Yep, that's an inclusive mask.
Morgan Hough wrote:
Thanks Doug,
Does a mask.mgh file get written out to the glmdir?
Cheers,
-Morgan
Doug Greve wrote:
If you're using glmfit, you can --label
$SUBJECTS_DIR/fsaverage/lh.Medial_wall.label --mask-inv
Morgan Hough wrote:
Hi Nick,
I see. I am just not sure how to put this back together again to
match the registered thickness files in the .mgh file. Do the
vertices have unique ids? Can I cat and sort them? Can I dump
matching information from my particular thickness group file and
then sort?
Is there someway to binarize the .annot file to use as a mask with
mri_glmfit?
Cheers,
-Morgan
Nick Schmansky wrote:
Morgan,
If you do this:
mri_annotation2label --subject <subjid> --hemi rh \
--outdir ./labels
you will get label files in the ./labels directory. Those label files
are text files listing the vertices and x,y.z of each label, including
'unknown' and 'corpuscallosum' (which are the only two that need
excluding). Will that provide you with what you need?
Nick
On Fri, 2007-04-06 at 21:35 +0100, Morgan Hough wrote:
I am using ICA on what would be input to mri_glmfit but since ICA
is multivariate I need to exclude regions that don't have
interpretable thickness or distortion measures. It seems like
unknown and corpus callosum are the two to exclude. Are there any
others I should exclude?
I would like to convert the ?h.aparc.annot files to .mgz if that
gets me surface files integer coded with the regions. That way I
can binarize and exclude those regions with avwtools as I will
need to convert to nifti to do the ICA analysis anyway. What would
you suggest for this?
Cheers,
-Morgan
Bruce Fischl wrote:
which parcellation are you using?
On Fri, 6 Apr 2007, Morgan Hough wrote:
Thanks Bruce,
Are there any other parcellation units that should be excluded?
That seems to be the only white matter PU.
Cheers,
-Morgan
Bruce Fischl wrote:
Hi Morgan,
no thickness should not be used in the cc.
Bruce
On Fri, 6 Apr 2007, Morgan Hough wrote:
I would like to make a mask for group analysis that excludes
the unknown parcellation area. Since I would like to apply the
same mask to all subjects (I have already brought them into
alignment with mri_preproc), I was wondering how I can convert
the average template label/?h.aparc.annot to a .mgz files
that I could then convert into a mask. Is there an alternative
way to produce such a mask?
As a follow-up question, I was wondering if parcellation areas
such as the corpus callosum should also be excluded from
analysis? Are the thickness estimates made in the corpus
callosum parcellation area interpretable?
Thanks in advance for your time.
Cheers,
-Morgan
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Douglas N. Greve, Ph.D.
MGH-NMR Center
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