P.S. If you’re a Matlab user, you can try using the spm_ss toolbox developed by Alfonso (which we here adapted for Python+BIDS)
github.com/alfnie/spm_ss

Many thanks to @evfedorenko.bsky.social & Alfonso Nieto-Castañon for developing these methods in Fedorenko et al (2010) and in subsequent works!
journals.physiology.org/doi/full/10....

For a detailed demo with code examples—check out our step-by-step guide 👉 funroi.readthedocs.io/en/latest/ex...

Built to be BIDS-compliant, funROI ensures your data is organized & reproducible. 📁

funROI also provides a wrapper for #Nilearn ’s first-level modeling - Easily run GLM analyses with support for event-related & block designs, customizable hemodynamic responses, confound regression, and statistical contrasts.

3 - Effect Estimation: Quantify the strength of neural responses in your fROIs.

4 - Spatial Correlation: Compare within-subject activation patterns across conditions.

5 - Overlap Estimation: Measure spatial overlap between parcels or fROIs.

2 - fROI Definition: Define subject-specific functional ROIs by selecting the top % of active voxels within each parcel (or use fixed voxel counts/p-value thresholds).

Key features include:

1 - Parcel Generation: Create group parcels (brain masks) from individual activation maps with customizable smoothing & thresholds.

funROI leverages subject-specific functional localization to boost the sensitivity & accuracy of your analyses.

It is also easy to use.