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fMRIPrep: a robust preprocessing pipeline for functional MRI

Nature Methods volume 16, pages 111–116 (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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Fig. 1: fMRIPrep is an fMRI preprocessing tool that adapts to the input dataset.
Fig. 2: Integration of visual assessment into the software testing framework effectively increases the quality of results.
Fig. 3: fMRIPrep affords researchers finer control over the smoothness of their analysis.
Fig. 4: Activation count maps from fMRIPrep are better aligned with the underlying anatomy than those from FEAT.

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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 at https://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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Author notes

  1. These authors contributed equally: Russell A. Poldrack, Krzysztof J. Gorgolewski.

Authors and Affiliations

  1. 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

  2. Max Planck Institute for Empirical Aesthetics, Hesse, Germany

    A. Ilkay Isik

  3. Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, Spain

    Asier Erramuzpe

  4. Neuroscience Program, University of Iowa, Iowa City, IA, USA

    James D. Kent

  5. McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

    Mathias Goncalves & Satrajit S. Ghosh

  6. Montreal Neurological Institute, McGill University, Montreal, QC, Canada

    Elizabeth DuPre

  7. Department of Psychiatry, Stanford Medical School, Stanford University, Stanford, CA, USA

    Madeleine Snyder

  8. Department of Neurosurgery, University of Iowa Health Care, Iowa City, IA, USA

    Hiroyuki Oya

  9. 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).

Corresponding authors

Correspondence to Oscar Esteban or Krzysztof J. Gorgolewski.

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The authors declare no competing interests.

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Supplementary Notes 1–5, Supplementary Results 1 and 2, Supplementary Figures 1–6, and Supplementary Table 1

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Esteban, O., Markiewicz, C.J., Blair, R.W. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16, 111–116 (2019). https://doi.org/10.1038/s41592-018-0235-4

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