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

项目概要/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，标准的神经心理学评估不足以反映潜在的 病理生理机制，这使得AD的神经生物学检查之间存在显着差距 病理学和临床诊断。越来越多的证据表明，AD是由两种异常的 β-淀粉样蛋白和tau蛋白。随着时间的推移，这些AD相关的神经病理学负担开始扩散 这导致AD症状的特征性进展。虽然惊人的努力 已经研究了获得淀粉样蛋白（A），tau蛋白（T）， 和神经退行性疾病[N]生物标志物，一个系统层面的了解，这些神经病理负担如何 促进神经退行性变以及为什么AD表现出特征性进展仍然很难理解。在本研究中， 我们将联合收割机结合系统生物学和网络神经科学的力量， 通过了解动态相互作用和扩散过程， AT[N]生物标志物的纵向神经影像学数据前所未有的数量。这件事的核心 该项目是我们最近开发的网络引导反应扩散模型，其特征不仅在于 AT[N]生物标志物在每个大脑区域的相互作用以及它们在大脑网络中的传播模式 偏微分方程（Partial Differential Equations）鉴于其在预测AT[N]演变方面的前景， 生物标志物，我们将进一步发展我们目前的PDE为基础的模型，结合时空适应 AT[N]生物标志物的机制途径。然后，我们将研究引导 目标1中认知能力下降的轨迹。之后，我们将开发一种新颖的深度学习方法来分层 将大脑老化成一组细粒度的类别（又名。亚型）具有不同的神经生物学基础， 其中预期相同亚型内的个体具有非常相似的认知衰退轨迹。我们 将使用ADNI数据库的纵向成像数据评估新的人群分层结果 目标2这个项目的成功将使我们对神经退化过程有一个新的认识 在认知连续谱中。这是一个重要的步骤，因为在早期减缓这种传播， 阶段可以预防或停止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.