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Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression
Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of...
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/PMC9199990/ https://www.ncbi.nlm.nih.gov/pubmed/35721531 http://dx.doi.org/10.3389/fphys.2022.858739 |
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author | Song, Yingjie Wang, Kejie Wei, Yu Zhu, Yongpeng Wen, Jinfeng Luo, Yuxi |
author_facet | Song, Yingjie Wang, Kejie Wei, Yu Zhu, Yongpeng Wen, Jinfeng Luo, Yuxi |
author_sort | Song, Yingjie |
collection | PubMed |
description | Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression. |
format | Online Article Text |
id | pubmed-9199990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91999902022-06-16 Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression Song, Yingjie Wang, Kejie Wei, Yu Zhu, Yongpeng Wen, Jinfeng Luo, Yuxi Front Physiol Physiology Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9199990/ /pubmed/35721531 http://dx.doi.org/10.3389/fphys.2022.858739 Text en Copyright © 2022 Song, Wang, Wei, Zhu, Wen and Luo. 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 | Physiology Song, Yingjie Wang, Kejie Wei, Yu Zhu, Yongpeng Wen, Jinfeng Luo, Yuxi Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title | Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title_full | Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title_fullStr | Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title_full_unstemmed | Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title_short | Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression |
title_sort | graph theory analysis of the cortical functional network during sleep in patients with depression |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199990/ https://www.ncbi.nlm.nih.gov/pubmed/35721531 http://dx.doi.org/10.3389/fphys.2022.858739 |
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