Select which options you have run fMRIPrep with to generate custom language we recommend to include in your paper.
Suceptibility Distortion Correction:
With slicetime correction:
BOLD-T1w coregistration degrees-of-freedom:
Results included in this manuscript come from preprocessing performed using
FMRIPREP version latest [1, 2, RRID:SCR_016216], a Nipype [3, 4, RRID:SCR_002502] based tool.
Each T1w (T1-weighted) volume was corrected for INU (intensity non-uniformity) using
N4BiasFieldCorrection v2.1.0  and skull-stripped using
antsBrainExtraction.sh v2.1.0 (using the
Brain surfaces were reconstructed using
recon-all from FreeSurfer v6.0.1 [6, RRID:SCR_001847],
and the brain mask estimated previously was refined with a custom variation of
the method to reconcile
ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter
of Mindboggle [21, RRID:SCR_002438].
Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c [7, RRID:SCR_008796]
was performed through nonlinear registration with the
tool of ANTs v2.1.0 [8, RRID:SCR_004757], using brain-extracted versions of both T1w volume and template.
Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
gray-matter (GM) was performed on the brain-extracted T1w using
fast  (FSL v5.0.9, RRID:SCR_002823).
Functional data was slice time corrected using
3dTshift from AFNI v16.2.07 [11, RRID:SCR_005927]
and motion corrected using
mcflirt (FSL v5.0.9 ).
Distortion correction was performed
using an implementation of the TOPUP technique  using
3dQwarp (AFNI v16.2.07 ).
Distortion correction was performed using fieldmaps
fugue  (FSL v5.0.9).
"Fieldmap-less" distortion correction was performed by co-registering the functional image to
the same-subject T1w image with intensity inverted [13,14] constrained with an average fieldmap
template , implemented with
This was followed by co-registration to the corresponding T1w using boundary-based registration 
twelve degrees of freedom, using
bbregister (FreeSurfer v6.0.1).
Motion correcting transformations,
field distortion correcting warp,
field distortion correcting warp,
field distortion correcting warp,
BOLD-to-T1w transformation and T1w-to-template (MNI) warp were concatenated and applied in
a single step using
antsApplyTransforms (ANTs v2.1.0) using Lanczos interpolation.
Physiological noise regressors were extracted applying CompCor . Principal components were estimated for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). A mask to exclude signal with cortical origin was obtained by eroding the brain mask, ensuring it only contained subcortical structures. Six tCompCor components were then calculated including only the top 5% variable voxels within that subcortical mask. For aCompCor, six components were calculated within the intersection of the subcortical mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run. Frame-wise displacement  was calculated for each functional run using the implementation of Nipype. ICA-based Automatic Removal Of Motion Artifacts (AROMA) was used to generate aggressive noise regressors as well as to create a variant of data that is non-aggressively denoised .
Many internal operations of FMRIPREP use Nilearn [22, RRID:SCR_001362], principally within the BOLD-processing workflow. For more details of the pipeline see http://fmriprep.readthedocs.io/en/latest/workflows.html.
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Note for reviewers and editors¶
The boilerplate text generated by fMRIPrep is intended to allow for clear, consistent description of the preprocessing steps used, in order to improve the reproducibility of studies. We fully intend for it to be copied verbatim, and have released it under the CC0 license, dedicating it to the public domain in jurisdictions that recognize the concept, and assert that we will take no action to enforce copyright in jurisdictions where we cannot disclaim it.
We firmly believe that requiring authors to modify this passage will serve no legitimate scientific or literary purpose and can, in fact, serve only to reduce the replicability of the analysis being described by making the preprocessing steps less clear.
We recognize that there may be automated plagiarism detection software that will flag the boilerplate text. We would be happy to discuss potential solutions for annotating boilerplate sections of documents to indicate automatic generation, and can update our software to make this annotation simpler for authors.
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) 2015-2019, the fMRIPrep developers and the CRN. All rights reserved.
Other relevant references¶
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.
Brett M, Leff AP, Rorden C, Ashburner J (2001) Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking. NeuroImage 14(2) doi:10.006/nimg.2001.0845.