Hi,

I'm just reposting an earlier question. Any help would be appreciated...


I have a question on computing betas related to the following thread:
[afni.nimh.nih.gov]

I have an experiment with 4 runs. In the past, I have fed each run into 3dDeconvolve (see below) and get an overall beta for each experimental condition of interest which I then use in group analyses. However, I now understand that it is best to compute beta coefficients seperately for each run and then either average them or use RUN as a within subjects variable in an ANOVA analysis so as to be able to look at order/time effects. My question is: Is the overall beta the average of all four betas (for each of the 4 runs) or is based on what would effectively be 1 long run?

Many thanks as always for your help.

Jatin



3dDeconvolve \
-input ${STUDY}${SUBJECTS[$n]}IMPEXP_EMOT_RUN1.zp.spikeShort.tshift.volreg.smooth.scaled.nii \
${STUDY}${SUBJECTS[$n]}IMPEXP_GEND_RUN1.zp.spikeShort.tshift.volreg.smooth.scaled.nii \
${STUDY}${SUBJECTS[$n]}IMPEXP_EMOT_RUN2.zp.spikeShort.tshift.volreg.smooth.scaled.nii \
${STUDY}${SUBJECTS[$n]}IMPEXP_GEND_RUN2.zp.spikeShort.tshift.volreg.smooth.scaled.nii \
-mask ${STUDY}${SUBJECTS[$n]}.Mask.nii \
-polort 4 \
-num_stimts 12 \
-stim_file 1 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Fear_Emot.1D \
-stim_label 1 Fear_Emot \
-stim_file 2 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Happy_Emot.1D \
-stim_label 2 Happy_Emot \
-stim_file 3 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Neutral_Emot.1D \
-stim_label 3 Neutral_Emot \
-stim_file 4 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Fear_Gend.1D \
-stim_label 4 Fear_Gender \
-stim_file 5 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Happy_Gend.1D \
-stim_label 5 Happy_Gender \
-stim_file 6 BehavDat/${STUDY}${SUBJECTS[$n]}IMPEXP.Neutral_Gend.1D \
-stim_label 6 Neutral_Gender \
-stim_file 7 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[1] \
-stim_label 7 roll -stim_base 7 \
-stim_file 8 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[2] \
-stim_label 8 pitch -stim_base 8 \
-stim_file 9 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[3] \
-stim_label 9 yaw -stim_base 9 \
-stim_file 10 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[4] \
-stim_label 10 dS -stim_base 10 \
-stim_file 11 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[5] \
-stim_label 11 dL -stim_base 11 \
-stim_file 12 motion_files/${STUDY}${SUBJECTS[$n]}IMPEXP.motionall.1D[6] \
-stim_label 12 dP -stim_base 12 \
-gltsym "SYM: Fear_Emot -Neutral_Emot" -glt_label 1 "F.E.v.N.E." \
-gltsym "SYM: Happy_Emot -Neutral_Emot" -glt_label 2 "H.E.v.N.E." \
-gltsym "SYM: Fear_Emot -Happy_Emot" -glt_label 3 "F.E.v.H.E." \
-gltsym "SYM: Fear_Gender -Neutral_Gender" -glt_label 4 "F.G.v.N.G." \
-gltsym "SYM: Happy_Gender -Neutral_Gender" -glt_label 5 "H.G.v.N.G." \
-gltsym "SYM: Fear_Gender -Happy_Gender" -glt_label 6 "F.G.v.H.G." \
-full_first -fitts ${STUDY}${SUBJECTS[$n]}Fitts.zp.spikeShort.tshift.volreg.smooth.scaled.Decon.wglt \
-tout \
-fout \
-bucket ${STUDY}${SUBJECTS[$n]}IMPEXP.zp.spikeShort.tshift.volreg.smooth.scaled.Decon.wglt