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Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models
Mattéo Dommanget-Kott1,2, Jorge Fernandez-de-Cossio-Diaz3,4, Monica Coraggioso1
1Institut de Biologie Paris-Seine (IBPS), Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France.
Plos Computational Biology
|January 6, 2026
Summary
This study links brain activity and zebrafish behavior using a Hidden Markov Model (HMM). The model connects neural states in the ARTR circuit to specific swimming patterns, revealing insights into action mechanisms.
Area of Science:
- Integrative neuroscience
- Computational neuroscience
- Systems neuroscience
Background:
- Understanding the brain's orchestration of behavior is a key challenge.
- Multimodal data interpretation requires unified models.
- The Anterior Rhombencephalic Turning Region (ARTR) circuit controls zebrafish swimming orientation.
Purpose of the Study:
- To jointly model zebrafish behavior and ARTR neural activity.
- To investigate the link between neural states and motor actions.
- To apply state-space models for interpreting complex biological data.
Main Methods:
- Utilized Hidden Markov Models (HMM) with three hidden states.
- Analyzed in vivo calcium imaging of the ARTR circuit.
- Recorded and analyzed video data of zebrafish larvae freely exploring.
Main Results:
- Both behavioral and neural data were accurately modeled by the HMM.
- Identified three behavioral states (left, right, forward swimming).
- Correlated neural states with ARTR circuit activation and swimming behavior.
Conclusions:
- State-space models effectively link neuronal and behavioral data.
- Demonstrated how neural activity in the ARTR circuit relates to swimming actions.
- Provided insights into the mechanisms of self-generated actions in zebrafish.
