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(NeuroImaging PREProcessing toolS)* application\n(`www.nipreps.org \u003chttps://www.nipreps.org\u003e`__) for the preprocessing of\ntask-based and resting-state functional MRI (fMRI).\n\n.. image:: https://img.shields.io/badge/RRID-SCR__016216-blue.svg\n  :target: https://doi.org/10.1038/s41592-018-0235-4\n  :alt: RRID:SCR_016216\n\n.. image:: https://img.shields.io/pypi/v/fmriprep.svg\n  :target: https://pypi.python.org/pypi/fmriprep/\n  :alt: Latest Version\n\n.. image:: https://circleci.com/gh/nipreps/fmriprep/tree/master.svg?style=shield\n  :target: https://circleci.com/gh/nipreps/fmriprep/tree/master\n\n.. image:: https://readthedocs.org/projects/fmriprep/badge/?version=latest\n  :target: https://fmriprep.org/en/latest/?badge=latest\n  :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/doi-10.1038%2Fs41592--018--0235--4-blue.svg\n  :target: https://doi.org/10.1038/s41592-018-0235-4\n  :alt: Published in Nature Methods\n\n.. image:: https://img.shields.io/badge/docker-nipreps/fmriprep-brightgreen.svg?logo=docker\u0026style=flat\n  :target: https://hub.docker.com/r/nipreps/fmriprep/tags/\n  :alt: Docker image available!\n\n.. image:: https://codeocean.com/codeocean-assets/badge/open-in-code-ocean.svg\n  :target: https://doi.org/10.24433/CO.ed5ddfef-76a3-4996-b298-e3200f69141b\n  :alt: Available in CodeOcean!\n\n.. image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg\n  :target: https://czi.co/EOSS\n  :alt: CZI's Essential Open Source Software for Science\n\nAbout\n-----\n.. image:: https://github.com/oesteban/fmriprep/raw/f4c7a9804be26c912b24ef4dccba54bdd72fa1fd/docs/_static/fmriprep-21.0.0.svg\n\n\n*fMRIPrep* is a functional magnetic resonance imaging (fMRI) data\npreprocessing pipeline that is designed to provide an easily accessible,\nstate-of-the-art interface that is robust to variations in scan acquisition\nprotocols and that requires minimal user input, while providing easily\ninterpretable and comprehensive error and output reporting.\nIt performs basic processing steps (coregistration, normalization, unwarping,\nnoise component extraction, segmentation, skull-stripping, etc.) providing\noutputs that can be easily submitted to a variety of group level analyses,\nincluding task-based or resting-state fMRI, graph theory measures, and surface\nor volume-based statistics.\n\n.. note::\n\n   *fMRIPrep* performs minimal preprocessing.\n   Here we define 'minimal preprocessing'  as motion correction, field\n   unwarping, normalization, bias field correction, and brain extraction.\n   See the `workflows section of our documentation\n   \u003chttps://fmriprep.readthedocs.io/en/latest/workflows.html\u003e`__ for more details.\n\nThe *fMRIPrep* pipeline uses a combination of tools from well-known software\npackages, including FSL_, ANTs_, FreeSurfer_ and AFNI_.\nThis pipeline was designed to provide the best software implementation for each\nstate of preprocessing, and will be updated as newer and better neuroimaging\nsoftware become available.\n\nThis tool allows you to easily do the following:\n\n- Take fMRI data from raw to fully preprocessed form.\n- Implement tools from different software packages.\n- Achieve optimal data processing quality by using the best tools available.\n- Generate preprocessing quality reports, with which the user can easily\n  identify outliers.\n- Receive verbose output concerning the stage of preprocessing for each\n  subject, including meaningful errors.\n- Automate and parallelize processing steps, which provides a significant\n  speed-up from manual processing or shell-scripted pipelines.\n\nMore information and documentation can be found at\nhttps://fmriprep.readthedocs.io/\n\nPrinciples\n----------\n*fMRIPrep* is built around three principles:\n\n1. **Robustness** - The pipeline adapts the preprocessing steps depending on\n   the input dataset and should provide results as good as possible\n   independently of scanner make, scanning parameters or presence of additional\n   correction scans (such as fieldmaps).\n2. **Ease of use** - Thanks to dependence on the BIDS standard, manual\n   parameter input is reduced to a minimum, allowing the pipeline to run in an\n   automatic fashion.\n3. **\"Glass box\"** philosophy - Automation should not mean that one should not\n   visually inspect the results or understand the methods.\n   Thus, *fMRIPrep* provides visual reports for each subject, detailing the\n   accuracy of the most important processing steps.\n   This, combined with the documentation, can help researchers to understand\n   the process and decide which subjects should be kept for the group level\n   analysis.\n\nCitation\n--------\n**Citation boilerplate**.\nPlease acknowledge this work using the citation boilerplate that *fMRIPrep* includes\nin the visual report generated for every subject processed.\nFor a more detailed description of the citation boilerplate and its relevance,\nplease check out the\n`NiPreps documentation \u003chttps://www.nipreps.org/intro/transparency/#citation-boilerplates\u003e`__.\n\n**Plagiarism disclaimer**.\nThe boilerplate text is public domain, distributed under the\n`CC0 license \u003chttps://creativecommons.org/publicdomain/zero/1.0/\u003e`__,\nand we recommend *fMRIPrep* users to reproduce it verbatim in their works.\nTherefore, if reviewers and/or editors raise concerns because the text is flagged by automated\nplagiarism detection, please refer them to the *NiPreps* community and/or the note to this\neffect in the `boilerplate documentation page \u003chttps://www.nipreps.org/intro/transparency/#citation-boilerplates\u003e`__.\n\n**Papers**.\n*fMRIPrep* contributors have published two relevant papers:\n`Esteban et al. (2019) \u003chttps://doi.org/10.1038/s41592-018-0235-4\u003e`__\n[`preprint \u003chttps://doi.org/10.1101/306951\u003e`__], and\n`Esteban et al. (2020) \u003chttps://doi.org/10.1038/s41596-020-0327-3\u003e`__\n[`preprint \u003chttps://doi.org/10.1101/694364\u003e`__].\n\n**Other**.\nOther materials that have been generated over time include the\n`OHBM 2018 software demonstration \u003chttps://effigies.github.io/fmriprep-demo/\u003e`__\nand some conference posters:\n\n* Organization for Human Brain Mapping 2018\n  (`Abstract \u003chttps://ww5.aievolution.com/hbm1801/index.cfm?do=abs.viewAbs\u0026abs=1321\u003e`__;\n  `PDF \u003chttps://files.aievolution.com/hbm1801/abstracts/31779/2035_Markiewicz.pdf\u003e`__)\n\n.. image:: _static/OHBM2018-poster_thumb.png\n   :target: _static/OHBM2018-poster.png\n\n* Organization for Human Brain Mapping 2017\n  (`Abstract \u003chttps://ww5.aievolution.com/hbm1701/index.cfm?do=abs.viewAbs\u0026abs=4111\u003e`__;\n  `PDF \u003chttps://f1000research.com/posters/6-1129\u003e`__)\n\n.. image:: _static/OHBM2017-poster_thumb.png\n   :target: _static/OHBM2017-poster.png\n\nLicense information\n-------------------\n*fMRIPrep* adheres to the\n`general licensing guidelines \u003chttps://www.nipreps.org/community/licensing/\u003e`__\nof the *NiPreps framework*.\n\nLicense\n~~~~~~~\nCopyright (c) the *NiPreps* Developers.\n\nAs of the 21.0.x pre-release and release series, *fMRIPrep* is\nlicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n`http://www.apache.org/licenses/LICENSE-2.0\n\u003chttp://www.apache.org/licenses/LICENSE-2.0\u003e`__.\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nAcknowledgements\n----------------\nThis work is steered and maintained by the `NiPreps Community \u003chttps://www.nipreps.org\u003e`__.\nThis work was supported by the Laura and John Arnold Foundation,\nthe NIH (grant NBIB R01EB020740, PI: Ghosh),\nand NIMH (R24MH114705, R24MH117179, R01MH121867, PI: Poldrack)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Ffmriprep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnipreps%2Ffmriprep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipreps%2Ffmriprep/lists"}