Dear FreeSurfer experts

 

I have one question regarding my data analysis and would be extremely thankful for any advice!

 

My data-set is as follows: I have repeated measures (time point 0 (T0), time point 1 (T1)) of several subjects. All individuals underwent an intervention at one of the time points and a placebo condition at the other time point in a fully randomized fashion. Thus, half of the subjects received treatment at T0 and half of them at T1. I am interested in the putative effect of the intervention on cortical thickness in a ROI. A major challenge is that the time between T0 and T1 varies between individuals and that I expect the time to impact on my dependent variable and to likely interact with the condition (treatment versus placebo).

 

I thought about conducting a simple repeated-measures ANOVA. However, as stated, I want to take the time between the two sessions into account. I also thought about an analysis of rates or percent changes. However, this approach does not model the correlation among the repeated measures and is thus associated with a reduction in power.

 

Accordingly, I am trying to use lme models to analyse my data. Since I have no between-group variable but a within-subjects design, I am concerned if my thoughts are correct and would be grateful for feedback.

 

I ran the longitudinal FS stream and followed the longitudinal lme model tutorial. I propose the following lme model with one random factor: thickness = intercept (random factor) + time since baseline + ICV + condition (placebo or treatment) + timeXcondition + Age (does not change across time interval) + gender

 

The analysis finishes with 0% non-covergence. Can you tell me if my model is suitable given the fact that it is a within-subjects design? I also started wondering if it was possible to model time as a random factor but I think that I read that this is not suitable if you only have two groups (in my case: conditions).

 

Thank you very much for help and advice!

 

All best wishes, Martina