Leveraging rich behavioral phenotyping to identify information processing changes due to a high-risk ASD gene

Animal behavior contains rich structure across many timescales, but there is a dearth of methods for the identification of relevant long run behavioral components. Inspired by the goals and techniques of genome-wide association studies, I will present our development of a data-driven method—the choice-wide behavioral association study: CBAS—that systematically identifies such behavioral features. CBAS breaks down the actions of subjects into all sequences of choices during behavior, then uses powerful, resampling-based, multiple comparisons methods to identify the sequences that either differ significantly between groups or significantly correlate with a covariate of interest. CBAS works across different tasks and species (flies, rats, and humans). I will focus on our application of CBAS to compare WT rats to those haploinsufficient for a high-confidence, large effect, autism spectrum disorder risk gene (Scn2a^+/-). CBAS identifies specific and consistent ways that Scn2a haploinsufficient rats differ in learning a spatial alternation task, and CBAS shows that Scn2a^+/- rats differentially rely on their hippocampus for behavior. Through identifying relevant choices during behavior, CBAS provides a uniquely informative framework to interpret neural function and its changes in the context of disease processes.