dependent of one another due to overlapping time windows. For example, the observations used to construct the dynamic networks at time t and t + 1 have L − 1 points in common, where L is the window length. Random reorderings of the excursion count time series would likely violate this interdependency. This procedure can also be viewed from a statistical point of view. In short, we want to test the null hypothesis that a dynamic functional connection is either significantly stronger or weaker than its corresponding static connection. We also calculated the global excursion matrix, X , whose element x$_{ij}$ was equal to the number of times, across all subjects, that the connection x$_{ij}$ par- ticipated in an excursion. As before, we were able to divide X into X + and X $^{−}$, which count the number of times a connection was stronger or weaker than expected, respectively. We used the dynamic functional connectivity matrices (in their raw form) in conjunction with modularity maximization (Newman and Girvan, 2004) to identify functional communities (Sporns and Betzel, 2016) and to assess their quality. This process entailed maximizing the modularity quality function for the dynamic functional connectivity matrix at time t : Here, B ( t ) = [ B$_{ij}$ ( t )] is the so-called modularity matrix, and is equal to B ( t ) = W ( t ) − P ( t ), where P ( t ) = [ P$_{ij}$ ( t )] is the expected weight of the functional connection between brain regions i and j at time t . The function δ ( g$_{i}$ ( t ) , g$_{j}$ ( t )) is the Kronecker delta and is equal to unity when the community assignments of regions i and j at time t , g$_{i}$ ( t ) and g$_{j}$ ( t ) respectively, are the same. Otherwise the delta function is equal to zero. Traditionally, the defini- 11 Q ( t ) = ∑ ij B$_{ij}$ ( t ) δ ( g$_{i}$ ( t ) , g$_{j}$ ( t )) (6) Community detection