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MARSS is a method to mitigate artifactual signal within slice groups in multiband data. It needs to be run on unprocessed data (pre-HMC+STC), so I think it would fit well into fMRIPrep.
The method essentially works by regressing out (1) the mean signal from slices outside the slice group and (2) motion parameters out of the mean signal from all slices in the slice group, except for the selected slice, for each slice. The artifact time series for that slice is the residuals from this regression. It then uses that slice-wise artifact time series, the other-slice group mean time series, and the motion parameters to regress the artifact out of each voxel in the slice.
Do you have any interest in helping implement the feature?
Yes
Additional information / screenshots
The official implementation is in MATLAB (https://github.com/CNaP-Lab/MARSS), but the actual method is pretty straightforward, so I would feel comfortable translating it to Python.
The method is currently only described in a preprint (Tubiolo, Williams, & Snellenberg, 2024) and I understand if the devs would rather wait until it's been peer-reviewed, but I thought it was at least worth bringing up.
The text was updated successfully, but these errors were encountered:
What would you like to see added in fMRIPrep?
MARSS is a method to mitigate artifactual signal within slice groups in multiband data. It needs to be run on unprocessed data (pre-HMC+STC), so I think it would fit well into fMRIPrep.
The method essentially works by regressing out (1) the mean signal from slices outside the slice group and (2) motion parameters out of the mean signal from all slices in the slice group, except for the selected slice, for each slice. The artifact time series for that slice is the residuals from this regression. It then uses that slice-wise artifact time series, the other-slice group mean time series, and the motion parameters to regress the artifact out of each voxel in the slice.
Do you have any interest in helping implement the feature?
Yes
Additional information / screenshots
The official implementation is in MATLAB (https://github.com/CNaP-Lab/MARSS), but the actual method is pretty straightforward, so I would feel comfortable translating it to Python.
The method is currently only described in a preprint (Tubiolo, Williams, & Snellenberg, 2024) and I understand if the devs would rather wait until it's been peer-reviewed, but I thought it was at least worth bringing up.
The text was updated successfully, but these errors were encountered: