I. INTRODUCTION
Functional connectivity is defined as the temporal correlations between spatially remote neurophysiological events [1] and describes the neural processes required for cognitive and motor tasks. Functional connectivity has been quantified by applying coherence or nonlinear synchronization measures to various neuroimaging data. In previous work, the bivariate relationships between neuronal populations have been represented as graphs composed of vertices and edges. These graphs were then analyzed using various measures from graph theory including small-world measures and centrality measures for hub classification. One remaining issue is to identify the functional modules in these neural networks through community detection. A community structure is defined as the natural tendency of a network's vertices to divide into modules containing a dense number of intra-connecting edges within each module and a sparse number of interconnecting edges between modules. Community detection methods can be categorized as divisive, agglomerative, or optimal where a particular objective function is maximized. These categories can include methods pertaining to spectral analysis [2], random walks [3], and min-cut problems [4].