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Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization
Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618576/ https://www.ncbi.nlm.nih.gov/pubmed/36310240 http://dx.doi.org/10.1038/s42003-022-04148-4 |
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author | Meng, Yao Yang, Siqi Xiao, Jinming Lu, Yaxin Li, Jiao Chen, Huafu Liao, Wei |
author_facet | Meng, Yao Yang, Siqi Xiao, Jinming Lu, Yaxin Li, Jiao Chen, Huafu Liao, Wei |
author_sort | Meng, Yao |
collection | PubMed |
description | Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles. |
format | Online Article Text |
id | pubmed-9618576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96185762022-11-01 Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization Meng, Yao Yang, Siqi Xiao, Jinming Lu, Yaxin Li, Jiao Chen, Huafu Liao, Wei Commun Biol Article Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles. Nature Publishing Group UK 2022-10-30 /pmc/articles/PMC9618576/ /pubmed/36310240 http://dx.doi.org/10.1038/s42003-022-04148-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Meng, Yao Yang, Siqi Xiao, Jinming Lu, Yaxin Li, Jiao Chen, Huafu Liao, Wei Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title | Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title_full | Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title_fullStr | Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title_full_unstemmed | Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title_short | Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
title_sort | cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618576/ https://www.ncbi.nlm.nih.gov/pubmed/36310240 http://dx.doi.org/10.1038/s42003-022-04148-4 |
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