Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.
Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.
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DOI：
10.3389/fninf.2023.1207721
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发表时间：
2023
影响因子：
3.5
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
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.
关键词：
尽管有丰富的可用数据，但协作性神经影像学研究经常受到技术、政策、管理和方法障碍的阻碍。COINSTAC（匿名计算的协同信息学和神经成像套件工具包）是一个通过联合分析成功解决这些挑战的平台，允许研究人员在不公开共享数据的情况下分析数据集。本文提出了对COINSTAC平台的一个重要增强：COINSTAC vault （CVs）。cv旨在通过托管标准化、持久性和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为自助服务分析提供了用户友好的界面，简化了协作，并消除了与数据所有者进行手动协调的需要。重要的是，CV也可以与开放数据结合使用，只需创建一个CV，托管您想要包含在分析中的开放数据，从而填补数据共享生态系统中的重要空白。我们通过几个功能和结构神经成像研究，利用联合分析证明了cv的影响，展示了它们在提高研究可重复性和增加神经成像研究样本量方面的潜力。
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.
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影响因子：
--
作者：
Ming J;Verner E;Sarwate A;Kelly R;Reed C;Kahleck T;Silva R;Panta S;Turner J;Plis S;Calhoun V
通讯作者：
Calhoun V
影响因子：
7.7
作者：
Markiewicz CJ;Gorgolewski KJ;Feingold F;Blair R;Halchenko YO;Miller E;Hardcastle N;Wexler J;Esteban O;Goncavles M;Jwa A;Poldrack R
通讯作者：
Poldrack R
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DOI：
10.1016/j.nicl.2020.102375
发表时间：
2020
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3
作者：
通讯作者：
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影响因子：
48
作者：
Esteban, Oscar;Markiewicz, Christopher J.;Gorgolewski, Krzysztof J.
通讯作者：
Gorgolewski, Krzysztof J.