Enhancement of the 1000 Functional Connectome Project

1000个功能连接体项目的增强

基本信息

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
项目类别：
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

项目摘要

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）：一旦一个遥远的目标，人类脑功能的发现科学现在已成为现实。通过静止状态fMRI（R-FMRI）利用数据共享，1000个功能连接组项目（FCP）通过通过实验室在世界各地独立收集的数据集进行了数据集，从而使神经成像社区充满活力，并在无限制的情况下公开可用。截至2009年12月11日，曾经努力获得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的创始成员进行的可行性分析证明了使用聚合数据集进行发现科学的能力。为了使Discovery Science在神经影像领域中占据，研究人员需要继续访问大规模成像数据集，这将同时挖掘和复制研究。为了满足这一需求，FCP启动了国际神经影像学计划（INDI）。旨在鼓励与成像数据集开放更详细的表型数据，并为前瞻性共享数据（即出版物）建立模型，在这方面取得了长足的进步。本提案旨在通过克服潜在研究人员面临的第二个关键障碍来扩展这种影响 - 即，科学家需要使用适当的工具来促进数据探索。这尤其适用于没有经验的调查员，因为fMRI图像分析的细微差别，或者缺乏处理和分析大型数据集所需的编程支持或资源。拟议工作的目的是创建一个开源用户界面，以通过新手和专家用户对R-FMRI数据集进行灵活的自动处理。另一个目的是提供基准结果，以便用户可以校准其本地结果。在项目的24个月内达到这些目标将大大加快人脑功能的发现科学轨迹。呢公共卫生相关性：1000个功能连接项目（FCP）通过汇总来自世界各地的数据集并使研究人员无限制地使用它们，从而激发了神经成像社区。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速度，尽管由于有限的神经成像分析经验有限，许多统计学家，数学家，工程师，工程师和神经科学家都无法参与参与。本提案旨在通过创建一个能够由新手和专家用户在FCP中自动处理数据集的软件工具来克服这一障碍。