The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents

摘要 公民科学，公众参与科学过程，利用人类认知的独特属性 将大量数据集的分析分发给公众。最近，人类计算研究所（Human Computation Institute） (HCI)使用这种方法来众包体内双光子激发显微镜（2PEF）数据的分析， 该研究将康奈尔大学的阿尔茨海默病（AD）研究加速了五倍， 在AD小鼠模型中毛细血管停滞的分子机制。对于这一行政补充， 我们现在建议开发和验证一个新的在线平台， 全切片图像（WSI）的人类大脑。这将有助于快速获取大量数据集 需要训练一个自动分类器，以支持我们现有的R01研究神经病理学 西班牙裔和非西班牙裔白色死者中AD的情况。得出研究级分类 从多源WSI标签需要数据质量的要求，超过了今天最好的自动化方法。 因此，我们打算开发一个群体驱动的分析管道，将组织病理学分析减少为 一系列可管理的在线任务，可由一般事务委员会的非专家成员完成， 公共的，以及“群体智慧”的方法，用于将许多个人的答案聚合成一个单一的专家式的 答案为了实现这些目标，我们的具体目标是开发流程、接口和聚合 支持群众动力分析管道和评估其内部有效性所需的方法。我们会委聘 重复交叉验证协议，以测试基于人群的分析将导致至少 相对于金，两种主要病理学类别（斑块和缠结）的灵敏度和特异性均为95% 专家注释者提供的标准数据。如果这些验证研究成功， 结合这个分析管道将用于执行有史以来第一次从头分析阿尔茨海默氏症 组织病理学图像由一群志愿者（面向那些西班牙裔遗产），被用来训练一个 神经网络模型的自动化分析，并将支持不断改进的可重复使用的数字 病理学工作流程工具，其对病理学目标的扩展库敏感。

ABSTRACT Citizen science, public participation in the scientific process, leverages the unique properties of human cognition to distribute the analysis of massive data sets to the general public. Recently, the Human Computation Institute (HCI) used this approach to crowdsource the analysis of in-vivo two-photon excitation microscopy (2PEF) data, which has accelerated Cornell University’s Alzheimer’s disease (AD) research by a factor of five, elucidating molecular mechanisms that underlie capillary stalling in mouse models of AD. For this administrative supplement, we now propose to develop and validate a new online platform that crowdsources the histopathological analysis of whole slide images (WSI) of the human brain. This would support the rapid acquisition of high-volume datasets needed to train an automated classifier in support of our existing R01 studies investigating the neuropathological landscape of AD within Hispanic and non-Hispanic White decedents. Deriving research-grade classification labels from multi-source WSI entails data quality requirements that exceed today’s best automated methods. Thus, we intend to develop a crowd-powered analytic pipeline that reduces the histopathological analysis into a step-wise series of manageable online tasks that could be accomplished by non-expert members of the general public, as well as “wisdom of crowd” methods for aggregating many individual answers into a single expert-like answer. In service of these objectives, our specific aims are to develop the process, interfaces, and aggregation methods needed to support the crowd-powered analytic pipeline and evaluate its internal validity. We will employ a repeated cross-validation protocol to test the hypothesis that the crowd-based analysis will result in at least 95% sensitivity and 95% specificity for two major pathology categories (plaques and tangles) relative to gold standard data provided by expert annotators. If these validation studies are successful, an online platform incorporating this analytic pipeline will be used to execute the first ever de novo analysis of Alzheimer’s histopathology imagery by a crowd of volunteers (geared towards those of Hispanic heritage), be used to train a neural network model for automating the analysis, and will support the ongoing improvement of a reusable digital pathology workflow tool that is sensitive to an expanding repertoire of pathology targets.