In that mri_segstats command, you will get a column for everything that
is not in a cluster as well as for each cluster. Make sure that you are
using the correct column. The other thing you can do is to run the post
hoc test in matlab, somthing like
y = load('file.stats');
X = load('Xg.dat');
C = load('contrast/C.dat');
[beta rvar] = fast_glmfit(y,X);
[F p gamma] = fast_fratio(beta,X,rvar,C);
On 6/21/17 5:41 AM, Damien MARIE wrote:
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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