Dear Gang,

Thank you for your help, I will move forward with one of 3dMVM or 3dANOVA3 (type 5).

Also I have a related question, I am particularly interested in testing what is sometimes called a "spreading interaction". That is, I'd like to specifically test for an interaction that captures a difference in posttreatment resting-state functional connectivity (rsfc) between groups when there is no difference in pretreatment rsfc between groups. I've pasted below some text from a relevant paper that does the this analysis that I am interested in.

My sense is that there is a way to do this using the GLT options with 3dMVM, but I do not know the syntax. I'd like to set up the contrast such that the contrast weights for increases in treatment_1 relative to treatment_2, i.e., contrast weights for [−1 (pretreatment, treatment_1), −1 (pretreatment, treatment_2), 3 (post-treatment, treatment_1), −1 (post-treatment, treatment_2]. Also, I am interested in testing both whether the treatment_2 group has EITHER an increase OR a decrease compared to the treatment_1 group. I am thinking to run two GLTs and then correct the alpha p-threshold by two. Is there a way to include the increase OR decrease in a single GLT and thus avoid the use of multiple comparisons correction? I imagine the GLT could be specified by something like the following, but I am certain that it is incorrect:
-gltLabel 1 spreading_interaction_increasesTreatment1 -gltCode 1 'Group : -1*treatment_1 -1*treatment_1 Time : 1*pretreatment 3*posttreatment' \

Many thanks for your help,

Matthew


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).