Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility
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 tack...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2023.1207721/full |
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| author | Dylan Martin Sunitha Basodi Sandeep Panta Kelly Rootes-Murdy Paul Prae Anand D. Sarwate Anand D. Sarwate Ross Kelly Javier Romero Bradley T. Baker Harshvardhan Gazula Jeremy Bockholt Jessica A. Turner Nathalia B. Esper Alexandre R. Franco Alexandre R. Franco Alexandre R. Franco Sergey Plis Vince D. Calhoun |
| author_facet | Dylan Martin Sunitha Basodi Sandeep Panta Kelly Rootes-Murdy Paul Prae Anand D. Sarwate Anand D. Sarwate Ross Kelly Javier Romero Bradley T. Baker Harshvardhan Gazula Jeremy Bockholt Jessica A. Turner Nathalia B. Esper Alexandre R. Franco Alexandre R. Franco Alexandre R. Franco Sergey Plis Vince D. Calhoun |
| author_sort | Dylan Martin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:33:46Z |
| format | Article |
| id | doaj.art-0aeb0c6c38a9444aad29dd4c5b554eb6 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-03-13T04:33:46Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-0aeb0c6c38a9444aad29dd4c5b554eb62023-06-19T07:52:07ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962023-06-011710.3389/fninf.2023.12077211207721Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibilityDylan Martin0Sunitha Basodi1Sandeep Panta2Kelly Rootes-Murdy3Paul Prae4Anand D. Sarwate5Anand D. Sarwate6Ross Kelly7Javier Romero8Bradley T. Baker9Harshvardhan Gazula10Jeremy Bockholt11Jessica A. Turner12Nathalia B. Esper13Alexandre R. Franco14Alexandre R. Franco15Alexandre R. Franco16Sergey Plis17Vince D. Calhoun18Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesDepartment of Electrical and Computer Engineering, Rutgers University–New Brunswick, Piscataway, NJ, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesCenter for the Developing Brain, Child Mind Institute, New York, NY, United StatesCenter for the Developing Brain, Child Mind Institute, New York, NY, United StatesCenter for Brain Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United StatesDepartment of Psychiatry, NYU Grossman School of Medicine, New York, NY, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United StatesCollaborative 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.https://www.frontiersin.org/articles/10.3389/fninf.2023.1207721/fullCOINSTACneuroimagingfederated learningreproducibilityopen sciencedatasets |
| spellingShingle | Dylan Martin Sunitha Basodi Sandeep Panta Kelly Rootes-Murdy Paul Prae Anand D. Sarwate Anand D. Sarwate Ross Kelly Javier Romero Bradley T. Baker Harshvardhan Gazula Jeremy Bockholt Jessica A. Turner Nathalia B. Esper Alexandre R. Franco Alexandre R. Franco Alexandre R. Franco Sergey Plis Vince D. Calhoun Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility Frontiers in Neuroinformatics COINSTAC neuroimaging federated learning reproducibility open science datasets |
| title | Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility |
| title_full | Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility |
| title_fullStr | Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility |
| title_full_unstemmed | Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility |
| title_short | Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility |
| title_sort | enhancing collaborative neuroimaging research introducing coinstac vaults for federated analysis and reproducibility |
| topic | COINSTAC neuroimaging federated learning reproducibility open science datasets |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2023.1207721/full |
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