communities. The approach we use here does not explicitly distinguish between positive and negative connections. However, because we essentially treat W$_{ij}$ = B$_{ij}$ , it means that only positive functional connections can contribute to Q and negative connections, when they fall within communities, serve to decrease the total modularity. We adopt this approach here, and use a Louvain-like algorithm (Blondel et al., 2008; Jutla et al., 2011) to maximize modularity 100 times for each dynamic functional connectivity matrix. The output of the Louvain algorithm is a set of community assignments, which specifies a partition of the brain, along with a score ranking the quality of this partition. The number and size of communities, in general, partially determined by the total density of the connections (Fortunato and Barth´ elemy, 2007), but can be varied by including a resolution parameter in the modularity expression that tunes the size and number of detected communities (Reichardt and Bornholdt, 2006). While this type of multi-resolution modularity maximization has been used to show that brain networks exhibit interesting modular structure at multiple organizational scales (Betzel et al., 2013), here we focus on a single-scale estimate of community structure. At the particular scale we investigate, the Louvain algorithm’s output typ- ically varies from run to run. Most analyses regard such variability as prob- lematic and attempt to resolve it by constructing, from the varied outputs, a consensus partition that represents the average community structure (Lanci- chinetti and Fortunato, 2012). Here, we embrace this variability and use it to characterize the “modularity landscape” for a given dynamic functional con- nectivity matrix. The modularity landscape refers to the space of all possible community divisions (which is far too large to enumerate in its entirety). Each 13