Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity.

Chendi Wang, Bernard Ng, Rafeef Garbi

Abstract: Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this article, we propose an approach for integrating fMRI and diffusion MRI data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test–retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the human connectome project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher left out data likelihood. The boundaries of multimodal parcels are observed to align to those based on cytoarchitecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.