Wang Yueting, Yang Defu, Li Quefeng, Kaufer Daniel, Styner Martin, Wu Guorong
Department of Biostatistics, University of North Carolina at Chapel Hill, USA.
Department of Psychiatry, University of North Carolina at Chapel Hill, USA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:292-295. doi: 10.1109/isbi45749.2020.9098513. Epub 2020 May 22.
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.
越来越多的证据表明，阿尔茨海默病（AD）是一种神经退行性疾病，表现为一种连接障碍综合征，即一个大规模脑网络被一个或多个神经病理过程逐渐破坏。然而，病理实体在脑网络中传播的机制在很大程度上尚不清楚。由于病理负担可能通过神经元进行传播，我们建议表征神经病理事件在受网络组织调节的相关脑网络中的传播模式。具体而言，我们提出了一种新颖的混合效应模型，以量化纵向网络改变与在特定脑区观察到的神经病理事件之间的关系，其中到枢纽节点的拓扑距离、高风险AD遗传学和环境因素（如教育程度）被视为预测变量。与许多横断面研究类似，我们发现与AD相关的神经病理学优先影响枢纽节点。此外，我们的统计模型提供了有力证据，表明异常的神经病理负担以朊病毒样方式从枢纽节点扩散到非枢纽节点，其传播模式遵循大规模脑网络的内在组织。
