, 2008). All ROIs are modeled as 10 mm diameter spheres centered upon ROI coordinates. For the voxelwise and modified voxelwise networks, all voxels (n = 40,100) within the AAL atlas (Tzourio-Mazoyer et al., 2002) were used as in Power et al. (2011). All voxels are cubes with sides of 3 mm. The subject-specific temporal masks formed from Motion Scrubbing were applied

to each subject’s reprocessed data, and a correlation matrix was calculated from node RSFC time courses (e.g., 264 nodes yields a 264 × 264 correlation matrix in each subject). For the main analyses, 120 subject average matrices were used. All averages and comparisons of correlations use Fisher z(r) transformations for calculations, followed see more by reconversion to Pearson r values for reporting. find more In Figure 2, for consistency with the previous literature, all correlations were used regardless of the distances between nodes. Short-distance correlations can arise from shared patterns of local neuronal activity, but they can also arise from data processing (e.g., blurring, reslicing) and from head motion (Power et al., 2012). To minimize the effects of questionable correlations on network structure, as in Power et al. (2011), short-distance correlations

(Euclidean distance <20 mm) were excluded from graph analyses in Figure 6, Figure 7, and Figure 8. Graphs were formed using the nodes and edges described above. Traditionally, analyses of weighted graphs must ignore

medroxyprogesterone negative edges and explore a range of thresholds to characterize the properties of a network (Rubinov and Sporns, 2010). Proposals have been made to modify some graph theoretic measures for unthresholded matrices (Rubinov and Sporns, 2011), but here we follow the traditional approach. Many real-world networks have edge densities of a few percent or less (see Figure 3), and the graph measures used in this paper are developed in such networks. Accordingly, we applied thresholds to graphs to bring them to similar levels of sparseness (∼10%–2% for the areal graph, 5%–0.5% for the voxel-based graphs) as in Power et al. (2011). In general, results are presented over a range of thresholds to give the reader a sense of the (lack of) dependence of a property upon thresholds, and no formal definition of threshold ranges is proposed since it is essentially arbitrary. Our thresholds matched the ranges used in Power et al. (2011), which were chosen to (1) yield complex and interesting community structures (more than four communities), and (2) occupy a range of edge densities often seen in the real-world networks in which techniques like Infomap and measures like participation coefficients were originally developed.

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