Graph theory identifies altered prefrontal microcircuit organization in Shank3 mice, a mouse Model of autism.

Graph theory provides unique tools to assess complex networks. It has been previously used with functional magnetic resonance imaging (fMRI) datasets to quantify macroscopic-scale connections among different brain regions, readily capturing brain network changes in subjects with Alzheimer's disease. Here, we apply graph theory to miniscope calcium imaging data recorded from the prefrontal cortex of freely behaving wild-type (WT) and Shank3fx mice (a mouse model of autism) during social behavior tasks to compare microscopic-scale functional connections among individual neurons. We demonstrate that Shank3fx mice display reduced population-level neural activity and a less-integrated and rigid prefrontal microcircuit. Furthermore, we employ machine learning to predict genotypes and behavioral differences between WT and Shank3fx mice using graph-theoretic metrics extracted from prefrontal microcircuits. Our results indicate strong links between altered prefrontal microcircuits and social behavior deficits in the Shank3fx mice, highlighting prefrontal microcircuitry as a potential diagnostic and therapeutic target for autism.