Hi all!
I have a decision-making experiment where I present a stimulus (for 500 ms) and then subjects make a binary decision by pressing a button (1 or 2) within 2 s. Afterwards there are other irrelevant (for my analysis) stimuli, accounting for ~13 s, and then the next trial starts. I acquire data all over the experiment (TR = 2.2s).
I am trying to analyze the fMRI data associated to two different conditions: whether the decision is correct or whether it is wrong. My design should be model-based, as the order of trials (correct vs. incorrect) depends on the subjects' responses. However, I couldn't find tutorials on how to perform this analysis in AFNI. The afni_proc.py script I put together (below) gives very random results (significant areas mostly outside the brain). Stim files are text files with one rows per TR, and 1 if it is the TR with onset at the stimulus that leads to correct/incorrect decision, or 0 otherwise.
Do you have any suggestions on how to model this analysis in AFNI? I have been bouncing my head on this problem for months!
Thanks!
Davide
afni_proc.py -subj_id $SUBJ \
-script proc.$SUBJ -scr_overwrite \
-blocks despike tshift align tlrc volreg blur mask scale regress \
-copy_anat $top_dir/$SUBJ/T1w_deobliqued.nii \
-tcat_remove_first_trs 2 \
-dsets $top_dir/$SUBJ/ep_*_deobliqued.nii.gz \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-tshift_opts_ts -tpattern alt+z \
-blur_size 4.0 \
-regress_use_stim_files \
-regress_stim_files \
$top_dir/$SUBJ/"$SUBJ"_correct_stim_file.1D \
$top_dir/$SUBJ/"$SUBJ"_incorrect_stim_file.1D \
-regress_stim_types file \
-regress_stim_labels \
correct incorrect \
-regress_basis SPMG3 \
-regress_motion_per_run \
-regress_censor_motion 1.0 \
-regress_censor_outliers 0.03 \
-regress_opts_3dD \
-gltsym 'SYM: correct -incorrect' -glt_label 1 correct-incorrect \
-jobs 8 \
-regress_make_ideal_sum sum_ideal.1D \
-regress_est_blur_epits \
-regress_est_blur_errts \
-regress_run_clustsim no \
end
