I am currently trying to compare 3dvolreg and SLOMOCO to see which head motion correction method I want to use. I know you can do 3dvolreg and censoring at the same time by using afni_proc. However, does anyone know how I can apply censoring to SLOMOCO so I can take out specific slices? The current code I use to generate SLOMOCO is
slicemoco_newalgorithem.sh -d $SubID$Time.fMRI1+orig
I am also wondering if there’s a way to record how many slices/volumes afni_proc is censoring.
Thanks in advance for any advice/ideas!
I cannot advise on the first part of this, but afni_proc.py reports at the end of the processing how many time points were censored, along with fractions per stim class and overall.
Slice-based censoring would be possible using 3dREMLfit (assuming there were a reasonable way to detect slice-based motion used for censoring), but there is no such method available within the framework of afni_proc.py.
But slice-based censoring might come with issues.
Were slice-based censoring done in resting-state analysis, it is not clear how correlations would then be carried out, as censoring would vary across space. And the method would rely on reasonably good slice-based motion correction, so that resampling and smoothing operation would not contaminate neighboring slices with censored motion effects.
Plus, any linear regression model would almost have to be done on the original slice grid, as the locations, angles and spacing of slices would change on any move to standard space. And that would lead to an extra resampling of the data when subsequently going to standard space, adding blur and possibly requiring recomputation of blur estimation.
And while slice-based censoring could be applied in the linear regression model to compute betas, tracking degrees of freedom to report accurate t-stats and such might not be simple.
There seem to be a lot of complications with the notion of slice-based motion censoring at first thought.
But getting back to volumetric censoring, if SLOMOCO produces motion parameters over time, they could be passed along to afni_proc.py.
SLOMOCO does not support the censoring option, but provides the slice wise motion index, possibly used for your censoring. Check slomoco.TDzmetric.txt file, which is slice wise time series (tdim x zdim) of motion index. You should consider the slice acquisition timing for EPI data, e.g. 3 tp data is 1st volume, 3rd slice acquisition, not the 3rd slice.