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Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts
Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical...
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/PMC10024695/ https://www.ncbi.nlm.nih.gov/pubmed/36934153 http://dx.doi.org/10.1038/s42003-023-04648-x |
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author | Hindriks, Rikkert Tewarie, Prejaas K. B. |
author_facet | Hindriks, Rikkert Tewarie, Prejaas K. B. |
author_sort | Hindriks, Rikkert |
collection | PubMed |
description | Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity. |
format | Online Article Text |
id | pubmed-10024695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100246952023-03-20 Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts Hindriks, Rikkert Tewarie, Prejaas K. B. Commun Biol Article Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity. Nature Publishing Group UK 2023-03-18 /pmc/articles/PMC10024695/ /pubmed/36934153 http://dx.doi.org/10.1038/s42003-023-04648-x 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 Hindriks, Rikkert Tewarie, Prejaas K. B. Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title | Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title_full | Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title_fullStr | Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title_full_unstemmed | Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title_short | Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
title_sort | dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024695/ https://www.ncbi.nlm.nih.gov/pubmed/36934153 http://dx.doi.org/10.1038/s42003-023-04648-x |
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