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.

项目摘要 /摘要 我们试图调查现有机器学习（ML）模型的基于代理的参与 众包系统，该系统可能次要加快生物医学图像分析而不会丢失数据质量 对于我们的R01研究中的AIMS 2-4。我们在先前的R01支持的工作中遇到了一个分析瓶颈，这是 试图揭示毛细管在大脑中停滞的基础的机制，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，我们与人类计算合作 Institute（HCI）使用在线公民科学平台摊位捕手进行分析，该摊位有 从数月到几周的时间，将典型数据集分析的时间减少了。超越几个 已发表的结果，有35,000名摊位捕手志愿者产生了超过140万个高质量的“人群” 注释是在最近的机器学习竞赛中作为丰富的培训，导致了创建 五十个不同的ML模型表现出广泛的灵敏度和偏见分布。这些模型本身都没有 满足我们严格的分析要求。但是，如果我们能够赋予这些模型，以提供足够的代理 作为真正的摊位捕手玩家参加，我们可以测试混合动力（人/机器）的假设 当我们使用我们的答案合并时，合奏将达到与人类合奏相同的数据质量 现有的“人群智慧”算法。开发用于将ML模型转换为的开源工具包 公民科学“机器人”将为有效地整合ML模型的直接途径 现有的人群动力分析管道，而无需进行密集的重新设计。加速生物医学 数据分析以这种方式可以使其他生物医学研究人员从较小的培训中获得立即价值 设置并使用更少的时间和资源调查更多假设。这个项目可以使一个低空 使用不完美的ML模型进行半自动化的途径，该途径可能会更快地利用ML 依赖人类认知资源，并为实现完全自动分析的途径作为 作为Citsci机器人，改进的ML型号被添加到人群中。在这种追求方面的成功将使我们能够合并 全日制citsci机器人进入摊位捕获器，这可以使我们可以将毛细管拖延研究的数量增加一倍 在给定的一年中进行阐明毛细管失速的更完整的机械模型。这会 加快我们识别有针对性干预措施的能力，其副作用降低，可以减轻认知 涉及痴呆症的障碍，例如阿尔茨海默氏病，同时有助于进步 具有广泛实用性的混合智力方法用于生物医学数据分析。