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Hi Daniel,
please find my responses below.
Hope this helps,
Kersten
External Email - Use Caution Hello FreeSurfer experts,
I am currently working through the linear mixed effects pipeline to assess longitudinal cortical thickness changes in non-contact/contact athletes and have some questions. My design matrix is as follows: X = [ones(size(M,1), 1), M(:,1), M(:,2), M(:,3), M(:,1).*M(:,3), M(:,4), M(:,1).*M(:,4), M(:,5), M(:,6), M(:,7), M(:,8), M(:,9)]; M(:,1) = group M(:,2) = scan time from baseline M(:,3) = age With the rest of the columns in X being other covariates from M or *time interaction, and the random intercept term being the only random effect.
1. I’m using the spatiotemporal approach, but would vertex-wise yield more powerful results? I know that a previous thread had a similar question, but I am wondering if one would be more favourable for looking at regional CT changes.
Hard to say, but I would not necessarily expect so, since the original paper demonstrated power increases for the spatiotemporal model as compared to the vertex-wise approach.
1. As well, the two groups have different distributions in timepoints and size. The contact athlete group is about double the size (42 vs 21) and has significantly more athletes with multi-year participation (2- or 3-years), whereas the non-contact athlete group is almost entirely single-year participation (one set of pre- and post-season scans). Is this something that the LME can inherently account for?
Yes, the LME model can handle for designs with unbalanced number of cases, and differences in the number and interval lengths of longitudinal observations.
1. My main goal is to test if there is a significant difference in the slopes of the two groups (using lme_mass_F). I currently have this setup for my contrast matrix: CM.C = [0 1 zeros(1,10)]. Since I only have two groups, is it logical to use a single row? Additionally, I am a bit unsure of how to assign beta weights.
Yes, using a single-row contrast like you say is fine. What exactly do you mean by "assign beta weights?"
1. After running the F-test, I am correcting the multiple comparisons using FDR2. Are there any other corrections/tests I should utilize before plotting data in tksurfer?
You might want to take into account correcting for the fact that you are (likely) doing the analysis in two hemispheres: the tutorial suggests to simply do a bonferroni correction.
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