Hi Sam,

1. Indeed, depending on what the further analysis entails, there should be a limit on the degrees of freedom lost/projected out. There is no hard number for that though, and it also depends on the quality of subjects. Sometimes a patient group is difficult to acquire, or might generally be expected to come with more motion.

Just think through the minimum fraction of degrees of freedom you would want remaining, and exclude subjects failing that.

2. Yes, 3dTproject is keeping the residuals of a least squares fit to the data. It would be the same as with 3dDeconvolve, but it is faster since the partial statistics are not bothered with.

3. Scaling the data would be up to you. Paul suggested that if you are computing ALFF for example, then scaling is important. Scaling would not affect correlations.

4. Yes, though it is the omitted frequency bands that are projected out. Note that if you do bandpassing with afni_proc.py, it will use 1dBport to output the sinusoids to regress out, and apply them in 3dDeconvolve or 3dTproject. If you do an example like that, the steps should be made clear.

For example, see the afni_proc.py command in AFNI_data6/FT_analysis/s06.ap.rest and the corresponding s16.proc.FT.rest processing script.

- rick