I'm sorry. I'm performing the cortical thickness analysis on three groups (2 pathological groups and 1 control group). I'm interested mainly to the differences between the two groups of disease. These are my questions: 1- Is qdec a good option for my study design or I should use other glm option that contains all 3 groups in the same matrix design? 2-As you suggest I'm using always mean cortical thickness as nuisance factor in qdec. If I'd like added 2 or more covariates, is correct if I add each covariate separately in my matrix.dat or I should add all covariates together in my matrix.dat? Thanks,
Stefano
----Messaggio originale----
Da: mharms@conte.wustl.edu
Data: 4-apr-2013 18.23
A: stdp82@virgilio.it, freesurfer@nmr.mgh.harvard.edu
Ogg: Re: [Freesurfer] R: Re: cortical thickness normalization
Hi,Sorry, but I don't understand what you're asking. -MH -- Michael Harms, Ph.D.-----------------------------------------------------------Conte Center for the Neuroscience of Mental DisordersWashington University School of MedicineDepartment of Psychiatry, Box 8134660 South Euclid Ave. Tel: 314-747-6173St. Louis, MO 63110 Email: mharms@wustl.edu From: stdp82@virgilio.it Reply-To: stdp82@virgilio.it Date: Thursday, April 4, 2013 9:31 AM To: freesurfer@nmr.mgh.harvard.edu Subject: [Freesurfer] R: Re: cortical thickness normalization
Thank you very much, Michael! I have just an other important question, please. I'm performing cortical thickness analysis on three groups. One of this is a control group and I'm using it as support because I'm interested only to difference between the two groups of disease. These are my questions: 1- Is correct if I'm applying qdec, testing before a) disease 1 vs disease 2; b) controls vs disease 1 and c) controls vs disease 2. 2- Addictionally to cortical thickness that I'm reporting always as nuisance factor in qdec, I'd test some covariates. Taking in account my study design, could I test each covariates separately on my model using qdec? Thanks,
Stefano
----Messaggio originale----
Da: mharms@conte.wustl.edu
Data: 4-apr-2013 15.45
A: stdp82@virgilio.it, freesurfer@nmr.mgh.harvard.edu
Ogg: Re: [Freesurfer] cortical thickness normalization
If you're studying thickness, I'm a fan of using mean cortical thickness as the covariate (since thickness is what you're studying). I've posted on this in the past. cheers,-MH -- Michael Harms, Ph.D.-----------------------------------------------------------Conte Center for the Neuroscience of Mental DisordersWashington University School of MedicineDepartment of Psychiatry, Box 8134660 South Euclid Ave. Tel: 314-747-6173St. Louis, MO 63110 Email: mharms@wustl.edu From: stdp82@virgilio.it Reply-To: stdp82@virgilio.it Date: Thursday, April 4, 2013 7:21 AM To: freesurfer@nmr.mgh.harvard.edu Subject: [Freesurfer] cortical thickness normalization
Hi list, I'm reading a lot of post on this list about cortical thickness normalization. I' m noting very different results on my data when I use mean thickness or ICV as nuisance factor than no factor. I'm confuse on this topic, could you advise the best way, please? Thanks,
Stefano _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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1) If you are ONLY interested in the difference between the 2 pathological groups, then you can include just those two. If you are also interested in comparing either of those with control, then I'd personally opt for a single model that includes all 3 groups, in which you investigate the difference between groups using appropriate contrasts.
2) If you want to control for all 3 covariates simultaneously, then all of them should be included (as separate, de-meaned columns) in the design matrix.
cheers, -MH
freesurfer@nmr.mgh.harvard.edu