Hi Amanda,

This statement refers to the average spacing of vertices e.g. along the 
midthickness.  Multiple voxels will almost always contribute to a give vertex’s 
input because of partial volume effects.  It is true that in ribbon mapping we 
intentionally collapse across the cortical thickness.  If one is interested in 
studying laminar fMRI, CIFTI can be expanded to allow this.

Peace,

Matt.

From: 
<[email protected]<mailto:[email protected]>>
 on behalf of Amanda Mejia <[email protected]<mailto:[email protected]>>
Date: Tuesday, April 17, 2018 at 9:11 AM
To: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: [HCP-Users] cortical surface vertex spacing in 32K

In the HCP FAQ and several HCP-related publications (e.g., A Multi-modal 
Parcellation of Human Cerebral Cortex by Glasser et al.), there is a statement 
about the surface vertices in 32K resolution and the 2mm isotropic voxels being 
on a "similar scale".  The aforementioned paper further states that the average 
cortical vertex spacing is 2mm.  If I'm reading this correctly, this would 
imply that the average distance in two dimensions between each vertex is 
similar to the distance between two 2mm voxels in a *single* layer.  This would 
seem to imply that since the cortical ribbon in volumetric space is several 
voxels thick, each vertex would be equivalent to multiple voxels.  But this 
isn't consistent with the fact that there are a similar number of cortical gray 
matter 2mm voxels and cortical surface vertices (~60,000 across both 
hemispheres).  It would seem to me that the only way to keep the number of 
locations the same, while transforming the data from multiple layers (of 
voxels) to a single layer (of vertices) would be to make the vertices closer 
together than 2mm.  Can someone please clarify?

Amanda

--
AMANDA F MEJIA, PhD
Assistant Professor
Department of Statistics
Indiana University Bloomington
https://mandymejia.wordpress.com/


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