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Hi experts,
I extracted network-wise cortical measures (i.e., 7 cortical thickness
values for 7 networks for Yeo atlas).
I was wondering if there is a way to get the cortical thickness of each ROI
within each of these networks e.g., cortical thickness
Hard to tell, Jenna!
Please do upload a sample subject using the instructions in the wiki, and I’ll
take a look.
Cheers,
/E
Juan Eugenio Iglesias
Senior research fellow
CMIC (UCL), MGH (HMS) and CSAIL (MIT)
http://www.jeiglesias.com
From: on behalf of Jenna Katherine
Blujus
Reply to: Freesur
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Hi Eugenio,
I might have fixed this particular issue. I ran AFNI's 3dUnifize on the T1
before submitting it to recon-all and this improved the segmentation (it now
overlays on the hippocampus). However, I still have some other files that this
soluti
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Hi Eugenio,
Yes, I can - which files would you like and where should I upload them?
Thanks,
Jenna
From: Jenna Katherine Blujus
Sent: Monday, July 13, 2020 6:29 PM
To: freesurfer@nmr.mgh.harvard.edu
Subject: Re: Free
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They didn’t have that data either. So perhaps there is some possibility of a
.gcs having improved performance over a simple surface registration depending
on how this was done (e.g. if the MSMAll registration were used to get the
individual lables a
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I found the .gcs files in a git repo:
https://github.com/faskowit/multiAtlasTT
El mar., 14 jul. 2020 a las 11:58, Glasser, Matthew ()
escribió:
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>
> So how exactly did you make a .gcs without the individual subjec
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So how exactly did you make a .gcs without the individual subject parcellations
or the multi-modal features used for classifying cortical areas? Note that we
are working on a tool for folks to do this.
Matt.
From: on behalf of Álvaro Deleglise
R
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Hi Alina and Matthew,
I think there is a simple solution to the problem. You can use the .gcs
files of the parcellation you are interested in! You have to run the
mris_ca_label command to apply the parcellation in individual subject space
and get de .an
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I would find using something like ciftify (https://github.com/edickie/ciftify)
potentially more straightforward (and then you can just use wb_command
-cifti-parcellate on
${StudyFolder}/${Subject}/MNINonLinear/fsaverage_LR32k/${Subject}.thickness.32k
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Hello Freesurfer support list,
I’m attempting to map the HCP-MMP1-Atlas to the individual brains of my
subjects to get the values of the parcellation, like aparc.stats but aparc
being HCP-MMP1. The aim ist to compare the cortical thickness of the
Pa
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