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Dynamic brain connectome and high risk of mental problem in clinical nurses
With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519872/ https://www.ncbi.nlm.nih.gov/pubmed/34331489 http://dx.doi.org/10.1002/hbm.25617 |
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author | Bai, Ling JI, Gong‐Jun Song, Yongxia Sun, Jinmei Wei, Junjie Xue, Fang Zhu, Lu Li, Rui Han, Yanfang Zhang, Liu Yang, Jinying Qiu, Bensheng Wu, Guo‐Rong Zhang, Jing Hong, Jingfang Wang, Kai Zhu, Chunyan |
author_facet | Bai, Ling JI, Gong‐Jun Song, Yongxia Sun, Jinmei Wei, Junjie Xue, Fang Zhu, Lu Li, Rui Han, Yanfang Zhang, Liu Yang, Jinying Qiu, Bensheng Wu, Guo‐Rong Zhang, Jing Hong, Jingfang Wang, Kai Zhu, Chunyan |
author_sort | Bai, Ling |
collection | PubMed |
description | With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL‐90) scale. A subgroup (n = 57) was further scanned by resting‐state functional MRI with two sessions. Based on the SCL‐90 scale, “somatic complaints” and “diet/sleeping” exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom‐imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for “diet/sleeping” problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention. |
format | Online Article Text |
id | pubmed-8519872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85198722021-10-22 Dynamic brain connectome and high risk of mental problem in clinical nurses Bai, Ling JI, Gong‐Jun Song, Yongxia Sun, Jinmei Wei, Junjie Xue, Fang Zhu, Lu Li, Rui Han, Yanfang Zhang, Liu Yang, Jinying Qiu, Bensheng Wu, Guo‐Rong Zhang, Jing Hong, Jingfang Wang, Kai Zhu, Chunyan Hum Brain Mapp Research Articles With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL‐90) scale. A subgroup (n = 57) was further scanned by resting‐state functional MRI with two sessions. Based on the SCL‐90 scale, “somatic complaints” and “diet/sleeping” exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom‐imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for “diet/sleeping” problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention. John Wiley & Sons, Inc. 2021-07-31 /pmc/articles/PMC8519872/ /pubmed/34331489 http://dx.doi.org/10.1002/hbm.25617 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Bai, Ling JI, Gong‐Jun Song, Yongxia Sun, Jinmei Wei, Junjie Xue, Fang Zhu, Lu Li, Rui Han, Yanfang Zhang, Liu Yang, Jinying Qiu, Bensheng Wu, Guo‐Rong Zhang, Jing Hong, Jingfang Wang, Kai Zhu, Chunyan Dynamic brain connectome and high risk of mental problem in clinical nurses |
title | Dynamic brain connectome and high risk of mental problem in clinical nurses |
title_full | Dynamic brain connectome and high risk of mental problem in clinical nurses |
title_fullStr | Dynamic brain connectome and high risk of mental problem in clinical nurses |
title_full_unstemmed | Dynamic brain connectome and high risk of mental problem in clinical nurses |
title_short | Dynamic brain connectome and high risk of mental problem in clinical nurses |
title_sort | dynamic brain connectome and high risk of mental problem in clinical nurses |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519872/ https://www.ncbi.nlm.nih.gov/pubmed/34331489 http://dx.doi.org/10.1002/hbm.25617 |
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