Just cross-referencing some information here about 3dMSS from another thread (where @Gang was helping me out with 3dLMEr), in case someone is using 3dMSS and has the same questions:

"My [original 3dLMEr] model is for a longitudinal study with two groups and [multiple] measurements (...)"

>Your current model [in 3dLMEr] assumes a linear relationship of Time. In case nonlinearity is of interest, consider 3dMSS.

"For 3dLMEr so far, my next steps are (...) 3dFWHMx and (...) 3dClustsim for cluster correction - is there a way to do this with 3dMSS?"

>No, but you don't have to adopt a harsh thresholding approach, and instead show the results in a gradation fashion.

"Would my model [in 3dLMEr, with Time=0-6: Time*Group+Age+Gender+(Time|Subj) ] translate to the following in 3dMSS?: -mrr 's(Time)+s(Time,by=Group)+(Age)+(Gender)'"

>The above specification assumes that there are 10 or more time points. With 7 time points, try
>-mrr 's(Time,k=7)+s(Time,by=Group,k=7)+Age+Gender'

"According to the 3dMSS [help file], I would create a pred.txt that include label, Time and Group (binarized).
Are Age and Gender okay to only be in the dataTable (not the pred.txt), and would Gender have to binarized for 3dMSS?"

>All predictors are required to be specified for prediction (in the file pred.txt). Dummy coding is needed when you want to account for the interaction with >nonlinearity (e.g., Group in your case). However, Gender does not have to be dummy-coded.