Multiple subject analysis of functional brain network communities through co-regularized spectral clustering

• Published in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 5992 - 5995
• Main Authors: Ozdemir, Alp, Mahyari, Arash Golibagh, Bernat, Edward M., Aviyente, Selin
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2014
• Subjects: Algorithms; Brain - physiology; Cluster Analysis; Clustering algorithms; Cognition; Communities; Electroencephalography; Humans; Optimization; Partitioning algorithms; Tensile stress; Time-frequency analysis
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2014.6944994

In recent years, the human brain has been characterized as a complex network composed of segregated modules linked by short path lengths. In order to understand the organization of the network, it is important to determine these modules underlying the functional brain networks. However, the study of these modules is confounded by the fact that most neurophysiological studies consist of data collected from multiple subjects. Typically, this problem is addressed by either averaging the data across subjects which omits the variability across subjects or using consensus clustering methods which treats all subjects equally irrespective of outliers in the data. In this paper, we adapt a recently introduced co-regularized multiview spectral clustering approach to address these problems. The proposed framework is applied to EEG data collected during a study of error-related negativity (ERN) to better understand the functional networks involved in cognitive control and to compare between the network structure between error and correct responses.