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

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

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.

• Jezik: Angleški jezik
• Ključne besede: neuroimaging, data processing, data analysis and reproducibility
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko; FF - Filozofska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 15 str.
• Številčenje: Vol. 4, art. ǂ1274
• PID: 20.500.12556/RUL-185559
• UDK: 004.6:616.8
• ISSN pri članku: 2837-6056
• DOI: 10.1162/IMAG.a.1274
• COBISS.SI-ID: 283171331
• Datum objave v RUL: 10.08.2026

Gradivo je del revije

• Naslov: Imaging neuroscience
• Skrajšan naslov: Imaging neurosci.
• Založnik: The MIT Press
• ISSN: 2837-6056
• COBISS.SI-ID: 176296451

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: nevrološko slikanje, procesiranje podatkov, analiza podatkov, ponovljivost