Matt.
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There are a variety of options out there. I personally do not endorse any of them as better than any others of them. I am not aware of a multi-modal subcortical parcellation made using a similar approach to the HCP’s cortical parcellation.
Matt.
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So you want to map the data into the individual’s volume space? FreeSurfer is a bit weird about both coordinate spaces and their heavy reliance on the native mesh. For the HCP we do things a bit differently and use a consistent coordinate space for all surfaces and volumes and do most of our analyses on standard meshes (which match the shape of the subjects surfaces but can be directly compared across subjects). For volume spaces we have ${StudyFolder}/${Subject}/T1w which is rigidly aligned to MNI space (no change in brain size/shape, but a consistent origin and orientation) and nonlinearly aligned MNI space. The native and 32k_fs_LR surfaces are available in both volume spaces and the 164k_fs_LR surfaces are in MNI space only. For surface meshes we have FreeSurfer’s native mesh, and 164k and 32k standard meshes. The 32k mesh is the main workhorse (2mm average vertex spacing).
Matt.
From: Panos Fotiadis <pfo...@gmail.com>
Reply-To: "hcp-...@humanconnectome.org" <hcp-...@humanconnectome.org>
Date: Thursday, October 7, 2021 at 1:57 PM
To: HCP-Users <hcp-...@humanconnectome.org>
Cc: "Glasser, Matt" <glas...@wustl.edu>
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If you like you can resample the parcellation onto the native mesh. There might be a minor improvement in accuracy.
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You would use the 32k spheres for current and the MSMAll registered native mesh spheres for new. adap_bary_area is correct.
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