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: 10323676
• 负责人: Juan Eugenio Iglesias Gonzalez
• 金额: $ 59.44万
• 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/10323676
• 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; Brain; Cessation of life; Clinical; Clinical Trials; Collaborations; Computer software; DNA-Binding Proteins; Data; Data Set; Dementia; Dementia with Lewy Bodies; Development; Diagnosis; Diagnostic; Disease; Dissection; Elderly; Goals; Hippocampus (Brain); 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; Research; Research Personnel; Resolution; Sample Size; Scanning; Semantics; Senile Plaques; Shapes; Site; Slice; Structure; Surface; Techniques; Technology; Testing; Thick; Three-Dimensional Image; Time; Transact; United States National Institutes of Health; Universities; Validation; Washington; base; 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; reconstruction; response; therapeutically effective; tool; 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.

项目摘要 标题： 诊断无法诊断的：通过神经病理计量学对阿尔茨海默病的模拟和混杂因素的研究 使用3D扫描对照片进行解剖 摘要： 虽然大多数老年痴呆症患者患有阿尔茨海默病(AD)，但也有重叠或 甚至模仿AD，混淆临床诊断，因此是准确预测发病率的障碍 进步和有效的治疗方法。这些疾病的例子包括伴随的TDP-43 病理、路易体痴呆(DLB)和与白色模糊相关的微血管病变 物质损伤。研究这些疾病的一个关键障碍是目前还没有可靠的死亡前证据。 生物标志物。在这里，我们建议与两个阿尔茨海默氏症研究中心合作，评估解剖学 这三种情况的签名，与AD形成对比，以便能够对它们进行研究，并最终 转回核磁共振，以便直接加强临床护理。 具体地说，我们建议使用高级机器学习(ML)技术来执行体积 马萨诸塞州阿尔茨海默病患者的尸检照片扫描 研究中心(MADRC)从解剖照片重建成像体积，这是常规的 在脑库和神经病理科获得的，将使我们能够将神经病理与 无需进行宏观测量(例如，脑结构的体积和形状、皮质厚度) 用于磁共振成像(MRI)数据。这一点至关重要，因为诊断性MRI并不总是获得的 接近尸检，或者根本没有，体外核磁共振是昂贵的，技术上的挑战，而且在许多情况下是不可用的 研究网站。因此，我们的技术有可能极大地增加样本量，特别是在 在生活中没有被扫描的无症状的人，他们可能会表现出最早和最纯粹的 神经病变。 我们的工具将把ML与3D形状扫描结合起来，这是一种越来越便宜的技术(1,000美元 -一台扫描仪10,000美元)，以非常准确地重建大脑形状。此外，我们还将 建立工具的“图集”版本，用概率图集取代3D扫描，从而能够分析 回溯数据。我们将与第二个亚洲发展研究中心合作开发这些工具。 华盛顿ADRC，它有大约1000个病例的切片照片。 新工具将用于密切研究MADRC的一个预期队列，该队列由200名受试者组成。我们 寻求识别上面提到的AD模拟物的神经成像特征，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，作为我们的神经成像包的一部分 免费浏览(超过40,000个全球许可)，因此它们可以被世界各地的研究网站使用 以很少的成本或免费的宏观形态测量手段增强神经病理学。