Evidence map›Paper›PMID 41293025›Full record

ArticlebioRxiv : the preprint server for biology2025

QuNex Recipes: Executable, Human-Readable Workflows for Reproducible Neuroimaging Research.

Jure Demšar, Aleksij Kraljič, Andraž Matkovič, Samuel Brege, Lining Pan, Zailyn Tamayo, Clara Fonteneau, Markus Helmer, Jie Lisa Ji, Alan Anticevic and 6 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Jure DemšarFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Aleksij KraljičMind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia.
Andraž MatkovičMind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia.
Samuel BregeDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Lining PanDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Zailyn TamayoDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Clara FonteneauDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Markus HelmerJanssen Research & Development, LLC, a Johnson & Johnson Company, Titusville, NJ, USA.
Jie Lisa JiJanssen Research & Development, LLC, a Johnson & Johnson Company, Titusville, NJ, USA.
Alan AnticevicJanssen Research & Development, LLC, a Johnson & Johnson Company, Titusville, NJ, USA.
Cole KorponayMcLean Hospital, Harvard Medical School, Belmont, MA, USA.
Melissa SalavrakosMcLean Hospital, Harvard Medical School, Belmont, MA, USA.
Matthew F GlasserDepartment of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
Lisa D NickersonMcLean Hospital, Harvard Medical School, Belmont, MA, USA.
Youngsun T ChoDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Grega RepovšMind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia.

Funding

Combining Computational Methods, RDoC, and Big Neuroimaging Data to Understand Mechanisms of Neuropsychiatric Symptoms in Alzheimer's DiseaseRF1AG078304 · NIA · MCLEAN HOSPITAL · PI HARPER, DAVID G, NICKERSON, LISA D · 2022 to 2025
$4.4M
NIA NIH HHS RF1 AG078304
6 · The paper itself

Abstract

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.

Indexed as

data analysisdata processingneuroimagingreproducibility

Identifiers

PMID41293025
PMCPMC12642669

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.