COINSTAC: Decentralizing the future of brain imaging analysis.
COINSTAC: Decentralizing the future of brain imaging analysis.
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DOI：
10.12688/f1000research.12353.1
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Calhoun V
中科院分区：
文献类型：
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作者：
Ming J;Verner E;Sarwate A;Kelly R;Reed C;Kahleck T;Silva R;Panta S;Turner J;Plis S;Calhoun V
在大数据时代，跨多个站点共享神经成像数据变得越来越重要。然而，想要进行集中式大规模数据共享和分析的研究人员必须经常应对诸如高数据库成本、长数据传输时间、大量手动工作以及敏感数据的隐私问题等问题。为了消除这些障碍，实现更轻松的数据共享和分析，我们在2016年为大脑成像数据引入了一种新的、分散的、支持隐私的基础设施模型，称为CONODAC。自COCOLAAC模型首次推出以来，我们一直在持续开发该模型。这种模型的挑战之一是调整所需的算法，使其在分散的框架内发挥作用。在本文中，我们报告了我们如何解决这个问题，沿着我们在几个方面的进展，包括额外的分散算法实现，用户界面增强，分散回归统计计算，以及完整的管道规范。
In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.