Dear Gang,

I have ran the GLT codes at you have suggested them, i.e.:
-gltLabel 1 interaction -gltCode 1 'Group : 1*treatment_1 -1*treatment_2 Time : 1*pretreatment -1*posttreatment' \
-gltLabel 2 interaction_t1 -gltCode 2 'Group : 1*treatment_1 Time : 1*pretreatment -1*posttreatment' \
-gltLabel 3 interaction_t2 -gltCode 3 'Group : 1*treatment_2 Time : 1*pretreatment -1*posttreatment' \
-gltLabel 4 interaction_pre -gltCode 4 'Group : 1*treatment_1 -1*treatment_2 Time : 1*pretreatment' \
-gltLabel 5 interaction_post -gltCode 5 'Group : 1*treatment_1 -1*treatment_2 Time : 1*posttreatment' \

My goal with this analysis was to test study predictions in the brain by specifying a time by group "spreading interaction" contrast that tested for pretreatment to posttreatment increases in rsFC in the treatment_2 group relative to no change in the treatment_1 group from pretreatment to posttreatment using contrast weights. I ran the above GLTs and the interaction_t2 exhibited a statistically significant cluster of voxels, but when I extracted the average z-scores for each individuals from the clusters and plotted averages for the groups for both time points I see that there is still a pretreatment difference between the groups that is driving the interaction, and no posttreatment difference between groups. I'm sorry if I wasn't clear in my previous question, but this makes me think that the GLTs coded above are not actually testing for a spreading interaction. Am I correct? if so, is there a way to use 3dMVM to test a "spreading interaction". If it might be helpful, I've pasted below text from a prior paper that described a spreading interaction that was implemented.

Many thanks for your help,

Matthew

TEXT FROM PRIOR STUDY
To test study predictions in the brain, we specified a time by group spreading interaction contrast that tested for baseline to postintervention increases in rsFC in the HEM program relative to no change in the HER program from baseline to postintervention using contrast weights: [−1 (pre, HEM), −1 (pre, HER), 3 (post, HEM), −1 (post, HER)]. This t contrast models the specific hypothesized differential group change from baseline to posttreatment. The strength of this approach (relative to testing for significant voxels using the more standard overall F contrast or just comparing the two groups at posttreatment only) is that it tests the specific prediction that the mindfulness meditation program increases rsFC from baseline to posttreatment compared with no change in the relaxation group (as opposed to other types of interaction patterns that might be significant with an F contrast analysis). Note that this approach compares the mindfulness group at posttreatment with the average of the other cells in this 2 × 2 design, testing the spreading interaction prediction (and not other interaction patterns, e.g., crossover interactions).