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

项目摘要 职务名称： 诊断无法诊断的疾病：通过“神经病理测量”研究阿尔茨海默病的模拟和混杂因素， 3D扫描解剖照片 总结： 虽然大多数晚期痴呆患者患有阿尔茨海默病（AD），但存在重叠或重叠的病症。 甚至模仿AD，混淆临床诊断，因此代表准确预测率的障碍 进展和有效的治疗方法。这些疾病的实例包括伴随的TDP-43 病理学、路易体痴呆（DLB）和与边界不清的白色 物质损伤研究这些疾病的一个关键障碍是目前没有可靠的死前检查， 生物标记物。在这里，我们建议与两个阿尔茨海默氏症研究中心合作，以评估解剖学 这三个条件的签名，与AD相反，为了使他们的研究，并最终 移植回MRI，以直接增强临床护理。 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。 照片扫描后尸检（在尸检），对病人看到在马萨诸塞州阿尔茨海默病 研究中心（MADRC）。根据解剖照片重建成像体积，这是常规操作 在脑库和神经病理学部门获得的，将使我们能够将神经病理学与 宏观测量（例如，脑结构的体积和形状、皮质厚度），而不需要 用于磁共振成像（MRI）数据。这是至关重要的，因为诊断MRI并不总是获得 离体MRI是昂贵的，技术上具有挑战性，并且在许多情况下不可用。 研究网站。因此，我们的技术有可能大大增加样本量，特别是 无症状的人在生活中没有扫描，谁可能会表现出最早和最纯粹的 神经病理学改变 我们的工具将联合收割机ML与3D形状扫描相结合，这是一种越来越便宜的技术（1000美元 - 扫描仪1万美元），以产生非常准确的大脑形状重建。此外，我们还将 建立一个“地图集”版本的工具，取代3D扫描的概率地图集，从而使分析 回顾性数据。我们将与第二个ADRC合作开发工具， 华盛顿ADRC，它有大约一千个病例的切片照片。 新工具将用于密切研究MADRC的前瞻性队列，由200名受试者组成。我们 寻求识别上述AD模拟物的神经成像特征，其可以移植到体内 核磁共振扫描。此外，我们还将分发和维护这些工具，作为我们神经成像软件包的一部分 FreeSurfer（全球超过40，000个许可证），因此它们可以被世界各地的研究站点使用， 以很少或没有成本的宏观形态测量来增强神经病理学。

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.