Functional random effects modeling of brain shape and connectivity

TL;DR

This paper introduces a Riemannian statistical framework for jointly modeling brain shape and functional connectivity, capturing their complex geometry and disentangling genetic and environmental influences.

Contribution

It develops a novel Riemannian-based joint modeling approach with functional random effects to analyze brain shape and connectivity simultaneously.

Findings

Identifies co-variation patterns between brain shape and connectivity.

Disentangles genetic and environmental variability in brain features.

Applies successfully to Human Connectome Project data.

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 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.