I think it will work properly if you use the raw data as input and
create an FSGD file with two classes: (1) the individual subject and (2)
healthy subjects. Also include age as a covariate. Use DOSS instead of
DODS. Then set your contrast to be 1 -1 0 and run mri_glmfit. I think
that should properly account for age.
On 5/7/2020 2:44 PM, Xiaojiang Yang wrote:
External Email - Use Caution
Dear FS experts,
I have a group of healthy subjects. Given a new individual subject, I
want to compare its cortical thickness to the healthy group so that I
can find where the thickness is abnormal (thickening or thinning).
I use fsaverage as the template subject to calculate mean and std of
the healthy group. Since subjects in the healthy group have different
ages, I do a “normalization” process to all the subjects in the group
BEFORE calculating the mean and std. Thus the normalized healthy group
can be regarded as all subjects having the same “standard” age. The
normalization process is just multiplying some pre-obtained scale
(varied by age, but not linearly ) to the file
*?h.thickness.fwhm0.fsaverage.mgh* of each subject that are already
calculated by “recon-all --qcache” command. So the mean and std of the
group are actually weighted in the sense of Freesurfer’s recon-all
results.
My questions are:
1)If I do vertex-wise t-tests by simply comparing individual subject’s
thickness to the group using mris_calc but WITHOUT using mri_glmfit,
can I still use mri_glmfit-sim to do multiple comparisons correction?
If yes, how?
2)If the answer is no for the above question, how should I use
mris_glmft to implement my above thoughts so that I can use use
mri_glmfit-sim? Specifically, how can I use the weighted mean for the
group?
3)Take a step back, if I consider the weight is linearly correlated
with the age, how to design the fsdg and contrast file?
Thank you very much!
Xiao
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