Detecting functional modules of the brain using eigen value decomposition of the signless Laplacian

Abstract

The human brain is organized into functionally specialized subnetworks, referred to as modules. Many methods have been employed to detect modules in the brain network, e.g. Newman's modularity and the Louvain method for community detection. However, these methods suffer from a resolution limit, and the detected number of modules is often inaccurate. In this work, we adopt Eigen Value Decomposition (EVD) on the signless Laplacian of the functional connectivity matrix to detect modules. This method is unaffected by the resolution-limit, and could identify the number of clusters more accurately. We tested the EVD method on two datasets. On a cat's cortex connectome, 5 modules were identified, agreeing with anatomical knowledge while Newman's and Louvain methods performed unstably. On the 872 fMRI scans in the Human Connectome Project, 9 modules were identified in the functional brain network, which complies well with the field knowledge.