Improving usage of the Aphasia Research Cohort (ARC) repository

改善失语症研究队列 (ARC) 存储库的使用

• 批准号: 10786684
• 负责人: JULIUS FRIDRIKSSON
• 金额: $ 23.34万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10786684
• 关键词: Address; Aphasia; Award; Brain Injuries; Breeding; Clinical; Clinical Data; Collaborations; Communities; Computer software; Computers; Data; Data Analyses; Data Set; Databases; Dependence; Education; Ensure; Environment; FAIR principles; Face; Feedback; Funding; Goals; Guidelines; Image; Impairment; Incidence; Individual; Instruction; Intervention; Lesion; Life Style; MRI Scans; Machine Learning; Maintenance; Maps; Masks; Measures; Methods; Mission; Modeling; Natural Selections; Performance; Persons; Population; Preventive measure; Problem Solving; Process; Property; Recovery; Reporting; Reproducibility; Research; Research Methodology; Research Personnel; Resource Sharing; Scanning; Scientist; Seminal; Series; Stroke; TRUST principles; Technical Expertise; Terminology; Training; Translating; United States National Institutes of Health; Visualization; Walking; Work; analysis pipeline; aphasia recovery; brain health; clinical predictors; clinically relevant; cohort; data repository; diagnostic value; disability; distributed data; expectation; experience; flexibility; image processing; imaging modality; improved; innovation; interoperability; language impairment; neuroimaging; neuroimaging marker; novel; open source; open source tool; prognostic value; repository; resilience; statistics; stroke survivor; survivorship; tool

Project Summary/Abstract The Center for the Study of Aphasia Recovery (C-STAR) explores recovery from language impairments following stroke. Over the course of the last 18 years, through both internally supported and NIH-funded awards, this database has grown to include over 5700 MRI scans from 250 stroke survivors. This trove of data is publicly accessible to researchers and scientists around the world via the OpenNeuro NIH-supported biomedical data repository known as the Aphasia Recovery Cohort (ARC). Historically, clinical neuroimaging has been analyzed by small, siloed teams like our own, who have developed specialized tools to handle the unusual properties of clinical data. Our objective is to develop a framework that will allow scientists around the world to process and analyze clinical neuroimaging datasets to make new discoveries. By creating a pipeline that handles the common pitfalls, experts from other domains can contribute innovative solutions, creating a virtuous feedback cycle. Our framework will also help other clinical teams share and exploit their datasets, easing compliance with resource sharing plans and exploiting large distributed datasets. By increasing compliance with FAIR (Findable, Accessible, Interoperable, and Re-usable) principles, we will increase the ability of researchers to examine not only our stroke data, but also stroke data from other repositories. Specifically, we will leverage NeuroDesk, an online platform preinstalled with image-processing software to disseminate a novel stroke-specific processing and analysis package, lesionPrep, which will facilitate usage of the ARC and similar datasets.

项目总结/摘要 失语症恢复研究中心（C-STAR）探索语言恢复 中风后的损伤在过去的18年里，通过内部 支持和NIH资助的奖项，这个数据库已经发展到包括超过5700个MRI扫描 从250名中风幸存者身上研究人员和科学家可以公开访问这些数据 通过OpenNeuro NIH支持的生物医学数据存储库（称为 失语症恢复队列（ARC）。从历史上看，临床神经影像学分析是通过 像我们这样的小型孤立团队，他们开发了专门的工具来处理不寻常的 临床数据的属性。我们的目标是建立一个框架， 处理和分析临床神经影像数据集， 发现。通过创建一个处理常见陷阱的管道， 可以贡献创新的解决方案，创造一个良性的反馈循环。我们的框架还将 帮助其他临床团队共享和利用其数据集，简化资源合规性 共享计划和利用大型分布式数据集。通过提高对FAIR的遵守程度 （可查找、可解释、可互操作和可重用）原则，我们将提高 研究人员不仅要检查我们的中风数据，还要检查来自其他存储库的中风数据。 具体来说，我们将利用NeuroDesk，这是一个预装图像处理的在线平台 软件传播一种新的中风特异性处理和分析包，lesionPrep， 这将促进ARC和类似数据集的使用。