Stalled capillary flow: a novel mechanism for hypoperfusion in Alzheimer disease

毛细血管血流停滞：阿尔茨海默病低灌注的新机制

• 批准号: 10412670
• 负责人: Nozomi Nishimura
• 金额: $ 22万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412670
• 关键词: Address; Algorithms; Alzheimer' s Disease; Automation; Blood capillaries; Blood flow; Brain; Classification; Cognitive; Collaborations; Computer software; Crowding; Data; Data Analyses; Data Compromising; Data Set; Dementia; Engineering; Exhibits; Future; Goals; Human; Hybrids; Image; Image Analysis; Imagery; Impaired cognition; Individual; Institutes; Intelligence; Intervention; Label; Laboratories; Machine Learning; Manuals; Methods; Modeling; Participant; Pathway interactions; Performance; Process; Publishing; Research; Research Personnel; Resources; Scientist; Sensitivity and Specificity; Speed; System; Testing; Time; Training; Validation; Work; automated analysis; base; bioimaging; citizen science; crowdsourcing; data quality; design; experience; fluorescence imaging; hypoperfusion; improved; member; novel; open source; open source tool; prevent; prototype; side effect; success; two-photon; volunteer

Project Summary / Abstract We seek to investigate the agent-based participation of machine learning (ML) models in an existing crowdsourcing system, which could substantially speed up biomedical image analysis without loss of data quality for Aims 2-4 in our R01 research. We encountered an analytic bottleneck in our prior R01-supported work, which seeks to reveal mechanisms that underlie capillary stalling in the brain and requires quantifying stall rates from 2PEF (2-photon excited fluorescence) image stacks. To address this, we partnered with the Human Computation Institute (HCI) to crowdsource the analysis using the online citizen science platform Stall Catchers, which has reduced the time to analyze a typical dataset from many months to just a few weeks. Beyond enabling several published results, 35,000 Stall Catchers volunteers have produced over 1.4 million high-quality “crowd” annotations, which served as a rich training set in a recent machine learning competition that led to the creation of fifty distinct ML models exhibiting a broad distribution of sensitivity and bias. None of these models, by itself, meets our stringent analytic requirements. However, if we could endow these models with sufficient agency to participate as bonafide Stall Catchers players, then we could test the hypothesis that hybrid (human/machine) ensembles will achieve the same data quality as human-only ensembles when answers are combined using our existing “wisdom of the crowd” algorithm. Developing an open source toolkit for transforming ML models into citizen science “bots” would enable a direct pathway for effectively integrating even substandard ML models into an existing crowd-powered analytic pipeline without requiring intensive re-engineering. Accelerating biomedical data analysis in this way could allow other biomedical researchers to derive immediate value from smaller training sets and investigate more hypotheses using less time and resources. This project could enable a low-overhead pathway for semi-automation using imperfect ML models, which could leverage ML sooner while reducing reliance on human cognitive resources, and provide a pathway for achieving fully automated analyses as improved ML models are added to the crowd as CitSci bots. Success in this pursuit would allow us to incorporate full-time CitSci bots into Stall Catchers, which could double the number of capillary stalling studies we can conduct in a given year toward elucidating a more complete mechanistic model of capillary stalling. This would speed up our ability to identify a targeted intervention with reduced side effects that could alleviate cognitive impairments in implicated dementias, such as Alzheimer’s disease while contributing to the advancement of hybrid intelligence methods with broad utility for biomedical data analysis.

项目概要/摘要 我们寻求在现有的机器学习（ML）模型中研究基于代理的参与 众包系统，可以在不损失数据质量的情况下大幅加快生物医学图像分析速度 对于 R01 研究中的目标 2-4，我们在之前 R01 支持的工作中遇到了分析瓶颈。 试图揭示大脑毛细血管失速的机制，并需要量化失速率 2PEF（2 光子激发荧光）图像堆栈为了解决这个问题，我们与 Human Computation 合作。 研究所 (HCI) 使用在线公民科学平台 Stall Catchers 众包分析，该平台已 将分析典型数据集的时间从几个月缩短到几周。 公布结果，3.5万名“抓地摊”志愿者培养了超140万优质“人群” 注释，在最近的机器学习竞赛中作为丰富的训练集，导致了创建 五十个不同的机器学习模型表现出广泛的敏感性和偏差，这些模型本身都没有。 满足我们的分析要求但是，如果我们能够赋予这些模型足够的代理能力。 作为真正的失速捕手玩家参与，然后我们可以测试混合（人/机器）的假设 当使用我们的方法组合答案时，集成将实现与纯人类集成相同的数据质量 开发一个开源工具包，将机器学习模型转换为现有的“群体智慧”算法。 公民科学“机器人”将提供一条直接途径，将甚至不合格的机器学习模型有效地集成到 现有的众包分析流程，无需进行密集的重新设计，加速生物医学。 以这种方式进行数据分析可以让其他生物医学研究人员从较小的培训中获得直接价值 使用更少的时间和资源来设置和研究更多的假设，该项目可以实现低开销。 使用不完美的机器学习模型实现半自动化的途径，可以更快地利用机器学习，同时减少 依赖人类认知资源，并提供实现全自动分析的途径 随着 CitSci 机器人的成功，我们可以将改进的 ML 模型纳入其中。 将全职 CitSci 机器人引入失速捕手，这可以使我们可以进行的毛细血管失速研究的数量增加一倍 在某一年进行的研究旨在阐明毛细管失速的更完整的力学模型。 加快我们确定有针对性的干预措施的能力，减少副作用，从而缓解认知障碍 与阿尔茨海默氏病等相关痴呆症有关的损害，同时有助于促进 混合智能方法对生物医学数据分析具有广泛的实用性。