure 4 legend.

    (6) Supplementary Figure 7: What are the ROI numbers for the different graphs in a and b? It is difficult to judge if the "significant" increase in activation level is due to a low level of neurons or a genuine biological result.

    We thank the reviewer for this helpful comment. To address the concern, we have revised the legend of Supplementary Figure 8 to clarify that mean ± S.E.M. values are computed across larvae using per‑larva averages and to report the ROI counts for each preference range. These changes make clear that each curve is supported by on the order of 102–103 ROIs pooled across the five larvae, rather than by a very small number of neurons.

    Reviewer #3 (Recommendations for the authors):

    (1) Shouldn't the ipsilateral tendency relative to each preceding flip across a series of tail flips decay to chance over repeated events? In Figure 2h, the teal curve appears to be flat over twenty seconds. Isn't the laterality index calculated relative to the event at n=0? Are the individual fish laterally biased (for spontaneous flips) over 20 bouts?

    We thank the reviewer for this insightful comment. In Figure 2h, the laterality index is computed relative to the tail flip at n = 0, and the sustained ipsilateral tendency (please see also Supplementary Figure 3c) over subsequent bouts reflects that spontaneous locomotion in larval zebrafish is not a memoryless random walk. To clarify this point, we now complement the citation on history‑dependent chaining of turns with an additional reference highlighting stable individual left–right motor asymmetries in larval zebrafish (page 5, paragraph 4). This added citation provides stronger context for our observation that tail flips can remain biased ipsilaterally to the initial event over the bout window we analyze.

    (2) Do zebrafish initiate a 'turn bout' by a preceding saccade also in free swimming conditions? Are there any data available with sufficient resolution to extract saccades in freely swimming larvae?

    We thank the reviewer for this important question. In the prior free‑swimming assay, we used a large field of view (60 mm × 30 mm) at approximately 0.1 mm per pixel to track multiple larvae and quantify their position and heading. Given this combination of field of view and spatial sampling, the larval eyes occupy only a few pixels, and the small angular displacements associated with saccades are very challenging to resolve with sufficient precision. For this reason, we did not extract eye movements or define turn bouts based on preceding saccades in the free‑swimming condition, and instead focused our saccade analyses on the head‑fixed imaging setup, where magnification and resolution are optimized for quantifying eye rotations.

    We have added a brief note in Methods (page 22, paragraph 2) to clarify this limitation.

  5. Version published to 10.7554/elife.109494.2 on eLife
  6. Version published to 10.7554/elife.109494 on eLife
  7. Version published to 10.1101/2024.10.27.620486v3 on bioRxiv
  8. eLife

    eLife Assessment

    Using their unique Fish-On-Chips optofluidics platform, the authors make three important findings: the presence of precise coupling between saccades and tail flips can be used to discriminate between turning or gliding behaviours; aversive and appetitive chemosensory cues differentially modulate these behaviours; transformation from cue valence to behaviour is encoded by the pallium. The evidence supporting these findings is solid. The work advances our understanding of the ancient interplay between chemosensation and motor output through the modulation of eye-body coordination.

  9. eLife

    Reviewer #1 (Public review):

    Summary:

    This study was designed to manipulate and analyze the effects of chemosensory cues on visuomotor control. They approach this by analyzing how eye-body coordination and brain-wide activity are altered with specific chemosensation in larval zebrafish. After analyzing the dynamics of coupled saccade-tail coordination sequences - directionally linked and typically coupled to body turns - the authors investigated the effects of sensory cues shown to be either aversive or appetitive on freely swimming zebrafish on the eye-body coordination. Aversive chemicals lead to an increase in saccade-tail sequences in both number and dynamics, seemingly facilitating behaviors like escape. Brain-wide imaging led the authors to neurons in the telencephalic pallium as a target to study eye-body coordination. Pallium neuron activity correlated with both aversive chemicals and coupled saccade-tail movements.

    Recommendations for improvement are minimal. So much of the data is ultimately tabular, and the figures are an impenetrable wall of datapoints. 1c is an excellent example: three concentrations are presented, but it's rare for the three averages to trend appropriately. The key point, which is that aversive odors are repulsive and attractive odors (sometimes) attractive just gets lost in showing the three concentrations individually; it also makes direct comparisons impossible. There are similar challenges abound in the violin plots in 4e-4h, the error bars on the "fits" in 4i-4m, and so on. We recommend selecting an illustrative subset of data to present to permit interpretation and putting the rest in a supplemental table. (Presenting) less is more (effective).

  10. eLife

    Reviewer #2 (Public review):

    Summary:

    The manuscript by Sy SKH. et al. on pallium encoded chemosensory impact of eye-body coordination describes how the valence of chemosensory stimuli can affect the coordination of eye saccades with tail flips. They show that aversive valence stimuli can increase both the strength and frequency of tail flips through a pallium-mediated circuit.

    Overall, the manuscript is well-written and easy to follow, although the figures are quite dense, the methodology is mostly sound, and the improvement to the fish on chips system is very interesting. The methods description is thorough and welcome, making the experiments clear. The limited number of animals, and the spread between 5 and 6dpf is a concern as most of the statistics seem to have been done on the individual events, and not the number of biological samples.

    The initial behavioural experiments are very promising. However, the conclusions surrounding the role of the pallium are a lot more speculative and not supported by the results.

    Comments:

    (1) The fish on chips 2.0 methods show a lot of promise for future studies of chemosensory stimuli, combined with whole-brain imaging. This will provide new avenues of research for zebrafish neuroscientists.

    (2) Chemosensory cues would have a very different timing than visual cues; timing is very important for multisensory integration. How do the authors suggest those are integrated? How would they differentiate between an integration of various cues or a different arousal state, as they describe in the introduction?

    (3) Studies have looked at chemosensation in Drosophila, including multisensory integration, which should be discussed by the authors (see the work of Mark Frye, amongst others).

    (4) In the brain imaging methods, there is a mention of robustly behaving larvae. Does that mean that an exclusion criterion was used to select only 5 larvae? If so, this should be stated clearly. The authors also do not mention how they avoid the switch to a passive state that one of the coauthors has observed in closed closed-loop setup. The authors should comment on this point.

    (5) Were the statistics in Figure 2 done with an n of 5, or do they assume that each tail flip and saccade is an independent event? I would imagine the latter would have inflated p-values and should be avoided.

    (7) Page 7: Why do the authors think that the cumulative effect of these minor differences could lead to very different behavioural goals? Especially when comparing to actual startle responses, which are extremely strong and stereotypical. How do their observations compare to the thermosensory navigation of larval zebrafish observed by Martin Haesemeyer, for example, or the work of the RoLi lab?

    (8) Page 8: Figure 5, I am confused by the y-axis of g, in e and f, the values are capped at 2, whereas in g they go up to 6, with apparently a number of cells whose preference is out of the y-axis limit (especially in Q2). Having the number of cells in each quadrant would also help to assess if indeed there is some preference in the pallium towards Q1.

    (9) Figure 6: How is the onset of neuronal activity determined compared to the motor stimulus? Looking at Supplementary Figure 8, it is quite unclear how the pallium is different from the OB or subpallium. The label of onset delay is also confusing in this figure.

    (10) Page 9: I do not think that the small differences observed in the pallium are as clear-cut as the authors make them out to be, or that they provide such strong evidence of their importance. As there are no interventions showing any causality in the presence of these pallium responses and the sensorimotor responses, these could represent different arousal states rather than any integration of sensory information.

  11. eLife

    Reviewer #3 (Public review):

    The manuscript investigates the coupling of saccadic eye movements (S) with directed tail flips (T). The remarkable discovery is that tail flips that are preceded by a conjugate sacced (S-T) can be credibly classified as specific "volitional" turns that are distinguished from the standard tail movements that seem to be more of a spontaneous and "impulsive" nature.

    They show that 'turning intent', as indicated by a small increase in S, is elevated by aversive odors, while 'gliding intent', as indicated by a decrease in S and an increase in undulation cycles, is elevated by appetitive odors.

    This is a very important finding, which is backed up by a thorough behavioral analysis, and the identification of neural populations in the pallium and sub-pallium that clearly distinguish between these kinds of turns is very promising. Here they identify neuronal populations that are preferentially active during - and predictive of - coupled (S-T) versus isolated (T) tail flips.

    Especially the fact that S-T turns (but not T turns) can be predicted already by pre-event, ramping, pallial activity is intriguing.

    The authors then go on and demonstrate that the frequency of (S-T) turns is modulated in fish exposed to appetitive or aversive odors.
    Specifically, they quantify the aversiveness and appetitive-ness of several odors in a free swimming assay. They select a couple of these odors based on their valence, and they demonstrate that these odors induce moderate modulation in the frequency of eye saccades (S) and tail flips (T) and (S-T) turns.

    The study is rigorous and thorough, and the findings are informative and novel.

    In important controls, they confirm that brain-wide imaging can distinguish between appetitive and aversive contexts, and they show that pallial activation by aversive odors is consistent with neural activity in the rhombencephalon that correlates with turning activity, whereas sub-pallial activation by appetitive odors correlates with rhombencephalic activity related to gliding.

    Overall, this manuscript is very good.

  12. eLife

    Author response:

    We thank the editors and all reviewers for the detailed evaluation of the work and the overall positive remarks, as well as the constructive feedback to improve our manuscript. Based on the integrated comments of the reviewers and advice of the reviewing editor, we will suitably address all comments raised by the reviewers, and we outline our revision plan below:

    Interpretation of findings

    ● We will carefully reframe our interpretation of the data regarding the role of the pallium in the coupled saccade-tail turning events, and clearly state that we have not established a causal role, which requires additional perturbation experiments.

    ● We will also acknowledge the confounding interpretation that the pallial activities recorded may also represent or include arousal state signals.

    Streamlining the presentation

    ● In the introduction, we will better contextualize our study with additional discussions on (i) the advantageous use of zebrafish to study chemosensation, factoring in differences in the spread of chemical cues in water vs. air, and (ii) the disruption of eye-body coordination and underlying neural circuits.

    ● We will streamline the presentation of data in Fig. 1 by keeping the overall responses of the larvae to each chemical across concentrations in the main figure, while moving suitable additional details to a supplementary figure.

    ● Similarly, for each of the subsequent main figures, wherever suitable we will select an illustrative, core set of panels to retain in the main figure, and move other more detailed plots to supplementary figures.

    ● We will incorporate additional references and discussions of the past literature, including relating our findings to (i) chemosensation/multisensory integration in Drosophila, (ii) thermosensation-driven and navigational behavior in larval zebrafish, and (iii) fleeing or escape behavior in zebrafish and other species.

    ● We will clarify our animal subject inclusion criteria, that all larval subjects with sufficiently high-quality, stable imaging were included (i.e., we only excluded larvae because of insufficient quality of imaging, but not other factors).

    ● For applicable plots, adding suitable additional details to the plots or legends (e.g., clarification of measures, specifying numbers of cells).

    Data analysis and statistics

    ● We will perform additional data analysis, by making comparisons with statistics performedon fish subject-level, and include confident intervals wherever applicable.

  13. Version published to 10.7554/elife.109494.1 on eLife
  14. Version published to 10.1101/2024.10.27.620486v2 on bioRxiv
  15. Version published to 10.1101/2024.10.27.620486v1 on bioRxiv