The essential point of this section is that using volumetric elements to form graphs results in network properties that may more closely represent volumetric properties of brain organization rather than the organization of information processing. We have highlighted two difficulties with degree-based hub identification in RSFC data: the influence of community size on degree in Pearson correlation networks and the susceptibility

of degree to distortion in volume-based brain networks. The latter problem can be ameliorated by proper network definition, but the former problem suggests that degree has a fundamentally ambiguous interpretation in RSFC correlation networks. If degree-based methods of hub identification are confounded, can other methods identify hubs in RSFC EPZ 6438 correlation networks? IGF-1R inhibitor Many other centrality measures based upon combinations of degree and path length exist to characterize hubs (e.g., betweenness, closeness, eigenvector, and PageRank centralities). Some of these measures have been used to identify RSFC hubs (Achard et al., 2006, He et al., 2009, Joyce et al., 2010, Lohmann et al., 2010 and Zuo et al., 2011). In many systems,

such as transit networks, these centrality measures, which combine information about path length and node degree, are appropriate and interpretable. However, in correlation networks, where degree is a problematic measure, and where path lengths are often created from thresholded correlation matrices (despite “distances” being already defined by the correlation coefficient), it is less clear how to interpret these measures. Other authors have used the node role approach, wherein centrality measures identify hubs (e.g., using within-module degree Z score

or betweenness centrality), and then participation coefficients classify hub type (He et al., 2009, Meunier et al., 2009 and Meunier et al., 2010). Possibly due to the variety of parcellation strategies employed (AAL atlas parcels, random parcellations), these studies have produced divergent Terminal deoxynucleotidyl transferase descriptions of hub locations. Due to the reservations we have expressed about degree-based measures and our lack of confidence in interpreting path-based measures in Pearson correlation networks, we have pursued different ways of identifying hubs. Recall that hubs are parts of networks that are critical for integrating and distributing information. In graph theory, such nodes are often identified by the number of edges a node has and by the importance of a node’s edges for facilitating network traffic ( Newman, 2010). In other words, it is not just the number but also the qualities of a node’s edges that establish its importance in a network.