Imaging Research Center, University of Texas Austin, TX, USA.
Front Neuroinform. 2013 Jul 8;7:12. doi: 10.3389/fninf.2013.00012. eCollection 2013.
The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.
大规模共享基于任务的功能神经影像学数据有可能使我们对大脑中精神功能的组织有新的认识，但神经影像学领域在数据共享资源的开发方面落后于其他生物科学领域。本文描述了 OpenFMRI 项目（可在 http://www.openfmri.org 在线访问），该项目旨在为神经影像学社区提供一个资源，以支持基于任务的 fMRI 研究的开放共享。我们描述了项目背后的动机，特别关注该项目如何解决基于任务的 fMRI 数据共享的一些众所周知的挑战。目前数据库的初步分析结果表明，该项目具有在跨主体高泛化精度下对任务对比进行分类的能力，并且具有从中以中等精度识别个体主体的能力。聚类分析表明，统计映射之间的相似关系与相关任务所涉及的精神功能之间存在有序关系。这些结果突出了该项目支持大规模多元分析精神过程与大脑功能之间关系的潜力。
