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A computational model of shared fine-scale structure in the human connectome

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual con...

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Detalles Bibliográficos
Autores principales: Guntupalli, J. Swaroop, Feilong, Ma, Haxby, James V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922579/
https://www.ncbi.nlm.nih.gov/pubmed/29664910
http://dx.doi.org/10.1371/journal.pcbi.1006120
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author Guntupalli, J. Swaroop
Feilong, Ma
Haxby, James V.
author_facet Guntupalli, J. Swaroop
Feilong, Ma
Haxby, James V.
author_sort Guntupalli, J. Swaroop
collection PubMed
description Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.
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spelling pubmed-59225792018-05-11 A computational model of shared fine-scale structure in the human connectome Guntupalli, J. Swaroop Feilong, Ma Haxby, James V. PLoS Comput Biol Research Article Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study. Public Library of Science 2018-04-17 /pmc/articles/PMC5922579/ /pubmed/29664910 http://dx.doi.org/10.1371/journal.pcbi.1006120 Text en © 2018 Guntupalli et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Guntupalli, J. Swaroop
Feilong, Ma
Haxby, James V.
A computational model of shared fine-scale structure in the human connectome
title A computational model of shared fine-scale structure in the human connectome
title_full A computational model of shared fine-scale structure in the human connectome
title_fullStr A computational model of shared fine-scale structure in the human connectome
title_full_unstemmed A computational model of shared fine-scale structure in the human connectome
title_short A computational model of shared fine-scale structure in the human connectome
title_sort computational model of shared fine-scale structure in the human connectome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922579/
https://www.ncbi.nlm.nih.gov/pubmed/29664910
http://dx.doi.org/10.1371/journal.pcbi.1006120
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