  We present a statistical framework that jointly models brain shape and
functional connectivity, which are two complex aspects of the brain that have
been classically studied independently. We adopt a Riemannian modeling approach
to account for the non-Euclidean geometry of the space of shapes and the space
of connectivity that constrains trajectories of co-variation to be valid
statistical estimates. In order to disentangle genetic sources of variability
from those driven by unique environmental factors, we embed a functional random
effects model in the Riemannian framework. We apply the proposed model to the
Human Connectome Project dataset to explore spontaneous co-variation between
brain shape and connectivity in young healthy individuals.
