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https://github.com/nipreps/fmriprep

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.
https://github.com/nipreps/fmriprep

bids brain-imaging fmri fmri-preprocessing image-processing neuroimaging

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fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

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README 

        
*fMRIPrep*: A Robust Preprocessing Pipeline for fMRI Data

=========================================================

*fMRIPrep* is a *NiPreps (NeuroImaging PREProcessing toolS)* application

(`www.nipreps.org `__) for the preprocessing of

task-based and resting-state functional MRI (fMRI).



.. image:: https://img.shields.io/badge/RRID-SCR__016216-blue.svg

  :target: https://doi.org/10.1038/s41592-018-0235-4

  :alt: RRID:SCR_016216



.. image:: https://img.shields.io/pypi/v/fmriprep.svg

  :target: https://pypi.python.org/pypi/fmriprep/

  :alt: Latest Version



.. image:: https://circleci.com/gh/nipreps/fmriprep/tree/master.svg?style=shield

  :target: https://circleci.com/gh/nipreps/fmriprep/tree/master



.. image:: https://readthedocs.org/projects/fmriprep/badge/?version=latest

  :target: https://fmriprep.org/en/latest/?badge=latest

  :alt: Documentation Status



.. image:: https://img.shields.io/badge/doi-10.1038%2Fs41592--018--0235--4-blue.svg

  :target: https://doi.org/10.1038/s41592-018-0235-4

  :alt: Published in Nature Methods



.. image:: https://img.shields.io/badge/docker-nipreps/fmriprep-brightgreen.svg?logo=docker&style=flat

  :target: https://hub.docker.com/r/nipreps/fmriprep/tags/

  :alt: Docker image available!



.. image:: https://codeocean.com/codeocean-assets/badge/open-in-code-ocean.svg

  :target: https://doi.org/10.24433/CO.ed5ddfef-76a3-4996-b298-e3200f69141b

  :alt: Available in CodeOcean!



.. image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg

  :target: https://czi.co/EOSS

  :alt: CZI's Essential Open Source Software for Science



About

-----

.. image:: https://github.com/oesteban/fmriprep/raw/f4c7a9804be26c912b24ef4dccba54bdd72fa1fd/docs/_static/fmriprep-21.0.0.svg



*fMRIPrep* is a functional magnetic resonance imaging (fMRI) data

preprocessing pipeline that is designed to provide an easily accessible,

state-of-the-art interface that is robust to variations in scan acquisition

protocols and that requires minimal user input, while providing easily

interpretable and comprehensive error and output reporting.

It performs basic processing steps (coregistration, normalization, unwarping,

noise component extraction, segmentation, skull-stripping, etc.) providing

outputs that can be easily submitted to a variety of group level analyses,

including task-based or resting-state fMRI, graph theory measures, and surface

or volume-based statistics.



.. note::



   *fMRIPrep* performs minimal preprocessing.

   Here we define 'minimal preprocessing'  as motion correction, field

   unwarping, normalization, bias field correction, and brain extraction.

   See the `workflows section of our documentation

   `__ for more details.



The *fMRIPrep* pipeline uses a combination of tools from well-known software

packages, including FSL_, ANTs_, FreeSurfer_ and AFNI_.

This pipeline was designed to provide the best software implementation for each

state of preprocessing, and will be updated as newer and better neuroimaging

software become available.



This tool allows you to easily do the following:



- Take fMRI data from raw to fully preprocessed form.

- Implement tools from different software packages.

- Achieve optimal data processing quality by using the best tools available.

- Generate preprocessing quality reports, with which the user can easily

  identify outliers.

- Receive verbose output concerning the stage of preprocessing for each

  subject, including meaningful errors.

- Automate and parallelize processing steps, which provides a significant

  speed-up from manual processing or shell-scripted pipelines.



More information and documentation can be found at

https://fmriprep.readthedocs.io/



Principles

----------

*fMRIPrep* is built around three principles:



1. **Robustness** - The pipeline adapts the preprocessing steps depending on

   the input dataset and should provide results as good as possible

   independently of scanner make, scanning parameters or presence of additional

   correction scans (such as fieldmaps).

2. **Ease of use** - Thanks to dependence on the BIDS standard, manual

   parameter input is reduced to a minimum, allowing the pipeline to run in an

   automatic fashion.

3. **"Glass box"** philosophy - Automation should not mean that one should not

   visually inspect the results or understand the methods.

   Thus, *fMRIPrep* provides visual reports for each subject, detailing the

   accuracy of the most important processing steps.

   This, combined with the documentation, can help researchers to understand

   the process and decide which subjects should be kept for the group level

   analysis.



Citation

--------

**Citation boilerplate**.

Please acknowledge this work using the citation boilerplate that *fMRIPrep* includes

in the visual report generated for every subject processed.

For a more detailed description of the citation boilerplate and its relevance,

please check out the

`NiPreps documentation `__.



**Plagiarism disclaimer**.

The boilerplate text is public domain, distributed under the

`CC0 license `__,

and we recommend *fMRIPrep* users to reproduce it verbatim in their works.

Therefore, if reviewers and/or editors raise concerns because the text is flagged by automated

plagiarism detection, please refer them to the *NiPreps* community and/or the note to this

effect in the `boilerplate documentation page `__.



**Papers**.

*fMRIPrep* contributors have published two relevant papers:

`Esteban et al. (2019) `__

[`preprint `__], and

`Esteban et al. (2020) `__

[`preprint `__].



**Other**.

Other materials that have been generated over time include the

`OHBM 2018 software demonstration `__

and some conference posters:



* Organization for Human Brain Mapping 2018

  (`Abstract `__;

  `PDF `__)



.. image:: _static/OHBM2018-poster_thumb.png

   :target: _static/OHBM2018-poster.png



* Organization for Human Brain Mapping 2017

  (`Abstract `__;

  `PDF `__)



.. image:: _static/OHBM2017-poster_thumb.png

   :target: _static/OHBM2017-poster.png



License information

-------------------

*fMRIPrep* adheres to the

`general licensing guidelines `__

of the *NiPreps framework*.



License

~~~~~~~

Copyright (c) the *NiPreps* Developers.



As of the 21.0.x pre-release and release series, *fMRIPrep* is

licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

`http://www.apache.org/licenses/LICENSE-2.0

`__.



Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.



Acknowledgements

----------------

This work is steered and maintained by the `NiPreps Community `__.

This work was supported by the Laura and John Arnold Foundation,

the NIH (grant NBIB R01EB020740, PI: Ghosh),

and NIMH (R24MH114705, R24MH117179, R01MH121867, PI: Poldrack)