Enhancement of the 1000 Functional Connectome Project

描述（由申请人提供）：人类大脑功能的科学发现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，1000个功能连接体项目（FCP）通过汇总世界各地实验室独立收集的数据集，并使它们不受限制地公开提供，为神经成像社区注入了活力。截至2009年12月11日，曾经努力获取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，由FCP创始成员进行的可行性分析证明了利用聚合数据集开展发现科学的能力。为了让发现科学在神经成像领域占据优势，研究人员需要持续访问大规模的成像数据集，这将使数据挖掘和复制研究成为可能。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）。旨在鼓励更详细的表型数据与成像数据集的开放共享，并建立一个前瞻性共享数据的模型（即出版前），INDI在这方面取得了重大进展。目前的提议试图通过克服未来的科学家面临的第二个关键障碍来扩大这种影响——即科学家需要获得适当的工具来促进数据探索。这尤其适用于对fMRI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。提出的工作的目的是创建一个开源用户界面，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测试结果，以便用户可以校准他们的本地结果。在项目的24个月内实现这些目标将大大加速人类大脑功能发现科学的轨迹。！

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. !