Mouse

The connectome is a comprehensive map of the structural and functional connections of the brain, derived from multiple neuroimaging modalities. This information-rich representation of the brain can be used to better understand previously intractable neurological disorders. However, existing methods to analyze connectomes are limited: either they can only operate on a single connectome or, if they operate on a population of connectomes, they lack a statistical model which makes their validity unclear. To fill this need, we introduce a set of algorithms that enable the discovery of significant biological components at multiple levels of the connectome. We validated these algorithms using high-resolution connectome data collected from well-validated mouse models of autism. Armed with these algorithms, researchers can interrogate existing connectome data sets and discover new biologically relevant markers to aid the diagnosis and treatment of disease.