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fMRIPrep: a robust preprocessing pipeline for functional MRI
- Oscar Esteban ORCID:orcid.org/0000-0001-8435-61911,
- Christopher J. Markiewicz ORCID:orcid.org/0000-0002-6533-164X1,
- Ross W. Blair1,
- Craig A. Moodie ORCID:orcid.org/0000-0003-0867-14691,
- A. Ilkay Isik ORCID:orcid.org/0000-0002-1652-92972,
- Asier Erramuzpe ORCID:orcid.org/0000-0002-9402-21843,
- James D. Kent4,
- Mathias Goncalves5,
- Elizabeth DuPre ORCID:orcid.org/0000-0003-1358-196X6,
- Madeleine Snyder7,
- Hiroyuki Oya8,
- Satrajit S. Ghosh ORCID:orcid.org/0000-0002-5312-67295,9,
- Jessey Wright1,
- Joke Durnez ORCID:orcid.org/0000-0001-9030-22021,
- Russell A. Poldrack1 na1 &
- …
- Krzysztof J. Gorgolewski ORCID:orcid.org/0000-0003-3321-75831 na1
Nature Methodsvolume 16, pages111–116 (2019)Cite this article
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Abstract
Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.
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Data availability
All original data used in this work are publicly available through the OpenNeuro platform (formerly OpenfMRI). Derivatives generated with fMRIPrep in this work are available athttps://s3.amazonaws.com/fmriprep/index.html. The expert ratings collected after visual assessment of all reports are available through FigShare (https://doi.org/10.6084/m9.figshare.6196994.v3). Source data for Fig.3 are available online.
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Acknowledgements
This work was supported by the Laura and John Arnold Foundation (R.A.P. and K.J.G.), the NIH (grant NBIB R01EB020740, S.S.G.), NIMH (R24MH114705 and R24MH117179, R.A.P.), and NINDS (U01NS103780, R.A.P.). J.D. has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement 706561. The authors thank S. Nastase and T. van Mourik for their thoughtful open reviews of a preprint version of this paper.
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These authors contributed equally: Russell A. Poldrack, Krzysztof J. Gorgolewski.
Authors and Affiliations
Department of Psychology, Stanford University, Stanford, CA, USA
Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie, Jessey Wright, Joke Durnez, Russell A. Poldrack & Krzysztof J. Gorgolewski
Max Planck Institute for Empirical Aesthetics, Hesse, Germany
A. Ilkay Isik
Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, Spain
Asier Erramuzpe
Neuroscience Program, University of Iowa, Iowa City, IA, USA
James D. Kent
McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Mathias Goncalves & Satrajit S. Ghosh
Montreal Neurological Institute, McGill University, Montreal, QC, Canada
Elizabeth DuPre
Department of Psychiatry, Stanford Medical School, Stanford University, Stanford, CA, USA
Madeleine Snyder
Department of Neurosurgery, University of Iowa Health Care, Iowa City, IA, USA
Hiroyuki Oya
Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
Satrajit S. Ghosh
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Contributions
O.E. contributed with conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing (original draft, review, and editing). C.J.M. contributed with conceptualization, data curation, methodology, software, validation, and writing (review and editing). R.W.B. contributed with software, validation, and writing (review and editing). C.A.M. contributed with methodology, software, and writing (review and editing). A.I.I. contributed with software and writing (review and editing). A.E. contributed with software and writing (review and editing). J.D.K. contributed with investigation, methodology, software, visualization, and writing (review and editing). M.G. contributed with software and writing (review and editing). E.D. contributed with software and writing (review and editing). M.S. contributed with software and writing (review and editing). H.O. contributed with data acquisition and writing (review and editing). S.S.G. contributed with conceptualization, software, and writing (review and editing). J.W. contributed with conceptualization and writing (review and editing). J.D. contributed with formal analysis, investigation, methodology, software, and writing (review and editing). R.A.P. contributed with conceptualization, formal analysis, investigation, methodology, validation, supervision, resources, funding acquisition, and writing (original draft, review, and editing). K.J.G. contributed with conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, supervision, resources, funding acquisition, and writing (original draft, review, and editing).
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Correspondence toOscar Esteban orKrzysztof J. Gorgolewski.
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Esteban, O., Markiewicz, C.J., Blair, R.W.et al. fMRIPrep: a robust preprocessing pipeline for functional MRI.Nat Methods16, 111–116 (2019). https://doi.org/10.1038/s41592-018-0235-4
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