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    • Given previous evidence indicating developmental maturity of

    2018-11-13

    Given previous evidence indicating developmental maturity of the R406 Supplier can be indexed by the degree of inverse functional coupling between limbic and PFC systems, representing the enhanced top-down inhibitory processing of the prefrontal system over the heavier bottom-up signaling from the limbic system (Bouwmeester et al., 2002a,b; Cressman et al., 2010; Cunningham et al., 2002; Gee et al., 2013a,b, 2014), the main goal of current study was to investigate the behavioral outcomes of functional connectivity between these two systems using rs-fMRI. rs-fMRI can provide a novel framework for investigating the functional systems in the large-scale organization of the developing brain in adolescents (Uddin et al., 2010), independent of stimulus-induced brain activity usually driven by either experimental demands or participants’ cognitive and emotional tendencies. In particular, we sought to provide a novel functional network-level account of adolescent brain connectivity associated with substance-use behavior by using independent component analysis (ICA) focusing on between-network coupling. Previous evidence has demonstrated that independent networks with opposing functions (e.g., top-down inhibitory processing of the prefrontal system versus bottom-up processing of the limbic system) are more likely to show an inverse correlation (i.e., negative connectivity at between-network level; Fox et al., 2005). Most importantly, this inverse coupling between opposite functional networks increases with age while sub-region connectivity strength increases in each functional network (i.e., positive connectivity at within-network level) as a result of enhanced efficiency in between- and within-network communication (Stevens et al., 2009). Although prior work has found limbic-prefrontal functional connectivity plays a role in adolescents’ sensitivity to rewards and substance-use behaviors (e.g., van Duijvenvoorde et al., 2016; Weissman et al., 2015), the approach they used was seed-based ROI which does not necessarily distinguish between whether the connectivity metric in certain paired voxels or regions is due to either between- or within network involvement (Xu et al., 2013). It is difficult to disentangle whether the source of the connectivity valence between seed regions (e.g., amygdala or VS) and their pairwise regions of interest (e.g., mPFC or dorsolateral PFC; dlPFC) is derived from either between- or within-network. Given the difficulty of evaluating the network level of brain systems (i.e., between- and within network connectivity), ICA holds several advantages over the seed-based approach in rs-fMRI. First, ICA identifies functional networks distinctly using spatial independence (Beckmann and Smith, 2004). By taking into account multiple simultaneous voxel-by-voxel time-course dynamics, ICA can decompose data into a linear mixture of spatially independent and temporally coherent course signals that are usually intermingled within a given voxel which would be indistinguishable in a conventional seed-based approach, and thus ICA can provide insight into whole-brain functional systems independently for both within- and between-network connectivity without a priori hypotheses for a specific seed-region (Jafri et al., 2008; Joel et al., 2011). Furthermore, ICA requires no a priori specification of non-resting state network relevant signal variability such as global signal fluctuation and noise such as physiological dynamics and head movement that commonly arises in seed-based correlation approach and leads to signal quality changes (e.g., Murphy et al., 2009; Power et al., 2012 Power et al., 2012) because an ICA characterizes individual-level spatiotemporal dynamics of each brain network by multiple regressions while controlling for the influence of other networks and sources of variability (e.g., noise and global signal; Filippini et al., 2009). In addition to the analytic aspect of ICA described above, ICA is relatively unaffected by different temporal sampling rates (see De Luca et al., 2006) thereby increasing flexibility in use of multiple datasets collected from multiple sites and scan protocols (e.g., Biswal et al., 2010).