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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Autonomic Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1535088
This article is part of the Research Topic Exploring Chronic Fatigue: Neural Correlates, Mechanisms, and Therapeutic Strategies View all 7 articles
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Chronic Fatigue Syndrome (CFS) is a disease characterized by unexplained fatigue and impaired cognition for more than six months. Recent studies have reported declines in large-scale brain networks' functional connections among patients with CFS, and these declines correlated with the patients' symptom severity. However, these reported networks are inconsistent. Brain structure serves as the essential architecture supporting brain functional fluctuations. Investigating structural alterations could provide insights into functional changes in different brain areas and facilitate the clinical diagnosis of CFS. In this study, we recruited 37 patients with CFS and 34 healthy controls to collect their clinical assessments and structural magnetic resonance imaging data. Multiple Voxel Pattern Analysis (MVPA) was employed to recognize chronic fatigue-related brain areas, and cortical thickness was compared between the two groups. By constructing a predictive MVPA classifier with 70% balanced accuracy, we identified five relevant brain areas, including the paracentral cortex, precentral cortex, central cortex, intraparietal cortex, and superior temporal cortex. Subsequently, the results showed that the thickness of these areas had associations with fatigue severity, healthy life status, and pain levels among our subjects. Furthermore, compared to healthy controls, the thickness reduction was observed in patients with CFS. In summary, our study revealed a pathological chronic fatigue pattern for understanding CFS and suggested associations between cortical atrophy and CFS, with the aim of highlighting potential impacts of chronic fatigue. The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2000032577).
Keywords: chronic fatigue syndrome, cortical atrophy, cortical thickness, MRI, machine learning
Received: 26 Nov 2024; Accepted: 25 Feb 2025.
Copyright: © 2025 Wu, Zou, Li, Hu, Wang, CHEN, Chen and Li. 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) or licensor 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.
* Correspondence:
Kuangshi Li, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100001, Beijing Municipality, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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