Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering

TLDR

Quantitative modeling and analysis of structural and functional brain networks based on DTI and fMRI have received extensive interest recently, but the regularity of these networks across modalities and individuals is largely unknown. This paper introduces a novel method to infer group‑wise consistent brain subnetworks from multimodal DTI and resting‑state fMRI data using multi‑view spectral clustering of cortical networks built on DICCCOL landmarks. The authors applied the method to DTI data from 100 healthy young females and 50 healthy young males, generating consistent multimodal brain networks within and across groups, and examined their functional roles. Experimental results show that the derived brain networks exhibit substantially improved inter‑modality and inter‑subject consistency.

Abstract

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.