Ty Doug, That gives 8 regressors, so it seems reasonable.
Kind regards, Egil ________________________________________ From: freesurfer-boun...@nmr.mgh.harvard.edu <freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Douglas N Greve <gr...@nmr.mgh.harvard.edu> Sent: 23 March 2015 17:44 To: freesurfer@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] Question regarding mri_glmfit DODS vs SPSS interaction terms in GLM analyses 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 > > > > _______________________________________________ > 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 Phone Number: 617-724-2358 Fax: 617-726-7422 Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . 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