title: Community detection for directional neural networks inferred from EEG data.
abstract: One major challenge in neuroscience is to identify the functional modules from multichannel, multiple subjects recordings. Most research on community detection has focused on finding the association matrix based on functional connectivity, instead of effective connectivity, thus not capturing the causality in the network. In this paper, we propose a community detection algorithm suitable for weighted and asymmetric (directed) networks representing effective connectivity, and apply the algorithm to multichannel electroencephalogram (EEG) data. In addition, we extend the algorithm to find one common community structure from multiple subjects.
authors.affiliation: Department of Electrical andComputer Engineering, Michigan State University, East Lansing, MI 48824, USA. liuying5@egr.msu.edu
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.forename: Jason
authors.initials: J
authors.lastname: Moser
authors.forename: Selin
authors.initials: S
authors.lastname: Aviyente
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D003071:Cognition; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009434:Neural Pathways; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted