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
On Wed, 11 Jul 2018, James Gullickson
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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
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
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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