Dear experts:
I would like to ask for your opinion on my design, if anyone has the time. I have an fMRI experiment with 2 conditions: ”ind” and ”dir” and two groups: group 1 and group 2. All participants was presented with both conditions (repeated measures), but each participant belongs to only one group. I am interested in the group difference for the difference between ind and dir, so basically the interaction between condition and group.
My idea is to construct contrasts at first level for ”ind vs baseline” and ”dir vs baseline” and then take these to the second level with a design matrix that looks like this:
First ”block” (as in the first consecutive rows) of rows: ”ind vs baseline” contrast values for all subjects in group 1
Second block of rows: ”dir vs baseline”, group 2
Third block of rows: ”ind vs baseline”, group 1
Fourth block of rows: ”dir vs baseline”, group 2
The first column represents the main effect of group and is = [-1 -1 1 1]’
The second column represents the main effect of condition and is = [1 -1 1 -1]’
The second column represents the interaction effect (the one I am interested in) an is = [1 -1 -1 1]’
The fourth row is just the grand mean = [1 1 1 1]
To compute the ANOVA, I would then use the SPM F-contrasts [1 0 0 0], [0 1 0 0] and [0 0 1 0] for the main effects and the interaction, respectively.
As I understand it, SPM will this way calculate the F-value based on the extra squared residuals for each effect by comparing to the reduced model(s), so that the F-value I get from the F-contrasts are the hypothesois test of these effects just like if I would have tested different GLMs, with and without these factors modelled, just like in any ANOVA.
Do you think I have understood it correctly or have I missed anything?
Thank you so much,
All the best,
Katarina
Sent from my iPad
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