time-varying functional connectivity. Keywords: Networks, Dynamic functional connectivity, Modularity Introduction The human brain can be described in terms of a network of neural elements and their connections (Bullmore and Sporns, 2009). In cases where the net- work refers to structural connectivity, the connections indicate the presence of an anatomical link, such as a synapse between two neurons or a white matter fascicle between brain regions, the complete set of which defines the human con- nectome (Sporns et al., 2005; Sporns, 2011). In cases where the network refers to functional connectivity, a connection indicates that a pair of neural elements is functionally related to one another, most commonly expressed through esti- mating statistical dependencies among neuronal time courses (Friston, 2011). The complete set of functional connections between all pairs of neural elements specifies a functional brain network . In most current applications, functional brain networks are constructed by calculating Pearson’s correlation coefficient of blood-oxygen-level-dependent (BOLD) time series for all pairs of ROIs, recorded over an extended period of time during rest or task conditions. The result is a full matrix, whose coefficients represent estimates of the magnitude of the functional connection between each pair of ROIs. In calculating the magnitude of a functional connection, one typically takes into account BOLD fluctuations over an extended scan session lasting on the order of 5-10 minutes or longer. The matrix of correlations generated for the en- tire scan session is here referred to as the static functional connectivity matrix. Recently, it has been proposed that such an approach overlooks potentially meaningful fluctuations in the magnitude of functional connections that take place on shorter time scales (Chang and Glover, 2010; Hutchison et al., 2013a; Calhoun et al., 2014; Kopell et al., 2014). A common approach to obtain an 2