Select which options you have run FMRIPREP with to generate custom language we recommend to include in your paper.

With Freesurfer:
Suceptibility Distortion Correction:
Skullstrip template:
With slicetime correction:

Results included in this manuscript come from preprocessing performed using FMRIPREP version latest [1] a Nipype [2,3] based tool. Each T1 weighted volume was corrected for bias field using N4BiasFieldCorrection v2.1.0 [4] and skullstripped using v2.1.0 (using OASIS template). Cortical surface was estimated using FreeSurfer v6.0.0 [5]. The skullstripped T1w volume was coregistered to skullstripped ICBM 152 Nonlinear Asymmetrical template version 2009c [6] using nonlinear transformation implemented in ANTs v2.1.0 [7].

Functional data was slice time corrected using AFNI [10] and motion corrected using MCFLIRT v5.0.9 [8]. This was followed by co-registration to the corresponding T1-weighted volume using boundary based registration 9 degrees of freedom - implemented in FreeSurfer v6.0.0 [15] . Motion correcting transformations, T1 weighted transformation and MNI template warp were applied in a single step using antsApplyTransformations v2.1.0 with Lanczos interpolation.

Three tissue classes were extracted from T1w images using FSL FAST v5.0.9 [16]. Voxels from cerebrospinal fluid and white matter were used to create a mask in turn used to extract physiological noise regressors using aCompCor [17]. Mask was eroded and limited to subcortical regions to limit overlap with gray matter, six principal components were estimated. Frame-wise displacement [18] was calculated for each functional run using Nipype implementation.

For more details of the pipeline see

FMRIPREP Available from: 10.5281/zenodo.852659

2. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform [Internet]. 2011 Aug 22;5(August):13. doi:10.3389/fninf.2011.00013

3. Gorgolewski KJ, Esteban O, Ellis DG, Notter MP, Ziegler E, Johnson H, Hamalainen C, Yvernault B, Burns C, Manhães-Savio A, Jarecka D, Markiewicz CJ, Salo T, Clark D, Waskom M, Wong J, Modat M, Dewey BE, Clark MG, Dayan M, Loney F, Madison C, Gramfort A, Keshavan A, Berleant S, Pinsard B, Goncalves M, Clark D, Cipollini B, Varoquaux G, Wassermann D, Rokem A, Halchenko YO, Forbes J, Moloney B, Malone IB, Hanke M, Mordom D, Buchanan C, Pauli WM, Huntenburg JM, Horea C, Schwartz Y, Tungaraza R, Iqbal S, Kleesiek J, Sikka S, Frohlich C, Kent J, Perez-Guevara M, Watanabe A, Welch D, Cumba C, Ginsburg D, Eshaghi A, Kastman E, Bougacha S, Blair R, Acland B, Gillman A, Schaefer A, Nichols BN, Giavasis S, Erickson D, Correa C, Ghayoor A, Küttner R, Haselgrove C, Zhou D, Craddock RC, Haehn D, Lampe L, Millman J, Lai J, Renfro M, Liu S, Stadler J, Glatard T, Kahn AE, Kong X-Z, Triplett W, Park A, McDermottroe C, Hallquist M, Poldrack R, Perkins LN, Noel M, Gerhard S, Salvatore J, Mertz F, Broderick W, Inati S, Hinds O, Brett M, Durnez J, Tambini A, Rothmei S, Andberg SK, Cooper G, Marina A, Mattfeld A, Urchs S, Sharp P, Matsubara K, Geisler D, Cheung B, Floren A, Nickson T, Pannetier N, Weinstein A, Dubois M, Arias J, Tarbert C, Schlamp K, Jordan K, Liem F, Saase V, Harms R, Khanuja R, Podranski K, Flandin G, Papadopoulos Orfanos D, Schwabacher I, McNamee D, Falkiewicz M, Pellman J, Linkersdörfer J, Varada J, Pérez-García F, Davison A, Shachnev D, Ghosh S. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python [Internet]. 2017. doi:10.5281/zenodo.581704

4. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging [Internet]. 2010 Jun;29(6):1310–20. doi:10.1109/TMI.2010.2046908

5. Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. Neuroimage. 1999;9(2):179–94. doi:10.1006/nimg.1998.0395

6. Fonov VS, Evans AC, McKinstry RC, Almli CR, Collins DL. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage; Amsterdam [Internet]. 2009 Jul 1;47:S102. doi:10.1016/S1053-8119(09)70884-5

7. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal [Internet]. 2008 Feb;12(1):26–41. doi:10.1016/

8. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage [Internet]. 2002 Oct;17(2):825–41. doi:10.1006/nimg.2002.1132

9. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage [Internet]. 2003 Oct;20(2):870–88. doi:10.1016/S1053-8119(03)00336-7

10. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res [Internet]. 1996 Jun;29(3):162–73. doi:10.1006/cbmr.1996.0014

11. Jenkinson M. Fast, automated, N-dimensional phase-unwrapping algorithm. Magn Reson Med [Internet]. 2003 Jan;49(1):193–7. doi:10.1002/mrm.10354

12. Huntenburg JM. Evaluating nonlinear coregistration of BOLD EPI and T1w images [Internet]. Freie Universität Berlin; 2014. Available from:

13. Wang S, Peterson DJ, Gatenby JC, Li W, Grabowski TJ, Madhyastha TM. Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI. Front Neuroinform [Internet]. 2017 [cited 2017 Feb 21];11. doi:10.3389/fninf.2017.00017

14. Treiber JM, White NS, Steed TC, Bartsch H, Holland D, Farid N, McDonald CR, Carter BS, Dale AM, Chen CC. Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images. PLoS One [Internet]. 2016 Mar 30;11(3):e0152472. doi:10.1371/journal.pone.0152472

15. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage [Internet]. 2009 Oct;48(1):63–72. doi:10.1016/j.neuroimage.2009.06.060

16. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging [Internet]. 2001 Jan;20(1):45–57. doi:10.1109/42.906424

17. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage [Internet]. 2007 Aug 1;37(1):90–101. doi:10.1016/j.neuroimage.2007.04.042

18. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage [Internet]. 2013 Aug 29;84:320–41. doi:10.1016/j.neuroimage.2013.08.048

19. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage [Internet]. 2015 May 15;112:267–77. doi:10.1016/j.neuroimage.2015.02.064


  • Organization for Human Brain Mapping 2017 (pdf)

License information

We use the 3-clause BSD license; the full license may be found in the LICENSE file in the fmriprep distribution.

All trademarks referenced herein are property of their respective holders.

Copyright (c) 2015-2017, the fmriprep developers and the CRN. All rights reserved.

Other relevant references

[Power2017]Power JD, Plitt M, Kundu P, Bandettini PA, Martin A (2017) Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection. PLOS ONE 12(9): e0182939. doi:10.1371/journal.pone.0182939.