Our results demonstrate drawbacks in some previous approaches, while offering new approaches that appear to more plausibly represent brain organization. It is important to recognize that these new approaches to graph definition are not equivalent or interchangeable. Note that in this article

we examine several graph theoretic properties of the areal graph, but restrict our discussions of modified voxelwise data to spatial observations. The areal graph is formed using our best estimates of the functional “units” in the brain, and many properties of this network should be fairly direct reflections of functional brain organization. On the other hand, the modified voxelwise graph is defined using volumetric elements (voxels),

and this graph reflects volumetric properties of Selleck ZD1839 functional organization. In this graph, most functional areas are probably represented by many voxels, and large functional areas (and functional systems) will dominate the graph structure regardless of their roles in information processing relative to smaller areas or systems. This volume-based definition thus warps representations of information processing, limiting the conclusions that can be drawn from this graph. The analyses presented here suggest several avenues for future inquiry. Within graphs that possess many subgraphs with strong correspondence to functional systems, we Screening Library have detected additional subgraphs with no such identity but with hints of shared activity in certain contexts (e.g., memory retrieval activity in the salmon and light blue subgraphs). Unifying functional attributes

among these subgraphs should be sought and tested. Our results demonstrate strong within-subgraph connectivity in sensory, motor and default mode systems, especially in contrast to task control systems, suggesting that these systems may differ in the dynamics of their relationships with other subgraphs over time. Our mafosfamide analyses only examined static pictures of graphs obtained by summarizing activity over entire epochs into a single correlation coefficient, and future work should explore if and how these relationships change over time. Perhaps the most obvious avenue for future work will lie in the comparison of graphs across the lifespan and in disease. A recognized limitation within graph theoretic investigations of structural and functional brain networks is the current lack of validated parcellation strategies (see Fornito et al., 2010, Wig et al., 2011 and Zalesky et al., 2010) for comprehensive discussions). We have derived and presented a graph of 264 putative functional areas that displays a plausible functional structure that should be sensitive to the organization of many functional systems. If the locations of functional areas do not greatly differ across populations (Barnes et al.