Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.

中文翻译：

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支持HAFNI的大型神经影像信息学平台（HELPNI）。

因此，在功能性MRI信息学系统的建立上进行了巨大的努力，该系统募集了用于fMRI数据分析的统计/计算方法的全面集合。但是，最新的功能磁共振成像信息系统是专门为特定的功能磁共振成像会议或研究而设计的，这些会议或研究的数据量并不是很大，因此难以处理功能磁共振成像“大数据”。由于神经影像技术的发展，fMRI数据的规模近来呈爆炸性增长，因此迫切需要一种能够处理和分析fMRI大数据的有效且高效的fMRI信息系统。为了解决这一挑战，在这项工作中，我们介绍了我们新开发的信息学平台，即“支持HAFNI的大型神经影像信息学平台（HELPNI）”。HELPNI实施了我们最近开发的全脑fMRI信号稀疏表示的计算框架，该框架称为fMRI数据分析的功能网络和相互作用的整体图集（HAFNI）。HELPNI提供了集成的解决方案，可以自动并在结构上存档和处理大规模fMRI数据，从原始fMRI数据中提取和可视化有意义的结果信息，以及通过Web与其他协作者共享开放访问的处理后的原始数据。我们使用公开可用的1000个功能性Connectomes数据集（包括1200多个主题）测试了建议的HELPNI平台。我们基于静止状态功能磁共振成像（rsfMRI）大数据，在个人和人群中确定了一致且有意义的功能性大脑网络。使用高效的采样模块，