Understanding Selectivity Mechanisms of Network Vulnerability and Resilience in Alzheimer's Disease by Establishing a Neurobiological Basis through Network Neuroscience

通过网络神经科学建立神经生物学基础，了解阿尔茨海默氏病网络脆弱性和恢复力的选择性机制

• 批准号: 10033069
• 负责人: Guorong Wu
• 金额: $ 193.99万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10033069
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Area; Atrophic; Biological Markers; Brain; Brain Mapping; Brain region; Clinic; Cognition; Cognitive; Communities; Computer software; Cross-Sectional Studies; Data; Development; Diagnostic; Discipline; Education; Event; Evolution; Exhibits; Factor Analysis; Genetic; Genetic Markers; Goals; Grain; Impaired cognition; Individual; Lead; Link; Longitudinal Studies; Measurement; Methodology; Methods; Modeling; Molecular; Nerve Degeneration; Network-based; Neurobiology; Neurodegenerative Disorders; Neurofibrillary Tangles; Neurons; Neurosciences; Occupations; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Physics; Positron-Emission Tomography; Process; Research; Risk; Risk Factors; Role; Senile Plaques; Single Nucleotide Polymorphism; Source Code; Structure; System; Testing; Thick; Vertebral column; base; biophysical analysis; cell type; computerized tools; connectome; flexibility; fluorodeoxyglucose positron emission tomography; genetic approach; genome wide association study; improved; modifiable risk; network dysfunction; neurocognitive disorder; neuroimaging; neuron loss; neuropathology; normal aging; novel; precision medicine; preservation; programs; resilience; spectral energy; statistics; tau Proteins; tool

Abstract. A plethora of neuroscience and neuroimaging studies have shown that Alzheimer’s disease (AD) differentially affects certain regions of the brain and specific cell types. Since AD-related pathological events often propagate trans-neuronally, the selective vulnerability to neuron loss and structure damage also manifest in the topological patterns of network alteration. Along with many other studies, the research team has found the strong evidence that (1) AD preferentially affects hub nodes in the network that are densely connected in the network, and (2) the propagation of neuropathological burdens such as amyloid plaques and neurofibrillary tangles exhibit unique topological patterns that are governed by the self-organized harmonic bases. However, the factors underlying this network vulnerability and the molecular mechanism regulating the selectivity in AD remain unclear. In this regard, we aim to continue the development of cutting-edge network analysis tools with a greater methodological understanding of how neuropathological events selectively affect certain harmonic bases (harmonic-selective network vulnerability) and how brain networks counteract AD pathology (network resilience). In this context, the backbone of this project is a harmonic factor analysis model that can be used as a neurobiological basis to accurately characterize the whole-brain mapping of neurodegeneration at a system level, where each harmonic factor explains how the ubiquitous propagation (wave) pattern of neuropathological event emerges from the particular structural connectome pathway. In Aim 1, we will leverage the well-studied biophysics concept of power and energy to identify a set of harmonic-selective vulnerable patterns that account for network vulnerability between normal aging and AD. Also, we will associatethe identified network vulnerability with couple factors from diverse research fields which include stochastics of selectivity (statistics), system criticality (physics), network organization (network neuroscience), and cognitive domains (clinic). After that, we will seek for the putative harmonic-genetics biomarker based on the discovered association between network vulnerability and genetics factor in Aim 2 and develop a harmonic-genetic approach to capture network resilience in Aim 3. In Aim 4, we will apply the computational approaches developed in Aim 1-3 to establish (1) a fine- grain understanding of network vulnerability and resilience across A (amyloid-PET), T (Tau-PET), and N (FDG- PET and cortical thickness) biomarkers, and (2) a longitudinal underpinning of the dynamics of network vulnerability by investigating the longitudinal change of AT[N] biomarkers. The diagnostic power of our novel harmonic-genetics biomarker and resilience will be evaluated in our current AD diagnostic engines. We will release the software (both binary program and source code), to facilitate the other AD biomarker projects and the neuroimaging studies of other neurocognitive disorders associated with brain network dysfunction.

摘要：大量神经科学和神经影像学研究表明，阿尔茨海默病（AD） 由于 AD 相关的病理事件，大脑的某些区域和特定的细胞类型受到不同的影响。 通常跨神经元传播，对神经元损失和结构损伤的选择性脆弱性也很明显 与许多其他研究一起，研究小组发现了网络改变的拓扑模式。 强有力的证据表明（1）AD优先影响网络中密集连接的中心节点 网络，以及（2）神经病理学负担的传播，例如淀粉样斑块和神经原纤维 缠结表现出由自组织谐波基控制的独特拓扑模式。 这种网络脆弱性背后的因素以及调节 AD 选择性的分子机制 在这方面仍不清楚，我们的目标是继续开发尖端的网络分析工具。 对神经病理事件如何选择性影响某些谐波有更深入的方法论理解 基础（谐波选择性网络脆弱性）以及大脑网络如何抵消 AD 病理学（网络 在这种情况下，该项目的支柱是一个调和因子分析模型，可以用作 准确表征系统神经退行性变的全脑图谱的神经生物学基础 水平，其中每个谐波因子解释了神经病理学的普遍传播（波）模式如何 事件从特定的结构连接组通路中出现。在目标 1 中，我们将利用经过充分研究的方法。 功率和能量的生物物理学概念，用于识别一组谐波选择性脆弱模式，这些模式解释了 对于正常老化和AD之间的网络漏洞，我们还将识别出的网络漏洞进行关联。 来自不同研究领域的几个因素，包括选择性随机（统计）、系统 之后，我们讨论关键性（物理学）、网络组织（网络神经科学）和认知领域（临床）。 将根据网络之间发现的关联寻找假定的和谐遗传学生物标志物 目标 2 中的脆弱性和遗传因素，并开发谐波遗传方法来捕获网络弹性 在目标 3 中。在目标 4 中，我们将应用目标 1-3 中开发的计算方法来建立 (1) 对 A (淀粉样蛋白-PET)、T (Tau-PET) 和 N (FDG- (2) 网络动态的纵向支撑 通过调查 AT[N] 生物标志物的纵向变化来确定我们的小说的诊断能力。 我们将在我们当前的 AD 诊断引擎中评估谐波遗传学生物标志物和恢复力。 发布软件（二进制程序和源代码），以促进其他 AD 生物标记项目和 与脑网络功能障碍相关的其他神经认知障碍的神经影像学研究。