Functional random effects modeling of brain shape and connectivity
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 4
Abstract
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 covariation 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.
Keywords
Functional data analysis, mixed effects models, neuroimaging, variance component models
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