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Analysis of task-based functional MRI data preprocessed with fMRIPrep


Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (, a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

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Fig. 1: Overall workflow of the fMRIPrep protocol.
Fig. 2: Running fMRIPrep on HPC.
Fig. 3: Output of the first-level analysis step.
Fig. 4: Group analysis results.

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Data availability

This protocol uses ds000003, a dataset publicly available at under the terms of the CC0 license. OpenNeuro and other neuroimaging archives contain a large number of open datasets in which this protocol can be exercised and evaluated. The outcomes of preprocessing ds000003 are available using DataLad ( under a CC0 license, with DataLad dataset locator ///labs/poldrack/ds003_fmriprep. Results of the analysis workflow described in this protocol are also distributed under the CC0 license and can be accessed with DataLad, with DataLad dataset locator ///labs/poldrack/NP-180740.

Code availability

fMRIPrep is open source, available at under a three-clause Berkeley Software Distribution license. The code for the exemplary analysis and visualizations is available at under an Apache-2.0 license.


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We thank the many community contributors who have helped fMRIPrep with code and documentation ( 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.) and NIMH (R24MH114705 and R24MH117179; R.A.P.). K.F. was supported by the Foundation for Polish Science, Poland (START 23.2018). D.E.P.G. was supported by a Marie Curie FP7-PEOPLE-2013-ITN ‘Initial Training Networks’ Action from the European Union (Project Reference Number: 608123). F.L. was supported by the University Research Priority Program ‘Dynamics of Healthy Aging’ at the University of Zurich. N.J. was supported by a National Science Foundation Graduate Research Fellowship (grant number DGE 16-44869). H.S. was supported by the Max Planck Society, Munich, Germany (grant number 647070403019). S.U. and E.D. were supported by Brain Canada.

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Authors and Affiliations



O.E., R.A.P. and K.J.G. contributed to conceptualization, data curation and funding acquisition. O.E., R.C. and K.F. contributed to formal analysis, investigation, methodology and validation and wrote the original draft. K.F. contributed visualizations. O.E., J.W. and W.H.T. contributed to interpretation and overall framing of the protocol. R.A.P. and K.J.G. contributed to project administration, resources and supervision. All the authors have contributed software and/or documentation, read the manuscript and edited/revised the original draft and later versions.

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Correspondence to Oscar Esteban.

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Peer review information Nature Protocols thanks Jo Etzel and Angela Laird for their contribution to the peer review of this work.

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Key references using this protocol

Esteban, O. et al. Nat. Methods 16, 111–116 (2019):

Key data used in this protocol

Xue, G. & Poldrack, R. A. J. Cogn. Neurosci. 19, 1643–1655 (2007):

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Esteban, O., Ciric, R., Finc, K. et al. Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nat Protoc 15, 2186–2202 (2020).

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