ArticleImaging neuroscience (Cambridge, Mass.)2024
A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A single-echo/multi-echo BOLD fMRI test-retest dataset.Data in brief · 2026Article
- ABCD-ReproNim: An educational program for responsible and reproducible analyses of ABCD data.Developmental cognitive neuroscience · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Two axes of white matter development.Nature communications · 2026Article
- IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.Frontiers in neuroinformatics · 2026Article
- An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function.bioRxiv : the preprint server for biology · 2025Article
- Context modulates brain state dynamics and behavioral responses during narrative comprehension.bioRxiv : the preprint server for biology · 2025Article
- A software ecosystem for brain tractometry processing, analysis, and insight.PLoS computational biology · 2025Article
- Open-source platforms to investigate analytical flexibility in neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- QuNex recipes: Executable, human-readable workflows for reproducible neuroimaging research.Imaging neuroscience (Cambridge, Mass.)Article
- Context modulates brain state dynamics and behavioral responses during narrative comprehension.Imaging neuroscience (Cambridge, Mass.)Article
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20 authors.
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Abstract
Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)-the BIDS App-has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging-especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad-a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.
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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.