RABIES: Rodent Automated Bold Improvement of EPI Sequences.

RABIES is an open source image processing pipeline for rodent fMRI. It conducts state-of-the-art preprocessing and confound correction, and supplies standard resting-state functional connectivity analyses. Visit our documentation at https://rabies.readthedocs.io/en/latest/.

What you can do with RABIES

The primary purpose of RABIES is to provide rodent fMRI research with a standard, flexible, and reliable image processing platform. The package is complemented with informative data diagnostic features for confound characterization and encourages best practices for quality control and reproducibility. The RABIES software is structured into three main processing stages: preprocessing, confound correction and analysis.

Preprocessing

The preprocessing workflow regroups essential fMRI preprocessing steps prior to analysis. It includes a robust registration workflow with automatically-adapting parameters allowing to succesfully process diverse acquisition types (i.e. rodent species, scan field strength, coil type, ...), and can conduct the following preprocessing steps:

• head motion correction
• susceptibility distortion correction
• resampling to native or common space
• slice timing correction (optional)
• despiking (optional)
• visual assessment of registration for quality control

Confound correction

Following preprocessing, RABIES provides a highly-customizable confound correction pipeline regrouping several common strategies:

• frame censoring (or scrubbing)
• confound regression (with several options for nuisance regressors)
• frequency filtering (highpass, lowpass, bandpass)
• ICA-AROMA
• spatial smoothing

Analysis

Simple resting-state connectivity analyses are made available after preprocessing and confound correction:

• seed-based functional connectivity
• whole-brain connectivity matrix
• group-ICA
• dual regression

Importantly, the analysis stage is also complemented by a data quality assessment report (executed with --data_diagnosis), which generates several indices of data quality and potential confounds, with the aim of improving preprocessing strategies, data interpretation and scientific transparency.

Notes on software design

Nipype workflows: The image processing pipelines are structured using the Nipype library, which allows to build dynamic workflows in the form of a computational graph. Each node in the graph consists of a processing step, and the required inputs/outputs define the links between nodes. In addition to supporting code organization, Nipype workflows also handle several plugin architectures for parallel execution as well as memory management. The computational time to run the entire RABIES pipeline will vary substantially depending on data size, but for most uses, it will range from a few hours to a day when using proper computational resources and parallel execution.

Reproducible and transparent research: RABIES aims to follow best practices for reproducible and transparent research, including the following:

• open source code https://github.com/CoBrALab/RABIES
• standardized input data format with BIDS
• easily shared, automatically-generated visual outputs for quality control
• containerized distribution of the software through Docker/Apptainer, and conveniently installed from our Github or Docker Hub servers.

Citation

Citing RABIES: Please cite the official publication Desrosiers-Grégoire, et al. Nat Commun 15, 6708 (2024). when referencing the software.

Boilerplate: a boilerplate summarizing the preprocessing and confound correction operations is automatically generated in the output folder. You can use the boilerplate to help describe your methods in a paper.

License

The RABIES license allows for uses in academic and educational environments only. Commercial use requires a commercial license from CoBrALab contact@cobralab.ca, http://cobralab.ca

Acknowledgements

This software was developed by the CoBrALab, located at the Cerebral Imaging Center of the Douglas Mental Health University Institute, Montreal, Canada, in affiliation with McGill University, Montreal, Canada. This work was supported by funding from Healthy Brains, Healthy Lives (HBHL), the Fonds de recherche du Québec - Santé (FRQS) and - Nature et technologies (FRQNT), and the Natural Sciences and Engineering Research Council (NSERC) of Canada. fMRIPrep was an important inspirational source for this project, in particular with regards to best practices for software reproducibility and code design using Nipype. We also thank the organizers of BrainHack School Montreal, which guided the initial steps of this project in 2018.

Ask for help

If you need support in using the software or experience issues that are not documented, we'll provide support on the Github discussion.