Metadata-Version: 2.5
Name: nibabies
Version: 26.0.1
Summary: Processing workflows for magnetic resonance images of the brain in infants
Project-URL: Documentation, https://nibabies.readthedocs.io/en/latest/
Project-URL: Source Code, https://github.com/nipreps/nibabies
Project-URL: Bug Tracker, https://github.com/nipreps/nibabies/issues
Project-URL: Docker Images, https://hub.docker.com/r/nipreps/nibabies
Author-email: The NiPreps Developers <nipreps@gmail.com>
License-Expression: Apache-2.0
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.12
Requires-Dist: acres
Requires-Dist: nibabel>=5.1.0
Requires-Dist: nipype>=1.9.0
Requires-Dist: nireports>=26.0.0
Requires-Dist: nitime
Requires-Dist: nitransforms>=25.0.1
Requires-Dist: niworkflows>=1.15.1
Requires-Dist: numpy>=2.0
Requires-Dist: packaging
Requires-Dist: pandas<3
Requires-Dist: psutil>=5.4
Requires-Dist: pybids>=0.15.0
Requires-Dist: requests
Requires-Dist: sdcflows>=2.17.0
Requires-Dist: smriprep>=0.20.0
Requires-Dist: tedana>=23.0.2
Requires-Dist: templateflow>=25.0.3
Requires-Dist: toml
Provides-Extra: all
Requires-Dist: coverage[toml]; extra == 'all'
Requires-Dist: fuzzywuzzy; extra == 'all'
Requires-Dist: migas>=0.4.0; extra == 'all'
Requires-Dist: myst-parser; extra == 'all'
Requires-Dist: pre-commit; extra == 'all'
Requires-Dist: pydot>=1.2.3; extra == 'all'
Requires-Dist: pytest; extra == 'all'
Requires-Dist: pytest-cov; extra == 'all'
Requires-Dist: pytest-env; extra == 'all'
Requires-Dist: pytest-xdist; extra == 'all'
Requires-Dist: python-levenshtein; extra == 'all'
Requires-Dist: shibuya; extra == 'all'
Requires-Dist: sphinx-argparse; extra == 'all'
Requires-Dist: sphinx-togglebutton; extra == 'all'
Requires-Dist: sphinx>=1.8; extra == 'all'
Requires-Dist: sphinxcontrib-bibtex; extra == 'all'
Provides-Extra: container
Requires-Dist: datalad; extra == 'container'
Requires-Dist: datalad-osf; extra == 'container'
Requires-Dist: migas>=0.4.0; extra == 'container'
Provides-Extra: dev
Requires-Dist: pre-commit; extra == 'dev'
Provides-Extra: doc
Requires-Dist: myst-parser; extra == 'doc'
Requires-Dist: pydot>=1.2.3; extra == 'doc'
Requires-Dist: shibuya; extra == 'doc'
Requires-Dist: sphinx-argparse; extra == 'doc'
Requires-Dist: sphinx-togglebutton; extra == 'doc'
Requires-Dist: sphinx>=1.8; extra == 'doc'
Requires-Dist: sphinxcontrib-bibtex; extra == 'doc'
Provides-Extra: docs
Requires-Dist: myst-parser; extra == 'docs'
Requires-Dist: pydot>=1.2.3; extra == 'docs'
Requires-Dist: shibuya; extra == 'docs'
Requires-Dist: sphinx-argparse; extra == 'docs'
Requires-Dist: sphinx-togglebutton; extra == 'docs'
Requires-Dist: sphinx>=1.8; extra == 'docs'
Requires-Dist: sphinxcontrib-bibtex; extra == 'docs'
Provides-Extra: duecredit
Requires-Dist: duecredit; extra == 'duecredit'
Provides-Extra: maint
Requires-Dist: fuzzywuzzy; extra == 'maint'
Requires-Dist: python-levenshtein; extra == 'maint'
Provides-Extra: telemetry
Requires-Dist: migas>=0.4.0; extra == 'telemetry'
Provides-Extra: test
Requires-Dist: coverage[toml]; extra == 'test'
Requires-Dist: pytest; extra == 'test'
Requires-Dist: pytest-cov; extra == 'test'
Requires-Dist: pytest-env; extra == 'test'
Requires-Dist: pytest-xdist; extra == 'test'
Provides-Extra: tests
Requires-Dist: coverage[toml]; extra == 'tests'
Requires-Dist: pytest; extra == 'tests'
Requires-Dist: pytest-cov; extra == 'tests'
Requires-Dist: pytest-env; extra == 'tests'
Requires-Dist: pytest-xdist; extra == 'tests'
Description-Content-Type: text/markdown

Magnetic resonance imaging (MRI) requires a set of preprocessing steps before
any statistical analysis. In an effort to standardize preprocessing,
we developed [fMRIPrep](https://fmriprep.org/en/stable/) (a preprocessing tool
for functional MRI, fMRI), and generalized its standardization approach to
other neuroimaging modalities ([NiPreps](https://www.nipreps.org/)). NiPreps
brings standardization and ease of use to the researcher, and effectively
limits the methodological variability within preprocessing. fMRIPrep is designed
to be used across wide ranges of populations; however it is designed for (and
evaluated with) human adult datasets. Infant MRI (i.e., 0-2 years) presents
unique challenges due to head size (e.g., reduced SNR and increased partial
voluming and rapid shifting in tissue contrast due to myelination. These and
other challenges require a more specialized workflow. *NiBabies*, an open-source
pipeline extending from fMRIPrep for infant structural and functional MRI
preprocessing, aims to address this need.

The workflow is built atop [Nipype](https://nipype.readthedocs.io) and encompasses 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/),
[Connectome Workbench](https://humanconnectome.org/software/connectome-workbench),
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.

*NiBabies* performs basic preprocessing steps (coregistration, normalization, unwarping,
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.
*NiBabies* 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.

[Repository](https://github.com/nipreps/nibabies)
[Documentation](https://nibabies.readthedocs.io/en/stable/)
