Hi Doug, 


I mean that when I run QDEC selecting gender and diagnosis as fixed factors, age as nuisance factor, and display the results, I obtain totally different significant statistical regions (different statistical maps) when ages are demeaned ((demeaned age = age of each subject – the mean of all subjects (grandmean)) than the case when ages aren't demeaned. Therefore, when I run the monte carlo simulation, when I demean the age variable I don’t get any clusters, but when age is not demeaned I obtain some clusters of considerable size. The contrast I used in mri_glmfit is:

 

0.5 -0.5 0.5 -0.5 0 0 0 0 ("is there a difference between patient and control regressing out the effects of gender and age?")

 

In QDEC it seems to correspond to the output 'Does the average thickness accounting for gender differ between patient and control? (age as nuisance variable)’ because the statistical maps look the same.

 

From what I understand, this difference in the results when age variable is demeaned is something I should expect when doing a DODS analysis. My main doubt would be if it was necessary to demean the age variable and enter it as a nuisance factor based on the characteristics of my population and the analysis I want to perform. I tried to focus my questions in that direction. I’m very sorry if I am not giving myself to understand. Thanks!


Rodrigo


De: freesurfer-bounces@nmr.mgh.harvard.edu <freesurfer-bounces@nmr.mgh.harvard.edu> en nombre de Douglas N Greve <greve@nmr.mgh.harvard.edu>
Enviado: martes, 29 de noviembre de 2016 03:22:15 p. m.
Para: freesurfer@nmr.mgh.harvard.edu
Asunto: Re: [Freesurfer] Demeaning variables
 
When you say that the results are different, what do you mean? What is
your contrast? What is your command line? What is your fsgd file?


On 11/29/2016 03:34 PM, Rodrigo Gonzalez Huerta wrote:
>
> Just to clarify, where is said N=18 I wanted to meant 18 controls + 18
> patients. Thanks in advance.
>
>
> Rodrigo
>
> ------------------------------------------------------------------------
> *De:* freesurfer-bounces@nmr.mgh.harvard.edu
> <freesurfer-bounces@nmr.mgh.harvard.edu> en nombre de Rodrigo Gonzalez
> Huerta <rghbecker2908@hotmail.com>
> *Enviado:* martes, 29 de noviembre de 2016 02:19:26 p. m.
> *Para:* freesurfer@nmr.mgh.harvard.edu
> *Asunto:* [Freesurfer] Demeaning variables
>
> Hi Freesurfers,
>
>
> For obtaining my undergraduate degree I’m performing a study using
> Freesurfer. I’m stuck with the centering (de-meaning) issue. I have
> two discrete variables, gender, and patient-control, and a continuous
> variable which is age. The age population range goes from 20 to 55
> years and is paired between patients and controls. Patient’s disease
> affects cortical thickness over time and every patient has been
> presenting the disease for 6 years (no more, no less). I’ve runned
> QDEC and mri_glmfit using DODS obtaining the same results, the problem
> is that when I demean the age (I used the grandmean) the results are
> very differente. If I don’t demean some clusters survive the
> monte-carlo multiple comparisons correction (p<0.05), but, when I
> demean no cluster survive. I have this questions:
>
>  1. Since most of my subjects are in the range of 20-35 years and only
>     a few have more than 35 years, it is correct to use the mean?
>     Wouldn’t be better to use the median or some other value?
>  2. This is maybe a silly question, but why should I care about the
>     intercept? I know that demeaning doesn’t change the slope, just
>     the intercept, but I don’t understand why I should care, I read in
>     some publications that demeaning is not always necessary.
>
> Other questions:
>
>  1. Most of my subjects are women (around 80%, N=18), Does it have an
>     advantage to remove the males to get rid of the gender variable?,
>     or should I continue as I’ve been doing, controlling for the
>     effect of gender.
>  2. Since I don’t know if this disease is more aggressive when is
>     developed at some stage of life, I mean, if young patients are
>     less affected than older patients after six years of presenting
>     it, it is correct to control the effect of age? (Entering the
>     variable as a nuisance factor), or should I search for an
>     interaction between age and the condition variable? I only care
>     about differences in cortical thickness.
>
> Any suggestion is very welcome, thanks in advance for your answer.
>
>
> Rodrigo
>
>
>
>
>
>
>
>
>
>
>
>
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> Freesurfer@nmr.mgh.harvard.edu
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--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
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