Enhancement of the 1000 Functional Connectome Project

1000个功能连接体项目的增强

• 批准号: 8241553
• 负责人: Bharat Bhusan Biswal
• 金额: $ 8.06万
• 依托单位: NATHAN S. KLINE INSTITUTE FOR PSYCH RES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-16 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8241553
• 关键词: Address; Aging; Archives; Benchmarking; Brain; Brain Mapping; Cerebrum; Childhood; Cities; Communities; Country; Data; Data Set; Distant; Engineering; Ensure; Event; Frequencies; Functional Magnetic Resonance Imaging; Generations; Goals; Human; Image; Image Analysis; International; Journals; Linux; Longevity; Maps; Measurement; Measures; Meta-Analysis; Metadata; Modeling; Molecular Genetics; National Institute of Mental Health; Output; Paper; Phase; Population; Preparation; Procedures; Process; Protocols documentation; Publications; Reading; Recruitment Activity; Reporting; Research; Research Personnel; Resources; Rest; Sampling; Science; Scientist; Seeds; Site; Software Tools; Specific qualifier value; Time; Work; base; data format; data mining; data sharing; design; experience; file format; flexibility; graphical user interface; independent component analysis; interest; member; neuroimaging; open source; operation; portability; preference; programs; prospective; prototype; quality assurance; success; tool

DESCRIPTION (provided by applicant): Once a distant goal, discovery science for human brain function is now a reality. Capitalizing on the ease of data-sharing with resting state fMRI (R-fMRI), the 1000 Functional Connectomes Project (FCP) has invigorated the neuroimaging community by aggregating datasets independently collected by labs around the world, and making them publicly available without restriction. As of Dec 11, 2009, researchers who once struggled to obtain 20 - 30 datasets for analyses suddenly had access to over 1200 datasets. Most importantly, feasibility analyses performed by the founding members of the FCP demonstrated the ability to carry out discovery science with the aggregate dataset. In order for discovery science to take hold in the neuroimaging field, researchers need continued access to large-scale imaging datasets that will enable both data mining and replication studies. To address this need, the FCP launched the International Neuroimaging Data-sharing Initiative (INDI). Designed to encourage the open sharing of more detailed phenotypic data with imaging datasets, and to establish a model for sharing data prospectively (i.e., prepublication), INDI is making significant strides in this regard. The present proposal seeks to extend this impact by overcoming a second key obstacle faced by prospective researchers - namely, scientists need access to appropriate tools to facilitate data exploration. This applies particularly to investigators who are inexperienced with the nuances of fMRI image analysis, or lack the programming support or resources necessary for handling and analyzing large- scale datasets. The aim of the proposed work is to create an open-source user interface for flexible, automated processing of R-fMRI datasets by both novice and expert users. An additional aim is to provide benchmark results so that users can calibrate their local results. Attaining these aims within the 24 months of the project will substantially accelerate the trajectory of discovery science of human brain function. ! PUBLIC HEALTH RELEVANCE: The 1000 Functional Connectomes Project (FCP) has invigorated the neuroimaging community by aggregating datasets from around the world and making them publicly available for researchers to use without restriction. By promoting a culture of open data-sharing, the FCP and similar efforts can rapidly accelerate the pace of neuroscientific and psychiatric discovery, though many statisticians, mathematicians, engineers and neuroscientists are deterred from participating due to limited experience with neuroimaging analysis. The present proposal aims to overcome this obstacle by creating a software tool capable of automated processing of datasets in the FCP by both novice and expert users.

描述（由申请人提供）：人类大脑功能的发现科学曾经是一个遥远的目标，现在已经成为现实。利用静息状态功能磁共振成像（R-fMRI）数据共享的便利性，1000个功能性连接体项目（FCP）通过聚合世界各地实验室独立收集的数据集，并将其公开提供给公众而不受限制，从而扩大了神经成像领域。截至2009年12月11日，曾经难以获得20 - 30个数据集进行分析的研究人员突然可以访问超过1200个数据集。最重要的是，FCP的创始成员进行的可行性分析证明了利用聚合数据集进行发现科学的能力。为了让发现科学在神经影像学领域站稳脚跟，研究人员需要持续访问大规模的成像数据集，以实现数据挖掘和复制研究。为了满足这一需求，FCP发起了国际神经影像数据共享倡议（INDI）。旨在鼓励与成像数据集开放共享更详细的表型数据，并建立前瞻性共享数据的模型（即，在这方面，INDI正在取得重大进展。本提案旨在通过克服未来研究人员面临的第二个关键障碍来扩大这一影响，即科学家需要获得适当的工具来促进数据探索。这尤其适用于那些对功能磁共振成像图像分析的细微差别缺乏经验的研究人员，或者缺乏处理和分析大规模数据集所需的编程支持或资源的研究人员。拟议的工作的目的是创建一个开源的用户界面，灵活，自动化处理的R-fMRI数据集的新手和专家用户。另一个目标是提供基准结果，以便用户可以校准其本地结果。在项目的24个月内实现这些目标将大大加快人类大脑功能科学发现的轨迹。! 公共卫生相关性：1000个功能性连接体项目（FCP）通过聚合来自世界各地的数据集并将其公开供研究人员无限制地使用，从而扩展了神经影像学界。通过促进开放数据共享的文化，FCP和类似的努力可以迅速加快神经科学和精神病学发现的步伐，尽管许多统计学家，数学家，工程师和神经科学家由于神经成像分析的经验有限而被阻止参与。本提案的目的是克服这一障碍，创建一个软件工具，使新手和专家用户都能自动处理FCP中的数据集。