Node-based Gaussian Graphical Model for identifying discriminative brain regions from connectivity graphs

Abstract

Despite that the bulk of our knowledge on brain function is established around brain regions, current methods for comparing connectivity graphs largely take an edge-based approach with the aim of identifying discriminative connections. In this paper, we explore a node-based Gaussian Graphical Model (NBGGM) that facilitates identification of brain regions attributing to connectivity differences seen between a pair of graphs. To enable group analysis, we propose an extension of NBGGM via incorporation of stability selection. We evaluate NBGGM on two functional magnetic resonance imaging (fMRI) datasets pertaining to within and between-group studies. We show that NBGGM more consistently selects the same brain regions over random data splits than using node-based graph measures. Importantly, the regions found by NBGGM correspond well to those known to be involved for the investigated conditions.