I apologize for the silly question but I am confused on how to setup the afni_proc script to analyze my finger tapping fMRI task. The task is basically a subject tapping with all 4 fingers 4 times on the left, then the right hand, then pause 20 seconds, and then repeat another time.
I used the GAM function and I am only able to visualize the left side of the brain results . This was my script:
afni_proc.py -subj_id test -script proc.test -scr_overwrite -blocks tshift \
align tlrc volreg blur mask scale regress -copy_anat \
/media/server2/crypto4/backup/intraop/volunteer/t1.nii -dsets \
-tcat_remove_first_trs 0 -volreg_align_to MIN_OUTLIER -volreg_align_e2a \
-volreg_tlrc_warp -blur_size 8.0 -regress_stim_times \
-regress_stim_labels stims.txt -regress_basis GAM \
-regress_censor_motion 0.3 -regress_apply_mot_types demean deriv \
-regress_motion_per_run -regress_reml_exec -regress_compute_fitts \
-regress_make_ideal_sum sum_ideal.1D -regress_est_blur_epits \
thank you a lot again
I think a good model/place to start for your example is with the “start-to-finish” example in the the AFNI Bootcamp. There is a two-task example there, for using multiple stimuli (2 there: an audio and visual). The afni_proc.py command specifically is (in the unzipped CD.tgz): AFNI_data6/FT_analysis/s05.ap.uber.
Therein, probably the “regress” block features there are the most useful, and you can make whatever contrasts are useful for your hypotheses. Some other blocks we would suggest other things, like how alignment to standard space is done (now often recommending an @SSwarper pre-step), for example. If you look at the @SSwarper help, you can see how that is integrated into the afni_proc.py command.
And feel free to ping back with any questions, too, of course.