Dear FreeSurfer experts,
Thank you for your fast reply. We are extracting cortical thickness values for statistical sub-analyses in SPSS (due to corrections required with regard to family relatedness within the data); therefore we want to be a 100% certain that the statistical model we use in SPSS is identical to the model used by mri_glmfit. Can you confirm that, out of the multiple linear regression types listed below, mri_glmfit makes use of the Direct (or Standard) regression model?
Type | Characteristics |
Direct (or Standard) | · All IVs are entered simultaneously |
Hierarchical | · IVs are entered in steps, i.e., some before others · Interpret: R2 change, F change |
Forward | · The software enters IVs one by one until there are no more significant IVs to be entered |
Backward | · The software removes IVs one to one until there are no more non-significant IVs to removed |
Stepwise | · A combination of Forward and Backward MLR |
Thank you again for your help,
Best wishes,
Lizanne
Date: Wed, 12 Mar 2014 11:04:00 -0400
From: Douglas Greve <greve@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] glmfit with correlated covariates
To: freesurfer@nmr.mgh.harvard.edu
Message-ID: <53207760.8010809@nmr.mgh.harvard.edu>
Content-Type: text/plain; charset="iso-8859-1"
It is just a standard multiple regression analysis where all regressors
are fit simultaneously. One weight does not include that of another weight.
doug
On 3/12/14 5:16 AM, L. Schweren wrote:
>
> Dear FreeSurfer experts,
>
> I am attempting to disentangle the effects of different features of
> pharmacological treatment on cortical thickness.
>
> I am running glmfit from the commandline, with multiple covariates
> (a.o. Z_Age, Z_TreatmentDuration and Z_StartAge) in the fsgd-file.
> These covariates are correlated up to approximately 0.6 , which to my
> understanding is not ideal yet not inducing collinearity. Running
> glmfit, I do not get any errors such as ill-designed matrix or so.
>
> My question regards the way the different regression weights are
> calculated in each voxel. If I test the variance in CT of voxel A
> explained by for example TreatmentDuration, and part of the variance
> in voxel A is explained by both TreatmentDuration and StartAge, will
> the regression weigth of TreatmentDuration than include the part that
> is also explained by StartAge? Or are all other covariates first
> "regressed out" of the variance, such that the variable I test can
> only explain the variance that was not explained by any of the other
> covariates?
>
> Thank you very much, your help is very much appreciated!
>
> Best wishes,
>
> Lizanne
>
>
>
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