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Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis

BACKGROUND: The latest guidance on chronic fatigue syndrome (CFS) recommends exercise therapy. Tai Chi, an exercise method in traditional Chinese medicine, is reportedly helpful for CFS. However, the mechanism remains unclear. The present longitudinal study aimed to detect the influence of Tai Chi o...

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Autores principales: Wu, Kang, Li, Yuanyuan, Zou, Yihuai, Ren, Yi, Wang, Yahui, Hu, Xiaojie, Wang, Yue, Chen, Chen, Lu, Mengxin, Xu, Lingling, Wu, Linlu, Li, Kuangshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714925/
https://www.ncbi.nlm.nih.gov/pubmed/36454926
http://dx.doi.org/10.1371/journal.pone.0278415
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author Wu, Kang
Li, Yuanyuan
Zou, Yihuai
Ren, Yi
Wang, Yahui
Hu, Xiaojie
Wang, Yue
Chen, Chen
Lu, Mengxin
Xu, Lingling
Wu, Linlu
Li, Kuangshi
author_facet Wu, Kang
Li, Yuanyuan
Zou, Yihuai
Ren, Yi
Wang, Yahui
Hu, Xiaojie
Wang, Yue
Chen, Chen
Lu, Mengxin
Xu, Lingling
Wu, Linlu
Li, Kuangshi
author_sort Wu, Kang
collection PubMed
description BACKGROUND: The latest guidance on chronic fatigue syndrome (CFS) recommends exercise therapy. Tai Chi, an exercise method in traditional Chinese medicine, is reportedly helpful for CFS. However, the mechanism remains unclear. The present longitudinal study aimed to detect the influence of Tai Chi on functional brain connectivity in CFS. METHODS: The study recruited 20 CFS patients and 20 healthy controls to receive eight sessions of Tai Chi exercise over a period of one month. Before the Tai Chi exercise, an abnormal functional brain connectivity for recognizing CFS was generated by a linear support vector model. The prediction ability of the structure was validated with a random forest classification under a permutation test. Then, the functional connections (FCs) of the structure were analyzed in the large-scale brain network after Tai Chi exercise while taking the changes in the Fatigue Scale-14, Pittsburgh Sleep Quality Index (PSQI), and the 36-item short-form health survey (SF-36) as clinical effectiveness evaluation. The registration number is ChiCTR2000032577 in the Chinese Clinical Trial Registry. RESULTS: 1) The score of the Fatigue Scale-14 decreased significantly in the CFS patients, and the scores of the PSQI and SF-36 changed significantly both in CFS patients and healthy controls. 2) Sixty FCs were considered significant to discriminate CFS (P = 0.000, best accuracy 90%), with 80.5% ± 9% average accuracy. 3) The FCs that were majorly related to the left frontoparietal network (FPN) and default mode network (DMN) significantly increased (P = 0.0032 and P = 0.001) in CFS patients after Tai Chi exercise. 4) The change of FCs in the left FPN and DMN were positively correlated (r = 0.40, P = 0.012). CONCLUSION: These results demonstrated that the 60 FCs we found using machine learning could be neural biomarkers to discriminate between CFS patients and healthy controls. Tai Chi exercise may improve CFS patients’ fatigue syndrome, sleep quality, and body health statement by strengthening the functional connectivity of the left FPN and DMN under these FCs. The findings promote our understanding of Tai Chi exercise’s value in treating CFS.
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spelling pubmed-97149252022-12-02 Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis Wu, Kang Li, Yuanyuan Zou, Yihuai Ren, Yi Wang, Yahui Hu, Xiaojie Wang, Yue Chen, Chen Lu, Mengxin Xu, Lingling Wu, Linlu Li, Kuangshi PLoS One Research Article BACKGROUND: The latest guidance on chronic fatigue syndrome (CFS) recommends exercise therapy. Tai Chi, an exercise method in traditional Chinese medicine, is reportedly helpful for CFS. However, the mechanism remains unclear. The present longitudinal study aimed to detect the influence of Tai Chi on functional brain connectivity in CFS. METHODS: The study recruited 20 CFS patients and 20 healthy controls to receive eight sessions of Tai Chi exercise over a period of one month. Before the Tai Chi exercise, an abnormal functional brain connectivity for recognizing CFS was generated by a linear support vector model. The prediction ability of the structure was validated with a random forest classification under a permutation test. Then, the functional connections (FCs) of the structure were analyzed in the large-scale brain network after Tai Chi exercise while taking the changes in the Fatigue Scale-14, Pittsburgh Sleep Quality Index (PSQI), and the 36-item short-form health survey (SF-36) as clinical effectiveness evaluation. The registration number is ChiCTR2000032577 in the Chinese Clinical Trial Registry. RESULTS: 1) The score of the Fatigue Scale-14 decreased significantly in the CFS patients, and the scores of the PSQI and SF-36 changed significantly both in CFS patients and healthy controls. 2) Sixty FCs were considered significant to discriminate CFS (P = 0.000, best accuracy 90%), with 80.5% ± 9% average accuracy. 3) The FCs that were majorly related to the left frontoparietal network (FPN) and default mode network (DMN) significantly increased (P = 0.0032 and P = 0.001) in CFS patients after Tai Chi exercise. 4) The change of FCs in the left FPN and DMN were positively correlated (r = 0.40, P = 0.012). CONCLUSION: These results demonstrated that the 60 FCs we found using machine learning could be neural biomarkers to discriminate between CFS patients and healthy controls. Tai Chi exercise may improve CFS patients’ fatigue syndrome, sleep quality, and body health statement by strengthening the functional connectivity of the left FPN and DMN under these FCs. The findings promote our understanding of Tai Chi exercise’s value in treating CFS. Public Library of Science 2022-12-01 /pmc/articles/PMC9714925/ /pubmed/36454926 http://dx.doi.org/10.1371/journal.pone.0278415 Text en © 2022 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Kang
Li, Yuanyuan
Zou, Yihuai
Ren, Yi
Wang, Yahui
Hu, Xiaojie
Wang, Yue
Chen, Chen
Lu, Mengxin
Xu, Lingling
Wu, Linlu
Li, Kuangshi
Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title_full Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title_fullStr Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title_full_unstemmed Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title_short Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis
title_sort tai chi increases functional connectivity and decreases chronic fatigue syndrome: a pilot intervention study with machine learning and fmri analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714925/
https://www.ncbi.nlm.nih.gov/pubmed/36454926
http://dx.doi.org/10.1371/journal.pone.0278415
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