Abstract
Abstract(1) Background: Network analysis allows investigators to explore the many facets of brain networks, particularly the proliferation of disease. One of the hypotheses behind the disruption in brain networks in Alzheimer’s disease is the abnormal accumulation of beta-amyloid plaques and tau protein tangles. In this study, the potential use of percolation centrality to study beta-amyloid movement was studied as a feature of given PET image-based networks; (2) Methods: The PET image-based network construction is possible using a public access database - Alzheimer’s Disease Neuroimaging Initiative, which provided 551 scans. For each image, the Julich atlas provides 121 regions of interest, which are the network nodes. Besides, using the collective influence algorithm, the influential nodes for each scan are calculated; (3) Analysis of variance (p<0.05) yields the region of interest Gray Matter Broca’s Area for PiB tracer type for five nodal metrics. In comparison, AV45: the Gray Matter Hippocampus region is significant for three of the nodal metrics. Pairwise variance analysis between the clinical groups yields five and twelve statistically significant ROIs for AV45 and PiB, capable of distinguishing between pairs of clinical conditions. Multivariate linear regression between the percolation centrality values for nodes and psychometric assessment scores reveals Mini-Mental State Examination is reliable(4) Conclusion: percolation centrality effectively (41% of ROIs) indicates that the regions of interest that are part of the memory, visual-spatial skills, and language are crucial to the percolation of beta-amyloids within the brain network to the other widely used nodal metrics. Ranking the regions of interest based on the collective influence algorithm indicates the anatomical areas strongly influencing the beta-amyloid network.
Publisher
Cold Spring Harbor Laboratory