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Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged...

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Autores principales: Batista Tsukahara, Victor Hugo, de Oliveira Júnior, Jordão Natal, de Oliveira Barth, Vitor Bruno, de Oliveira, Jasiara Carla, Rosa Cota, Vinicius, Maciel, Carlos Dias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399918/
https://www.ncbi.nlm.nih.gov/pubmed/36034337
http://dx.doi.org/10.3389/fncir.2022.747910
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author Batista Tsukahara, Victor Hugo
de Oliveira Júnior, Jordão Natal
de Oliveira Barth, Vitor Bruno
de Oliveira, Jasiara Carla
Rosa Cota, Vinicius
Maciel, Carlos Dias
author_facet Batista Tsukahara, Victor Hugo
de Oliveira Júnior, Jordão Natal
de Oliveira Barth, Vitor Bruno
de Oliveira, Jasiara Carla
Rosa Cota, Vinicius
Maciel, Carlos Dias
author_sort Batista Tsukahara, Victor Hugo
collection PubMed
description Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.
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spelling pubmed-93999182022-08-25 Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis Batista Tsukahara, Victor Hugo de Oliveira Júnior, Jordão Natal de Oliveira Barth, Vitor Bruno de Oliveira, Jasiara Carla Rosa Cota, Vinicius Maciel, Carlos Dias Front Neural Circuits Neuroscience Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399918/ /pubmed/36034337 http://dx.doi.org/10.3389/fncir.2022.747910 Text en Copyright © 2022 Batista Tsukahara, de Oliveira Júnior, de Oliveira Barth, de Oliveira, Rosa Cota and Maciel. 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 Neuroscience
Batista Tsukahara, Victor Hugo
de Oliveira Júnior, Jordão Natal
de Oliveira Barth, Vitor Bruno
de Oliveira, Jasiara Carla
Rosa Cota, Vinicius
Maciel, Carlos Dias
Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title_full Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title_fullStr Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title_full_unstemmed Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title_short Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis
title_sort data-driven network dynamical model of rat brains during acute ictogenesis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399918/
https://www.ncbi.nlm.nih.gov/pubmed/36034337
http://dx.doi.org/10.3389/fncir.2022.747910
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