Event:

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 an informative framework to interpret neural function and its changes in the context of disease processes.