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: 10288783
• 负责人: Guorong Wu
• 金额: $ 16.54万
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288783
• 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。 对于临床症状，标准的神经心理学评估不足以反映潜在的症状 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是由两种异常的积累引起的。 随着时间的推移，这些与 AD 相关的神经病理负担开始扩散。 整个大脑，这导致了 AD 症状的特征性进展。 已研究淀粉样蛋白 (A)、tau 蛋白 (T) 获得背后的神经生物学因素， 和神经退行性变 [N] 生物标志物，对这些神经病理负担如何进行系统水平的理解 促进神经退行性变以及 AD 表现出特征性进展的原因在这项研究中仍然难以捉摸。 我们将结合系统生物学和网络神经科学的力量来理清异质性 通过了解动态相互作用和扩散过程来了解 AD 人群认知能力下降的轨迹 来自数量空前的纵向神经影像数据的 AT[N] 生物标志物是这一研究的支柱。 项目是我们最近开发的网络引导反应扩散模型，它不仅表征 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 使用 PDE（偏微分方程）来预测 AT[N] 的演化具有良好的结果。 生物标记物，我们将通过结合时空自适应来进一步开发我们当前的基于偏微分方程的模型 然后，我们将研究引导 AT[N] 生物标志物的系统行为。 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 将老化的大脑分为一组具有不同神经生物学基础的细粒度类别（又称亚型）， 同一亚型的个体预计会有非常相似的认知衰退轨迹。 将使用 ADNI 数据库的纵向成像数据评估新的人口分层结果 目标2。这个项目的成功将使我们对神经退行性变过程有一个新的认识 在认知连续谱中，这是重要的一步，因为尽早减缓这种传播。 阶段可能会预防或阻止 AD 的症状。