community structure or a division of a network into internally dense and externally sparse modules (Fortunato, 2010; Sporns and Betzel, 2015). The most popular method for identifying a network’s communities and evaluating their fitness is to maximize a ”modularity” function (Newman and Girvan, 2004): In this expression, g$_{u}$ ∈ { 1 , . . . , K } is the community to which node u is assigned, δ ( g$_{u}$, g$_{v}$ ), is the Kronecker delta function and is equal to unity when g$_{u}$ = g$_{v}$ , and p$_{u}$v indicates the expected number of connections between u and v under a particular null model (typically p$_{uv}$ = k$_{u}$k$_{v}$ 2 m , which is the expected weight under the null model where each node’s degree is preserved but connections are otherwise made randomly). In general, high quality modules produce large values of Q . To maximize Q , then, one needs to ensure that connections satisfying ( a$_{uv}$ − p$_{uv}$ ) > 0 fall within communities. The process of modularity maximization is computationally intractable for all but the most trivial cases, though many heuristics are available for identifying near-optimal modules. Here, we use the Louvain algorithm (Blondel et al., 2008) to produce 100 near-optimal estimates of modules. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. 6. Acknowledgements 28 Q = 1 2 m ∑ uv [ a$_{uv}$ − p$_{uv}$ ] δ ( g$_{u}$, g$_{v}$ ) (4)