On Wed, Dec 21, 2011 at 5:45 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:

Hi Martijn, sorry for the delay. Your contrast matrices look correct. The differences between demeaning and not demeaning is somewhat expected. When you do not demean, you are testing whether there is a difference between groups at age=0 (ie, birth). When you demean, you are testing for a difference at age=MeanAge. If the slope of each group with respect to age is the same, then this will yield the same result since the regression lines will be parallel and the distance between parallel lines will always be the same. If the slopes differ, then the distance will change with age. For example, there will be an age where the lines cross. If you test at this age, you are assured not to see a difference! For this reason, it is better to test for a difference in the slopes, and, if there is no difference, then reanalyze with DOSS which forces the lines to be parallel. In your case, you found that there is some difference in insula. If this is not the area that you are interested in, then I would not worry about it. You should just keep in mind that you should not try to draw conclusions from this area.
doug

Martijn Steenwijk wrote:

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.edu <mailto:greve@nmr.mgh.harvard.edu>> wrote:

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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