Jie Lisa Ji
QuNex – An Integrative Platform for Reproducible Neuroimaging Analytics
Ji, Jie Lisa; Demšar, Jure; Fonteneau, Clara; Tamayo, Zailyn; Pan, Lining; Kraljič, Aleksij; Matkovič, Andraž; Purg, Nina; Helmer, Markus; Warrington, Shaun; Winkler, Anderson; Zerbi, Valerio; Coalson, Timothy S.; Glasser, Matthew F.; Harms, Michael P.; Sotiropoulos, Stamatios N.; Murray, John D.; Anticevic, Alan; Repovš, Grega
Authors
Jure Demšar
Clara Fonteneau
Zailyn Tamayo
Lining Pan
Aleksij Kraljič
Andraž Matkovič
Nina Purg
Markus Helmer
Dr SHAUN WARRINGTON Shaun.Warrington1@nottingham.ac.uk
Research Fellow
Anderson Winkler
Valerio Zerbi
Timothy S. Coalson
Matthew F. Glasser
Michael P. Harms
STAMATIOS SOTIROPOULOS STAMATIOS.SOTIROPOULOS@NOTTINGHAM.AC.UK
Professor of Computational Neuroimaging
John D. Murray
Alan Anticevic
Grega Repovš
Abstract
Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability.
Methods: To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a “turnkey” command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features.
Results: The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform.
Discussion: Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
Citation
Ji, J. L., Demšar, J., Fonteneau, C., Tamayo, Z., Pan, L., Kraljič, A., …Repovš, G. (2023). QuNex – An Integrative Platform for Reproducible Neuroimaging Analytics. Frontiers in Neuroinformatics, 17, Article 1104508. https://doi.org/10.1101/2022.06.03.494750
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 21, 2023 |
Online Publication Date | Apr 5, 2023 |
Publication Date | Apr 5, 2023 |
Deposit Date | Apr 21, 2023 |
Publicly Available Date | Apr 21, 2023 |
Journal | Frontiers in Neuroinformatics |
Electronic ISSN | 1662-5196 |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 17 |
Article Number | 1104508 |
DOI | https://doi.org/10.1101/2022.06.03.494750 |
Keywords | Neuroimaging, data processing, functional MRI, diffusion MRI, multi-modal analyses, containerization, cloud integration, high-performance computing |
Public URL | https://nottingham-repository.worktribe.com/output/9085373 |
Publisher URL | https://www.frontiersin.org/articles/10.3389/fninf.2023.1104508/full |
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Copyright Statement
© 2023 Ji, Demšar, Fonteneau, Tamayo, Pan, Kraljiˇc, Matkoviˇc, Purg, Helmer, Warrington, Winkler, Zerbi, Coalson, Glasser, Harms, Sotiropoulos, Murray, Anticevic and Repovš.
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