Hello,

To state the conclusion first, I would just add the full regression to the current afni_proc.py command and reprocess. That will take more computer time, but might save quite a bit of your time. But to babble...


afni_proc.py would apply the last pb*.HEAD datasets, matching all_runs, except the using multiple files tells 3dDeconvolve the run breaks.

Even if the regression is not fully carried out at first with afni_proc.py, it is good to put in a regress block (akin to treating it as rest), just for the extra QC that comes with it. The extra cost is disk space.

For this step, and since you will presumably add regression to the afni_proc.py commands for future subjects, it might be good to start by making a complete afni_proc.py command now, including regressors of interest. Then add the option -write_3dD_script (and a script name) to the command. It will create a 3dDeconvolve command for you, which assumes all of the other processing.

On the flip side, that single command script will not create censor and enorm time series (if censoring is being used), so you might still have to run 1d_tool.py. But at least it will suggest what to do.

This also shows the benefit of adding a regress block early on, even without the tasks of interest. All of the preparation would be in place to use for a new 3dDeconvolve command script.

At this point, it might be good to create a full AP script with regression, and work from there an example. OR preferably, you could just reprocess those subjects with the AP script has everything it needs (full regression). That is what I would do. Your time is more important than computer time.

- rick