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Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus
Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097701/ https://www.ncbi.nlm.nih.gov/pubmed/37046050 http://dx.doi.org/10.1038/s42003-023-04796-0 |
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author | Katsumi, Yuta Zhang, Jiahe Chen, Danlei Kamona, Nada Bunce, Jamie G. Hutchinson, J. Benjamin Yarossi, Mathew Tunik, Eugene Dickerson, Bradford C. Quigley, Karen S. Barrett, Lisa Feldman |
author_facet | Katsumi, Yuta Zhang, Jiahe Chen, Danlei Kamona, Nada Bunce, Jamie G. Hutchinson, J. Benjamin Yarossi, Mathew Tunik, Eugene Dickerson, Bradford C. Quigley, Karen S. Barrett, Lisa Feldman |
author_sort | Katsumi, Yuta |
collection | PubMed |
description | Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function. |
format | Online Article Text |
id | pubmed-10097701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100977012023-04-14 Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus Katsumi, Yuta Zhang, Jiahe Chen, Danlei Kamona, Nada Bunce, Jamie G. Hutchinson, J. Benjamin Yarossi, Mathew Tunik, Eugene Dickerson, Bradford C. Quigley, Karen S. Barrett, Lisa Feldman Commun Biol Article Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function. Nature Publishing Group UK 2023-04-12 /pmc/articles/PMC10097701/ /pubmed/37046050 http://dx.doi.org/10.1038/s42003-023-04796-0 Text en © The Author(s) 2023 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 Katsumi, Yuta Zhang, Jiahe Chen, Danlei Kamona, Nada Bunce, Jamie G. Hutchinson, J. Benjamin Yarossi, Mathew Tunik, Eugene Dickerson, Bradford C. Quigley, Karen S. Barrett, Lisa Feldman Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title | Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title_full | Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title_fullStr | Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title_full_unstemmed | Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title_short | Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
title_sort | correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097701/ https://www.ncbi.nlm.nih.gov/pubmed/37046050 http://dx.doi.org/10.1038/s42003-023-04796-0 |
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