Dear Doug,
Thanks again for your reply. Based on that I did some further work.
I first demeaned the age of all subjects. Actually, I have a third group which I would like to compare to, so my contrast matrices will be [.5 .5 -.5 -.5 0 0 0 0 0 0 0 0] [0 0 .5 .5 -.5 -.5 0 0 0 0 0 0] [.5 .5 0 0 -.5 -.5 0 0 0 0 0 0] to test for CT differences between all the groups while correcting for age and sex. Surprisingly, I'm observing a big difference in the results compared to the results without demeaning. Could you explain the reson for this? In the FSGD-examples (eg http://surfer.nmr.mgh.harvard.edu/fswiki/FsgdFormat), age is also not normalized. Does normalizing the variance to 1 also influence the results? Given this big difference, I started wondering whether it would maybe be better to analyze the data in pairs of two groups (and then demean by the mean of the two groups). Would this be a better approach?
Concerning your second suggestion: if I test the data for differences in group slope, a number of small area's are significantly different. Regions popping up are especially in the neighborhood of the insula. Unfortunately this suggests that I cannot use the DOSS model, or am I wrong?
Looking forward to your reply, With best regards, Martijn
On Sat, Dec 10, 2011 at 7:16 PM, Douglas Greve greve@nmr.mgh.harvard.eduwrote:
Yes, that is correct, though I think your matrix should be [.5 .5 -.5 -.5 0 0 0 0]. You should also remove the mean from the age (mean computed from all subjects). Or even better, first test whether there is a group difference in age slope with [0 0 0 0 .5 .5 -.5 -.5]. If there is nothing that is significant, then re-run your analysis using the Different Offset Same Slope (DOSS) model with this contrast [.5 .5 -.5 -.5 0].
doug
On 12/10/11 4:15 AM, Martijn Steenwijk wrote:
Dear all,****
I’m relatively new with Freesurfer, but slowly getting more and more used to it’s great possibilities. To be ‘sure’, I’ve a question about the design of a GLM. ****
I want to compare CT in Healthy Controls vs Diseased, and control for age and sex. It appears to me that factors (eg sex) cannot be used as covariate/variable, which forces me to model them as a separate class although I’m not interested in sex differences. This brings me to the following FSGD file:****
# HcDis.fsgd****
GroupDescriptorFile 1****
Title HcDis****
Class Hc_Male****
Class Hc_Female****
Class Dis_Male****
Class Dis_Female****
Variables Age****
Input subjid1 Hc_Male 35****
Input subjid2 Dis_Female 30****
….****
Then the difference between Hc and Dis, corrected for age and sex is given by the contrast matrix****
#Hc-vs-Dis.mtx****
0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 ****
Is this correct? ****
Best,****
Martijn****
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