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Description:

rabies 0.5.1

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/stable/.

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
brain parcellation
slice timing correction (optional)
despiking (optional)
visual assessment of registration for quality control

Confound correction
Following preprocessing, a range of strategies to correct fMRI confounds (e.g. motion) can then be conducted within RABIES:

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

Analysis
Simple resting-state connectivity analyses are made available after preprocessing and confound correction. RABIES also provides a 'data diagnosis' workflow, which generates several indices of data quality and potential confounds, and conversaly, aims to improve the correction of confounds and transparency with regards to data quality:

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

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 hosted on Docker Hub which can be downloaded via Docker and Singularity platforms

Citation
Citing RABIES: A preprint introducing the software is available on bioXriv, which should be cited accordingly when using RABIES.
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 developped 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.
Contributing to RABIES
Read our dedicated documentation