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

抽象的。大量的神经科学和神经成像研究表明，阿尔茨海默病(AD) 不同地影响大脑的某些区域和特定的细胞类型。由于与AD相关的病理事件 经常通过神经元传播，也表现出对神经元丢失和结构损伤的选择性易感性 在网络变化的拓扑模式中。与许多其他研究一起，研究小组发现 强有力的证据表明：(1)AD优先影响网络中密集连接的集线器节点 网络，以及(2)神经病理负荷的传播，如淀粉样斑块和神经原纤维 缠结表现出由自组织调和基所支配的独特的拓扑模式。然而， 阿尔茨海默病网络脆弱性的因素及其选择性调控的分子机制 目前仍不清楚。在这方面，我们的目标是继续开发尖端网络分析工具 对神经病理事件如何选择性地影响某些谐波有更好的方法论理解 基础(选择谐波的网络脆弱性)以及大脑网络如何抵消AD病理(网络 恢复力)。在此背景下，该项目的主干是调和因子分析模型，该模型可用作 准确描述系统神经退行性变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无处不在的神经病理传播(波)模式 事件从特定的结构连接体途径中出现。在目标1中，我们将利用经过充分研究的 生物物理学的功率和能量概念，以识别一组对谐波具有选择性的脆弱模式，这些模式 对于正常老化和AD之间的网络漏洞。此外，我们还会将识别出的网络漏洞 来自不同研究领域的耦合因素，包括选择性(统计)的随机性，系统 临界性(物理学)、网络组织(网络神经科学)和认知域(临床)。之后，我们 将根据已发现的网络之间的关联来寻找可能的和谐遗传学生物标志物 AIM 2中的脆弱性和遗传因素，并开发一种和谐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中发展的计算方法来建立(1)罚款- 深入了解A(淀粉样蛋白-PET)、T(Tau-PET)和N(FDG-PET)的网络脆弱性和恢复能力 PET和皮质厚度)生物标志物，以及(2)网络动力学的纵向支撑 通过研究AT[N]生物标志物的纵向变化来研究脆弱性。我们小说的诊断能力 我们目前的AD诊断引擎将评估谐波遗传学生物标记物和弹性。我们会 发布软件(包括二进制程序和源代码)，以促进其他AD生物标记物项目和 与大脑网络功能障碍相关的其他神经认知障碍的神经成像研究。

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.