I don't use SPSS so I can't really comment with any authority. But I 
think it is group*gender and group*gender*age
doug

On 03/20/2015 10:08 AM, Egil Nygaard wrote:
>
> How are the DODS analyses in Freesurfer comparable to interaction 
> terms in GLM analyses in SPSS? I notice that others have had similar 
> questions, but it is still unclear to me.
>
> To get comparable interaction terms in SPSS GLM analyses and 
> individual slopes in mri-glmfit Freesurfer DODS analyses, I have used 
> the following independent variables in the SPSS GLM: group, gender, 
> age*group*gender (age was centralized before calculating the 
> interaction term).
>
> Another alternative suggested by a colleague was to include all main 
> effects and possible interaction effects, thus group, gender, age, 
> age*group, age*gender, group*gender, age*group*gender.
>
> I also saw an earlier reply on the support page suggesting the 
> following independent variables: group*age, gender*age, 
> group*gender*age, but I am uncertain whether the main effects should 
> also be included.
>
> Thus, I wonder which independent variables you would suggest should be 
> included in the GLM analyses in SPSS to be as comparable to mri_glmfit 
> DODS analyses in Freesurfer as possible?
>
> Below is more information about what analyses I have done:
>
> *Overview analyses:*
>
> We investigate difference between two groups. We have to sets of data, 
> one with skewed age and gender distribution between groups (n = 89) 
> (older participants and more girls in the risk group), and one with 33 
> extra control participants (n = 122) making the risk and control group 
> having similar age and gender distribution.
>
> The ROI area and thickness from APARC exported files are analyzed 
> using GLM analyses in SPSS. We have done the analyses both without any 
> covariate, and controlling for gender or/and age. We have also run 
> analyses controlling for gender and the interaction term 
> group*gender*age (with age being centralized). Here is an example of 
> the SPSS syntax for the full model trying to be as similar as possible 
> to the vertex based mri_glmfit DODS analyses in Freesurfer as possible:
>
> GLM lh_bankssts_area  BY group gender  WITH  zage
>   /PRINT=DESCRIPTIVE PARAMETER
>   /DESIGN = gender group gender*group*zage.
>
> The vertex based analyses are done with mri_glmfit in Freesurfer 
> version 5.3. I have analyzed the data both with DOSS and DODS. Both 
> without covariates, controlling for age (demeaned continuous variable) 
> or controlling for gender, or controlling for both age and gender. We 
> have used smoothing fwhm 10. The results were controlled for multiple 
> comparisons with the standard mri_glmfit-sim method in Freesurfer with 
> cache 1.3 abs (we did not use 2spaces because we will also compare the 
> results from mri_glmfit with results from Qdec).
>
> We have used tksurfer (DOSS and DODS) and qdec (DODS) to visualize the 
> results, but mainly used results from 
> cache.th13.abs.sig.cluster.summary files to compare with the analyses 
> from SPSS.
>
> The contrast in the full DODS model in the mri_glmfit is 1 1 -1 -1 0 0 
> 0 0, with the fsgd file being:
>
> GroupDescriptorFile 1
> Title n89-33
> Class group1risk_gender1girl
> Class group1risk_gender2boy
> Class group2control_gender1girl
> Class group2control_gender2boy
> Variables age
> Input UN001 group1control_gender2boy -1.0172131148
> Etc
>
> Preproc was done with:
>
> mris_preproc --target fsaverage --hemi lh --meas area --fsgd 
> $glm/demographicn122.fsgd --out $preproc
>
> Smoothing was done with:
> mri_surf2surf --hemi lh --s fsaverage --sval $preproc --fwhm 10 
> --cortex --tval $smoothing10
>
> GLM was done with:
>
> mri_glmfit --glmdir $contrast --y $smoothing10 --fsgd 
> $glm/demographicn122.fsgd --C $glm$contrast_kjal.mat --surf fsaverage 
> lh --cortex
>
> Correction for multiple comparisons was done with:
>
> mri_glmfit-sim --glmdir $contrast --cache 1.3 abs
>
> Kind regards,
>
> Egil Nygaard
>
>
>
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-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
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