Dear all.

I have the following scenario. I have two types of stimuli, A and B. They are being shown in blocks. Each block can have 1, 2, 4, or 8 different A stimuli or 1,2,4, or 8 different B stimuli (e.g.. 2 different A stimuli would be A1 A2 A1 A2 A1...).

I want to see which regions modulate by the number of different stimuli in each block. Therefore I want to use the stim_times_AM2 option where each stimulus time is associated with an amplitude (which would here be 1, 2, 4, or 8). This will give me two T-stats for A and two T-stats for B (#0_Tstat and #1_Tstat) and I should look at the latter to look for regions modulated by the number of different As or Bs, right?

However, I am also interested in seeing which regions module to a _greater_ extent with increasing number of As than with increasing number of Bs. I therefore thought that I could do the contrast n_A-n_B, see below. However, I am not exactly sure whether this is the right way to get at that, nor exactly how to interpret it. It will give me something labeled n_A-n_B_GLT#0_Tstat. What, exactly, is it contrasting? The two #0_Tstats from A and B (which I don't want)?

Also, an unrelated question, but I might as well post it since I am writing this anyway. For this particular dataset, I am having trouble aligning the anatomical and functionals (volreg step). I am running this mostly from the script generated by afni_proc.py. Is there an option to put there that would make the alignment more likely to succeed? Individually, all the datasets look pretty much fine.

3dDeconvolve -input pb04.$subj.r??.scale+tlrc.HEAD \
-censor motion_${subj}_censor.1D \
-polort 2 \
-num_stimts 8 \
-stim_times_AM2 1 stimuli/cond01_block_A_times_x_amps.txt 'BLOCK(16,1)' \
-stim_label 1 n_A \
-stim_times_AM2 2 stimuli/cond02_block_B_times_x_amps.txt 'BLOCK(16,1)' \
-stim_label 2 n_B \
-stim_file 3 motion_demean.1D'[0]' -stim_base 3 -stim_label 3 roll \
-stim_file 4 motion_demean.1D'[1]' -stim_base 4 -stim_label 4 pitch \
-stim_file 5 motion_demean.1D'[2]' -stim_base 5 -stim_label 5 yaw \
-stim_file 6 motion_demean.1D'[3]' -stim_base 6 -stim_label 6 dS \
-stim_file 7 motion_demean.1D'[4]' -stim_base 7 -stim_label 7 dL \
-stim_file 8 motion_demean.1D'[5]' -stim_base 8 -stim_label 8 dP \
-jobs 2 \
-gltsym 'SYM: n_A -n_B' \
-glt_label 1 n_A-n_B \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D \
-fitts fitts.$subj \
-errts errts.$subj \
-bucket stats.$subj