A SYSTEM TO SHARE AND EXPLORE HUMAN BRAIN MAPPING DATA

共享和探索人脑图谱数据的系统

• 批准号: 7955654
• 负责人: KEN SMITH
• 金额: $ 0.34万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955654
• 关键词: Area; Brain; Brain Mapping; Brain region; Communication; Communities; Computer Retrieval of Information on Scientific Projects Database; Creativeness; Data; Data Set; Databases; Development; Funding; Grant; Human; Imaging technology; Informatics; Information Management; Institution; International; Laboratories; Language; Magnetic Resonance Imaging; Management Information Systems; Modeling; Neurosciences; Policies; Population; Productivity; Research; Research Personnel; Resources; Sampling; Source; Structure; System; Techniques; Testing; United States National Institutes of Health; Vision; computational anatomy; data modeling; data sharing; design; flexibility; innovation; interest; neuroinformatics; open source; peer; system architecture

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recent advances in imaging technologies have provided human brain mapping researchers with awide repertoire of experimental approaches to elucidate brain structure and function at a variety of spatial scales. Over the entire neuroscience community, a vast amount of human brain mapping data is being gathered. However, lack of suitable a information management system keeps the value and scientific impact of this data from reaching its full potential, as it is explored almost exclusively by the lab of origin using data and resources which are locally accessible. We envision a worldwide information management network capable of increasing the creativity and productivity of neuroscience investigators, as they use shared human brain mapping data to generate and test ideas that were unanticipated by the data's originators. To achieve this vision, we propose to design an open source information management system that enables the exploration, analysis, and controlled sharing of structural magnetic resonance imaging (MRI) data. This system will function flexibly in multiple modes: as an international repository and data exploration platform for the neuroscience community, as part of a peer-to-peer sharing network, or as a lightweight stand-alone laboratory information management system. We expect that increased creativity and productivity will derive from the ability to: 1) access a large number of MRI datasets that sample a broad demographic spectrum; 2) query by example (e.g. "retrieve volumes which look like this example") and over specific brain region attributes (e.g., volume) to generate and test neuroscientific hypotheses; 3) create, visualize, and manipulate average brain representations tailored to a demographic population of interest; and 4) rely on a common set of basic terms and sharing principles to faciliate communication in peer relationships. The system's design will entail the development of innovative informatics techniques in the areas of data modeling, query languages, and systems architecture. It will be scalable in size, extensible in capabilities, flexible in data sharing policy options, and compatible in format with other neuroinformatic systems.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 成像技术的最新进展为人脑成像研究人员提供了广泛的实验方法，以在不同的空间尺度上阐明大脑结构和功能。在整个神经科学界，人们正在收集大量的人脑图谱数据。然而，由于缺乏合适的信息管理系统，这些数据的价值和科学影响无法充分发挥出来，因为这些数据几乎完全由原产地实验室使用当地可获得的数据和资源进行探索。我们设想一个全球信息管理网络，能够提高神经科学研究人员的创造力和生产率，因为他们使用共享的人脑映射数据来生成和测试数据发起者意想不到的想法。为了实现这一愿景，我们建议设计一个开放源码的信息管理系统，使结构磁共振成像(MRI)数据的探索、分析和受控共享成为可能。该系统将以多种模式灵活运作：作为神经科学界的国际储存库和数据探索平台，作为点对点共享网络的一部分，或者作为一个轻量级的独立实验室信息管理系统。我们预计，创造力和生产率的提高将源于以下能力：1)访问大量对广泛人口统计光谱进行采样的MRI数据集；2)按示例进行查询(例如“检索看起来像这个示例的体积”)和特定大脑区域属性(例如体积)以生成和测试神经科学假设；3)创建、可视化和操纵针对感兴趣的人口统计人群量身定做的平均大脑表示；以及4)依赖一组通用的基本术语和共享原则来促进同伴关系中的沟通。该系统的设计将需要在数据建模、查询语言和系统架构领域开发创新的信息学技术。它的规模是可扩展的，功能是可扩展的，数据共享策略选项是灵活的，并且在格式上与其他神经信息系统兼容。