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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