Hi Doug,
Thanks for your reply again. It's getting more and more clear now.
I've however one question remaining, which is regarding the correction for sex. What I did not tell (my fault ;-) ), is that 75% of the cohort is female. Comparing the sex-corrected results with male-only and female-only results, it appears to me that the relatively small male-group partly 'drives' the results in the sex-corrected results. I guess this is because the males and females are currently equally weighted in the contrast matrices. Shouldn't the differences in sex also be represented in the contrast matrices, like [.25 .75 -.25 -.75 0 0 0 0 0 0 0 0] [0 0 .25 .75 -.25 -.75 0 0 0 0 0 0] [ .25 .75 0 0 -.25 -.75 0 0 0 0 0 0]
? Or am I wrong?
Best, Martijn
On Wed, Dec 21, 2011 at 5:45 PM, Douglas N Greve greve@nmr.mgh.harvard.eduwrote:
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/FsgdFormathttp://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 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
______________________________**_________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.** harvard.edu Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurferhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
______________________________**_________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.** harvard.edu Freesurfer@nmr.mgh.harvard.edu>
https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurferhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**compliancelinehttp://www.partners.org/complianceline. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html