We have recently defined and described a network of 264 putative functional areas (Power et al., 2011). This graph is a first-draft model of area-level relationships in the brain, and communities PLX4032 mw in this network correspond well to functional systems (Power et al., 2011). In this areal graph, nodes that participate in multiple systems could potentially support or integrate different types of information. Our first

method therefore identifies putative hubs as nodes in this areal network that have edges to many different communities. To find such nodes, we alter the node role approach of Guimera and Amaral: we discard the traditional measure of centrality due to the reservations expressed above and instead use the participation coefficient as the sole measure

of node importance. Figure 6A shows a network with three communities (yellow, green, and pink) and the participation coefficient of each node. Nodes in blue have no relationships outside their community and low participation coefficients, whereas the red node has relationships to every community and the highest participation coefficient in the network. Our approach searches the areal network for nodes like the red node. In the first half of this paper, in order to replicate and expand on previous findings related to degree-based hubs, graphs were formed in ways corresponding INK1197 to the previous literature. In the second half of the paper, graphs will be formed using our preferred methodology (Power et al., 2011), which excludes short-distance relationships (less than 20 mm apart). This exclusion is performed because short-distance correlations are inflated by unavoidable steps in image processing (realigning, registration, reslicing), partial voluming, and head motion (Power et al., 2012). Additionally, short-distance correlations 17-DMAG (Alvespimycin) HCl are virtually always high (the bloom around any seed in a seed map), thus acting as a

spatial lattice of high short-range correlations that provide little distinguishing information between nodes. Eliminating correlations spanning less than 20 mm removes 4% of the edges in both the areal and voxelwise graph and does not alter our observations about the confounding relationship between community size and degree in RSFC graphs (Figure S1). An areal network was formed in 120 healthy young adults, and community assignments were obtained over many thresholds (10%–2% edge density in 1% steps) as in Power et al. (2011). Figure 6B shows the participation coefficients in the average network at a single threshold. The participation coefficients were summed over thresholds to identify nodes that routinely participate in multiple communities, and the summed participation coefficients are plotted in Figure 6C. Several control analyses were performed to establish the robustness of these results. Identical analyses performed in matched 40 subject subcohorts of the main cohort yielded very similar results (Table S1 and Figure S2; correlations between subcohorts = 0.87 ± 0.04).