Those timeseries don’t seem to be the same length or scale. You could try single regression of the ICA timeseries into your unsmoothed data and then regressing them out.
Matt.
From: FSL - FMRIB's Software Library <[log in to unmask]<mailto:[log in to unmask]>> on behalf of Chirag Limbachia <[log in to unmask]<mailto:[log in to unmask]>>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]<mailto:[log in to unmask]>>
Date: Tuesday, October 9, 2018 at 11:44 AM
To: "[log in to unmask]<mailto:[log in to unmask]>" <[log in to unmask]<mailto:[log in to unmask]>>
Subject: [FSL] ICA AROMA: Denoising unsmooth func data
FSL Experts,
Carrying out denoising on the unsmooth data seems to introduce more noise. The following is the method which was recommend to me carry out denoising on the unsmooth data:
1) perform ica aroma on the smooth data.
2) Then using dual regression, regress the spatial components (melodic_IC.nii.gz) found in step 1 onto the unsmooth data to get the corresponding timeseries for every component for the unsmooth data.
3) Lastly, use the timeseries (from step 2) of the components there were classified as motion in step 1 to filter them out of the unsmooth data using fsl_regfilt.
Here is the actual code of how denoising was carried out:
python $main_dir/ICA_AROMA.py \
-in <smooth func data> \
-out $outdir \
-mc <six motion parameters text file>
dual_regression $outdir/melodic.ica/melodic_IC.nii.gz 1 -1 0 $outdir/unsmooth.dr <unsmooth func data>
fsl_regfilt -i <unsmooth func data> \
-d $outdir/unsmooth.dr/dr_stage1_subject00000.txt \
-o <donoised unsmooth func data> \
-f `cat $outdir/classified_motion_ICs.txt`
All my func data are registered to standard space
The above method made sense, but when I compared (visually) the timeseries of unsmooth data before and after denoising, I saw a lot of new noise introduced in the data post denoising. Attached figure shows the timeseries of the unsmooth data before (in blue) and after (black) denoisig.
Thank you,
Chirag
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