Dear Rick,

this is very good information. In fact, the solution to question number 2 was much easier than I thought. Both options, that is, the one via the GUI and the one via the -batch method work now.

I would have another question beside question 1 in my first post, also in relation to this topic. My dataset contains 25 subjects and I am analyzing three sessions:

- One resting-state session (1 run per subject)
- One task-based with 6 runs but processed as if it were a resting-state run (for dynamic measurements and their comparison with the resting-state session)
- One task-based with 6 runs (same as above) but properly processed as task-based.

The dataset has physiological recordings (both cardiac and respiration) for almost all subjects and runs. However, some physiological recordings are missing:
- Subject 3: cardiac and respiration recordings are missing for run 5 and 6
- Subject 8: respiration is missing for run 6
- Subject 16: both physiological recordings are missing for the resting-state

My question is if I should apply a bandpassfilter of 0.01-0.05 Hz for these subjects’ runs or simply preprocess these subjects’ runs without bandpassing, even though they will consequently lack physiological regression? I learnt all the deficits that come with bandpassing from you AFNI people and I generally try to avoid bandpassing now. I was just thinking if it is appropriate to skip the physiological regression in these specific runs without bandpassing and to proceed like this for further resting-state as well as task-based measurements.