Hi,
I am working on R1 maps. I ran a very simple glm on smoothed R1 maps, FWHM = 8
mm, population 1, population 2 , and age as nuisance factor.
After POS MC simulation CW correction, I had two significant clusters resulting
from this in the planum temporale and Heschl’s sulcus. The contrast being
population 1 - population 2, my interpretation was higher R1 values in these
two clusters in Pop 1 as compared to Pop 2.
After that I wanted to run some post-hoc tests let say, in a statistical
software similar to SPSS, JMP, on the mean R1 values from each of these
clusters.
The strategy I picked was to stay in the common space, fsaverage, and apply
mri_segstats:
#Extract mean R1 in montecarlo's significantcluster
for i in `cat subjects`
do mri_segstats --mul 1000 --annot fsaverage lh PT_mc-z.pos.sig.ocn --i
$SUBJECTS_DIR/${i}/surf/lh.R1.fsaverage.fwhm8.mgh --sum lh.PT.R1.${i}.stats
done
for i in `cat subjects`
do mri_segstats --mul 1000 --annot fsaverage lh HS_mc-z.pos.sig.ocn --i
$SUBJECTS_DIR/${i}/surf/lh.R1.fsaverage.fwhm8.mgh --sum lh.HS.R1.${i}.stats
done
Everything when fine but as compared to my glm analysis when looking at mean
group differences in my stat software I have the opposite : larger R1 values
in Pop 2 as compared to Pop 1 (after removing the effect of age or not, and
removing one outlier or not).
I went back to my glm and checked everything, fsgd file, matrix and so on,
everything seems ok I am not mistaken on the direction of my glm contrast (pop
1 - pop 2) so I do not get it at all.
Any advices?
For now I am thinking about going back in the individual space, registering my
clusters in the individual surfaces (label2surf something like that) and then
extract my mean R1 values, or extract values in non-smoothed maps, but I do
not think it will change anything.
Damien
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