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Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia
General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3744550/ https://www.ncbi.nlm.nih.gov/pubmed/23967131 http://dx.doi.org/10.1371/journal.pone.0070899 |
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author | Shin, Jeongkyu Mashour, George A. Ku, Seungwoo Kim, Seunghwan Lee, Uncheol |
author_facet | Shin, Jeongkyu Mashour, George A. Ku, Seungwoo Kim, Seunghwan Lee, Uncheol |
author_sort | Shin, Jeongkyu |
collection | PubMed |
description | General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network “backbones.” We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks. |
format | Online Article Text |
id | pubmed-3744550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37445502013-08-21 Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia Shin, Jeongkyu Mashour, George A. Ku, Seungwoo Kim, Seunghwan Lee, Uncheol PLoS One Research Article General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network “backbones.” We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks. Public Library of Science 2013-08-15 /pmc/articles/PMC3744550/ /pubmed/23967131 http://dx.doi.org/10.1371/journal.pone.0070899 Text en © 2013 Shin 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Shin, Jeongkyu Mashour, George A. Ku, Seungwoo Kim, Seunghwan Lee, Uncheol Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title | Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title_full | Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title_fullStr | Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title_full_unstemmed | Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title_short | Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia |
title_sort | subgraph “backbone” analysis of dynamic brain networks during consciousness and anesthesia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3744550/ https://www.ncbi.nlm.nih.gov/pubmed/23967131 http://dx.doi.org/10.1371/journal.pone.0070899 |
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