Hi Gang,

I have a few questions about setting up 3dLME. I have two within-subjects variables (Condition: Standard vs. Reward; and Emotion: Happy vs. Neutral) and one continuous covariate of interest (anxiety severity). I also want to control for four covariates of non-interest: age, race (non-white vs. white), IQ and gender.

Here are my questions:

1. As I understand LMEs, I need to include random slopes for the two sources of non-independence, namely the two within-subjects variables (Condition and Emotion). However, previous discussions on the message board seem to suggest that I should only model the random intercept, and that I would only include a random slope if the within-subjects variable changed for each subject in each condition (e.g., reaction times). First, should I only estimate a random intercept? And if so, how does the model account for the non-independence caused by the repeated measures of Condition and Emotion?
2. I'm mainly interested in the interactions involving anxiety (Cond:Anx, Emo:Anx, and Cond:Emo:Anx). These are automatically generated by the model. However, I'm also interested in the effect of anxiety at each of the within-subject levels. To model these effects, I ran the four GLTs below. Is that what I'm modeling? Or am I modeling, for example, the effect of the standard condition at the average anxiety score?

Here's my current script:

3dLME \
-prefix LME.nii \
-jobs 4 \
-mask mask.group_anat.nii \
-model "Cond*Emo*Anx+age+race+IQ+gen" \
-qVars 'Anx,age,race,IQ,gen' \
-qVarCenters '23.3,32.94,0,97.65,0' \
-ranEff '~1' \
-SS_type 3 \
-num_glt 4 \
-gltLabel 1 'Standard_Anx' -gltCode 1 'Cond : 1*Standard Anx : ' \
-gltLabel 2 'Reward_Anx' -gltCode 2 'Cond : 1*Reward Anx : ' \
-gltLabel 3 'Happy_Anx' -gltCode 3 'Emo : 1*Happy Anx : ' \
-gltLabel 4 'Neutral_Anx' -gltCode 4 'Emo : 1*Neutral Anx : '  \
-dataTable \
Subj   Cond       Emo     Anx     age   race   IQ     gen    InputFile \
s1     Standard   Happy   13      45    -0.5   109   -0.5    stats.s1.nii'[Standard_Happy_GLT#0_Coef]' \
s2     Standard   Happy   22      35    -0.5   117   -0.5    stats.s2.nii'[Standard_Happy_GLT#0_Coef]' \
...
s1     Standard   Neutral 13      45    -0.5   109   -0.5    stats.s1.nii'[Standard_Neutral_GLT#0_Coef]' \
s2     Standard   Neutral 22      35    -0.5   117   -0.5    stats.s2.nii'[Standard_Neutral_GLT#0_Coef]' \
...
s1     Reward     Happy   13      45    -0.5   109   -0.5    stats.s1.nii'[Reward_Happy_GLT#0_Coef]' \
s2     Reward     Happy   22      35    -0.5   117   -0.5    stats.s2.nii'[Reward_Happy_GLT#0_Coef]' \
...
s1     Reward     Neutral 13      45    -0.5   109   -0.5    stats.s1.nii'[Reward_Neutral_GLT#0_Coef]' \
s2     Reward     Neutral 22      35    -0.5   117   -0.5    stats.s2.nii'[Reward_Neutral_GLT#0_Coef]' \
...