Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks.

A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.