Dear all,

I’m analyzing data from an fMRI experiment with 17 subjects and one within-subject factor with 6 levels (“WITHIN”). I also want to include 2 covariates of interest in my model (one I call “score” and one not varying over the levels, which is “age”). I’m using SPM5, if this is of importance.

I checked the relevant posts on this matter and I think I understood roughly which way to go, but I still have some questions and would welcome any input you can give me on this.

1) I first performed a 1st-level analysis for each subject with one regressor for each level of “WITHIN” plus 6 motion regressors. From this analysis I get number-of-sessions*6 beta-images of interest. Now I can calculate a t-contrast for each condition to obtain the weighted sum of parameters for each condition (one con.img for WITHIN 1, one for WITHIN 2 etc).
First question: Some of my subjects are missing one session. Do I have to adjust my contrasts for these subjects?

2) Now, as I understand the second level is a bit more complicated: Basically, I should do a one-way repeated measures ANCOVA with 2 covariates (a mixed model, because I have both within-subject  and between-subjects effects, the covariates). So, as SPM doesn’t partition the residual variance for each effect I cannot evaluate the statistics for each effect in one model, right?
Some posts in the list have suggested splitting up the model, each having the adequate error term, to test for the different effects.

a) I would use flexible factorial to model one ANCOVA design with 1 “WITHIN” factor, 1 “subjects” factor (because it is a repeated measures design) and 1 “score” covariate (the covariate with different values per condition). I wouldn’t include “age” in this model, because it’s collinear with the subject factor, right? 
Then, I would include the main effect for “subjects”, the main effect for “WITHIN” and the interaction WITHIN *score (under the “covariate” and “interaction with factor” option) in the model.
This results in a matrix with 17 regressors for “subjects”, 6 regressors for “WITHIN” and 6 additional regressors (I guess each-level-of-WITHIN *score?). Is this ok?
And how do I test for the WITHIN*score interaction? zeros(1,23) -2.5 -1.5 -0.5 0.5 1.5 2.5 ? 

b) For the main effect of “score” I use a second design: I would take the averaged parameter estimates for all conditions from the 1st-level (beta1+beta2+betam)/m), resulting in one contrast image per subject. Then I’d do a one-sample t-test in the 2nd-level with the “score” covariate. 
Is this how I can test for “score” and can I also test for “age” in this model this way?

And what about WITHIN *age? I’ve read it’s tricky to include covariates like age, but I don’t want to just leave it out. Any way how to deal with it? 

I also recently found a poster online that was on display in the HBM 2011 in Quebec (“Repeated measures Designs overestimate between-subject effects in fMRI packages using one error term”) by McLaren, Schultz, Locascio, Sperling and Atri. In this study the authors used in-house flexible factorial scripts using the correct error terms for within- and between-subject effects. I wonder if they are available, because I think they could be useful for my kind of design.

Thanks for your help

Best regards

Lorenzo Cordani