Ming Jing, Verner Eric, Sarwate Anand, Kelly Ross, Reed Cory, Kahleck Torran, Silva Rogers, Panta Sandeep, Turner Jessica, Plis Sergey, Calhoun Vince
Datalytic Solutions, Albuquerque, NM, 87106, USA.
The Mind Research Network, Albuquerque, NM, 87106, USA.
F1000Res. 2017 Aug 18;6:1512. doi: 10.12688/f1000research.12353.1. eCollection 2017.
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.
在大数据时代,跨多个站点共享神经影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常常要应对诸如数据库成本高、数据传输时间长、大量人工操作以及敏感数据的隐私问题等难题。为了消除这些障碍,使数据共享和分析更加便捷,我们在2016年引入了一种名为COINSTAC的全新的、去中心化的、支持隐私保护的脑成像数据基础设施模型。自首次引入该模型以来,我们一直在持续开发COINSTAC。这种模型面临的挑战之一是使所需算法在去中心化框架内运行。在本文中,我们报告了我们是如何解决这个问题的,以及我们在几个方面取得的进展,包括额外的去中心化算法实现、用户界面增强、去中心化回归统计计算以及完整的管道规范。