Python-based Reliability in MRI (PyReliMRI)

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PyReliMRI is a Python package designed to address the increasing interest for reliability assessment in MRI research, particularly in task fMRI and resting state fMRI. Researchers use various methods to calculate reliability, but there is a lack of open-source tools that integrate multiple metrics for both individual and group analyses.

Purpose of PyReliMRI

PyReliMRI (pronounced: Pi-Rely-MRI) aims to fill the gap by providing an open-source Python package for estimating multiple reliability metrics on fMRI (or MRI) data in standard space. It supports analysis at both the group and individual levels, facilitating comprehensive reporting in multi-run and/or multi-session MRI studies. Even with single-session and single-run data, PyReliMRI remains useful. For example:

  • Assessing reliability or similarity metrics on individual files by splitting the run and modeling them separately.

  • Using group-level maps (e.g., from neurovault or across studies) to compute various similarity metrics.

Modules Overview

PyReliMRI comprises several modules tailored to different use cases:

  • `icc`: Computes various components used in ICC calculations, including ICC(1), ICC(2,1), or ICC(3,1), confidence intervals, between-subject variance, and within-subject variance.

  • `brain_icc`: Calculates voxelwise and ROI-based ICCs across multiple sessions, integrating with Nilearn datasets for atlas options.

  • `conn_icc`: Estimates ICC for precomputed correlation matrices, useful for connectivity studies.

  • `similarity`: Computes similarity coefficients (Dice, Jaccard, tetrachoric, Spearman) between 3D Nifti images, including pairwise comparisons across multiple images.

  • `tetrachoric_correlation`: Calculates tetrachoric correlation between binary vectors.

  • `masked_timeseries`: Extracts and processes timeseries data from BOLD image paths, facilitating ROI-based analysis and event-locked responses.

Each module is designed to answer specific questions about data reliability, supporting a range of MRI analyses in standard spaces like MNI or Talairach.

Citation

If you use PyReliMRI in your research, please cite it using the following Zenodo DOI:

Demidenko, M., Mumford, J., & Poldrack, R. (2024). PyReliMRI: An Open-source Python tool for Estimates of Reliability in MRI Data (2.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.12522260