Resting-state functional connectivity provides a measure of neural activation in spatially-distributed brain networks under conditions of rest (i.e., when participants are not engaged in an explicit, goal-directed task; Beckmann et al., 2005). Unlike task-based measures of brain activation, resting-state connectivity is relatively stable over time (Zuo and Xing, 2014), suggesting it indexes more trait-like brain networks and, therefore, acts as a potentially valuable medium for investigating genetic influences on brain networks. Indeed, there is growing data to support the heritability of resting-state neural activity, including aberrant connectivity in these networks in psychiatric populations (Glahn et al., 2010; Smit et al., 2008). Recently developed graph theory tools can be applied to resting-state connectivity data to investigate brain organization and function in psychiatric populations, potentially identifying brain networks that exhibit differential features across psychiatric disorders (Fornito and Bullmore, 2015). Graph theory is an analytic tool involving the calculation of network properties that can be used to examine the organization of network connections and, thus, the functional capabilities of a network and the role that nodes (brain regions) play in global and local
