The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
Open accessPublished version of a preprint
Abstract
The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Source data files were converted to NifTI format using dcm2niix and organized and named following the Brain Imaging Data Structure (BIDS).
“To provide a single standard format, the sources data files were converted to NifTI format files using the dcm2niix 54 conversion program and are organized and named following the Brain Imaging Data Structure 55 (BIDS).”
From OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019) · cites this paper as [55]
MRtrix3 provides functionalities for handling data in the Brain Imaging Data Structure (BIDS).
“In addition, MRtrix3 provides the requisite functionalities for handling data in the Brain Imaging Data Structure (BIDS) , a recent standard for organising and sharing neuroimaging data (Gorgolewski et 2 al., 2016) .”
From MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation (2019) · cites this paper as Gorgolewski et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Extensible Markup Language XML
Hitzler, P., Krotzsch, M., & Rudolph, S. (2009) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420090512-18 · Cited by 4,836 · see citing works
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Gorgolewski, K. J., Burns, C. D., Madison, C. M., Clark, D., Halchenko, Y. O., Waskom, M., & Ghosh, S. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00013 · Cited by 2,206 · see citing works
Self-citationThe extensible neuroimaging archive toolkit
Marcus, D. S., Olsen, T. R., Ramaratnam, M., & Buckner, R. L. (2007) · Neuroinformatics · doi:10.1385/ni:5:1:11 · Cited by 462 · see citing works
The cognitive atlas: toward a knowledge foundation for cognitive neuroscience
Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., & Bilder, R. M. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00017 · Cited by 385 · see citing works
Self-citationToward open sharing of task-based fMRI data: the OpenfMRI project
Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, S., & Milham, M. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00012 · Cited by 341 · see citing works
Self-citationComparison of JSON and XML Data Interchange Formats: A Case Study.
Nurseitov, N., Paulson, M. G., Reynolds, R., et al. (2009) · Computer Applications in Industry and Engineering · Cited by 263 · see citing works
COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets
Scott, A., Courtney, W., Wood, D., de la Garza, R., Lane, S., King, M. D., Wang, R., Roberts, J., Turner, J. A., & Calhoun, V. D. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00033 · Cited by 178 · see citing works
Self-citationLORIS: a web-based data management system for multi-center studies
Das, S., Zijdenbos, A. P., Harlap, J., Vins, D., & Evans, A. C. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00037 · Cited by 165 · see citing works
Self-citationThe Cognitive Paradigm Ontology: Design and Application
Turner, J. A., & Laird, A. R. (2011) · Neuroinformatics · doi:10.1007/s12021-011-9126-x · Cited by 156 · see citing works
Self-citationCusack, R., Vicente-Grabovetsky, A., Mitchell, D. J., Wild, C. J., Auer, T., Linke, A. C., & Peelle, J. E. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00090 · Cited by 149 · see citing works
Self-citationEastman, K. M., & Huk, A. C. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00001 · Cited by 135 · see citing works
Special Issue: The First Provenance Challenge
Moreau, L., Ludäscher, B., Altıntaş, İ., Barga, R., Bowers, S., Callahan, S. P., Chin, G., Clifford, B., Cohen, S., Cohen‐Boulakia, S., et al. (2007) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1233 · Cited by 133 · see citing works
Self-referential forces are sufficient to explain different dendritic morphologies
Memelli, H., Torben-Nielsen, B., & Kozloski, J. (2013) · Front. Neuroinform. · doi:10.3389/fninf.2013.00001 · Cited by 127 · see citing works
Keator, D. B., Grethe, J. S., Marcus, D., Özyurt, İ. B., Gadde, S., Murphy, S., Pieper, S. D., Greve, D. N., Notestine, R. J., Bockholt, H. J., et al. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.917893 · Cited by 123 · see citing works
Self-citationSperka, D. J., & Ditterich, J. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00001 · Cited by 120 · see citing works
Making Data Sharing Count: A Publication-Based Solution
Gorgolewski, K. J., Margulies, D., & Milham, M. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00009 · Cited by 99 · see citing works
Self-citationIs it time to re-prioritize neuroimaging databases and digital repositories?
Horn, J. D. V., & Toga, A. W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.086 · Cited by 80 · see citing works
Towards structured sharing of raw and derived neuroimaging data across existing resources
Keator, D. B., Helmer, K. G., Steffener, J., Turner, J. A., van Erp, T. G. M., Gadde, S., Ashish, N., Burns, G., & Nichols, B. N. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.094 · Cited by 76 · see citing works
Self-citationSmith, K. (2012) · Nature · doi:10.1038/484024a · Cited by 71 · see citing works
MacKenzie‐Graham, A. J., Horn, J. D. V., Woods, R. P., Crawford, K. L., & Toga, A. W. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.186 · Cited by 66 · see citing works
XCEDE: An Extensible Schema for Biomedical Data
FBIRN, M., Gadde, S., Aucoin, N., et al. (2011) · Neuroinformatics · doi:10.1007/s12021-011-9119-9 · Cited by 49 · see citing works
Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database
Özyurt, İ. B., Keator, D. B., Dingying, W., et al. (2010) · Neuroinformatics · doi:10.1007/s12021-010-9078-6 · Cited by 48 · see citing works
Self-citationSchwartz, Y., Barbot, A., Thyreau, B., Frouin, V., Varoquaux, G. P., Aditya, S., Marcus, D. S., & Poline, J. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00012 · Cited by 39 · see citing works
Self-citationA General XML Schema and SPM Toolbox for Storage of Neuro-Imaging Results and Anatomical Labels
Keator, D. B., Gadde, S., Grethe, J. S., et al. (2006) · Neuroinformatics · doi:10.1385/ni:4:2:199 · Cited by 37 · see citing works
Self-citationBook, G. A., Anderson, B. M., Stevens, M. C. G., et al. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9194-1 · Cited by 27 · see citing works
Nichols, B. N., & Pohl, K. M. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9293-x · Cited by 25 · see citing works
Self-citationData management to support reproducible researchPreprint
Wandell, B. A., Rokem, A., Perry, L., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.06900 · Cited by 17 · see citing works
Self-citationDeveloping a Brain Informatics Provenance Model
Han, Z., Chen, J., Kotake, T., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02753-1_44 · Cited by 9 · see citing works
The development and generality of self -control
Cohen, J. R. (2009) · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Keator D. B. et al. A General XML Schema and SPM Toolbox for Storage of Neuro-Imaging Results and Anatomical Labels. Neuroinformatics 4, 199– 211 (2006). 16845169
- Cox R. W. et al. A (Sort of) New Image Data Format Standard: NIfTI-1. in Proceedings of the 10th Annual Meeting of Organisation of Human Brain Mapping (2003).
- Zhong H. et al. in Brain and Health Informatics 439– 449 (Springer International Publishing, 2013).
- Crockford D. JSON: The fat-free alternative to XML. in Proc. of XML 2006 (2006).
Cited by
Sorted by citations
fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905 · see citing works
Self-citationMRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974 · see citing works
Scanning the horizon: towards transparent and reproducible neuroimaging research
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.167 · Cited by 1,410 · see citing works
Self-citationMRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920 · see citing works
Self-citationLaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779 · see citing works
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809 · see citing works
Best practices in data analysis and sharing in neuroimaging using MRI
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4500 · Cited by 714 · see citing works
Self-citationAnalysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315 · see citing works
Self-citationGorgolewski, 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., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311 · see citing works
Self-citationA Shared Vision for Machine Learning in Neuroscience
Vu, M.-A. T., Adalı, T., Ba, D., Buzsáki, G., Carlson, D., Heller, K., Liston, C., Rudin, C., Sohal, V. S., Widge, A. S., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0508-17.2018 · Cited by 191 · see citing works
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
Self-citationTAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169 · see citing works
Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging
Kennedy, D. N., Abraham, S. A., Bates, J. F., Crowley, A., Ghosh, S., Gillespie, T., Goncalves, M., Grethe, J. S., Halchenko, Y. O., Hanke, M., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00001 · Cited by 113 · see citing works
Self-citationProgress toward openness, transparency, and reproducibility in cognitive neuroscience
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13325 · Cited by 95 · see citing works
Digital open science—Teaching digital tools for reproducible and transparent research
Toelch, U., & Ostwald, D. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006022 · Cited by 80 · see citing works
EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces
Won, K., Kwon, M., Ahn, M., & Jun, S. C. (2022) · Scientific Data · doi:10.1038/s41597-022-01509-w · Cited by 68 · see citing works
Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51 · see citing works
Burgos, N., & Colliot, O. (2020) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000838 · Cited by 53 · see citing works
A collaborative resource platform for non-human primate neuroimaging
Messinger, A., Sirmpilatze, N., Heuer, K., Loh, K. K., Mars, R. B., Sein, J., Xu, T., Glen, D., Jung, B., Seidlitz, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117519 · Cited by 52 · see citing works
Suppression weakens unwanted memories via a sustained reduction of neural reactivation
Meyer, A.-K., & Benoit, R. G. (2022) · eLife · doi:10.7554/elife.71309 · Cited by 27 · see citing works
A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation
Dargahi Nobari, K., & Bertram, T. (2024) · Scientific Data · doi:10.1038/s41597-024-03137-y · Cited by 29 · see citing works
Simistira Liwicki, F., Gupta, V., Saini, R., De, K., Abid, N., Rakesh, S., Wellington, S., Wilson, H., Liwicki, M., & Eriksson, J. (2023) · Scientific Data · doi:10.1038/s41597-023-02286-w · Cited by 28 · see citing works
Standardized Effect Sizes and Image-Based Meta-Analytical Approaches for fMRI Data
Bossier, H., Nichols, T. E., & Moerkerke, B. (2019) · doi:10.1101/865881 · Cited by 14 · see citing works
Self-citationThe Personal Data Is Political
Greshake Tzovaras, B., & Tzovara, A. (2019) · Philosophical Studies Series · doi:10.1007/978-3-030-04363-6_8 · Cited by 7 · see citing works
Mindfulness training impacts brain network dynamics linked to stress response in young adolescents
Gaviria, J., Celen, Z., Smith, M. M., Peek, L., Brosset, S., Vuilleumier, P., Van De Ville, D., Merglen, A., Klauser, P., & Piguet, C. (2024) · doi:10.1101/2024.10.16.616959 · Cited by 1 · see citing works
- Authors
- Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A.
- Affiliations
- Stanford University (US)
- MRC Cognition and Brain Sciences Unit (GB)
- Mind Research Network (US)
- Nathan Kline Institute for Psychiatric Research (US)
- Montreal Neurological Institute and Hospital (CA)
- University of Oxford (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Harvard University (US)
- Lyon 1 Université (FR)
- Dartmouth College (US)
- National Institute of Mental Health (US)
- Center for Behavioral Brain Sciences (DE)
- University of California, Irvine (US)
- The Ohio State University (US)
- University of Warwick (GB)
- SRI International (US)
- Child Mind Institute (US)
- University of California, Berkeley (US)
- University of Washington (US)
- Corresponding author
- Gorgolewski, Krzysztof J.
- ORCID
- Gorgolewski, Krzysztof J.
- Auer, Tibor
- Calhoun, Vince D.
- Craddock, R. Cameron
- Das, Samir
- Duff, Eugene P.
- Flandin, Guillaume
- Ghosh, Satrajit S.
- Glatard, Tristan
- Halchenko, Yaroslav O.
- Handwerker, Daniel A.
- Hanke, Michael
- Keator, David
- Li, Xiangrui
- Maumet, Camille
- Nichols, B. Nolan
- Nichols, Thomas E.
- Pellman, John
- Poline, Jean-Baptiste
- Rokem, Ariel
- Sochat, Vanessa
- Triplett, William
- Turner, Jessica A.
- Poldrack, Russell A.
- Venue
- Scientific Data
- Published
- 2016-06-21
- Volume
- 3
- Issue
- 1
- Pages
- 160044-160044
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC4978148
- OpenAlex
- W2951103577
- Cited by
- 1,775
- References
- 29
- Citations by year
- 2026: 220 · 2025: 301 · 2024: 280 · 2023: 221 · 2022: 205 · 2021: 207 · 2020: 131 · 2019: 102 · 2018: 50 · 2017: 41 · 2016: 14 · 2013: 2
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1429999, P20-GM103472)
- Laura and John Arnold Foundation
- Wellcome Trust (100309/A/12/Z)
- Center for Behavioral Brain Sciences
- National Institutes of Health (AA021697-04S1, AA021697)
- Medical Research Council (MC-A060-53144)
- European Regional Development Fund
- National Institute on Alcohol Abuse and Alcoholism (U01AA021697, AA021697-04S1, AA021697)
- National Institute of Mental Health
- National Institute of General Medical Sciences (P20GM103472)
- National Institute of Biomedical Imaging and Bioengineering (R01EB020407, P41EB019936, R01EB006841)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- BIDS, neuroimaging data standardization, metadata, data sharing, data reuse, functional MRI, diffusion-weighted imaging, quality assurance
- MeSH terms
- Data Collection, Humans, Magnetic Resonance Imaging, Neuroimaging, Datasets as Topic
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 ›
Are you an author, or do you see a problem with this page? Request a review
