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

Kang Wu, Yuanyuan Li, Yihuai Zou, Yi Ren, Yahui Wang, Xiaojie Hu, Yue Wang, Chen Chen, Mengxin Lu, Lingling Xu, Linlu Wu, Kuangshi Li
Author Information
  1. Kang Wu: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China. ORCID
  2. Yuanyuan Li: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  3. Yihuai Zou: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  4. Yi Ren: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  5. Yahui Wang: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  6. Xiaojie Hu: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  7. Yue Wang: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  8. Chen Chen: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  9. Mengxin Lu: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  10. Lingling Xu: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  11. Linlu Wu: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  12. Kuangshi Li: Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China. ORCID

Abstract

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.

Associated Data

ChiCTR | ChiCTR2000032577

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MeSH Term

Humans
Fatigue Syndrome, Chronic
Tai Ji
Magnetic Resonance Imaging
Pilot Projects
Longitudinal Studies
Machine Learning

Word Cloud

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