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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

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Authors

Jie Lisa Ji

Jure Demšar

Clara Fonteneau

Zailyn Tamayo

Lining Pan

Aleksij Kraljič

Andraž Matkovič

Nina Purg

Markus Helmer

Anderson Winkler

Valerio Zerbi

Timothy S. Coalson

Matthew F. Glasser

Michael P. Harms

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., Matkovič, A., Purg, N., Helmer, M., Warrington, S., Winkler, A., Zerbi, V., Coalson, T. S., Glasser, M. F., Harms, M. P., Sotiropoulos, S. N., Murray, J. D., Anticevic, 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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Licence
https://creativecommons.org/licenses/by/4.0/

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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