Applying this general strategy to all five syndromic atrophy patterns, we used group-level goodness-of-fit (GOF) analyses (see Experimental Procedures) to reveal

five sets of distinct and focal epicenters (Figure 3 and see Figure S1 and Table S1 available online), whose large-scale connectivity maps in health showed highest GOF to the binarized syndromic atrophy patterns. Remarkably, although atrophy Pfizer Licensed Compound Library severity values made no contribution to epicenter identification, the epicenters uncovered here were seated in or near the most atrophic regions identified in our previous work (Seeley et al., 2009; Figure S1), suggesting that epicenters—in addition to being broadly connected with regions atrophied in a disease—are often among the most atrophied (and perhaps earliest affected) regions in that disease. Although the terms “epicenter” and “hub” have been used interchangeably to describe Alectinib research buy transmodal convergence zones within healthy large-scale brain networks (Mesulam, 2012), we chose “epicenter” to describe the regions identified here because (1) “epicenter” carries a pathogenic connotation, describing a region that is often but not necessarily the site of maximal damage and (2) “hub” evokes a brain region with high node centrality (“hub-ness”), as defined within the network science lexicon. Our epicenter identification strategy, however, did not include graph theoretical measures and thus provided no

guarantee that the identified epicenters would represent true network hubs. Having identified a set of focal epicenters within each atrophy pattern, we next sought to examine where the epicenters fit within their target network’s functional architecture. To this end, we generated five intra-network

healthy connectivity matrices covering all ROIs, including the epicenters, contained within the five binary spatial atrophy patterns (Figure 3). Specifically, we first generated unthresholded subject-level intranetwork matrices, using ROIs as nodes and connectivity z scores between ROI pairs as the weights of the undirected edges (see Experimental Procedures). Group-level intranetwork healthy connectivity matrices were then derived for each network using below one-sample t tests. Significant edges were determined by thresholding at p < 0.01, false discovery rate (FDR) corrected for multiple comparisons across the matrix; nonsignificant edges were assigned a weight of zero. Examination of these matrices revealed that the epicenters related to each disease showed broad-based connectivity with other nodes in the target network, consistent with the manner in which they were identified (Figure 3). We further questioned whether these epicenters, though defined by their healthy ICN’s resemblance to the (binary) parent atrophy pattern, might also serve as functional hubs, defined as nodes with high weighted degree centrality (total connectional flow) within the target network (Sporns et al., 2007).