Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models

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.Author summary: How does spontaneous brain activity give rise to self-initiated actions? Using larval zebrafish, we combine two kinds of data: freely-swimming trajectories and neuronal recordings of a small hindbrain circuit (ARTR) known to control swim orientation . We train three-state Hidden Markov Models (HMM) separately on both datasets, enabling us to link the alphabet of actions - forward, leftward and rightward swim bouts - with stereotypical ARTR states. This parallel modeling sheds a new light on how the ARTR endogenous dynamics constrain fish swimming statistics, and its dependence on water temperature. We further show that the neuronal HMM can be used to generate naturalistic swimming sequences, while the behavioral model captures idiosyncratic features of individual fish. Overall, this work provides a generic approach to link neuronal and behavioral data.