QuNex recipes : executable, human- readable workflows for reproducible neuroimaging research

Demšar, Jure (author), Kraljič, Aleksij (author), Matkovič, Andraž (author), Brege, Samuel (author), Pan, Lining (author), Tamayo, Zailyn (author), Fonteneau, Clara (author), Helmer, Markus (author), Ji, Jie Lisa (author), Anticevic, Alan (author), Korponay, Cole (author), Salavrakos, Melissa (author), Drucker, Daniel (author), Glasser, Matthew F. (author), Nickerson, Lisa D. (author), Cho, Youngsun T. (author), Repovš, Grega (author)

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows—encompassing data onboarding, preprocessing, and analysis—in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

• Language: English
• Keywords: neuroimaging, data processing, data analysis and reproducibility
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FF - Faculty of Arts
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 15 str.
• Numbering: Vol. 4, art. ǂ1274
• PID: 20.500.12556/RUL-185559
• UDC: 004.6:616.8
• ISSN on article: 2837-6056
• DOI: 10.1162/IMAG.a.1274
• COBISS.SI-ID: 283171331
• Publication date in RUL: 10.08.2026

Record is a part of a journal

• Title: Imaging neuroscience
• Shortened title: Imaging neurosci.
• Publisher: The MIT Press
• ISSN: 2837-6056
• COBISS.SI-ID: 176296451

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: nevrološko slikanje, procesiranje podatkov, analiza podatkov, ponovljivost