CRCNS:NeuroBridge: Connecting Big Data for Reproducible Clinical Neuroscience

在对神经影像学发展至关重要的复制、大分析和荟萃分析中， 研究中，一个关键问题是如何有效地利用已经收集的数据进行复制， 严格地目前关于再现性科学的许多努力都假设适当的数据集是 available.虽然存在许多不同的神经成像数据库，但是它们具有不同的语言、格式和配置。 他们通常不会互相交流。此外，神经成像数据收集在数百个 实验室，形成“科学的长尾”数据。这些数据中的大部分都在期刊中描述。 出版物，但仍未得到充分利用。因此，在获得足够的正确数据方面存在着一个关键的差距 给科学家。我们提出了神经桥，一个数据共享基础设施，结合创新的 技术和既定的技术在一个平台，这将大大提高重用的关键 神经科学数据集。 相关性（参见说明）： 我们的研究开发了解决大数据临床神经科学研究中关键差距的技术。 我们提议的研究包括与成瘾项目的合作，如CONOMAC和ENIGMA- 成瘾这些努力将利用能够“扩大越来越大的 数据库.沿着分析、计算和信息技术的快速发展”， 提高科学的严谨性和可重复性，这将导致“提高成瘾的质量和可信度 药物使用和成瘾问题研究的进展”， 国家药物滥用研究所（NIDA）。此外，我们专注于开发先进的信息学工具， “大数据和计算科学”与神经科学部门的目标直接一致 与NIDA的行为（DNB）。我们上述提出的合作可以进一步推动NIDA的使命， “确定药物成瘾的神经回路及其功能特性”和“药物成瘾的原因” 以及药物滥用和成瘾在整个生命周期中的后果，并指导治疗策略。”

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.”