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Complexity Synchronization of Organ Networks
The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606256/ https://www.ncbi.nlm.nih.gov/pubmed/37895514 http://dx.doi.org/10.3390/e25101393 |
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author | West, Bruce J. Grigolini, Paolo Kerick, Scott E. Franaszczuk, Piotr J. Mahmoodi, Korosh |
author_facet | West, Bruce J. Grigolini, Paolo Kerick, Scott E. Franaszczuk, Piotr J. Mahmoodi, Korosh |
author_sort | West, Bruce J. |
collection | PubMed |
description | The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the ‘complexity matching effect’, and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks’ multifractal dimensions. |
format | Online Article Text |
id | pubmed-10606256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106062562023-10-28 Complexity Synchronization of Organ Networks West, Bruce J. Grigolini, Paolo Kerick, Scott E. Franaszczuk, Piotr J. Mahmoodi, Korosh Entropy (Basel) Article The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the ‘complexity matching effect’, and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks’ multifractal dimensions. MDPI 2023-09-28 /pmc/articles/PMC10606256/ /pubmed/37895514 http://dx.doi.org/10.3390/e25101393 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article West, Bruce J. Grigolini, Paolo Kerick, Scott E. Franaszczuk, Piotr J. Mahmoodi, Korosh Complexity Synchronization of Organ Networks |
title | Complexity Synchronization of Organ Networks |
title_full | Complexity Synchronization of Organ Networks |
title_fullStr | Complexity Synchronization of Organ Networks |
title_full_unstemmed | Complexity Synchronization of Organ Networks |
title_short | Complexity Synchronization of Organ Networks |
title_sort | complexity synchronization of organ networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606256/ https://www.ncbi.nlm.nih.gov/pubmed/37895514 http://dx.doi.org/10.3390/e25101393 |
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