BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
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Abstract
Abstract The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness richness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms. Author Summary Magnetic Resonance Imaging (MRI) is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. There are thousands MRI studies performed every year generating a substantial amount of data. At the same time, many new data analysis methods are being developed every year. The potential of using new analysis methods on the variety of existing and newly acquired data is hindered by difficulties in software deployment and lack of support for standardized input data. Here we propose to use container technology to make deployment of a wide range of data analysis techniques easy. In addition, we adapt the existing data analysis tools to interface with data organized in a standardized way. We hope that this approach will enable researchers to access a wider range of methods when analyzing their data which will lead to accelerated progress in human neuroscience.
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MRtrix3 can import and export dMRI gradient encoding and other metadata in JSON and BIDS bvec/bval format.
“These include importing and exporting dMRI gradient encoding and other metadata in JSON and BIDS bvec/bval format, ultimately enabling integrating MRtrix3 commands in BIDS Apps (Gorgolewski et al., 2017) .”
From MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation (2019) · cites this paper as Gorgolewski et al., 2017
The Results table (Available BIDS Apps; page not provided) lists an MRtrix3 connectome app for diffusion-weighted imaging, supporting the point that MRtrix3 is represented among BIDS Apps. It does not describe MRtrix3 importing or exporting gradient encoding or other metadata in JSON or BIDS bvec/bval format; searches for those terms found no such passage. (LLM, conf=0.87)
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Frontoparietal action-oriented codes support novel instruction implementation
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- Authors
- Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., Eklund, A., Esteban, O., Flandin, G., Ghosh, S. S., Guntupalli, J. S., Jenkinson, M., Keshavan, A., Kiar, G., Liem, F., Raamana, P. R., Raffelt, D., Steele, C. J., Quirion, P.-O., Smith, R. E., Strother, S. C., Varoquaux, G., Wang, Y., Yarkoni, T., & Poldrack, R. A.
- Affiliations
- Stanford University (US)
- Royal Holloway University of London (GB)
- Institut Universitaire de Gériatrie de Montréal (CA)
- Intel (United States) (US)
- Douglas Mental Health University Institute (CA)
- St. Michael's Hospital (CA)
- Boston Children's Hospital (US)
- Nathan Kline Institute for Psychiatric Research (US)
- Linköping University (SE)
- Wellcome Centre for Human Neuroimaging (GB)
- Harvard University (US)
- Dartmouth College (US)
- University of Oxford (GB)
- University of California, San Francisco (US)
- Johns Hopkins University (US)
- University of Zurich (CH)
- University of Toronto (CA)
- Florey Institute of Neuroscience and Mental Health (AU)
- The University of Texas at Austin (US)
- ORCID
- Gorgolewski, Krzysztof J.
- Alfaro-Almagro, Fidel
- Auer, Tibor
- Bellec, Pierre
- Capotă, Mihai
- Chakravarty, M. Mallar
- Churchill, Nathan W.
- Cohen, Alexander Li
- Craddock, R. Cameron
- Devenyi, Gabriel A.
- Eklund, Anders
- Esteban, Oscar
- Flandin, Guillaume
- Ghosh, Satrajit S.
- Guntupalli, J. Swaroop
- Jenkinson, Mark
- Keshavan, Anisha
- Kiar, Gregory
- Liem, Franziskus
- Raamana, Pradeep Reddy
- Raffelt, David
- Steele, Christopher J.
- Quirion, Pierre-Olivier
- Smith, Robert E.
- Strother, Stephen C.
- Wang, Yida
- Yarkoni, Tal
- Poldrack, Russell A.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-03-09
- Volume
- 13
- Issue
- 3
- Pages
- e1005209
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5363996
- OpenAlex
- W2738782983
- Cited by
- 311
- References
- 52
- Citations by year
- 2026: 25 · 2025: 37 · 2024: 35 · 2023: 28 · 2022: 50 · 2021: 35 · 2020: 29 · 2019: 33 · 2018: 19 · 2017: 19 · 2016: 1
- FWCI
- 44.03
- Type
- journal article
- Language
- en
- Update notices
- new_version (2017-03-23)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- UCL Discovery (University College London) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- PLoS eBooks
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Laura and John Arnold Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- BIDS-App, BIDS, neuroimaging data analysis, containerization, singularities, reproducibility, software portability, software versioning, data archiving
- MeSH terms
- Algorithms, Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Radiology Information Systems, Software, User-Computer Interface, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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