A multi-modal parcellation of human cerebral cortex
Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/ https://www.ncbi.nlm.nih.gov/pubmed/27437579 http://dx.doi.org/10.1038/nature18933 |
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author | Glasser, Matthew F Coalson, Timothy S Robinson, Emma C Hacker, Carl D Harwell, John Yacoub, Essa Ugurbil, Kamil Andersson, Jesper Beckmann, Christian F Jenkinson, Mark Smith, Stephen M Van Essen, David C |
author_facet | Glasser, Matthew F Coalson, Timothy S Robinson, Emma C Hacker, Carl D Harwell, John Yacoub, Essa Ugurbil, Kamil Andersson, Jesper Beckmann, Christian F Jenkinson, Mark Smith, Stephen M Van Essen, David C |
author_sort | Glasser, Matthew F |
collection | PubMed |
description | Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease. |
format | Online Article Text |
id | pubmed-4990127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
record_format | MEDLINE/PubMed |
spelling | pubmed-49901272017-02-11 A multi-modal parcellation of human cerebral cortex Glasser, Matthew F Coalson, Timothy S Robinson, Emma C Hacker, Carl D Harwell, John Yacoub, Essa Ugurbil, Kamil Andersson, Jesper Beckmann, Christian F Jenkinson, Mark Smith, Stephen M Van Essen, David C Nature Article Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease. 2016-08-11 /pmc/articles/PMC4990127/ /pubmed/27437579 http://dx.doi.org/10.1038/nature18933 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Glasser, Matthew F Coalson, Timothy S Robinson, Emma C Hacker, Carl D Harwell, John Yacoub, Essa Ugurbil, Kamil Andersson, Jesper Beckmann, Christian F Jenkinson, Mark Smith, Stephen M Van Essen, David C A multi-modal parcellation of human cerebral cortex |
title | A multi-modal parcellation of human cerebral cortex |
title_full | A multi-modal parcellation of human cerebral cortex |
title_fullStr | A multi-modal parcellation of human cerebral cortex |
title_full_unstemmed | A multi-modal parcellation of human cerebral cortex |
title_short | A multi-modal parcellation of human cerebral cortex |
title_sort | multi-modal parcellation of human cerebral cortex |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/ https://www.ncbi.nlm.nih.gov/pubmed/27437579 http://dx.doi.org/10.1038/nature18933 |
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