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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease pathology Alzheimer&apos s disease patient Alzheimer&apos 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

项目摘要

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.

在大多数情况下，阿尔茨海默氏病（AD）与令人惊讶的高度病理异质性相关。在尸检中被诊断患有 AD 的个体中，大脑不仅存在 β-淀粉样蛋白和 tau 蛋白病变， AD 的标志，还有一种或多种共同病理，包括 TDP-43、α-突触核蛋白、非 AD tau 蛋白病、和脑小血管病 (SVD) 的主要病理学和共病理学均导致 AD 的发生。 AD 中的神经退行性变，但它们在不同大脑区域的相对贡献以及共同作用的程度人们普遍认识到，病理学调节原发性病理学的进展。对于 AD 的临床试验来说，解释这些神经退行性变的额外驱动因素非常重要，但有一个因素缺乏能够可靠地检测和量化病理异质性的体内生物标志物可能会有所帮助。解释为什么针对单一病理机制的 AD 治疗基本上无效。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该项目利用了 AD 中神经损失和皮质变薄的主要 AD 病理学和共病理学。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其中将包括完整大脑半球的高分辨率 7 特斯拉 MRI，并在选定的地点共同注册组织学灰质位置和白质病变周围此外，颞叶是大脑的一部分。最早和最严重的 AD 相关神经变性发生，将以 9.4 特斯拉进行扫描，并进行系列治疗组织学成像，可对 tau 病理学（缠结、线状等）和神经元进行三维绘图这个独特的离体成像数据集将代表整个颞叶的密度。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轨迹和病理-神经变性关系。建议如下：目标 1 是开发基于深度学习的 7 Tesla 整体图像分析技术。目前缺乏的半球MRI，包括皮质灰质、白质病变的分割，正常外观的白质和皮层下结构；离体和体内 MRI 的分组配准；模板；提取基于 MRI 和组织学的特征来表征白质病变与 SVD 相关的目标 2 是分析完整的 100-120 个样本数据集以表征分布。颞叶和整个大脑中的 tau 蛋白病理学、神经元丢失和皮质变薄目标 3 描述共病理对这些分布及其之间关系的影响。利用从分析该离体数据集中提取的病理特异性“特征”来提高灵敏度用于推断共同病理的存在和跟踪疾病进展的体内生物标志物。