Hi,

Example 6 in 'afni_proc.py -help' says that the '-volreg_align_e2a' switch along with '-do_block align' should create an align_epi_anat.py command in which the epi is aligned to the anat (the -epi2anat switch in that program). But, running that example using afni_proc.py (version 2.13) outputs an align_epi_anat.py command with the '-anat2epi' switch. It's easy enough to fix that manually. I just wanted to report that as a small bug.

Also, I have a question about the following bit from 'afni_proc.py -help':

"If the user wishes to include statistics as part of the group analysis
(e.g. using 3dMEMA.R), this warping becomes more needed. Warping to
standard space *after* statistics are generated is not terribly valid."

This seems potentially inconsistent with the steps outlined in the "AFNI Soup to Nuts" presentation from the latest bootcamp (http://afni.nimh.nih.gov/pub/dist/edu/2009_09_fall_4day/afni16_soup_to_nuts/afni16_soup_to_nuts.pdf). In that presentation, warping is done on the betas output by 3dDeconvolve. Are the betas not considered to be statistics, in the relevant sense? If not, what is the sense of "statistics" used above? And why isn't it valid to warp them?

If I had to take a guess, I would say that the betas aren't considered statistics in this case, because they haven't been divided by the corresponding standard error. If they had, that would make them t-statistics (I think), which would make them statistics in the relevant sense used above (i.e., inferential statistics). The only thing I don't have a clue about is why it might be valid to warp betas but not t-statistics (for example).

I'd appreciate any insight that folks could shed on that question.

-Joe