article · 05/01/2026
Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models
Résumé
Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.
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Dommanget-Kott, M., Fernandez-de-Cossio-Diaz, J., Coraggioso, M., Bormuth, V., Monasson, R., Debrégeas, G., & Cocco, S. (2026). Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models. PLoS Comput Biol, 22(1): e1013762. https://doi.org/https://doi.org/10.1371/journal.pcbi.1013762@article{DommangetKott2026_470,
author = {Dommanget-Kott, Matteo and Fernandez-de-Cossio-Diaz, Jorge and Coraggioso, Monica and Bormuth, Volker and Monasson, Rémi and Debrégeas, Georges and Cocco, Simona},
year = {2026},
month = {1},
title = {Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models},
journal = {PLoS Comput Biol},
volume = {22(1): e1013762},
abstract = {Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013762},
doi = {https://doi.org/10.1371/journal.pcbi.1013762},
}TY - JOUR
AU - Dommanget-Kott, Matteo
AU - Fernandez-de-Cossio-Diaz, Jorge
AU - Coraggioso, Monica
AU - Bormuth, Volker
AU - Monasson, Rémi
AU - Debrégeas, Georges
AU - Cocco, Simona
PY - 2026
DA - 2026/01/05
TI - Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models
JO - PLoS Comput Biol
VL - 22(1): e1013762
AB - Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.
DO - https://doi.org/10.1371/journal.pcbi.1013762
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013762
ER -