Hi, I realize this is fairly straightforward for most of you, but I am relatively new to freesurfer and want to make sure I’m proceeding correctly with group analysis.

 

We have ~35 TBI subjects and a similar number of age- and sex-matched controls, and we’re trying to see if we can identify differences between groups in cortical thickness, curvature and volume. We’re using parasagittal T1 post-contrast 3D volumes obtained as part of routine clinical care, and hope our data show that it is feasible to do retrospective analysis like this, when axial 3D T1 MRI volumes are unavailable.

 

So we have:

 

Nclasses=4 (TBI+, TBI-, M, and F)

Nvariables=1 (age)

NregressorsDODS = 8

 

Each of the contrasts is repeated for lh (left hemisphere) and rh (right hemisphere), and the 3 values for MeasurementName. The “Title” in the fsgd file is changed to specify a separate output directory for each contrast. We figure there should be 24 lh directories, and 24 rh directories when we’re all done, with 8 contrasts (below) for thickness, volume and curvature (8*3), for each hemisphere (i.e., 48 total).

 

The contrast names and matrix specifications are:

 

Male.TBI-vs-Controls.intercept.mtx 1 -1 0 0 0 0 0 0

Male.TBI-vs-Controls.slope.mtx 0 0 0 0 1 -1 0 0

Male-female.intercept.mtx 0.5 0.5 -0.5 -0.5 0 0 0 0

Male-female.slope.mtx 0 0 0 0 0.5 0.5 -0.5 -0.5

TBI-Controls.intercept.mtx 0.5 -0.5 0.5 -0.5 0 0 0 0

TBI-Controls.slope.mtx 0 0 0 0 0.5 -0.5 0.5 -0.5

Gender-x-TBI.intercept.mtx 0.5 -0.5 -0.5 0.5 0 0 0 0

Gender-x-TBI.slope.mtx 0 0 0 0 0.5 -0.5 -0.5 0.5

 

Should we do other contrasts?

 

Female.TBI-vs-Controls.intercept.mtx

Female.TBI-vs-Controls.slope.mtx

Age-x-TBI.intercept.mtx

Age-x-TBI.slope.mtx

Finally, does a permutation analysis need to be done once, or once for each contrast?

 

Here are the first few lines of the FSGD file:

 

GroupDescriptorFile 1                                 

Title lh.gender_age.glmdir                                         

MeasurementName thickness                   

Class TBIMale plus blue                               

Class ControlMale square red                                   

Class TBIFemale circle pink                                        

Class ControlFemale black triangle                                         

Variables Age                                  

Input      TBI1_002             TBIMale               45.000

Input      TBI1_003             TBIFemale           46.000

 

Does this approach look correct? Apologies for the long post.

 

Thanks,

 

John

 

John R. Absher, MD, FAAN

Department of Internal Medicine, Division of Neurology

 

Prisma Health–Upstate

200 Patewood Drive, Suite 350B

Greenville, SC 29615

864-454-4500 (office)

864-350-6655 (mobile)

864-454-4505 (fax)

 

 

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