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.
- 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.
Install funROI via pip:
pip install funROIFor more details and examples, please refer to the full documentation at: https://funroi.readthedocs.io/en/latest/
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
This toolbox implements the parcel definition, fROI definition, and fROI effect size estimation methods described in Fedorenko et al. (2010). It builds heavily on the spm_ss toolbox, which provides a Matlab-based implementation for fROI analyses. We thank Alfonso Nieto-Castañon and Ev Fedorenko for developing these methods.