This is a Python and MATLAB pipeline developed for use in simultaneous multi-slice (multiband; MB) fMRI data.
MARSS is a regression-based method that mitigates an artifactual shared signal between simultaneously acquired slices in unprocessed MB fMRI.
Software Authors: Philip N. Tubiolo, John C. Williams, Ashley Zhao, Mahika Gupta, and Jared X. Van Snellenberg
Accompanies the following manuscript:
Tubiolo PN, Williams JC, Van Snellenberg JX.
Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices.
Hum Brain Mapp. 2024 Nov;45(16):e70066. doi: 10.1002/hbm.70066. PMID: 39501896; PMCID: PMC11538719.
This software has been tested on the following operating systems, but should be compatible with MacOS as well:
Linux: Red Hat Enterprise Linux 7.9
Windows: Windows 10 Home 64-bit
MARSS should only require the minimum RAM to handle a single fMRI timeseries (approximately 2GB). However, it has been tested with these minimum specifications:
RAM: 16 GB
Processor: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz
With the above specifications, the total time taken for MARSS to complete on a single fMRI timeseries of 563 volumes is approximately 10 minutes.
Prior to running MARSS, it must be added to the MATLAB path via the following command:
addpath(genpath(('/path/to/MARSS/'))
For more information, see the MATLAB README.
To install this package, run the following command:
pip install MARSS
For more information, see the Python README.
When using MARSS, please cite the following:
Tubiolo PN, Williams JC, Van Snellenberg JX.
Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices.
Hum Brain Mapp. 2024 Nov;45(16):e70066. doi: 10.1002/hbm.70066. PMID: 39501896; PMCID: PMC11538719.
This software is released under the GNU General Public License Version 3.