Hi FSL Experts,
I've searched through the message board and the archives and it seems like there's quite a lot of questions about the design files for contrasts and matrix. I just wanted to make sure that I'm setting things up correctly for my experiment.
I have 2 separate groups (Older adults and Young adults; n=18 and n=21 respectively) that I'm attempting to perform VBM on, but beyond the significant volumetric differences between the two groups, I'd like to know how to create the contrasts for testing the correlation/association of these specific brain regions with memory performance measure(s). I've been able to run the whole-brain analyses with controlling for ICV by creating the following matrix:
EV1= Old (OA), EV2= Young (YA), EV3= ICV (scaling factor centered)
1 0 1.2345
1 0 .8652
1 0 .6587
0 0 1.1365
0 0 .9874
0 0 1.3245
I then created the following contrasts based off the two group difference adjusted for covariate:
c1 = 1 -1 0 --> Old > YA
c2= -1 1 0 --> YA > OA
c3= 0 0 1 --> Pos effect of ICV/scaling factor
c4= 0 0 -1 --> Neg effect of ICV/scaling factor
Are these setup correctly?
If so, I'd now like to modify the analyses and investigate whether these significant differences in brain volume between OA and YA are also associated with one or more of our behavioral performance measures (ie, Memory interference). From what I've read, I can just add another EV (Proactive Interference score centered) of interest into the GLM, but how exactly do I setup the contrasts? I assume that I'd want to put the behavioral variable of interest into the GLM before controlling for any nuisance variables, which would then make EV3= memory score centered and EV4= ICV. But again, I'm confused at how to setup the contrasts.
For your reference, I'd like to look and see whether there is a correlation/association between GM volume and memory performance overall, as well as for each group separately. Any help you can give me would be greatly appreciated. Thank you so much for your time and please let me know if you need any additional information.
Best Regards,
Jonathan Siegel
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