Our previous

work showed that each of five distinct neurodegenerative syndromes featured an atrophy pattern that mirrored the healthy functional ICN seeded by the cortical region most atrophied in patients with that syndrome (Seeley et al., 2009). The present study, in contrast, examined every brain region within the five disease-related atrophy selleck products maps to identify the regions whose connectivity pattern in health most resembled the atrophy map seen in each syndrome (see Figure 2 for a methods schematic). The resulting dataset fully specified the node pair connectivity strengths across all regions atrophied in any of the five diseases; collectively, these regions traversed most cerebral cortical and subcortical structures. With this information in hand, we used graph theoretical analyses to test model-based predictions of how network architecture in health relates to disease-associated tissue loss (Figure 1). Although previously described spatial atrophy patterns (Seeley et al., 2009) specified the brain regions interrogated for the current study, all network connectivity analyses were performed on an independent dataset of 16 healthy subjects aged 57 to 70 (8 females, all right-handed and psychoactive medication-free; see Experimental Procedures). The resulting connectivity patterns and graph metrics were used to relate each region’s healthy connectivity profile to that region’s disease-specific vulnerability,

defined as its atrophy severity in patients. In previous work (Seeley tuclazepam et al., 2009), we identified Tenofovir cost regional atrophy maxima for five neurodegenerative syndromes: Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), semantic

dementia (SD), progressive nonfluent aphasia (PNFA), and corticobasal syndrome (CBS). Then, using healthy subjects scanned with task-free fMRI, we used these five atrophy maxima as “seed” regions to derive five ICNs, representing regions whose blood-oxygen level-dependent (BOLD) signal time-series significantly correlated with that of the seed. The atrophy maxima seeded ICNs that resembled the parent atrophy maps, supporting the view that neurodegenerative disease patterns are network based. By studying only one seed region per atrophy pattern, however, this approach could not determine which regions featured maximal connectivity to the other vulnerable regions. We anticipated that each disease-associated pattern would harbor focal “epicenters,” regions whose connectivity patterns—in the healthy brain—most closely mirrored the disease vulnerability pattern. To seek out these epicenters, here we took a more comprehensive, data-driven approach by studying all regions within each of the five atrophy patterns. For example (Figure 2), we created 1,128 4 mm radius spherical regions of interest (ROIs) covering the entire bvFTD atrophy pattern and built 1,128 functional ICN maps, one seeded by each ROI, for each of our 16 healthy subjects.