Improving usage of the Aphasia Research Cohort (ARC) repository

项目摘要/摘要 失语症康复研究中心(C-STAR)探索语言康复 中风后的损害。在过去的18年里，通过内部 这个数据库得到了NIH的支持和资助，现已包含超过5700次MRI扫描 从250名中风幸存者中选出。研究人员和科学家可以公开访问这些数据宝库 通过OpenNeuro NIH支持的生物医学数据存储库 失语症康复队列(ARC)。从历史上看，临床神经影像分析 像我们这样孤立的小团队，他们开发了专门的工具来处理不寻常的事情 临床数据的属性。我们的目标是开发一个框架，让科学家 在世界各地处理和分析临床神经成像数据集，以做出新的 发现。通过创建一条处理常见陷阱的管道，来自其他领域的专家 能够贡献创新的解决方案，创造良性的反馈循环。我们的框架还将 帮助其他临床团队共享和利用他们的数据集，简化资源合规性 共享计划和利用大型分布式数据集。通过增加对公平的遵守 (可查找、可访问、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。 具体地说，我们将利用预装了图像处理的在线平台NeuroDesk 用于分发新的特定于中风的处理和分析包LesionPrep的软件， 这将促进ARC和类似数据集的使用。

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.