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Visibility graph analysis for brain: scoping review

In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds...

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Detalles Bibliográficos
Autores principales: Sulaimany, Sadegh, Safahi, Zhino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570536/
https://www.ncbi.nlm.nih.gov/pubmed/37841678
http://dx.doi.org/10.3389/fnins.2023.1268485
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author Sulaimany, Sadegh
Safahi, Zhino
author_facet Sulaimany, Sadegh
Safahi, Zhino
author_sort Sulaimany, Sadegh
collection PubMed
description In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others. This research presents a scoping review of scholarly articles published in reputable English-language journals and conferences, focusing on VG-based analysis methods related to brain disorders. The aim is to provide a foundation for further and future research endeavors, beginning with an introduction to the VG and its various types. To achieve this, a systematic search and refinement of relevant articles were conducted in two prominent scientific databases: Google Scholar and Scopus. A total of 51 eligible articles were selected for a comprehensive analysis of the topic. These articles categorized based on publication year, type of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined, such as Epilepsy, Alzheimer’s disease, Autism, Alcoholism, Sleep disorders, Fatigue, Depression, and other related conditions. Moreover, there are recommendations for future advancements in research, which involve utilizing cutting-edge techniques like graph machine learning and deep learning. Additionally, the exploration of understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson’s disease is also suggested.
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spelling pubmed-105705362023-10-14 Visibility graph analysis for brain: scoping review Sulaimany, Sadegh Safahi, Zhino Front Neurosci Neuroscience In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others. This research presents a scoping review of scholarly articles published in reputable English-language journals and conferences, focusing on VG-based analysis methods related to brain disorders. The aim is to provide a foundation for further and future research endeavors, beginning with an introduction to the VG and its various types. To achieve this, a systematic search and refinement of relevant articles were conducted in two prominent scientific databases: Google Scholar and Scopus. A total of 51 eligible articles were selected for a comprehensive analysis of the topic. These articles categorized based on publication year, type of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined, such as Epilepsy, Alzheimer’s disease, Autism, Alcoholism, Sleep disorders, Fatigue, Depression, and other related conditions. Moreover, there are recommendations for future advancements in research, which involve utilizing cutting-edge techniques like graph machine learning and deep learning. Additionally, the exploration of understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson’s disease is also suggested. Frontiers Media S.A. 2023-09-29 /pmc/articles/PMC10570536/ /pubmed/37841678 http://dx.doi.org/10.3389/fnins.2023.1268485 Text en Copyright © 2023 Sulaimany and Safahi. 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
Sulaimany, Sadegh
Safahi, Zhino
Visibility graph analysis for brain: scoping review
title Visibility graph analysis for brain: scoping review
title_full Visibility graph analysis for brain: scoping review
title_fullStr Visibility graph analysis for brain: scoping review
title_full_unstemmed Visibility graph analysis for brain: scoping review
title_short Visibility graph analysis for brain: scoping review
title_sort visibility graph analysis for brain: scoping review
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570536/
https://www.ncbi.nlm.nih.gov/pubmed/37841678
http://dx.doi.org/10.3389/fnins.2023.1268485
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