Hi, Joy-

Oooook, this set of EPI-anat align opts worked well (see attached APQC HTML piece; and I did verify it by running afni_proc.py with both full EPIs entered this time...):

-align_opts_aea \
   -big_move -cmass cmass \

Some other thoughts struck me about regression modeling here: you might want to add these opts:

      -regress_compute_fitts                                   \
      -regress_make_ideal_sum sum_ideal.1D                     \
      -regress_motion_per_run                                  \

... to (in order):
+ output the full time series fit by the model
+ output the "ideal" response curve, based on your input stimuli timings and chosen HRF
+ and, well, I'll just display part of the afni_proc.py help for this last opt:

-regress_motion_per_run : regress motion parameters from each run

                default: regress motion parameters catenated across runs

            By default, motion parameters from the volreg block are catenated
            across all runs, providing 6 (assuming 3dvolreg) regressors of no
            interest in the regression block.

            With -regress_motion_per_run, the motion parameters from each run
            are used as separate regressors, providing a total of (6 * nruns)
            regressors.

            This allows for the magnitudes of the regressors to vary over each
            run, rather than using a single (best) magnitude over all runs.
            So more motion-correlated variance can be accounted for, at the
            cost of the extra degrees of freedom (6*(nruns-1)).

--pt



Edited 1 time(s). Last edit at 04/04/2020 12:10PM by ptaylor.