Network Community Structure Detection for Directional Neural Networks Inferred From Multichannel Multisubject EEG Data

In many neuroscience applications, one is interested in identifying the functional brain modules from multichannel, multiple subject neuroimaging data. However, most of the existing network community structure detection algorithms are limited to single undirected networks and cannot reveal the common community structure for a collection of directed networks. In this paper, we propose a community detection algorithm for weighted asymmetric (directed) networks representing the effective connectivity in the brain. Moreover, the issue of finding a common community structure across subjects is addressed by maximizing the total modularity of the group. Finally, the proposed community detection algorithm is applied to multichannel multisubject electroencephalogram data.