Interactive Effects of Aging and AD on Brain Networks

衰老和 AD 对大脑网络的交互影响

基本信息

批准号：
10624812
负责人：
Hadi Hosseini
金额：
$ 59.57万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10624812
关键词：
Acceleration Adult Affect Age Aging Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer&apos s disease risk Alzheimer&apos s disease therapeutic Amyloid beta-Protein Animal Experimentation Brain Brain imaging Brain region Clinical Cognitive Communities Compensation Consensus Data Data Set Development Diagnosis Diffusion Disease Outcome Elderly Goals Growth Human Impaired cognition Inflammation Knowledge Late Onset Alzheimer Disease Lipids Magnetic Resonance Imaging Measurement Measures Medial Memory Loss Methods Modeling Morphology Myelin Neurites Neurosciences Non-linear Models Organizational Change Parietal Patients Phenotype Positron-Emission Tomography Process Property Reporting Research Priority Resources Rest Risk Risk Factors Sampling Signal Transduction Structure Synapses Techniques Testing Tissues Vascular Diseases age effect age related aging brain amnestic mild cognitive impairment amyloid pathology biomarker development cohort compare effectiveness connectome density disease phenotype gray matter improved in vivo indexing longitudinal design magnetic resonance imaging biomarker mild cognitive impairment mitochondrial dysfunction neural neural circuit neural network neuroimaging new therapeutic target normal aging novel novel therapeutics pre-clinical response tau Proteins white matter

项目摘要

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.

项目概要在已知的迟发性 AD 风险因素中，年龄被认为是最大的，65 岁以后，发生 AD 的风险最大。尽管人们一致认为，迟发性 AD 主要影响的是 AD。随着大脑老化，衰老对 AD 发生和进展的直接影响大多被忽视。事实上，大多数 AD 人类神经影像学研究在报告时都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。损失和血管功能障碍可能会导致 AD。和白质，例如神经突形态，比传统方法能够可靠地检测到它们要早几年当大脑重组以补偿这些时，MRI 测量也会影响网络级计算。知识上的一个空白是，大脑区域的微观结构中表现出与年龄相关的变化最多。 MRI 技术的进步为我们提供了更容易受到 AD 病理影响的组织。能够探测皮质和白质的微观结构组织，例如人类神经突形态为了弥补这一差距并响应高优先级研究主题 PAR-19-070（NOT-AG-18-051：在大脑老化的背景下理解AD），我们提出了一项多层次的研究来检查微观结构（例如，皮质神经突形态）和大脑网络的连接组级组织特性我们将追求三个目标：（1）检查微观结构。灰质和白质的特性在衰老过程中最脆弱且受 AD 影响最大我们将利用斯坦福 ADRC PET-MR 和深度表型分析资源，收集新颖的、脑微观结构的定量 MRI 标记，包括神经突形态的测量和 120 名具有以下任一临床共识诊断的老年人的大分子组织体积 (MTV) 认知正常对照 (HC) 或轻度认知障碍 (MCI)，并将被确认为 Aβ- 或 Aβ+ PET；（2）研究衰老和AD对人类连接组组织特性的相互作用。我们将利用 ADNI 神经影像数据来实现这一目标，并将使用 (3) 表征变化轨迹正常衰老和向 AD 表型转变期间大脑网络的组织特性。 ADNI纵向数据，我们将应用连接组分析，具有混合效应的加速纵向设计建模以模拟正常衰老过程中大脑网络组织变化的轨迹并测试成功完成本研究将显着改善 AD 不同阶段的轨迹。我们对衰老背景下的 AD 的理解将为 AD 新疗法的开发提供信息针对衰老机制。