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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.
Thank you in advance,
Daniel
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