Hi Doug,

 

That’s extremely helpful, thank you! And just to verify:

1. Since I have 1 group and I just want to compare the two surface maps (without adjusting for other covariates like age/sex at the moment), my contrast matrix should be [1 0], right?
2. If I add other covariates (let’s say age for instance), then if I want to test the hypothesis that the two regional maps are sig. different from each other after regressing the effects of age, would my contrast matrix then be [1 0 0]?

 

Best,

Panos

 

Try this


To regress include a per-vertex regressor (PVR, --pvr) in mri_glmfit.
This procedure will essentially add a column to the design matrix for
each vertex. Eg, if your FSGD file (my.fsgd) has two groups, then, by
itself, the design matrix would be two columns, one for each group.
You will pass mri_glmfit two maps (one with --y, the input), and one
the PVR; each map will have a frame for each subject. For a given
vertex, the PVR values for each subject will be extracted and added as
a column to the design matrix; the design matrix will be fit to the
y-values for that vertex, the contrast matrices will be applied, and
p-values computed. The extra column in the design matrix needs to be
accounted for in the contrast matrix. Eg, if you have two groups, then
you might have a contrast of +1 -1. If you are going to treat the PVR
as a nuisance variable, then you would add a 0 (so +1 -1 0). If the
PVR is a regressor of interest, then you could use 0 0 +1. Multiple
PVRs can be added to make more complicated designs and test more
complicated PVR hypotheses. DOSS and DODS do not apply to PVR. All the
same rules about design matrices do apply (eg, scaling and
colinearity). Permutation does not work with PVR.

Eg, create two maps, thickness and curvature, with

mris_preproc --f my.fsgd --hemi lh --meas thickness --o thickness.lh.sm00.mgz

mris_preproc --f my.fsgd --hemi lh --meas curv --o curv.lh.sm00.mgz

When you create your contrast matrices, make sure to include an item
for the PVR.

mri_glmfit --y thickness.lh.sm00.mgz --fsgd my.fsgd --C contrast.mtx
--pvr curv.lh.sm00.mgz --o my.glm --surf fsaverage lh

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Hello FS experts,

 

I have a cohort of subjects for whom I created cortical thickness and T1w/T2w signal ratio surface maps, projected them to fsaverage’s surface, smoothed them, and then created average thickness and T1w/T2w maps using mris_preproc. Is there a way to statistically compare the two averaged surface maps (i.e., come up with a third surface map displaying correlation patterns between thickness and T1w/T2w signal across the surface)?  

 

Thanks in advance!

 

Best,

Panos

 

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