The PyReliMRI package intergrates several modules designed to facilitate reliability estimation on MRI data. The code is simplified by leveraging features from Nilearn. These modules can be categorized into two main groups:
- similarity.py: Computes similarity coefficients (Dice, Jaccard, etc.) between 3D Nifti images. Includes functions like image_similarity for pairwise comparisons.
- tetrachoric_correlation.py: Calculates the tetrachoric correlation between binary vectors, useful for certain types of data analysis.
- icc.py: Computes various components used in ICC calculations, such as ICC(1), ICC(2,1), or ICC(3,1), along with confidence intervals and variance components.
- brain_icc.py: Calculates voxelwise and ROI-based ICCs across multiple runs/sessions. Integrates with Nilearn datasets for atlas options, facilitating quick atlas integration.
- conn_icc.py: Estimates ICC for precomputed correlation matrices, useful for connectivity analyses.
The masked_timeseries.py module provides functionality for extracting and processing stimulus-locked timeseries data from BOLD images. It includes methods for ROI-based analysis and event-locked responses.
Combined, these modules collectively support a wide range of reliability assessments in MRI studies, from basic similarity metrics to advanced ICC calculations and timeseries analysis.