CHARACTERIZING THE PROPAGATION PATTERN OF NEURODEGENERATION IN ALZHEIMER'S DISEASE BY LONGITUDINAL NETWORK ANALYSIS

Converging evidence shows that Alzheimer's disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may propagate trans-neuronally, we propose to characterize the propagation pattern of neuropathological events spreading across relevant brain networks that are regulated by the organization of the network. Specifically, we present a novel mixed-effect model to quantify the relationship between longitudinal network alterations and neuropathological events observed at specific brain regions, whereby the topological distance to hub nodes, high-risk AD genetics, and environmental factors (such as education) are considered as predictor variables. Similar to many cross-section studies, we find that AD-related neuropathology preferentially affects hub nodes. Furthermore, our statistical model provides strong evidence that abnormal neuropathological burden diffuses from hub nodes to non-hub nodes in a prion-like manner, whereby the propagation pattern follows the intrinsic organization of the large-scale brain network.