Uncovering the Heterogeneity of Neurodegeneration Trajectories in Alzheimer's Disease Using a Network Guided Reaction-Diffusion Model

项目摘要/摘要 阿尔茨海默病(AD)是一种异质性、多因素的神经退行性疾病。由于多样性 临床症状，标准的神经心理评估不能充分反映潜在的 病理生理机制，这使得AD的神经生物学检查之间存在显著差距 病理学和临床诊断。越来越多的证据表明，阿尔茨海默病是由两种异常堆积而成的 蛋白质，β-淀粉样蛋白和tau。随着时间的推移，这些与AD相关的神经病理负担开始蔓延 在整个大脑中，这导致了阿尔茨海默病症状的典型进展。尽管做出了惊人的努力 研究了淀粉样蛋白(A)，蛋白tau(T)， 和神经退行性[N]生物标记物，系统水平上了解这些神经病理负担是如何 促进神经退行性变以及为什么AD表现出特征性的进展在很大程度上仍然难以捉摸。在这项研究中， 我们将结合系统生物学和网络神经科学的力量来解开异构性 从动态相互作用和扩散过程了解AD人群认知功能衰退的轨迹 从史无前例的纵向神经成像数据中提取AT[N]生物标志物。这其中的主心骨 Project是我们最近开发的网络引导反应-扩散模型，该模型不仅描述了 AT[N]生物标记物在每个脑区的相互作用及其在脑网络中的传播模式 使用偏微分方程(PDE)。鉴于其在预测AT[N]演化方面的良好结果 生物标记物，我们将进一步发展我们目前的基于PDE的模型，将时空自适应 AT[N]生物标志物的机制途径。然后，我们将调查引导 目标中认知衰退的轨迹1.在此之后，我们将开发一种新的深度学习方法来分层 老化的大脑分为一组细粒度的类别(又名。亚型)具有不同的神经生物学基础， 同一亚型中的个体预计会有非常相似的认知衰退轨迹。我们 我将使用ADNI数据库的纵向成像数据来评估新的人口分层结果 这个项目的成功将使我们对神经退变过程有一个新的理解 在认知连续体光谱中。这是重要的一步，因为在早期减缓这种传播 Stage可预防或阻止AD的症状。

Project Summary/Abstract Alzheimer’s disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder. Due to the multiplicity of clinical symptoms, standard neuropsychological assessments inadequately reflect the underlying pathophysiological mechanisms, which renders a significant gap between neurobiological examinations of AD pathology and clinical diagnoses. Mounting evidence shows that AD is caused by the build-up of two abnormal proteins, beta-amyloid and tau. Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD. Although striking efforts have been made to investigate the neurobiological factors behind the acquisition of amyloid (A), protein tau (T), and neurodegeneration [N] biomarkers, a system-level understanding of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is still largely elusive. In this study, we will combine the power of systems biology and network neuroscience to disentangle the heterogeneous trajectories of cognitive decline in AD population by understanding the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal neuroimaging data. The backbone of this project is our recently developed network guided reaction-diffusion model that characterizes not only the interaction of AT[N] biomarkers at each brain region but also their propagation pattern across the brain networks using PDEs (partial differential equations). Given its promising results in predicting the evolution of AT[N] biomarkers, we will further develop our current PDE-based model by incorporating spatiotemporal-adaptive mechanistic pathways of AT[N] biomarkers. Then, we will investigate the system behaviors that steer the trajectory of cognitive decline in Aim 1. After that, we will develop a novel deep learning approach to stratify aging brains into a set of fine-grained categories (aka. subtypes) with distinct neurobiological underpinnings, where individuals within the same subtype are expected to have very similar trajectories of cognitive decline. We will evaluate the novel population stratification result using the longitudinal imaging data from the ADNI database in Aim 2. The success of this project will allow us to have a new understanding of the neurodegeneration process in the cognitive continuum spectrum. This is an important step because slowing down this spread at an early stage might prevent or halt the symptoms of AD.