Pandya Sneha, Kuceyeski Amy, Raj Ashish
J Alzheimers Dis. 2017;55(4):1639-1657. doi: 10.3233/JAD-160090.
Alzheimer's disease (AD), one of the most common causes of dementia in adults, is a progressive neurodegenerative disorder exhibiting well-defined neuropathological hallmarks. It is known that disease pathology involves misfolded amyloid-β (Aβ) and tau proteins, and exhibits a relatively stereotyped progression over decades. The relationship between AD neuropathological hallmarks (Aβ, hypometabolism, and tau proteins) and imaging biomarkers (MRI, AV-45/FDG-PET) is not fully understood. In addition, biomarker pathologies are oftentimes discordant, wherein it may show varying levels of abnormality across brain regions. Evidence based on recent elucidation of trans-neuronal "prion-like" transmission and other available data already suggests that disease spread follows the brain's fiber connectivity network. Thereby, the brain's connectome information can be used to predict the process of disease spread in AD. A recently established mathematical model of AD pathology spread using a connectome-based network diffusion model was successful in encapsulating neurodegenerative progression. Motivated by these network-based findings, the current study explores whether and how network connectivity mediates the interactions between various AD biomarkers. We hypothesized that the structural connectivity matrix will mediate the cross-sectional association between regional AD-associated hypometabolism and Aβ deposition. Given recent reports of inherent or lifetime activity of brain regions as strong predictors of Aβ deposition in patients, we also tested whether healthy metabolism exerts a network-mediated effect on Aβ deposition and hypometabolism in AD patients. We found that regional Aβ deposition is best predicted by a linear combination of both regional healthy local metabolism and connectome-mediated regional healthy metabolism.
阿尔茨海默病（AD）是成人痴呆最常见的病因之一，是一种具有明确神经病理学特征的进行性神经退行性疾病。已知疾病病理涉及错误折叠的淀粉样β蛋白（Aβ）和tau蛋白，并在数十年间呈现出相对固定的进展过程。AD神经病理学特征（Aβ、代谢减退和tau蛋白）与成像生物标志物（MRI、AV - 45/FDG - PET）之间的关系尚未完全明确。此外，生物标志物病理情况常常不一致，即不同脑区可能呈现出不同程度的异常。基于近期对跨神经元“朊病毒样”传播的阐明以及其他现有数据的证据表明，疾病传播遵循大脑的纤维连接网络。因此，大脑的连接组信息可用于预测AD中疾病传播的过程。最近建立的一个使用基于连接组的网络扩散模型的AD病理传播数学模型成功地概括了神经退行性进展。受这些基于网络的研究结果的启发，本研究探讨网络连接是否以及如何介导各种AD生物标志物之间的相互作用。我们假设结构连接矩阵将介导区域AD相关代谢减退与Aβ沉积之间的横断面关联。鉴于近期有报道称脑区的固有或终生活动是患者Aβ沉积的强预测因子，我们还测试了健康代谢是否对AD患者的Aβ沉积和代谢减退产生网络介导效应。我们发现，区域Aβ沉积最好通过区域健康局部代谢和连接组介导的区域健康代谢的线性组合来预测。
