Hypergraph Overlapping Community Detection for Brain Networks

TL;DR

This paper introduces a spectral clustering method for detecting overlapping communities in hypergraphs constructed from fMRI data, capturing high-order brain region dependencies to better understand brain network organization.

Contribution

It presents a novel approach combining hypergraph construction from fMRI data with spectral clustering for overlapping community detection in brain networks.

Findings

Successfully identified overlapping communities in brain hypernetworks.

Revealed high-order dependencies among brain regions.

Applied to HCP data to find consensus community structures.

Abstract

Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantifying pairwise relationships between ROIs ignoring higher order dependencies between multiple brain regions. Recently, hypergraph construction methods from fMRI time series data have been proposed to characterize the high-order relations among multiple ROIs. While there have been multiple methods for constructing hypergraphs from fMRI time series, the question of how to characterize the topology of these hypergraphs remains open. In this paper, we make two key contributions to the field of community detection in brain hypernetworks. First, we construct a hypergraph for each subject capturing high order dependencies between regions. Second, we introduce a spectral clustering based approach on hypergraphs to detect overlapping community structure. Finally, the proposed method is implemented to detect the consensus community structure across multiple subjects. The proposed method is applied to resting state fMRI data from Human Connectome Project to summarize the overlapping community structure across a group of healthy young adults.