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Dear Freesurfer experts,
I have some doubts while running LME analysis on longitudinal data. The main goal is to check whether longitudinal BMI scores have any impact on the longitudinal cortical thickness changes. The model I’m thinking of is as follows:
Y_ij = b0 + b1*time_ij + b2*BMI_ij + b3*Skyra_ij + b4*Prisma_ij + b5*gender_i + b6*bslAge_i.
And here is an example design matrix X for the model above:
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ones|time|BMI |Skyra|Prisma|gender|bslAge|
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1 |0 |0.62|0 |0 |0 |1.89 |
1 |3.2 |1.56|0 |0 |0 |1.89 |
1 |7.2 |2.04|1 |0 |0 |1.89 |
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1 |0 |1.1 |0 |0 |1 |1.67 | 1 |1.5 |0.9 |0 |0 |1 |1.67 | 1 |4 |0.9 |1 |0 |1 |1.67 | 1 |5.3 |1.3 |0 |1 |1 |1.67 |
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1 |0 |0.7 |0 |0 |0 |0.89 | 1 |1.2 |0.5 |0 |0 |0 |0.89 |
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Note that BMI and bslAge are z-scored, and there are 3 different scanners: Avanto (base), Skyra and Prisma. I’m trying to account for differences between scanners as well. For some participants, the first two timepoints were scanned with one scanner and the last time point with a different one, so it varies within the subject. Do I account for the scanner differences in a correct way? Should I add interactions with the time variable, as time*Skyra?
Another question. Since BMI is longitudinal, time-variant, should I add an interaction with time as well?
Assuming that the above design matrix is “correct”, I would use the following contrast [0 0 1 0 0 0 0] to answer my question whether longitudinal BMI scores have any impact on longitudinal cortical thickness changes, right?
P.S. I’m running spatiotemporal models with one random effect - intercept.
Thank you for the answer!
Best,
Donatas