Uncovering multiscale structure in the variability of larval zebrafish navigation

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(47)

Published: Nov. 15, 2024

Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences bridge timescales enable a quantitative comparison behavioral phenotypes among individuals. Applied to larval zebrafish responding diverse sensory cues, uncover hierarchy dominated by changes orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at population level: while fish cruise light, they wander response aversive stimuli, or search for appetitive prey. As our method encodes dynamics each individual transitions coarse-grained use it hierarchical phenotypic exploration–exploitation trade-offs. Across wide range major source variation is driven prior and/or immediate exposure prey induces exploitation phenotypes. A large degree not explained unravels hidden override context contrasting Altogether, extracting strategies deployed during navigation, approach exposes individuals reveals tuned experience.

Language: Английский

Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 22, 2024

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question integrative neuroscience. Addressing this requires models that can offer unified interpretation of multimodal data. In study, we jointly examine video-recordings zebrafish larvae freely exploring their environment and calcium imaging Anterior Rhombencephalic Turning Region (ARTR) circuit, which known to control swimming orientation, recorded vivo under tethered conditions. We show both behavioral neural data be accurately modeled using Hidden Markov Model (HMM) with three hidden states. context behavior, states correspond leftward, rightward, forward swimming. The HMM robustly captures key statistical features motion, including bout-type persistence its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For data, left- right-lateral activation ARTR govern selection left vs. right reorientation, balanced state, likely corresponds state. To further unify two analysis, exploit generative nature HMM, sequences generate synthetic trajectories whose properties are similar Overall, work demonstrates state-space used link providing insights into mechanisms self-generated action.

Language: Английский