I would like to model longitudinal intervention effects (pre to post) of change in functional connectivity (FC fisher’s z maps as input as nifti files) for 3 groups compared to a reference control group. I'm interested in 3dLME because I'd like to run a voxel-wise seed-based model in the same way that I've run lmer models on network average FC values, which include subjects that have missing data at post test. In this model the fixed effects are session*group and the random effects are for subject intercept (1 | sub). Session is coded as 0=pre and 1=post, and group contrasts are set up as dummy codes compared to reference control (Ctr) group:

A B C
Ctr 0 0 0
A 1 0 0
B 0 1 0
C 0 0 1

Would the following be a correct setup for 3dLME, if I'm primarily interested in group x time effects for each group A, B, C with regard to the reference group? I'm not sure if session should be explicitly coded as -1*pre 1*post with symbolic form. Any other suggestions appreciated. Many thanks

3dLME -prefix LongitModel -jobs 1 \
-model "session*group" \
-ranEff '~1' \
-SS_type 3 \
-num_glt 3 \
-gltLabel 1 ‘session_A’ -gltCode 1 ‘session : 1*post group: 1*A’ \
-gltLabel 2 'session_B’ -gltCode 2 ‘session : 1*post group: 1*B’ \
-gltLabel 3 'session_C’ -gltCode 3 ‘session : 1*post group: 1*C’ \
-num_glf 1 \
-glfLabel 1 ‘group_time’ -glfCode 1 'Group : 1*dance & 1*walk & 1*walkplus session : 1*post’ \
-dataTable \
Subj group session InputFile \
s1 Ctr pre path_to_nifti.nii \
s1 Ctr post path_to_nifti.nii \
s2 A pre path_to_nifti.nii \
s2 A post path_to_nifti.nii \
s3 B pre path_to_nifti.nii \
s3 B post path_to_nifti.nii \
s4 C pre path_to_nifti.nii \
s4 C post path_to_nifti.nii \
....