Multiple Voxel Pattern Analysis Shows Associations Between Chronic Fatigue Syndrome and Cortical Atrophy, 2025, Wu et al

Kang Wu, Yihuai Zou, Yuanyuan Li, Xiaojie Hu, Yahui Wang, TZ CHEN, Yuhang Chen, Kuangshi Li

Abstract

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).

Only abstract available: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1535088/abstract

 

I'm not sure if this is true ME/CFS research or not. May be just chronic fatigue.

 

I googled the registration number, and it’s identical to this study:
https://www.s4me.info/threads/tai-c...ith-machine-learning-and-fmri-analysis.30717/

Might just be a mistake. But if it’s a part of the same protocol, they used Fukuda in the 2022 study.