Diagnosing the undiagnosable: studies of Alzheimer disease mimics and confounders via "neuropathometry" of dissection photos with 3D scanning

诊断无法诊断的疾病：通过 3D 扫描解剖照片的“神经病理学”研究阿尔茨海默病的模拟和混杂因素

• 批准号: 10533801
• 负责人: Juan Eugenio Iglesias Gonzalez
• 金额: $ 58.15万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10533801
• 关键词: 3-Dimensional; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Amyloid; Amyloid beta-Protein; Anatomy; Artificial Intelligence; Atlases; Autopsy; Back; Bayesian learning; Biological Assay; Biological Markers; Blood Vessels; Brain; Cessation of life; Clinical; Clinical Trials; Collaborations; Computer software; DNA-Binding Proteins; Data; Data Analyses; Data Set; Dementia; Dementia with Lewy Bodies; Development; Diagnosis; Diagnostic; Disease; Dissection; Elderly; Goals; Hippocampus; Image; Image Analysis; Individual; Laboratories; Laboratory Research; Lesion; Licensing; Life; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Massachusetts; Measurement; Measures; Methods; Microscopic; Nerve Degeneration; Pathology; Patients; Pattern; Photography; Positron-Emission Tomography; Prospective cohort; Qualifying; Research; Research Personnel; Sample Size; Scanning; Semantics; Shapes; Site; Slice; Structure; Surface; Techniques; Technology; Testing; Thick; Three-Dimensional Image; Time; United States National Institutes of Health; Universities; Validation; Washington; brain shape; cerebral atrophy; clinical care; clinical diagnosis; cost; design; diagnostic criteria; economic impact; effective therapy; heterogenous data; image registration; improved; in vivo; in-vivo diagnostics; machine learning algorithm; multidisciplinary; neuroimaging; neuropathology; novel; object shape; open source; prevent; protein TDP-43; reconstruction; response; therapeutically effective; tool; ultra high resolution; white matter

Project Summary Title: Diagnosing the undiagnosable: studies of Alzheimer disease mimics and confounders via "neuropathometry" of dissection photos with 3D scanning Summary: While most patients with late life dementia have Alzheimer’s disease (AD), there are conditions that overlap or even mimic AD, confounding clinical diagnosis, and thus representing a barrier to accurate predictions of rate of progression and to effective therapeutics. Examples of these diseases include concomitant TDP-43 pathology, Dementia with Lewy bodies (DLB), and microvascular lesions associated with poorly defined white matter lesions. A critical barrier to studying these diseases is that there currently is no reliable premortem biomarker. Here we propose a collaboration with two Alzheimer’s Research Centers to evaluate anatomical signatures of these three conditions, in contrast to AD, in order to enable research into them, and ultimately port back to MRI in order to directly enhance clinical care. Specifically, we propose to use advanced machine learning (ML) techniques to perform volumetric photographic scanning post mortem (at autopsy), on patients seen at the Massachusetts Alzheimer Disease Research Center (MADRC). Reconstructing imaging volumes from dissection photographs, which are routinely acquired at brain banks and neuropathology departments, will enable us to correlate neuropathology with macroscopic measurements (e.g., volume and shape of brain structures, cortical thickness) without the need for magnetic resonance imaging (MRI) data. This is crucial because diagnostic MRI is not always acquired close to autopsy, or at all, and ex vivo MRI is expensive, technically challenging, and not available at many research sites. Therefore, our technique has the potential of greatly increasing sample sizes, especially with asymptomatic individuals who were not scanned in life, and who would likely manifest the earliest and purest neuropathological changes. Our tools will combine ML with 3D shape scanning, which is an increasingly inexpensive technology ($1,000 - $10,000 for a scanner), to produce very accurate reconstructions of the brain shape. Moreover, we will also build an “atlas” version of the tool, that replaces 3D scanning by a probabilistic atlas, thus enabling analysis of retrospective data. We will develop the tools in collaboration with a second ADRC, the University of Washington ADRC, which has slice photographs for approximately one thousand cases. The new tools will be used to closely study a prospective cohort at MADRC, consisting of 200 subjects. We seek to identify neuroimaging signatures of the AD mimics mentioned above, which can be ported to in vivo MRI scanning. Moreover, we will also distribute and maintain the tools as part of our neuroimaging package FreeSurfer (over 40,000 worldwide licenses), so they can be used by research sites around the world to augment neuropathology with macroscopic morphometric measures at little or no cost.

项目概要 标题： 诊断无法诊断的疾病：通过“神经病理学”对阿尔茨海默病的模拟和混杂因素进行研究 3D 扫描解剖照片 概括： 虽然大多数晚年痴呆症患者都患有阿尔茨海默病 (AD)，但有些情况是重叠的或 甚至模仿AD，混淆临床诊断，从而对准确预测发病率构成障碍 进展和有效的治疗方法。这些疾病的例子包括伴随的 TDP-43 病理学、路易体痴呆 (DLB) 以及与边界不清的白种人相关的微血管病变 物质病变。研究这些疾病的一个关键障碍是目前没有可靠的验尸方法 生物标志物。在这里，我们建议与两个阿尔茨海默病研究中心合作来评估解剖学 与 AD 相比，这三个条件的特征，以便能够对它们进行研究，并最终 移植回 MRI 以直接加强临床护理。 具体来说，我们建议使用先进的机器学习（ML）技术来执行体积测量 对在马萨诸塞州阿尔茨海默病中心就诊的患者进行尸检摄影扫描（尸检时） 研究中心（MADRC）。从解剖照片重建成像体积，这是常规的 在脑库和神经病理学部门获得的信息将使我们能够将神经病理学与 宏观测量（例如，大脑结构的体积和形状、皮质厚度），无需 用于磁共振成像 (MRI) 数据。这一点至关重要，因为诊断性 MRI 并不总是能够获得 接近尸检，或者根本没有，离体 MRI 价格昂贵，技术上具有挑战性，而且很多地方都没有 研究地点。因此，我们的技术有可能大大增加样本量，特别是 无症状个体在生前没有接受过扫描，并且可能最早、最纯粹地表现出来 神经病理改变。 我们的工具将 ML 与 3D 形状扫描相结合，这是一种越来越便宜的技术（1,000 美元） - 扫描仪花费 10,000 美元），以非常准确地重建大脑形状。此外，我们还将 构建该工具的“图集”版本，用概率图集代替 3D 扫描，从而能够分析 回顾性数据。我们将与第二个 ADRC（即英国大学）合作开发这些工具。 华盛顿 ADRC，拥有大约 1000 个病例的切片照片。 新工具将用于密切研究 MADRC 的一个由 200 名受试者组成的前瞻性队列。我们 寻求识别上述 AD 模拟物的神经影像特征，这些特征可以移植到体内 核磁共振扫描。此外，我们还将分发和维护这些工具，作为我们的神经影像包的一部分 FreeSurfer（超过 40,000 个全球许可证），因此世界各地的研究站点都可以使用它们来 通过宏观形态测量来增强神经病理学，只需很少的成本或无需成本。