Joint Modeling for Geometry and Functionality of Cerebral Cortical Surface Images

TL;DR

This paper introduces a joint modeling framework for analyzing the relationship between the geometry and functionality of cerebral cortical surfaces, revealing new insights into brain structure-function interactions.

Contribution

It presents a novel mixed effects model that jointly captures geometry and functionality effects in high-dimensional brain surface data with efficient estimation techniques.

Findings

Identified relationships between cortical shape and neuronal activation.

Discovered new modes of shape-function correspondence related to emotion processing.

Analyzed data from the Human Connectome Project to validate the model.

Abstract

We propose a framework for jointly modeling the geometry and functionality in high dimensional functional surfaces. The proposed mixed effects model characterizes effects of subject-specific covariates and exogenous stimuli on functional surfaces while accounting for potential mutual-influence of their geometry and functionality. This is achieved through a computationally efficient estimation method that incorporates regularized estimation of the precision matrix of the random effects. We perform a thorough analysis of cerebral cortical surface structural MRI and task fMRI data from the Human Connectome Project and discover relationships between the geometric shapes of cortical surface and neuronal activation responding to task stimuli. Our findings highlight new modes of correspondence between cortical surface shape and functional activation relevant to emotion processing.