Thank you very much for your reply and the warm welcome, Gang!

>With a longitudinal study, you may try to account for varying slope effect:
>-model "Time*Group+Age+Gender+(Time|Subj)" \

That is a good idea, thank you! Btw, in lme4 (Time|Subj) is the same as (1+Time|Subj), if I am not mistaken? (Random intercept and random slope)

> By default, 3dLMEr centers a quantitative variable around its overall average across all subjects.

Thanks, I forgot about that.

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

That looks like a promising tool. While I whink I can justify using a linear approach for now, as the variable "Time" is actually only 0 to 6, which can probably be considered to be"confined within a narrow range", to quote your post about 3dMSS, it definitely is interesting for future use!
For 3dLMEr so far, my next steps are input the residuals into 3dFWHMx and then use 3dClustsim for cluster correction
- is there a way to do this with 3dMSS?

Would my model translate to the following in 3dMSS?:

 -mrr 's(Time)+s(Time,by=Group)+(Age)+(Gender)'             \
 -qVars 'Time,Group,Age'                            \

- Does this compare the trend along Time between the two groups while correcting for the effects of Age and Gender?


Also, another question, if I may ask:

Is it possible to test for correlation of test outcome scores in 3dLMEr (and/or 3dMSS) without correcting for them?
I'm thinking that if I changed the model to Time*Group+Age+Gender+outcome1+outcome2+outcome3+(Time|Subj) , in order to be able to look at the effects of those outcome scores, I am basically correcting for them, am I not?

Kind regards,
AFNIuser007/ Agent 007 ;)