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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要失语症恢复研究中心（C-Star）探索了从语言中恢复中风后的障碍。在过去的18年中，两者都在内部该数据库已获得支持和NIH资助的奖项，包括超过5700次MRI扫描来自250名中风幸存者。研究人员和科学家可以公开访问这一数据通过Openneuro NIH支持的生物医学数据存储库，被称为失语恢复队列（ARC）。从历史上看，临床神经影像学已通过像我们自己一样的小型，孤立的团队，他们开发了专业工具来处理异常临床数据的特性。我们的目标是开发一个框架，该框架将允许科学家在世界各地处理和分析临床神经影像学数据集以使新发现。通过创建处理共同陷阱的管道，来自其他领域的专家可以贡献创新的解决方案，从而创造出良性的反馈周期。我们的框架也将帮助其他临床团队共享并利用其数据集，从而降低对资源的遵守共享计划并利用大型分布式数据集。通过增加对公平的遵守（可找到，可访问，可互操作和可重复使用）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用于传播新型中风的处理和分析软件包的软件，病变prep，这将有助于使用弧和类似数据集。