I don't think you are modeling gender and scanner appropriately. It 
looks like you have them as continuous covariates? The right way is to 
create classes. In your case, you'd have 8 classes, Group1MaleScanner1, 
Group1FemaleScanner2, etc.


On 09/15/2016 10:48 AM, Eryilmaz, H. Hamdi wrote:
> Dear Doug/FreeSurfers,
>
> We are looking at effects of age-squared on cortical thickness in two 
> different groups.  We are interested in age-squared by group 
> interactions on cortical thickness across the surface.  Nuisance 
> covariates include age, sex, scanner, and total brain volume.
>
> The sig map for the interaction shows numerous highly significant 
> regions. When we extract cortical thickness from individual subjects 
> for these regions, and then plot the relationship between age-squared 
> and cortical thickness (after adjusting for the covariates) in group 1 
> versus group 2, the interaction is very clear.
>
> However, using linear regression in SPSS, we are not able to reproduce 
> these results.  I suspect we are not using the same model in SPSS that 
> FreeSurfer is using to do the interaction analysis.
>
> This is how our fsgd file looks like:
>
> GroupDescriptorFile 1
> Title GroupAgesqInteraction
> Class Group1
> Class Group2
> Variables Agesq Age Gender Scanner TBV
>
> Input    3662291        Group1    0.607    0.654 -1.061    1.041    0.706
> Input    3665043        Group1    1.294    1.237 -1.061    1.041    -0.747
> .
> .
>
> This is the contrast we used:
>
> [0 0 1 -1 0 0 0 0 0 0 0 0]
>
> What would be the translation of this contrast to a linear model, 
> where we are interested in the age -squared by group interaction on 
> cortical thickness after controlling for the four nuisance covariates?
>
> Thanks very much!
>
> Hamdi and Josh
>
>
>
>
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> Freesurfer mailing list
> Freesurfer@nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
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