FlyVISTA, an Integrated Machine Learning Platform for Deep Phenotyping of Sleep in <i>Drosophila</i>.

ABSTRACT: Animal behavior depends on internal state. While subtle movements can signify significant changes in internal state, computational methods for analyzing these "microbehaviors" are lacking. Here, we present FlyVISTA, a machine-learning platform to characterize microbehaviors in freely-moving flies, which we use to perform deep phenotyping of sleep. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep-learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep-associated microbehaviors in flies. We further show that stimulation of dorsal fan-shaped body neurons induces micromovements, not sleep, whereas activating R5 ring neur

SUBMITTER: Keles MF 

PROVIDER: S-EPMC10635029 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature