Spokes: MEDIUM: WEST: Breaking down barriers for reproducible neuroimaging data analyses

辐条：中：西：打破可重复神经影像数据分析的障碍

• 批准号: 1760950
• 负责人: Russell Poldrack
• 金额: $ 59.99万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760950&HistoricalAwards=false
• 关键词: Spokes; MEDIUM; WEST; Breaking; down

This project will enable the reproducible analysis and sharing of large human brain imaging datasets. Imaging the brain using magnetic resonance imaging (MRI) is an essential tool for the study of the human brain, but the processing of large neuroimaging datasets is often a difficult and time-consuming process. This project will improve the ability of researchers to analyze these data more effectively and share the results. This work will build upon the OpenNeuro project, which provides researchers with the ability to easily upload and share neuroimaging data. The first aim of this project is to develop the ability to process these datasets using national supercomputing resources. Because these resources are both much more powerful and more cost-effective than commercial cloud computing resources, this extension will allow researchers to process larger datasets using more sophisticated analysis procedures. The second aim is to extend the reach of data storage and processing beyond standard neuroimaging datasets to include heterogeneous datasets with clinical and psychological data in addition to brain imaging data. A large study of human brain development will be used as a test case for this extension. The third aim of this project is to engage researchers and software developers in order to develop a broad community that will further extend the reach and capabilities of the proposed technical developments and promote the sustainability of the resources beyond the grant period.The first aim will implement the ability to execute computational workflows using national supercomputing resources through a Science-As-A-Service model, via technologies such as the Agave API and the Singularity container system. This will provide the ability for researchers to execute complex containerized workflows using large datasets, providing a long-term sustainable computational platform for analysis of open data. The second aim will involve the extension of the current OpenNeuro platform to represent data from a large longitudinal neurodevelopmental study, in order to allow the joint analysis of imaging, clinical, psychological, and genetic data. The third aim will engage the relevant user and developer communities through workshops and code sprints.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将使大型人脑成像数据集的可重复分析和共享成为可能。使用磁共振成像（MRI）对大脑进行成像是研究人类大脑的重要工具，但处理大型神经成像数据集通常是一个困难且耗时的过程。该项目将提高研究人员更有效地分析这些数据并分享结果的能力。这项工作将建立在OpenNeuro项目的基础上，该项目为研究人员提供了轻松上传和共享神经成像数据的能力。该项目的第一个目标是开发利用国家超级计算资源处理这些数据集的能力。因为这些资源都比商业云计算资源更强大，更具成本效益，这个扩展将允许研究人员使用更复杂的分析程序来处理更大的数据集。第二个目标是扩展数据存储和处理的范围，超越标准的神经成像数据集，除了脑成像数据外，还包括临床和心理数据的异构数据集。人类大脑发育的大型研究将被用作此扩展的测试案例。该项目的第三个目标是吸引研究人员和软件开发人员，以建立一个广泛的社区，进一步扩大拟议技术发展的范围和能力，并促进资助期后资源的可持续性。第一个目标是通过Agave API和Singularity容器系统等技术，通过科学即服务模型实现使用国家超级计算资源执行计算工作流的能力。这将为研究人员提供使用大型数据集执行复杂容器化工作流程的能力，为开放数据分析提供一个长期可持续的计算平台。第二个目标将涉及扩展现有的OpenNeuro平台，以表示来自大型纵向神经发育研究的数据，以便对成像、临床、心理和遗传数据进行联合分析。第三个目标将通过研讨会和代码冲刺吸引相关的用户和开发人员社区。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。