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Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures
In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks—in which edges indicate strong enough coupling between brain regions—are consistent with the notion of percolation, which is a phenomeno...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310035/ https://www.ncbi.nlm.nih.gov/pubmed/37327238 http://dx.doi.org/10.1371/journal.pcbi.1011188 |
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author | Zhu, Xiaojing Shappell, Heather Kramer, Mark A. Chu, Catherine J. Kolaczyk, Eric D. |
author_facet | Zhu, Xiaojing Shappell, Heather Kramer, Mark A. Chu, Catherine J. Kolaczyk, Eric D. |
author_sort | Zhu, Xiaojing |
collection | PubMed |
description | In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks—in which edges indicate strong enough coupling between brain regions—are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy. |
format | Online Article Text |
id | pubmed-10310035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103100352023-06-30 Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures Zhu, Xiaojing Shappell, Heather Kramer, Mark A. Chu, Catherine J. Kolaczyk, Eric D. PLoS Comput Biol Research Article In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks—in which edges indicate strong enough coupling between brain regions—are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy. Public Library of Science 2023-06-16 /pmc/articles/PMC10310035/ /pubmed/37327238 http://dx.doi.org/10.1371/journal.pcbi.1011188 Text en © 2023 Zhu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhu, Xiaojing Shappell, Heather Kramer, Mark A. Chu, Catherine J. Kolaczyk, Eric D. Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title | Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title_full | Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title_fullStr | Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title_full_unstemmed | Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title_short | Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
title_sort | distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310035/ https://www.ncbi.nlm.nih.gov/pubmed/37327238 http://dx.doi.org/10.1371/journal.pcbi.1011188 |
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