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Fmriprep docker build from rwblair fork of the project.
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.. include:: links.rst

fmriprep: A Robust Preprocessing Pipeline for fMRI Data

This pipeline is developed by the Poldrack lab at Stanford University <https://poldracklab.stanford.edu/>
for use at the Center for Reproducible Neuroscience (CRN) <http://reproducibility.stanford.edu/>
,
as well as for open-source software distribution.

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

.. image:: https://readthedocs.org/projects/fmriprep/badge/?version=latest
:target: http://fmriprep.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status

.. image:: https://img.shields.io/pypi/v/fmriprep.svg
:target: https://pypi.python.org/pypi/fmriprep/
:alt: Latest Version

About

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 differences 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, skullstripping etc.) providing outputs that make
running a variety of group level analyses (task based or resting state fMRI, graph theory measures, surface or volume, etc.) easy.

.. 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_ for more details.

The fmriprep pipeline primarily
utilizes FSL tools, but also utilizes ANTs tools at several stages such as
skull stripping and template registration. 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 full 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 typical linear, manual processing.

More information and documentation can be found here:

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.

Acknowledgements

Please acknowledge this work mentioning explicitly the name of this software (fmriprep)
and the version, along with the link to the GitHub repository
(https://github.com/poldracklab/fmriprep).

License information

We use the 3-clause BSD license; the full license is in the file LICENSE 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.

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