From a network dynamics perspective the amPFC is

From a network dynamics perspective, the amPFC is part of the midline core components of the DMN and may underlie the evaluation of experiences with a high personal significance (Andrews-Hanna et al., 2010). In a detailed study of the structural and functional changes that occur in the DMN, Supekar et al. (2010); Sato et al. (2015a,b) showed that the connectivity between the amPFC and PCC is immature in children. Our finding of a relative absence of the PCC from the group of the most consistent hub regions, in contrast to the amPFC, may be in line with this previous observation. Similarly, the IPS and IPL are central integrative nodes of the frontoparietal component of the executive control network (Dosenbach et al., 2007) and are thought to underlie top-down control. Our results support a view in which the IPL/IPS and amPFC constitute a stable (across the age range investigated) and replicable core of the control/default mode network, with the PCC/precuneus and other parts of the medial prefrontal cortex included as hubs. Finally, we tested the hypothesis of an association between the disruption of hub regions and the expression of psychiatric symptoms in children and adolescents. As expected, lower values of centrality in a replicable hub region, the right IPS, correlated with the increased expression of psychiatric symptoms. Higher right IPS activity was previously found to be positively correlated with better executive function among populations at risk for Zalcitabine disorders including mild cognitive impairment (Jacobs et al., 2012) and among carriers of genes associated with predisposition to Parkinson’s disease (Thaler et al., 2013). Moreover, right IPS activity was correlated with reduced symptom severity in a subgroup of patients with schizophrenia (Bleich-Cohen et al., 2014). This finding demands further investigation of the potential role of hub impairment, particularly of right IPS, as a potential marker of vulnerability or as part of the natural history of several neuropsychiatric pathologies. Interestingly, in addition to the correlation of lower EVC values in the right but not left IPS with psychiatric symptoms, the majority of hubs identified were concentrated in the right hemisphere. Higher connectivity and centrality of the right but not the left IPS was previously shown to be associated with improved executive task performance (Markett et al., 2014; Seeley et al., 2007). Furthermore, there is evidence for asymmetric arrangements of large-scale networks, including the DMN (Saenger et al., 2012). By simultaneously acquiring EEG and fMRI, Biazoli et al. (2013) showed that BOLD activity in the nodes of resting-state networks is correlated with an increased flow of information from the right to the left hemisphere. Accordingly, stronger connectivity was found in the right hemisphere, and causality measures suggested a crucial functional role of this hemisphere during the resting state (Medvedev, 2014). Fig. 3 highlights the replicated hubs in yellow, while the regions depicted in red and green show the non-overlapping regions. Some of these regions are nodes of the DMN, such as precuneus/posterior cingulate and right temporal gyrus. In these cases, we believe the divergence between the two sites may be due to sampling fluctuations. In addition, occipital and cerebellar regions seem to be specific to one of the sites. Indeed, although the age and gender distributions were similar between the two sites, the two samples considerably differed in terms of psychiatric symptoms and levels of head motion, as shown in Table 1 . The participants at site 1 presented a wider range of symptom severity and head motion, which may explain the between-sites differences in brain hubs Tables 2 and 3. Finally, it is important to mention some of the limitations of the current study. Although we used a scrubbing method, head motion artifacts may still affect the analyses. This is an inherent challenge in neuroimaging studies involving children, and the challenge is even greater when psychiatric symptoms are present. We also tried to include FD as a possible confounder in the GLM and discarded regions in which the p-value depended on FD. However, head motion effects cannot be completely eliminated because these artifacts may also be due to non-linear influences of motion on the signal or field inhomogeneity. Despite reducing the number of comparisons by discarding the analyses more prone to motion effects, the remaining four GLM analyses with CBCL were not adjusted for multiple testing. Although the findings did not persist after Bonferroni correction, the association between EVC and CBCL in the IPS was replicated across the two sites when uncorrected p-values were considered. In addition, we acknowledge that by using the absolute values of the correlation coefficients as functional connectivity estimates, we could not differentiate correlated and “anti-correlated” (negatively correlated) networks. This assumption may not be an accurate representation of the underlying neurophysiology, which may complicate the interpretation of the results. We acknowledge that this approach is not optimal, but there is no established framework (conceptual and methodological) for handling anti-correlated networks in whole-brain analyses. When analyzing these data considering solely the positive correlations between the ROIs, the results are very different because the graph connectivity structure is altered. However, two of the main resting-state networks are the default-mode and control networks (which were part of our findings). Because the activities of both networks are negatively correlated, using solely the connections with positive weights to define the graphs would mask this antagonism. Moreover, some studies suggest that this opposition is intrinsic, and the dynamic of cognitive control involves the union of both systems (Fox et al., 2005; Hellyer et al., 2014). Finally, we emphasize that one of the main limitations of the current study was the choice of parcellation scheme. Indeed, the results are expected to vary according to the parcellation atlas used because atlases differ with respect to the number of ROIs and the size and location of the parcels. These differences strongly impact the estimated functional connectomes. We opted to use an anatomical parcellation based on predefined cortical and subcortical areas, but we recognize this is an arbitrary choice.