Hi Tamara,

(1) That looks reasonable, and seems similar to "Example 6b" from the help. But it might be worth you considering some of the differences.

Add this to your command:

-compare_opts 'example 6b'

(and possibly -verb 2), and see what it says.

One example is that we suggest running @SSwarper on each subject before running afni_proc.py, and then passing the warp info as in Example 6b.

For QC, it is nice to include the -radial_correlate_blocks option.
Example 6 also uses 3dREMLfit for the regression, which is somewhat preferable to the OLSQ in 3dDeconvolve.

Also, -html_review_style pythonic will give nicer plots (if you have matplotlib) in the QC report.


(2) With the QC specified, note that the final output from the analysis should be a suggestion to open a QC HTML report. That report is a nice QC review and should be the minimum review for each subjects. For the first couple/few subjects, it is suggested to review the data at the level of the bootcamp class "Start to Finish". We have videos on that (17 videos covering 6 hours, it is detailed).


(3) Those are reasonable parameters to focus on, but the levels depend on the subjects. Note that for healthy adults, those censor limits are high. But some subjects are much more prone to motion.

Motion (damaging estimates of the betas) can subsequently hurt the group results. So if you end up dropping 5-20% of the subjects, it might actually help. There are no universal numbers because of how they vary across subject groups.

- rick