Ex Vivo Imaging of the Aging Brain to Discover Morphology/Pathology Associations

衰老大脑的离体成像以发现形态学/病理学关联

• 批准号: 10608603
• 负责人: Paul A. Yushkevich
• 金额: $ 207.77万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608603
• 关键词: 3-Dimensional; 3D Print; Address; Algorithms; Alzheimer associated neurodegeneration; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Amyloid; Amyloid beta-Protein; Atlases; Atrophic; Autopsy; Benefits and Risks; Biological Markers; Blood; Blood Vessels; Brain; Brain imaging; Brain region; Cardiovascular Diseases; Cerebral hemisphere; Cerebral small vessel disease; Chemicals; Clinical; Clinical Trials; Cognitive; Data; Data Set; Dementia; Deposition; Detection; Development; Diagnosis; Disease; Disease Marker; Disease Progression; Future; Grant; Heterogeneity; Histologic; Histology; Histopathology; Human; Image; Image Analysis; Impaired cognition; Individual; Infarction; Knowledge; Lesion; Link; Literature; Location; Magnetic Resonance Imaging; Maps; Measures; Medial; Methods; Microvascular Dysfunction; Molds; Molecular Abnormality; Morphology; Nerve Degeneration; Neurofibrillary Tangles; Neurologist; Neurons; Participant; Pathologic; Pathology; Pattern; Pennsylvania; Positron-Emission Tomography; Research; Resolution; Scanning; Slide; Specimen; Structure; Surface; Tauopathies; Techniques; Temporal Lobe; Testing; Therapeutic; Thick; Thinness; Time; Tracer; Translating; Universities; White Matter Hyperintensity; Work; aging brain; alpha synuclein; automated segmentation; brain cell; brain magnetic resonance imaging; cerebral atrophy; clinical practice; cohort; deep learning; density; ex vivo imaging; gray matter; hippocampal atrophy; histological image; human imaging; imaging biomarker; improved; in vivo; in vivo imaging; individual patient; magnetic resonance imaging biomarker; morphometry; multimodality; neuron loss; neuropathology; novel; open source tool; prospective; protein TDP-43; success; tau Proteins; tau aggregation; tool; treatment response; two-dimensional; vascular risk factor; white matter

Alzheimer's disease (AD) is associated with surprisingly high degree of pathologic heterogeneity. In most individuals diagnosed with AD at autopsy, the brain not only harbors the β-amyloid and tau pathologies that are the hallmarks of AD, but also one or more co-pathologies, including TDP-43, α-synuclein, non-AD tauopathy, and cerebral small vessel disease (SVD). The primary AD pathologies and co-pathologies all contribute to neurodegeneration in AD, but their relative contribution in different brain regions and the degree in which co- pathologies modulate the progression of primary pathologies is not well understood. It is widely recognized that it is important for clinical trials in AD to account for these additional drivers of neurodegeneration, but there is a lack of in vivo biomarkers that can reliably detect and quantify co-pathology. Pathologic heterogeneity may help explain why AD treatments targeting a single pathological mechanism have been largely ineffective. This project seeks to address this limitation by using ex vivo human brain MRI to characterize the contributions of primary AD pathologies and co-pathologies to neuronal loss and cortical thinning in AD. The project leverages a prospective dataset from 100-120 autopsies conducted at the University of Pennsylvania AD Research Center that will include high-resolution 7 Tesla MRI of intact brain hemispheres with co-registered histology at selected gray matter locations and around white matter lesions. Moreover, the temporal lobe, part of the brain where earliest and most severe AD-related neurodegeneration occurs, will be scanned at 9.4 Tesla, and undergo serial histological imaging, allowing three-dimensional mapping of tau pathology (tangles, threads, etc.) and neuronal density across the entire temporal lobe. This unique ex vivo imaging dataset will represent a convergence of structural and pathological imaging data in the same 3D space, allowing a broad range of studies analyzing trajectories of pathology deposition and pathology-neurodegeneration relationships. The specific aims of the proposal are as follows. Aim 1 is to develop deep learning-based image analysis techniques for 7 Tesla whole- hemisphere MRI, which are currently lacking, including segmentation of cortical gray matter, white matter lesions, normal-appearing white matter, and subcortical structures; groupwise registration to both ex vivo and in vivo MRI templates; and extraction of both MRI-based and histological features to characterize white matter lesions associated with SVD. Aim 2 is to analyze the complete 100-120 specimen dataset to characterize the distribution of tau pathology, neuronal loss, and cortical thinning both in the temporal lobe and in the whole brain and to describe the impact of co-pathologies on these distributions and on the relationships between them. Aim 3 is to leverage pathology-specific “signatures” extracted from analyzing this ex vivo dataset to improve the sensitivity of in vivo biomarkers for inferring the presence of co-pathology and tracking disease progression.

阿尔茨海默病（AD）与令人惊讶的高度病理异质性相关。在大多数 在尸检时被诊断患有AD的个体中，大脑不仅含有β-淀粉样蛋白和tau病理， AD的标志，以及一种或多种共病，包括TDP-43、α-突触核蛋白、非AD tau蛋白病， 脑小血管病（SVD）主要AD病理和共病理都有助于 AD中的神经退行性变，但它们在不同脑区的相对贡献和共同作用的程度， 病理学调节原发性病理学的进展还没有很好的理解。人们普遍认识到 对于AD的临床试验来说，解释这些额外的神经变性驱动因素是很重要的，但是 缺乏能够可靠地检测和定量共病的体内生物标志物。病理异质性可能有助于 解释为什么针对单一病理机制的AD治疗在很大程度上无效。 该项目旨在通过使用离体人脑MRI来表征贡献， 原发性AD病理和AD中神经元丢失和皮质变薄的共同病理。该项目利用 宾夕法尼亚大学AD研究中心进行的100-120例尸检的前瞻性数据集 这将包括高分辨率7特斯拉MRI的完整大脑半球与共同登记的组织学在选定的 灰质位置和白色物质病变周围。此外，大脑的颞叶， 发生最早和最严重的AD相关神经变性，将在9.4特斯拉下扫描，并进行连续 组织学成像，允许tau病理学（缠结、线等）的三维映射和神经元 整个颞叶的密度。这种独特的离体成像数据集将代表 同一3D空间中的结构和病理成像数据，允许进行广泛的研究分析 病理沉积的轨迹和病理-神经变性关系。该委员会的具体目标 建议如下。目标1是开发基于深度学习的图像分析技术，用于7特斯拉的全自动汽车。 目前缺乏半球MRI，包括皮质灰质、白色物质病变 外观正常的白色物质和皮质下结构;离体和体内MRI的分组配准 模板;以及提取基于MRI的特征和组织学特征以表征白色物质病变 与SVD有关。目的2是分析完整的100-120份标本数据集，以表征分布 在颞叶和整个大脑中的tau病理学，神经元损失和皮质变薄， 描述共同病理学对这些分布的影响以及它们之间的关系。目标3是 利用从分析该离体数据集中提取的病理学特异性“特征”来提高灵敏度 体内生物标志物用于推断共同病理学的存在和跟踪疾病进展。