Multiple-View Spectral Clustering for Group-wise Functional Community Detection
This work addresses the problem of efficiently and consistently detecting functional brain communities across multiple subjects, which is incremental as it adapts existing clustering methods to a specific domain.
The authors tackled group-wise functional community detection in the human brain by applying multiple-view spectral clustering, achieving more consistent communities and being several orders of magnitude faster than the competing Joint Diagonalization of Laplacians method on 291 subjects from the Human Connectome Project.
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.