Interactive Effects of Aging and AD on Brain Networks

项目总结 在已知的晚发性AD的危险因素中，年龄被认为是最大的危险因素。65岁以后，患上 开发AD每五年翻一番。虽然有一种共识，即晚发性AD主要影响 脑老化对阿尔茨海默病发生发展的直接影响大多被忽视。在……里面 事实上，大多数阿尔茨海默病的人类神经成像研究在报告时将年龄视为一个混杂因素 AD的结果。一些与年龄相关的过程，包括炎症、线粒体功能障碍、突触 丢失和血管功能障碍可能与AD有关。这些过程影响了灰的显微组织性能 以及脑白质，如轴突形态，几年后才能使用常规方法可靠地检测到 核磁共振检查。它们还会影响网络级别的计算，因为大脑会重新组织以补偿这些 改变。知识上的一个缺口是大脑中显示出大多数与年龄相关的微结构变化的区域 组织可能更容易受到AD病理的影响。核磁共振技术的进步为我们提供了 探索人类皮质和白质的微结构组织，如轴突形态的能力 在活体内。为了弥补这一差距，并回应高度优先的研究主题PAR-19-070(非AG-18-051： 在脑老化的背景下理解阿尔茨海默病)，我们提出了一个多层次的研究来检查微结构 (例如，皮质神经突起形态)和大脑网络的连接组级组织特性 受影响最大的是衰老，并可能导致AD。我们将追求三个目标：(1)研究微观结构 最易受衰老和AD影响的灰质和白质的特性 病理学。我们将利用斯坦福ADRC PET-MR和深度表型资源，并将收集新的， 脑微结构的MRI定量标记物，包括轴突形态测量和 120名临床公认的老年患者的大分子组织体积(MTV) 认知正常对照(HC)或轻度认知障碍(MCI)，并将被确认为β-或Aβ 用正电子发射计算机断层扫描(PET)；(2)研究衰老和阿尔茨海默病对人结缔组织结构的影响。 我们将利用ADNI神经成像数据来实现这一目标，并将使用 独立数据集，即斯坦福ADRC数据集；(3)描述 正常衰老和向阿尔茨海默病表型转变过程中脑网络的组织特性。利用 ADNI纵向数据，我们将应用连通性分析，加速纵向混合效应设计 模拟正常衰老时脑网络组织变化的轨迹并检验 阿尔茨海默病不同阶段的轨迹变化。这项研究的成功完成将显著改善 我们在衰老背景下对AD的理解，并将为AD新疗法的发展提供信息 瞄准衰老机制。

PROJECT SUMMARY Among the known risk factors for late-onset AD, age is considered the greatest. After age of 65, the risk of developing AD doubles every five years. While there is a consensus that late-onset AD mainly impacts the aging brain, the direct effects of aging on development and progression of AD have been mostly overlooked. In fact, the majority of human neuroimaging studies of AD consider age as a confounding factor when reporting the AD outcomes. Several age-related processes including inflammation, mitochondrial dysfunction, synaptic loss and vascular dysfunction may contribute to AD. These processes impact microstructural properties of gray and white matter such as neurite morphology years before they can be reliably detected using conventional MRI measures. They also impact network-level computations as brain reorganizes to compensate for these changes. A gap in knowledge is that brain regions that show most age-related changes in their microstructural organization may be more vulnerable to AD pathology. Advances in MRI techniques have provided us with the ability to probe microstructural organization of cortical and white matter such as neurite morphology in human in vivo. To bridge this gap and in response to the high-priority research topic PAR-19-070 (NOT-AG-18-051: understanding AD in the context of aging brain), we propose a multi-level study to examine microstructural (e.g., cortical neurite morphology) and connectome-level organizational properties of brain networks that are most affected in aging and may contribute to AD. We will pursue three Aims: (1) To examine microstructural properties of gray matter and white matter that are most vulnerable in aging and are most impacted by AD pathology. We will leverage Stanford ADRC PET-MR and deep phenotyping resources and will collect novel, quantitative MRI markers of brain microstructure including measures of neurite morphology and macromolecular tissue volume (MTV) in 120 older adults who have a clinical consensus diagnosis of either cognitively normal controls (HC) or mild cognitive impairment (MCI), and will be confirmed to be Aβ- or Aβ+ with PET; (2) To examine the interaction of aging and AD on organizational properties of human connectome. We will leverage ADNI neuroimaging data to achieve this goal and will validate the findings using an independent dataset, namely the Stanford ADRC dataset; (3) To characterize the trajectory of changes in organizational properties of brain networks in normal aging and during transition to AD phenotypes. Leveraging ADNI longitudinal data, we will apply connectomic analysis, accelerated longitudinal design with mixed effect modelling to model the trajectory of organizational changes of brain networks in normal aging and test the alterations of trajectories at different stages of AD. Successful completion of this study will significantly improve our understanding of AD in the context of aging and will inform development of novel therapeutics of AD targeting aging mechanisms.