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Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology
With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from th...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579369/ https://www.ncbi.nlm.nih.gov/pubmed/36277610 http://dx.doi.org/10.3389/fncom.2022.1024205 |
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author | Guo, Guangxing Zhao, Yanli Liu, Chenxu Fu, Yongcan Xi, Xinhua Jin, Lizhong Shi, Dongli Wang, Lin Duan, Yonghong Huang, Jie Tan, Shuping Yin, Guimei |
author_facet | Guo, Guangxing Zhao, Yanli Liu, Chenxu Fu, Yongcan Xi, Xinhua Jin, Lizhong Shi, Dongli Wang, Lin Duan, Yonghong Huang, Jie Tan, Shuping Yin, Guimei |
author_sort | Guo, Guangxing |
collection | PubMed |
description | With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris–Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors. |
format | Online Article Text |
id | pubmed-9579369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95793692022-10-20 Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology Guo, Guangxing Zhao, Yanli Liu, Chenxu Fu, Yongcan Xi, Xinhua Jin, Lizhong Shi, Dongli Wang, Lin Duan, Yonghong Huang, Jie Tan, Shuping Yin, Guimei Front Comput Neurosci Computational Neuroscience With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris–Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9579369/ /pubmed/36277610 http://dx.doi.org/10.3389/fncom.2022.1024205 Text en Copyright © 2022 Guo, Zhao, Liu, Fu, Xi, Jin, Shi, Wang, Duan, Huang, Tan and Yin. 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 | Computational Neuroscience Guo, Guangxing Zhao, Yanli Liu, Chenxu Fu, Yongcan Xi, Xinhua Jin, Lizhong Shi, Dongli Wang, Lin Duan, Yonghong Huang, Jie Tan, Shuping Yin, Guimei Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title | Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title_full | Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title_fullStr | Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title_full_unstemmed | Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title_short | Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
title_sort | method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology |
topic | Computational Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579369/ https://www.ncbi.nlm.nih.gov/pubmed/36277610 http://dx.doi.org/10.3389/fncom.2022.1024205 |
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