Tracking the Progression & Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study using PET images

Abstract

Network analysis allows investigators to explore the many facets of brain networks, particularly the proliferation of disease using graph theory to model the disease movement. The disruption in brain networks in Alzheimer’s disease (AD) is due to the abnormal accumulation of beta-amyloid plaques and tau protein tangles. In this study, the potential use of percolation centrality to study the movement of beta-amyloid plaques, as a feature of given PET image-based networks, is studied. The PET image-based network construction is possible using the public access database - Alzheimer’s Disease Neuroimaging Initiative, which provided 1522 scans, of which 429 are of AD patients, 583 of patients with mild cognitive impairment, and 510 of cognitively normal. For each image, the Julich atlas provides 121 regions of interest/network nodes. Additionally, the influential nodes for each scan are calculated using the collective influence algorithm. Through this study, it is possible to use percolation centrality values to indicate the regions of interest that reflect the disease’s spread and show potential use for early AD diagnosis. Analysis of variance (ANOVA) shows the regions of interest for which percolation centrality is a valid measure, irrespective of the tracer type. A multivariate linear regression between the percolation centrality values for each of the nodes and psychometric assessment scores reveals that models Mini-Mental State Examination (MMSE) scores performed better than ones with Neuropsychiatric Inventory Questionnaire (NPIQ) scores as the target variable. Similar to ANOVA, the multivariate linear regression yields regions of interest for which percolation centrality is a good differentiator. Finally, a ranking of the regions of interest is made based on the collective influence algorithm to indicate the anatomical areas strongly influencing the beta-amyloid network.