Hi Emily,

Given Paul's suggestion of:

3dRSFC -nodetrend -mask MASKNAME -bp_at_end -prefix PREFIX 0.01 0.1 INPUT_NAME

and Ziad's comment that 3dRSFC would want to remove the same
model as 3dD, except for the bandpass regressors, it makes sense to
give 3dRSFC the input to 3dDeconvolve, plus a matrix of regressors
to orthogonalize to. Also, censoring would presumably not be done.

So...

1. The input dataset to 3dRWFC would be the same as to 3dDeconvolve.

2. Generate a matrix of regressors to pass to 3dRWFC via -ort.
2a. Start with X.no.censor.xmat.1D (censoring was not done here).
2b. Extract everything but the bandpass regressors. You may have to
get the column indices by viewing the text file itself. I will add an
option to 1d_tool.py to help with this soon.

3. Use the command as Paul suggested, including -nodetrend and
passing the new matrix via -ort.

How does that seem?

I will add afni_proc.py options for this pretty soon.

- rick