Good day!
I would like to bandpass filter my resting state data and nuisance regressors before performing regression as described in Hallquist et al (Neuroimage, 2013). Am I implementing it in AFNI correctly (code below)?
Are my inputs to the 3dDeconvolve -ortvec options (here, white matter and csf time series) correct? Within 3dbandpass, I do filter the regressor time series with the -ort option but what I eventually put into the regression (-ortvec) are my unfiltered regressors.
Thank you very much!
Best,
JDC
resting state data - rest.nii.gz
white matter regressor time series - wm.1D
csf regressor time series - csf.1D
censor file - fdanddvars.1D
Code:
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# bandpass filter EPI and regressors
3dBandpass -nodetrend -mask brainmask.nii.gz \
-ort wm.1D -ort csf.1D \
-prefix rest.bpass.nii.gz 0.009 0.08 rest.nii.gz
# create regression matrix
${afnipath}/3dDeconvolve -input rest.bpass.nii.gz \
-mask brainmask.nii.gz \
-censor fdanddvars.1D \
-ortvec wm.1D ROI.WMe \
-ortvec csf.1D ROI.CSFe \
-polort 3 -float \
-jobs 8 \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D \
-fitts fitts.$subj \
-errts finaldata.nii.gz \
-x1D_stop \
-bucket stats.$subj \
# perform regression
3dTproject -polort 0 -input rest.bpass.nii.gz \
-censor fdanddvars.1D -cenmode ZERO \
-ort X.nocensor.xmat.1D -prefix finaldata.nii.gz
