Introduction Graph theory has been increasingly applied

Introduction Graph theory has been increasingly applied in the analysis of connectivity in neuroimaging data. Graph-theory-derived centrality measures, especially eigenvector centrality (EVC), can rank the relevance of a node in a complex network (Zuo et al., 2012) in terms of strategic location. This feature is particularly useful for exploring the etiology of mental disorders that are associated with functional alterations at a network rather than a focal level (Fornito et al., 2015; Sporns, 2014; He et al., 2016). However, there is currently a lack of consistent findings from independent large samples in the literature (Horga et al., 2014). In particular, few studies have investigated if central functional buy stavudine regions in children and adolescents are replicable across different sites of acquisition (Zuo et al., 2012). In addition, even fewer studies have explored the replicability of associations between network-level disruptions and dimensional psychopathology in childhood. Previous studies of human structural and functional brain networks have identified a set of highly connected brain regions (i.e., hubs) (Power et al., 2013). These hubs are believed to play a central role in the flow of information throughout the brain, integrating parallel and distributed networks (Gong et al., 2009; Hagmann et al., 2008; Hwang et al., 2013). Structural connectivity studies have shown that hubs are mainly located at integrative cortical areas in adults (Power et al., 2013; Sporns et al., 2007). In the adult brain, functional cortical hubs include the medial prefrontal cortex, posterior cingulate cortex (PCC), precuneus, inferior parietal lobule (IPL; particularly the angular gyrus) and medial temporal cortex (Buckner et al., 2009). Moreover, Betzel et al. (2014) have noted that functional hub regions are affected by changes in white matter connections and that the relationship between functional and structural connectivity changes with age. Two major gaps can be drawn from the current neuroimaging literature of children and adolescents: (i) the replicability across different samples; and (ii) the differential effects of hubs over neurodevelopment. Several resting-state fMRI studies of large samples of children and adolescents have been carried out, but in healthy developing children as opposed to clinical samples (Grayson et al., 2014; Sato et al., 2015a,b; Supekar et al., 2009; Uddin et al., 2011; Hwang et al., 2013). Evidence for a typical developmental trajectory emerged from these studies: large-scale networks apparently shift from a locally segregated to a more distributed and integrated organizational pattern (Fair et al., 2008, 2009, 2007; Fransson et al., 2011; Gao et al., 2009; Supekar et al., 2009). Interestingly, in infants, hubs have been identified in primary sensorimotor regions as opposed to the association cortex, where hubs have been identified in adults (Fransson et al., 2011). Hwang et al. (2013) showed that the connectivity between frontal hubs and other brain regions increases from childhood to adolescence. However, Khundrakpam et al. (2013) described developmental changes in the topological properties of the networks that suggested an organizational shift towards a more random configuration. Moreover, Zuo et al. (2012) demonstrated the test-retest reliability of EVC in functional connectivity networks over a subject age range of 7–85 years old. Therefore, despite the emergence of several brain regions as potential hubs of intrinsic brain connectivity in children, few studies have investigated which regions are most replicable across different samples (Horga et al., 2014). This is a crucial point for progress in biological psychiatry. In addition, many findings from early resting-state fMRI studies were limited by the issue of head micro-motion artifacts (Power et al., 2012). These movement artifacts are also related to age and influence functional connectivity estimates, complicating the interpretation of results.