Enhancement of the 1000 Functional Connectome Project

1000个功能连接体项目的增强

• 批准号: 8412999
• 负责人: Bharat Bhusan Biswal
• 金额: $ 7.74万
• 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 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8412999
• 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. !

描述(申请人提供)：人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。利用与静息状态功能磁共振成像(R-fMRI)轻松共享数据的优势，1000功能连接项目(FCP)通过聚合由世界各地的实验室独立收集的数据集，并将其不受限制地公开提供，从而活跃了神经成像社区。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1200多个数据集。最重要的是，FCP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成像领域站稳脚跟，研究人员需要持续访问大规模成像数据集，以便能够进行数据挖掘和复制研究。为了满足这一需求，FCP发起了国际神经成像数据共享倡议(INDI)。INDI旨在鼓励更详细的表型数据与成像数据集公开共享，并建立一个预期共享数据的模式(即出版前)，INDI在这方面正在取得重大进展。目前的提案试图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fMRI图像分析的细微差别缺乏经验，或缺乏处理和分析大规模数据集所需的编程支持或资源的调查人员。拟议工作的目的是创建一个开放源码的用户界面，供新手和专家用户灵活、自动化地处理R-fMRI数据集。另一个目标是提供基准结果，以便用户可以校准他们的本地结果。在该项目的24个月内实现这些目标，将大大加快人脑功能发现科学的发展轨迹。好了！