Dear FreeSurfers and LME experts, I've just started using the LME toolbox by Bernal-Rusiel et al (2012, 2013) in Matlab, apart from FreeSurfer. My experimental design includes one between-subjects factor (group with two levels, 24 vs 22 subjects), and two within-subjects (WS) factors (day with two levels, and condition with three levels). As far as I understand, the LME toolbox can be used for longitudinal data and for investigating modulations of neural activity with behavioral measures. However, it's been difficult for me to set up both the design matrix and the input of the LME functions given three fixed factors in my design (I haven't included behavior yet). So I have a few questions that I hope you can help me answer. I follow these steps:
1) Following the wiki, I've created a pre-design matrix, M, with: First column: Day factor coded as 0 (first day) and 7 (7 days later, as during acquisition). Second column: Group factor binary coded. Third column: Condition factor coded with dummy variables 1 to 3 (three conditions in total). I don't know if this is correct; I have failed to find in the mailing list any reference to an additional repeated measure besides time in LME models. >From here, I've created the design matrix X adding a column of 1's for the >intercept, adding the pre design matrix M, and adding columns for every >possible interaction by multiplying element-wise the columns of M (including >two-way and three-way interactions). 2) I then use lme_mass_fit_vw, but I have doubts about some inputs Zcols: column 1 in X ni: created a vector with 6 for all subjects (in total there are 6 repeated measures, 2 of day and 3 of condition) I kept the others as default. 3) lme_mass_F for statistics of fixed effects. I check main effects and interactions with contrasts based on the design matrix X: placing a 1 in the column position corresponding to each effect of interest. 4) lme_mass_FDR2 for correcting the F stats for multiple comparisons. I would like to compare the results using AlphaSim's Monte-Carlo simulations, however I'm not sure what image to use to estimate the smoothness. Would you have a suggestion? Could you please let me know if this procedure is sound? Also, if I'm interested in specific comparisons or post-hoc tests, e.g. Group 1$Day 1$Condition 3 vs. Group 1$Day 2$Condition 3, would I need a separate model for that? And lastly, for the analysis including the behavioral measures, should I just include them as a forth column in the pre-design matrix M and add the relevant effects in X? I hope I haven't overwhelmed you with so many questions. I would greatly appreciate any suggestion you can provide. Best regards, Laura Rueda
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