Metadata-Version: 2.1
Name: fmriprep
Version: 20.1.0rc2
Summary: fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data.
Home-page: https://github.com/poldracklab/fmriprep
Author: The CRN developers
Author-email: nipreps@gmail.com
License: 3-clause BSD
Project-URL: Documentation, https://fmriprep.readthedocs.io/
Project-URL: Paper, https://doi.org/10.1038/s41592-018-0235-4
Project-URL: Docker Images, https://hub.docker.com/r/poldracklab/fmriprep/tags/
Description: Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize
        the data before statistical analysis.
        Generally, researchers create ad hoc preprocessing workflows for each dataset,
        building upon a large inventory of available tools.
        The complexity of these workflows has snowballed with rapid advances in
        acquisition and processing.
        fMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and
        reproducible preprocessing for task-based and resting fMRI data.
        fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
        virtually any dataset, ensuring high-quality preprocessing without manual intervention.
        fMRIPrep robustly produces high-quality results on diverse fMRI data.
        Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed
        with commonly used preprocessing tools.
        fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing
        workflow, which can help ensure the validity of inference and the interpretability
        of results.
        
        The workflow is based on `Nipype <https://nipype.readthedocs.io>`_ and encompases a large
        set of tools from well-known neuroimaging packages, including
        `FSL <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>`_,
        `ANTs <https://stnava.github.io/ANTs/>`_,
        `FreeSurfer <https://surfer.nmr.mgh.harvard.edu/>`_,
        `AFNI <https://afni.nimh.nih.gov/>`_,
        and `Nilearn <https://nilearn.github.io/>`_.
        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 becomes
        available.
        
        fMRIPrep performs basic preprocessing steps (coregistration, normalization, unwarping, noise
        component extraction, segmentation, skullstripping 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, surface or volume-based statistics, etc.
        fMRIPrep allows you to easily do the following:
        
          * Take fMRI data from *unprocessed* (only reconstructed) to ready for analysis.
          * Implement tools from different software packages.
          * Achieve optimal data processing quality by using the best tools available.
          * Generate preprocessing-assessment reports, with which the user can easily identify problems.
          * 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.
        
        [Nat Meth doi:`10.1038/s41592-018-0235-4 <https://doi.org/10.1038/s41592-018-0235-4>`_]
        [Documentation `fmriprep.org <https://fmriprep.readthedocs.io>`_]
        [Software doi:`10.5281/zenodo.852659 <https://doi.org/10.5281/zenodo.852659>`_]
        [Support `neurostars.org <https://neurostars.org/tags/fmriprep>`_]
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: datalad
Provides-Extra: doc
Provides-Extra: docs
Provides-Extra: duecredit
Provides-Extra: resmon
Provides-Extra: sentry
Provides-Extra: tests
Provides-Extra: all
