Identifying functional clusters in the brain using phase synchrony

One particular challenge in the study of the brain as a complex system is the identification of dynamic functional networks underlying observed neural activity. In this study, we focus on inferring the functional connectivity of the brain and the underlying network patterns from electroencephalography (EEG) data. The interactions between the different neuronal populations are quantified through a dynamic measure of phase synchrony. These interactions are then analyzed by applying a graph clustering algorithm known as the Cluster-Overlap Newman Girvan Algorithm (CONGA) and generating a three dimensional model relating modularity, degree, and number of clusters. The importance of each electrode in forming clusters is quantified using a `participation score´ and an optimal clustering arrangement is found with respect to the degree, number of clusters and the `participation score´. The proposed measures are applied to an EEG study containing the error-related negativity (ERN) to determine the organization of the brain during a decision making task.