Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.
COINSTAC
collaborative analysis
datasets
federated learning
neuroimaging
open science
privacy
reproducibility
Journal
Frontiers in neuroinformatics
ISSN: 1662-5196
Titre abrégé: Front Neuroinform
Pays: Switzerland
ID NLM: 101477957
Informations de publication
Date de publication:
2023
2023
Historique:
received:
18
04
2023
accepted:
02
06
2023
medline:
5
7
2023
pubmed:
5
7
2023
entrez:
5
7
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37404336
doi: 10.3389/fninf.2023.1207721
pmc: PMC10315678
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1207721Subventions
Organisme : NIDA NIH HHS
ID : R01 DA040487
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA049238
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH121246
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
Copyright © 2023 Martin, Basodi, Panta, Rootes-Murdy, Prae, Sarwate, Kelly, Romero, Baker, Gazula, Bockholt, Turner, Esper, Franco, Plis and Calhoun.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Neuroinformatics. 2022 Jan;20(1):261-275
pubmed: 34846691
Neuroinformatics. 2022 Jan;20(1):91-108
pubmed: 33948898
Neuroinformatics. 2017 Oct;15(4):343-364
pubmed: 28812221
Proc Natl Acad Sci U S A. 2010 Mar 9;107(10):4734-9
pubmed: 20176931
Neuroimage Clin. 2020;28:102375
pubmed: 32961402
Sci Data. 2017 Dec 19;4:170181
pubmed: 29257126
Sci Data. 2016 Jun 21;3:160044
pubmed: 27326542
PLoS Genet. 2008 Aug 29;4(8):e1000167
pubmed: 18769715
Elife. 2021 Oct 18;10:
pubmed: 34658334
Indian J Psychol Med. 2020 Jan 06;42(1):102-103
pubmed: 31997873
IEEE Trans Signal Process. 2021;69:6355-6370
pubmed: 35755147
F1000Res. 2017 Aug 18;6:1512
pubmed: 29123643
Neuroimage. 2017 Jan 15;145(Pt B):389-408
pubmed: 26658930
Nat Methods. 2023 Jan;20(1):34
pubmed: 36635546
Neuroinformatics. 2023 Apr;21(2):287-301
pubmed: 36434478
Genome Res. 2011 Jul;21(7):1001-7
pubmed: 21632745
Nat Methods. 2019 Jan;16(1):111-116
pubmed: 30532080
Neuroimage. 2017 Jan;144(Pt B):259-261
pubmed: 26048618
Schizophr Bull. 2015 Sep;41(5):1133-42
pubmed: 25548384
Brain Imaging Behav. 2014 Jun;8(2):153-82
pubmed: 24399358
Neuroinformatics. 2022 Apr;20(2):377-390
pubmed: 34807353
Front Neurosci. 2016 Aug 19;10:365
pubmed: 27594820
Hum Brain Mapp. 2022 Jun 1;43(8):2707-2721
pubmed: 35142409
Neuroinformatics. 2013 Jul;11(3):367-88
pubmed: 23760817