CRCNS: US-France Data Sharing Proposal: Lowering the barrier of entry to network neuroscience

CRCNS：美法数据共享提案：降低网络神经科学的准入门槛

• 批准号: 9916138
• 负责人: Danielle Smith Bassett
• 金额: $ 21.51万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9916138
• 关键词: Address; Aging; Biophysics; Brain; Cloud Computing; Cognition; Communities; Complex Analysis; Computer software; Data; Data Set; Development; Disease; Documentation; Ecosystem; Education; Elements; France; Funding; Generations; Graph; Growth; Instruction; Knowledge; Language; Lead; Libraries; Link; Literature; Machine Learning; Mathematics; Methods; Modeling; Neurosciences; Pathway Analysis; Play; Process; Reporting; Reproducibility; Research; Research Personnel; Resources; Role; Running; Science; Sociology; Standardization; Structure; Study models; System; Techniques; Time; Training; analysis pipeline; brain computer interface; cloud based; cyber infrastructure; data sharing; experience; innovation; insight; learning strategy; network architecture; network models; neuroimaging; open data; prevent; relating to nervous system; statistics; tool

The field of network neuroscience has developed powerful analysis tools for studying brain networks and holds promise for deepening our understanding of the role played by brain networks in health, disease, development, and cognition. Despite widespread interest, barriers exist that prevent these tools from having broader impact. These include (1) unstandardized practices for sharing and documenting software, (2) long delays from when a method is first introduced to when it becomes publicly available, and (3) gaps in theoretic knowledge and understanding leading to incorrect, delays due to mistakes, and errors in reported results. These barriers ultimately slow the rate of neuroscientific discovery and stall progress in applied domains. To overcome these challenges, we will use open science methods and cloud-computing, to increase the availability of network neuroscience tools. We will use the platform "brainlife.io" for sharing these tools, which will be packaged into self-contained, standardized, reproducible Apps, shared with and modified by a community of users, and integrated into existing brainlife.io analysis pipelines. Apps will also be accompanied by links to primary sources, in-depth tutorials, and documentation, and worked-through examples, highlighting their correct usage and offering solutions for mitigating possible pitfalls. In standardizing and packaging network neuroscience tools as Apps, this proposed research will engage a new generation of neuroscientists, providing them powerful new and leading to new discoveries. Second, the proposed research will contribute growing suite of modeling analysis that can be modified to suit specialized purposes. Finally, the Brainlife.io platform will serve as part of the infrastructure supporting neuroscience research. Altogether, these advances will lead to new opportunities in network neuroscience research and further stimulate its growth while increasing synergies with other domains in neuroscience.

网络神经科学领域已经开发出强大的分析工具来研究大脑网络和 有望加深我们对大脑网络在健康、疾病、 发展和认知。尽管人们普遍感兴趣，但仍存在障碍，使这些工具无法 更广泛的影响。这些问题包括(1)共享和记录软件的非标准化做法，(2)冗长 从一种方法首次引入到它公开可用的时间延迟，以及(3)理论上的差距 知识和理解导致不正确、因错误而延误和报告结果中的错误。 这些障碍最终减缓了神经科学发现的速度，并阻碍了应用领域的进展。 为了克服这些挑战，我们将使用开放科学方法和云计算，以增加 网络神经科学工具的可用性。我们将使用“Brainlife.io”平台来分享这些工具，其中 将打包成独立的、标准化的、可重现的应用程序，与 用户社区，并集成到现有的Brainlife.io分析管道中。应用程序也将随同 通过指向主要来源的链接、深入的教程和文档以及已完成的示例， 强调它们的正确使用，并提供减少可能陷阱的解决方案。 在将网络神经科学工具标准化和打包为应用程序的过程中，这项拟议的研究将参与 新一代神经科学家，为他们提供了强大的新成果，并导致了新的发现。第二， 拟议的研究将有助于不断增长的建模分析套件，这些分析可以进行修改以适应专门的 目的。最后，Brainlife.io平台将作为支持神经科学的基础设施的一部分 研究。总之，这些进展将带来网络神经科学研究和 进一步刺激其增长，同时增加与神经科学其他领域的协同效应。