Multiple-View Spectral Clustering for Group-wise Functional Community Detection

TL;DR

This paper introduces a multiple-view spectral clustering method for group-wise functional brain community detection, demonstrating improved consistency and computational efficiency over existing techniques in analyzing human connectome data.

Contribution

The paper proposes a novel multiple-view spectral clustering approach for functional community detection, outperforming existing methods in consistency and speed.

Findings

Multiple-view spectral clustering yields more consistent communities than JDL.

The proposed methods are several orders of magnitude faster than JDL.

Experiments on 291 subjects validate the effectiveness of the approach.

Abstract

Functional connectivity analysis yields powerful insights into our understanding of the human brain. Group-wise functional community detection aims to partition the brain into clusters, or communities, in which functional activity is inter-regionally correlated in a common manner across a group of subjects. In this article, we show how to use multiple-view spectral clustering to perform group-wise functional community detection. In a series of experiments on 291 subjects from the Human Connectome Project, we compare three versions of multiple-view spectral clustering: MVSC (uniform weights), MVSCW (weights based on subject-specific embedding quality), and AASC (weights optimized along with the embedding) with the competing technique of Joint Diagonalization of Laplacians (JDL). Results show that multiple-view spectral clustering not only yields group-wise functional communities that are more consistent than JDL when using randomly selected subsets of individual brains, but it is several orders of magnitude faster than JDL.