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Hi Doug,

Thank you so much for your respond. Greatly appreciate it!!

I have one more question. If I run glm with FSGD and contrast file
mentioned in my previous email, it should be DOSS, correct? However, when I
tried to run it using the same FSGD, contrast and command mentioned in my
previous email, the screen prints are telling me it is DODS as below:
INFO: gd2mtx_method is dods

I just want to confirm that I am using DOSS in this condition. And, if I
want to use DODS, I will have to make two thickness file as -pvr inputs for
each group, is this correct?

Thank you so much for your time.
Vicky



On Fri, Jan 10, 2020 at 11:22 AM Greve, Douglas N.,Ph.D. <
dgr...@mgh.harvard.edu> wrote:

> Your FSGD and command look correct. As for whether it is a good idea or
> not, I'm not sure (you're probably the best person to decide that). One
> thing to keep in mind is that if there are true thickness differences you
> might see some differences due to partial voluming. I would also remove the
> mean of the thickness. You can so this with
> mri_glmfit --surf fsaverage lh --osgm --y lh.thickness.sm10.mgz --o
> glm.osgm.lh --eres-save :
> Then use glm.osgm.lh/eres.mgh as the PVR
>
> On 1/8/2020 9:45 PM, Vicky Shi wrote:
>
>         External Email - Use Caution
> Dear Freesurfer team,
>
> I am running the glm analysis on CBF maps. I have two class and I want to
> see the group difference regressing out the cortex thickness. I know that I
> can add the average cortical thickness as one covariable in FSDG file.
> However, I think it might worth trying to do this with GLM by using a per
> vertex regressor (thickness).
>
> My fsgd file is like below:
>
> Class Patient
> Class CTL
> INPUT subj1 Patient
> INPUT subj2 CTL
> ....
>
> My command is below:
> mri_glmfit --y lh.fsaverage.cbf.sm10.mgz --fsgd fsgd.txt --C contrast.mtx
> --glmdir lh.output.glmdir --surf fsaverage lh --pvr lh.thickness.sm10.mgz
>
> My contrast matrix is -1 1 0.
>
> I am wondering if what I do is correct or not. Does it make sense or I
> should use the mean thickness as regressor?
>
> Thank you for your time!
>
> Best regards,
> Vicky
>
>
>
>
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