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Introduction: Multivariate metabolic brain patterns obtained by SSM/PCA analysis of [18F]FDG-PET scans represent discrete functional brain networks. They were identified and validated in most neurodegenerative dementias and are routinely used in clinical practice. However, the internal structure of these networks remains unknown. In this study, we explored metabolic connectivity with graph theory methods within the predefined SSM/PCA derived networks in two neurodegenerative dementias: Creutzfeldt-Jakob’s disease (CJD) (in vivo model of neurodegeneration due to fast progression/pathological availability) and frontotemporal dementia (FTD) (for robust and consistent network).
Methods: Topographic maps of CJD-related (CJDRP) and FTD-related patterns (FTDRP) were transformed to 95 regions-of-interest. Regions with standardized weights above and below one standard deviation were defined as disease specific nodes. For each node, normalized metabolic activity was calculated for corresponding patients and normal controls (NC). Metabolic data were used to construct matrices of node-to-node pairwise correlations separately for patients and NC. Bootstraping (100 iterations) was used to estimates correlation pairs. Global connectivity differences between patients and NC networks in both spaces were studied by degree centrality, clustering coefficient, characteristic path length, small-worldness, and assortativity at varying graph thresholds for all the bootstrap iterations.
Results: In both vectors spaces (CJDRP and FTDRP) we observed signifficant connectivity changes. While degree centrality was elevated and clustering coefficient/small worldness were decreased in CJDRP space in CJD patients compared to NC, it was the opposite in the FTDRP space/FTD patients. However, path length and assortativity were elevated in both diseases showing reduced efficiency of information transfer. In CJDRP, we observed disruption of normal connections with compensatory reconnections, while only severe disrupting was observed in FTDRP.
Conclusions: Functional connectivity exploration within disease-specific network spaces enable us to understand network’s internal organization. Connectivity measure changes are not constant across different disorders and should be understood in context of network disruption and adaptations.
| Topic Selection | Brain Imaging |
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