Hi, Joy-

Thanks for sending that info.

Your AFNI version is a bit old-- nearly a year old now-- so I would recommend updating:

@update.afni.binaries -d

There will have been lots of minor updates, additions, fixes and tweaks in that time. I don't believe that that would necessary change anything here directly, but it would probably be useful for you.

Your datasets look *very* well aligned to start, so it is odd that they end up so far apart after. It is a little bit hard to see the tissue contrast clearly because of the color range, but I'll assume it is mostly normal (if it isn't standard EPI kind of tissue contrast--bright ventricles, dark WM--then using a different cost function could be useful, but that would be a somewhat rare case in human data). One thing you could do is leave out the giant_move part, because that is mainly for helping cases that start *far apart*, by removing this line:

-align_opts_aea -giant_move \

(from the align_epi_anat.py help:

-giant_move : even larger movement required - uses cmass, two passes and
                  very large angles and shifts. May miss finding the solution
                  in the vastness of space, so use with caution

)

One issue could be skullstripping; I see that you aren't aligning your data to standard space, so perhaps you don't want to use @SSwarper (which combines both skullstripping and nonlinear alignment to standard space), but you could use that program still perhaps with the "-skipwarp" option to save some time, to perform skullstripping---fair warning, it will be fairly slow, but if you are running on a multicore machine, you can use parallel threading to speed it up. Let me/us know if you are interested in that (but you can check how the skullstripping looks at present).

Another note: it looks like you are including the derivatives of motion in your regressors---that is typically a step for resting state processing, and I'm not sure you would want/need to do that for task data, such as you have. So, you might want to leave out:

-regress_apply_mot_types demean deriv \

here.

Also, if you have Python and matplotlib, then I would strongly recommend adding:

-html_review_style pythonic

in order to have the nicer QC HTML file output.

--pt