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加强协作神经影像学研究:引入用于联合分析和可重复性的COINSTAC Vaults

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.

作者信息

Martin Dylan, Basodi Sunitha, Panta Sandeep, Rootes-Murdy Kelly, Prae Paul, Sarwate Anand D, Kelly Ross, Romero Javier, Baker Bradley T, Gazula Harshvardhan, Bockholt Jeremy, Turner Jessica A, Esper Nathalia B, Franco Alexandre R, Plis Sergey, Calhoun Vince D

机构信息

Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States.

Department of Electrical and Computer Engineering, Rutgers University-New Brunswick, Piscataway, NJ, United States.

出版信息

Front Neuroinform. 2023 Jun 19;17:1207721. doi: 10.3389/fninf.2023.1207721. eCollection 2023.

Abstract

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.

摘要

尽管有大量可用数据,但协作神经影像学研究常常受到技术、政策、管理和方法学等方面的障碍阻碍。COINSTAC(用于匿名计算的协作信息学和神经影像学套件工具包)是一个通过联邦分析成功应对这些挑战的平台,它允许研究人员在不公开共享数据的情况下分析数据集。本文介绍了对COINSTAC平台的一项重大改进:COINSTAC Vaults(CVs)。CVs旨在通过托管标准化、持久且高可用性的数据集来进一步减少障碍,同时与COINSTAC的联邦分析功能无缝集成。CVs提供了一个便于用户进行自助分析的界面,简化了协作流程,并且无需与数据所有者进行人工协调。重要的是,通过简单地创建一个托管想要纳入分析的开放数据的CV,CVs也可以与开放数据结合使用,从而填补了数据共享生态系统中的一个重要空白。我们通过利用联邦分析的几项功能和结构神经影像学研究展示了CVs的影响,凸显了它们在提高神经影像学研究的可重复性和增加样本量方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7034/10315678/81e0248dd875/fninf-17-1207721-g0001.jpg

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