• Xing Su, Youjun Li, Huiping Liu, Simeng An, Nan Yao, Chenxi Li, Meiling Shang, Ling Ma, Jing Yang, Jianlong Li, Ming Zhang, Wanghuan Dun, and Zi-Gang Huang.
• The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China; Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
• J Pain. 2024 Oct 1; 25 (10): 104618104618.

AbstractThe human brain is a dynamic system that shows frequency-specific features. Neuroimaging studies have shown that both healthy individuals and those with chronic pain disorders experience pain influenced by various processes that fluctuate over time. Primary dysmenorrhea (PDM) is a chronic visceral pain that disrupts the coordinated activity of brain's functional network. However, it remains unclear whether the dynamic interactions across the whole-brain network over time and their associations with neurobehavioral symptoms are dependent on the frequency bands in patients with PDM during the pain-free periovulation phase. In this study, we used an energy landscape analysis to examine the interactions over time across the large-scale network in a sample of 59 patients with PDM and 57 healthy controls (HCs) at different frequency bands. Compared with HCs, patients with PDM exhibit aberrant brain dynamics, with more significant differences in the slow-4 frequency band. Patients with PDM show more indirect neural transition counts due to an unstable intermediate state, whereas neurotypical brain activity frequently transitions between 2 major states. This data-driven approach further revealed that the brains of individuals with PDM have more abnormal brain dynamics than HCs. Our results suggested that unstable brain dynamics were associated with the strength of brain functional segregation and the Pain Catastrophizing Scale score. Our findings provide preliminary evidence that atypical dynamics in the functional network may serve as a potential key feature and biological marker of patients with PDM during the pain-free phase. PERSPECTIVE: We applied energy landscape analysis on brain-imaging data to identify relatively stable and dominant brain activity patterns for patients with PDM. More atypical brain dynamics were found in the slow-4 band and were related to the strength of functional segregation, providing new insights into the dysfunction brain dynamics.Copyright © 2024 United States Association for the Study of Pain, Inc. Published by Elsevier Inc. All rights reserved.

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