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
>
> Input3662291Group10.6070.654 -1.0611.0410.706
> Input3665043Group11.2941.237 -1.0611.041-0.747
> .
> .
>
> 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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Douglas N. Greve, Ph.D.
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