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

使用网络引导反应扩散模型揭示阿尔茨海默病神经退行性轨迹的异质性

• 批准号: 10461847
• 负责人: Guorong Wu
• 金额: $ 15.15万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10461847
• 关键词: Alzheimer' s Disease; Alzheimer' s disease pathology; Amyloid; Amyloid Proteins; Amyloid beta-Protein; Base of the Brain; Behavior; Biological Markers; Brain; Brain Mapping; Brain region; Categories; Characteristics; Clinical; Cognitive; Communities; Complex; Consensus; Data; Data Analyses; Databases; Development; Diagnostic; Differential Equation; Diffusion; Diffusion Magnetic Resonance Imaging; Disease Progression; Event; Evolution; Exhibits; Goals; Grain; Heterogeneity; Impaired cognition; Individual; Language; Lead; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Mathematics; Measurement; Measures; Memory; Modeling; Nerve Degeneration; Neurobiology; Neurodegenerative Disorders; Neurofibrillary Tangles; Neuropsychology; Neurosciences; Outcome; Pathologic; Pathway interactions; Pattern; Population; Process; Proteins; Reaction; Research; Risk; Senile Plaques; Severities; Stratification; Symptoms; System; Systems Biology; Technology; Time; Vertebral column; Yang; aging brain; base; clinical diagnosis; deep learning; fluorodeoxyglucose positron emission tomography; insight; method development; neurobiological mechanism; neuroimaging; novel; population stratification; pre-clinical; precision medicine; prevent; serial imaging; spatiotemporal; success; tau Proteins; white matter

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.

项目摘要/摘要 阿尔茨海默氏病（AD）是一种异质的多因素神经退行性疾病。由于多重性 临床症状，标准神经心理学评估不足反映了基础 病理生理机制，它在AD的神经生物学检查之间存在很大的差距 病理和临床诊断。越来越多的证据表明，AD是由于两个异常的积累而引起的 蛋白质，β-淀粉样蛋白和tau。随着时间的流逝，这些与广告相关的神经病理学伯恩斯开始扩散 通过大脑，导致AD中症状的特征进展。虽然努力 已经进行了研究淀粉样蛋白（a），蛋白质tau（t）， 和神经变性[n]生物标志物，对这些神经病理伯良的系统级别的理解 促进神经变性以及为什么AD表现出特征进展仍然难以捉摸。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，以解开异质性 通过了解动态相互作用和扩散过程，AD人群认知能力下降的轨迹 来自空前数量的纵向神经影像数据的AT [N]生物标志物。骨干 项目是我们最近开发的网络指导反应扩散模型，不仅表征 在每个大脑区域的AT [N]生物标志物的相互作用，也是它们在大脑网络中的传播模式的相互作用 使用PDE（部分微分方程）。鉴于它的承诺结果可以预测[n]的演变 生物标志物，我们将通过合并时空自适应来进一步开发我们当前的基于PDE的模型 AT [N]生物标志物的机械途径。然后，我们将研究引导的系统行为 目标1的认知能力下降轨迹1。之后，我们将开发一种新颖的深度学习方法来分层 衰老的大脑成一组具有不同神经生物学基础的细粒类别（又称亚型）， 同一亚型内的个体的认知能力下降轨迹非常相似。我们 将使用来自ADNI数据库的纵向成像数据评估新的人口分层结果 在AIM 2中。该项目的成功将使我们能够对神经退行性过程有了新的了解 在认知连续性频谱中。这是一个重要的一步 阶段可能会阻止或阻止AD的症状。