CRCNS:NeuroBridge: Connecting Big Data for Reproducible Clinical Neuroscience

CRCNS：NeuroBridge：连接大数据以实现可重复的临床神经科学

• 批准号: 10318395
• 负责人: Jose Luis Ambite
• 金额: $ 42.24万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-11 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318395
• 关键词: CRCNS; NeuroBridge; Connecting; Big; Data

In replication, mega analysis, and meta-analysis that are critical to the advancement of neuroimaging research, a critical question is how to harness already-collected data for replication purposes efficiently and rigorously. Much of the present efforts on reproducibility science assumes that appropriate datasets are available. While many different neuroimaging databases exist, they have different languages, formats, and usually do not communicate with each other. Moreover, neuroimaging data are collected in hundreds of laboratories each year, forming the “long tail of science” data. Much of this data are described in journal publications but remain underutilized. A critical gap therefore exists in getting enough data of the right kind to the scientist. We propose the NeuroBridge, a data sharing infrastructure that combines innovative techniques and established technologies in a platform that will greatly enhance the reuse of critical neuroscience data sets. RELEVANCE (See instructions): Our research develops technologies that address critical gaps in big data clinical neuroscience research. Our proposed research includes collaboration with addiction projects such as COINSTAC and ENIGMA- addiction. These efforts will leverage technologies that can “expand access to increasingly larger databases … along with rapid advances in analytic, computational, and information technologies,” and increase scientific rigor and reproducibility that will lead to “improved quality and credibility of addiction research and advancement in research on drug use and addiction,” key cross-cutting themes of the National Institute on Drug Abuse (NIDA). Moreover, our focus on developing advanced informatics tools for “Big Data and Computational Science” is in direct alignment with the goals of the Division of Neuroscience and Behavior (DNB) with NIDA. Our above proposed collaborations can further the NIDA mission of “identifying the neural circuits underlying drug addiction and their functional properties,” and “the causes and consequences of drugs of abuse and addiction across the lifespan and to guide treatment strategies.”

在复制，大型分析和荟萃分析是至关重要的神经影像学的进步