Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex

For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personaliz...

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Autores principales: Doyen, Stephane, Nicholas, Peter, Poologaindran, Anujan, Crawford, Lewis, Young, Isabella M., Romero‐Garcia, Rafeael, Sughrue, Michael E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837585/
https://www.ncbi.nlm.nih.gov/pubmed/34826179
http://dx.doi.org/10.1002/hbm.25728
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author Doyen, Stephane
Nicholas, Peter
Poologaindran, Anujan
Crawford, Lewis
Young, Isabella M.
Romero‐Garcia, Rafeael
Sughrue, Michael E.
author_facet Doyen, Stephane
Nicholas, Peter
Poologaindran, Anujan
Crawford, Lewis
Young, Isabella M.
Romero‐Garcia, Rafeael
Sughrue, Michael E.
author_sort Doyen, Stephane
collection PubMed
description For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity‐based parcellation approach that can be applied at the single‐subject level. Utilizing normative diffusion data, we first developed a machine‐learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient‐specific maps independent of brain shape and pathological distortion. The supervised ML classifier re‐parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re‐parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas‐based registration from healthy subjects by employing a voxel‐wise connectivity approach based on individual data.
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spelling pubmed-88375852022-02-14 Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex Doyen, Stephane Nicholas, Peter Poologaindran, Anujan Crawford, Lewis Young, Isabella M. Romero‐Garcia, Rafeael Sughrue, Michael E. Hum Brain Mapp Research Articles For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity‐based parcellation approach that can be applied at the single‐subject level. Utilizing normative diffusion data, we first developed a machine‐learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient‐specific maps independent of brain shape and pathological distortion. The supervised ML classifier re‐parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re‐parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas‐based registration from healthy subjects by employing a voxel‐wise connectivity approach based on individual data. John Wiley & Sons, Inc. 2021-11-26 /pmc/articles/PMC8837585/ /pubmed/34826179 http://dx.doi.org/10.1002/hbm.25728 Text en © 2021 Omniscient Neurotechnology Pty limited. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Doyen, Stephane
Nicholas, Peter
Poologaindran, Anujan
Crawford, Lewis
Young, Isabella M.
Romero‐Garcia, Rafeael
Sughrue, Michael E.
Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title_full Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title_fullStr Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title_full_unstemmed Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title_short Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
title_sort connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837585/
https://www.ncbi.nlm.nih.gov/pubmed/34826179
http://dx.doi.org/10.1002/hbm.25728
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