(2011) (see Figure S6 for flat maps). Nodes with high participation coefficients tend to be adjacent to regions of high community density, though this is not always the case (e.g., left

intraparietal sulcus). This proximity is consonant with our reasoning that brain regions in which multiple functional systems are represented would be good locations for hubs. This proximity is also consistent with an argument that high participation coefficients arise from signal blurring due to proximity to several distinct signals. The data were therefore reanalyzed without spatial blurring as part of functional connectivity processing, yielding results very similar to those with blurring (r = 0.94, Figure S3). The subjects studied thus far are mainly university students who met strict inclusion criteria. To determine whether selleck chemical our results generalize to more typical populations, a 40 subject cohort (40F, 30.0 ± 3.2 years old) from a prospective twin study in the general population was also examined, including subjects with PARP inhibitor psychiatric and neurologic disease

and psychotropic medication use. Analyses identical to those shown in Figure 8 were performed on this cohort. Results in the main 120 subject cohort explained 74% of the variance in summed participation coefficients and 77% of the variance in summed community density in this accessory cohort (Figure S7). Hubs exist in many real-world networks, and they often play critical roles in facilitating network traffic and maintaining network integrity (Albert et al.,

1999, Albert et al., 2000 and Jeong et al., 2001). In this report, we aimed to advance the study of brain hubs by clarifying some important issues and by providing some conceptually straightforward methods to identify putative hubs in RSFC correlation networks. We now discuss our findings and their implications for previous and future work. Several points are worth noting when considering how to interpret degree-based hubs. First, unlike many real-world networks, RSFC networks formed using Pearson correlations do not tend to contain nodes that are and convincing outliers in strength, meaning that any degree-based hubs in RSFC networks are rather weak hubs from a graph theoretic perspective (Figure 4). Second, given the block-like structure of correlation networks, these hubs tend to be provincial, meaning their connections are largely restricted to their community (Figure 4). This provincial quality stands in contrast to hubs found in many real-world networks, which often connect strongly to a wide variety of other communities (Figure 4 and Figure S1). Third, strength in RSFC graphs strongly reflects community size, which is indirectly related to the physical sizes of areas and systems (Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5).