Statistical signature of subtle behavioral changes in large-scale assays

The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines.