Multimodal Cortical Parcellation Based on Anatomical and Functional Brain Connectivity

Abstract

Reliable cortical parcellation is a crucial step in human brain network analysis since incorrect definition of nodes may invalidate the inferences drawn from the network. Cortical parcellation is typically cast as an unsupervised clustering problem on functional magnetic resonance imaging (fMRI) data, which is particularly challenging given the pronounced noise in fMRI acquisitions. This challenge manifests itself in rather inconsistent parcellation maps generated by different methods. To address the need for robust methodologies to parcellate the brain, we propose a multimodal cortical parcellation framework based on fused diffusion MRI (dMRI) and fMRI data analysis. We argue that incorporating anatomical connectivity information into parcellation is beneficial in suppressing spurious correlations commonly observed in fMRI analyses. Our approach adaptively determines the weighting of anatomical and functional connectivity information in a data-driven manner, and incorporates a neighborhood-informed affnity matrix that was recently shown to provide robustness against noise. To validate, we compare parcellations obtained via normalized cuts on unimodal vs. multimodal data from the Human Connectome Project. Results demonstrate that our proposed method better delineates spatially contiguous parcels with higher test-retest reliability and improves inter-subject consistency.