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:
—————————————————————————————————
# 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