Dear Jorge and FreeSurfers, Would you mind having a look at my set-up and advising on whether it is correct or needs changing?
I have participants with one or two scans, who are divided into two groups. I'm interested in the longitudinal effects of aging, and the difference in the effect of age on the two groups. Not all people were scanned at exactly the same age, and the interscan interval in not exactly the same for each person. Rather, the age at scan looks like a bimodal distribution around the mean 1st and 2nd timepoint. I believe I read on the list that you should have more timepoints than random effects. I guess I should then only name one random effect (intercept). At the moment I have my model set up as follows: X = Intercept Group Age(at scan) Group*Age Gender [stats, st] = lme_mass_fit_vw(X,1,Y,ni,Mask,[],12); CM.C = [0 0 0 1 0] I guess I should then run * lme_mass_F(stats,CM);* and *lme_mass_FDR2.* Here I am trying to look find the areas that develop differently between the two groups over the timespan we are studying. If you would be able to advise me as to whether I have set up the model wrong it would be greatly appreciated. Age in this case refers to the the age at scan time, regardless of whether it is the first or second scan, so many participants will have two distinct values in the age column. Also, is it possible within this set-up to test the effect of age within a group, or would that require re-doing the design matrix? Finally, is it possible to include the by-subject random slopes for the effect of age? In R (lmer) it would be something like (1+age|subject). Best wishes y gracias, Seán
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