Hi James

you could use techniques that compare the whole distribution of thicknesses across subject populations. You could do a t-test or something non-parametric like a Kolmogorov-Smirnov or use permutation testing. I'll cc Tom Nichols so he can chime in with something more sophisticated or specific.


On Wed, 11 Jul 2018, James Gullickson wrote:

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I am comparing cortical thickness between subjects with and without mild 
traumatic brain injury
(mTBI). So far the contrasts in QDEC have not been significant after correcting 
for multiple
comparisons. I am not necessarily surprised at this due to the heterogeneous 
nature of mTBI in our
sample, i.e. we do not expect any two subjects to have damage in the same area. 
I am interested in
ways to compare cortical thickness that are not dependent on a single ROI 
having an effect across
subjects. One way I have tried is calculating z-scores for the values in the 
aparc.stats file, and
using the number of abnormally low ROIs as a dependant variable to compare 
between groups. 

Is there a way to look at thickness differences at an even more general level? 
E.g. by comparing the
number of vertices with abnormally low thickness? If so how would one go about 
that with Freesurfer

This paper takes a similar approach with DTI. I'd like to do something analogous to 
their "number of
voxels with low FA" analysis.



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