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We present a statistical framework that jointly models brain shape andfunctional connectivity, which are two complex aspects of the brain that havebeen classically studied independently. We adopt a Riemannian modeling approachto account for the non-Euclidean geometry of the space of shapes and the spaceof connectivity that constrains trajectories of co-variation to be validstatistical estimates. In order to disentangle genetic sources of variabilityfrom those driven by unique environmental factors, we embed a functional randomeffects model in the Riemannian framework. We apply the proposed model to theHuman Connectome Project dataset to explore spontaneous co-variation betweenbrain shape and connectivity in young healthy individuals.

Functional random effects modeling of brain shape and connectivity