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.

阿尔茨海默氏病（AD）与高度高的病理异质性有关。大多数 诊断为AD尸检的人，大脑不仅含有β-淀粉样蛋白和TAU病理 AD的标志，但也有一个或多种共同病理，包括TDP-43，α-突触核蛋白，非AD tauopathy， 和脑小血管疾病（SVD）。主要的广告病理和副病理都有助于 AD中的神经变性，但它们在不同大脑区域的相对贡献以及共同的程度 病理调节原发性病理的进展尚不清楚。广泛认识到 对于AD中的临床试验来说，考虑到这些其他神经变性驱动因素很重要，但是有一个 缺乏可以可靠地检测和量化共生病理学的体内生物标志物。病理异质性可能有帮助 解释为什么针对单个病理机制的AD治疗基本上是无效的。 该项目旨在通过使用离体人脑MRI来表征贡献来解决这一限制 AD中神经元丧失和皮质稀疏的主要AD病理和共同病理学的。该项目利用 宾夕法尼亚大学广告研究中心进行的100-120尸检的潜在数据集 这将包括完整脑半球的高分辨率7特斯拉MRI，并在选定的组织学处共同注册的组织学 灰质的位置以及周围的白质病变。而且，临时叶，大脑的一部分 最早，最严重的与广告相关的神经变性发生，将在9.4 Tesla扫描，并经历串行 组织学成像，允许TAU病理学（缠结，线等）和神经元的三维映射 整个临时叶的密度。这个独特的离体成像数据集将代表 在相同3D空间中的结构和病理成像数据，可以进行广泛的研究 病理沉积和病理 - 神经变性关系的轨迹。特定目标 建议如下。目的1是为7个特斯拉的整体制定基于学习的深度图像分析技术 半球MRI目前缺乏，包括对皮质灰质，白质病变的分割， 正常的白质和皮层结构； groupwise注册到Ex Vivo和In Vivo MRI 模板；并提取基于MRI的基于MRI和组织学特征以表征白质病变 与SVD相关。 AIM 2是分析完整的100-120标本数据集以表征分布 临时叶和整个大脑中的tau病理学，神经元丧失和皮质稀疏 描述副病理对这些分布以及它们之间的关系的影响。目标3是 利用从分析该离体数据集中提取的特定于病理学的“签名”以提高灵敏度 体内生物标志物，用于推断出存在和跟踪疾病进展的存在。