The second argument is concerned with amplifications of the first argument that can occur when systems are not modeled at their inherent levels of organization, such as when brains (cortically organized at levels of columns,

areas, and systems [Churchland and Sejnowski, 1988 and Felleman and Van Essen, 1991]) are modeled as voxels (an arbitrary volumetric element). Since some classic methods of hub identification are confounded in correlation networks, we develop two alternative methods for identifying Nintedanib cost hubs that are more suited to RSFC correlation networks. Both methods aim to identify regions of the brain that are well-situated to support and/or integrate multiple types of information. Both methods leverage the correspondence between functional brain systems (e.g., dorsal attention system) and graph subnetworks

observed in recently described RSFC graphs (Power et al., 2011; see also Yeo et al., 2011). First, using a model of the brain at the level of functional areas, we identify nodes that participate in many subnetworks of the brain (e.g., a node that has relationships with members of multiple brain systems, such as visual, default mode, or frontoparietal control systems). These nodes www.selleckchem.com/products/Cisplatin.html are candidate brain hubs. We identify these candidate hubs using the established measure of participation coefficients (Guimerà and Nunes Amaral, 2005). Second, we examine a high-resolution brain network to identify spatial locations where many subnetworks are present within a small volume (e.g., finding, within a small sphere, voxels representing the dorsal attention, visual, frontoparietal control, and default mode systems). We call these locations articulation points—they are not hubs in the traditional graph theoretic sense, but they are locations where such hubs might be situated. Both methods identify similar sets of brain regions in the anterior insula, anterior, middle and superior frontal cortex, medial either superior frontal

cortex, medial parietal cortex, inferior parietal, and temporo-occipital cortex. Notably, these regions do not emphasize the default mode system. Several influential reports have identified brain hubs in RSFC networks using (variations of) a measure called degree (or degree centrality), which is the number of edges on a node (Buckner et al., 2009, Cole et al., 2010, Fransson et al., 2011, Tomasi and Volkow, 2010, Tomasi and Volkow, 2011 and van den Heuvel et al., 2008). Hubs, when identified by high degree, are nodes with many edges. In weighted networks, the analogous measure, strength, is defined as the sum of the weights of the edges on a node. Degree (or strength) is usually an appropriate measure for identifying hubs (e.g.