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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...

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Autores principales: Zhu, Xiaojing, Shappell, Heather, Kramer, Mark A., Chu, Catherine J., Kolaczyk, Eric D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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