Metadata-Version: 2.4
Name: funROI
Version: 1.0.0.post1
Summary: A Python package for functional ROI analyses of fMRI data
Keywords: fMRI,ROI,neuroimaging,data analysis
Author-email: Ruimin Gao <ruimin.gao@gatech.edu>, "Anna A. Ivanova" <a.ivanova@gatech.edu>
Description-Content-Type: text/x-rst
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
License-File: LICENSE
Requires-Dist: sphinx>=4.0
Requires-Dist: sphinx_rtd_theme
Requires-Dist: h5py>=3.0.0
Requires-Dist: nibabel>=5.2.0
Requires-Dist: numpy>=1.22.4
Requires-Dist: pandas>=2.2.0
Requires-Dist: scipy>=1.8.0
Requires-Dist: statsmodels
Requires-Dist: nilearn

funROI
========================

|docs|

.. |docs| image:: https://readthedocs.org/projects/funroi/badge
    :alt: Documentation Status
    :target: https://funroi.readthedocs.io/en/latest/?badge=latest

The **funROI** (FUNctional Region Of Interest) toolbox is designed to provide robust analytic methods for fMRI data analyses that accommodate inter-subject variability in the precise locations of functional activations. 

.. image:: https://github.com/GT-LIT-Lab/funROI/raw/main/doc/source/funROI-collage.png
   :width: 800px
   :align: center

Features
--------

- **Parcel generation:** generates parcels (brain masks) based on individual activation maps, which can serve as a spatial constraint for subsequent subject-level analyses. (This step can be skipped if you already have parcels of interest).

- **fROI definition:** defines functional regions of interest (fROIs) by selecting a subset of functionally responsive voxels within predefined parcels.

- **Effect estimation:** extracts average effect sizes for each subject-specific fROI.

- **Spatial correlation estimation:** quantifies the similarity of within-subject activation patterns across conditions (within either a parcel or an fROI).

- **Spatial overlap estimation:** calculates the overlap between parcels and/or fROIs from different subjects or definitions.

Installation
------------
Install funROI via pip:

.. code-block:: bash

   pip install funROI

Usage
-------------
For more details and examples, please refer to the full documentation at:
https://funroi.readthedocs.io/en/latest/

Citation
--------
If you use funROI in your work, please cite it as follows:

   Gao, R., & Ivanova, A. A. (2025). *funROI: A Python package for functional ROI analyses of fMRI data* (Version 1). Figshare. https://doi.org/10.6084/m9.figshare.28120967.v1

Acknowledgements
----------------

This toolbox implements the parcel definition, fROI definition, and fROI effect size estimation methods described in `Fedorenko et al. (2010) <https://pmc.ncbi.nlm.nih.gov/articles/PMC2934923/>`_. It builds heavily on the `spm_ss <https://github.com/alfnie/spm_ss>`_ toolbox, which provides a Matlab-based implementation for fROI analyses. We thank Alfonso Nieto-Castañon and Ev Fedorenko for developing these methods. 

