Community detection for directional neural networks inferred from EEG data

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 ca...

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Bibliographic Details
Published in2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 7155 - 7158
Main Authors Ying Liu, Moser, J., Aviyente, S.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2011
Subjects
Algorithm design and analysis
Algorithms
Brain - pathology
Brain - physiology
Brain Mapping - methods
Cluster Analysis
Clustering algorithms
Cognition
Communities
Computer Simulation
Detection algorithms
Electroencephalography
Electroencephalography - methods
Humans
Models, Neurological
Models, Statistical
Neural Networks (Computer)
Neural Pathways
Neuroscience
Partitioning algorithms
Reproducibility of Results
Signal Processing, Computer-Assisted
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Summary: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.
ISBN:9781424441211
1424441218
ISSN:1094-687X
1557-170X
DOI:10.1109/IEMBS.2011.6091808