FMRIPrep statistics

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Protocols

FMRIPrep specifications

Information


Unique identifier OMICS_29144
Name FMRIPrep
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Output data Some visual quality assessment reports, pre-processed imaging data and additional data including FreeSurfer derivatives or confounds.
Operating system Unix/Linux
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 1.0.12
Stability Stable
Requirements
Nipype, FSL, ANTs, AFNI, C3D, FreeSurfer, ICA-AROMA
Maintained Yes

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Versioning


No version available

Documentation


Maintainers


  • person_outline Krzysztof Gorgolewski
  • person_outline Oscar Esteban
  • person_outline Ross Blair
  • person_outline Christopher Markiewicz
  • person_outline Craig Moodie

Additional information


http://fmriprep.readthedocs.io/en/latest/ https://github.com/poldracklab/fmriprep/wiki

Publication for FMRIPrep

FMRIPrep citations

 (3)
library_books

The control of tonic pain by active relief learning

2018
eLife
PMCID: 5843408
PMID: 29482716
DOI: 10.7554/eLife.31949.029

[…] session’s slice timing output by the scanner. Resulting images were then preprocessed using the fmriprep software (build date 09/03/2017, freesurfer option turned off, https://github.com/poldracklab/fmriprep), a pipeline that performs motion correction, field unwarping, normalisation, field bias correction, and brain extraction using a various set of neuroimaging tools available. The normalised i […]

call_split

Preprocessed Consortium for Neuropsychiatric Phenomics dataset

2017
F1000Res
PMCID: 5664981
PMID: 29152222
DOI: 10.5256/f1000research.12934.r24599
call_split See protocol

[…] om 272 participants, seven were missing T1 weighted scans (see ) and thus only data from 265 participants were preprocessed.Results included in this manuscript come from preprocessing performed using FMRIPREP version 0.4.4 ( http://fmriprep.readthedocs.io). This recently developed tool is a robust preprocessing pipeline based on the Nipype workflow engine . FMRIPREP aims at combining different imp […]

library_books

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

2017
PLoS Comput Biol
PMCID: 5363996
PMID: 28278228
DOI: 10.1371/journal.pcbi.1005209

[…] is currently underway to define BIDS Derivatives specification that would cover description and organization of data analysis outputs. Adoption of such standard will allow chaining multiple apps. The FMRIPREP app already supports an early version of the BIDS Derivatives specification. In a similar vein, two extensions of the BIDS standard are in final stages of development: BIDS MEG and BIDS PET. […]

Citations

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FMRIPrep institution(s)
Department of Psychology, Stanford University, Stanford, CA, USA; Medical School Center, Stanford University, Stanford, CA, USA; Max Planck Institute for Empirical Aesthetics, Hesse, Germany; Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, Spain; Neuroscience Program, University of Iowa, Iowa City, IA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology: MIT, Cambridge, MA, USA; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychiatry, Stanford Medical School, Stanford University, Stanford, CA, USA; Department of Neurosurgery, University of Iowa Health Care, Iowa City, IA, USA; Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
FMRIPrep funding source(s)
Supported by the Laura and John Arnold Foundation, NIH R01 EB020740, NIH 1R24MH114705-01, the NINDS grant 1U01NS103780-01; and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 706561.

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