Dear Gang,

Thanks for your help.

> More accurately, GLT #4 looks for the difference
> of association between beta and 'Corr' between the
> two group when Category is fixed at 'iaps'.

Am I right in interepting association as correlation? If not could you please explain what you mean with association?
My main struggle is to find ways to make this 3-way interaction interpretable.
How would you suggest to proceed in order to understand what this significant cluster reflects?

>If 'Corr' is a within-subject quantitative variable, consider
> -ranEff '~1+Corr' \

Unfortuantely the model still failed. I noticed that when adding an additional within-subject level in the table the same script run fine.
This setup wont be the appropriate way of performing the analysis since the Covariate is within-subject but I thought that this information might help with figuring out where the error might be. Any ideas why?

3dMVM -prefix MVM_corr -jobs 35 \
-bsVars "Group*Corr" \
-wsVars Category \
-qVars Corr \
-ranEff'~1+Corr' \
-num_glt 1 \
-gltLabel 1 Group -gltCode 1 'Group : 1*control -1*patient' \
-dataTable @do_mvm_corr_table.txt \
-mask GM_MNI_resample+tlrc


Subj Group Category Corr InputFile
s01 control iaps 0.00 singlesub/C_sub01.results/stats.C_sub01+tlrc.BRIK'[iaps#0_Coef]'
s02 control iaps -0.05 singlesub/C_sub02.results/stats.C_sub02+tlrc.BRIK'[iaps#0_Coef]'
p01 patient iaps 2.36 singlesub/P_sub01.results/stats.P_sub01+tlrc.BRIK'[iaps#0_Coef]'
p02 patient iaps 0.05 singlesub/P_sub02.results/stats.P_sub02+tlrc.BRIK'[iaps#0_Coef]'
s01 control shape 0.00 singlesub/C_sub01.results/stats.C_sub01+tlrc.BRIK'[iaps#0_Coef]'
s02 control shape -0.05 singlesub/C_sub02.results/stats.C_sub02+tlrc.BRIK'[iaps#0_Coef]'
p01 patient shape 2.36 singlesub/P_sub01.results/stats.P_sub01+tlrc.BRIK'[iaps#0_Coef]'
p02 patient shape 0.05 singlesub/P_sub02.results/stats.P_sub02+tlrc.BRIK'[iaps#0_Coef]'


Thank you for your help!

All the best,
Irene