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Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and...

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Autores principales: Ke, Ming, Wang, Changliang, Liu, Guangyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036585/
https://www.ncbi.nlm.nih.gov/pubmed/36969802
http://dx.doi.org/10.3389/fnbeh.2023.1123534
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author Ke, Ming
Wang, Changliang
Liu, Guangyao
author_facet Ke, Ming
Wang, Changliang
Liu, Guangyao
author_sort Ke, Ming
collection PubMed
description Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.
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spelling pubmed-100365852023-03-25 Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy Ke, Ming Wang, Changliang Liu, Guangyao Front Behav Neurosci Behavioral Neuroscience Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients. Frontiers Media S.A. 2023-03-10 /pmc/articles/PMC10036585/ /pubmed/36969802 http://dx.doi.org/10.3389/fnbeh.2023.1123534 Text en Copyright © 2023 Ke, Wang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Behavioral Neuroscience
Ke, Ming
Wang, Changliang
Liu, Guangyao
Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title_full Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title_fullStr Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title_full_unstemmed Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title_short Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
title_sort multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
topic Behavioral Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036585/
https://www.ncbi.nlm.nih.gov/pubmed/36969802
http://dx.doi.org/10.3389/fnbeh.2023.1123534
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