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Papers of the Week

Papers: 29 Jun 2024 - 5 Jul 2024

2024 Jun 28

J Pain


Brain network dynamics in women with primary dysmenorrhea during the pain-free periovulation phase.


Su X, Li Y, Liu H, An S, Yao N, Li C, Shang M, Ma L, Yang J, Li J, Zhang M, Dun W, Huang Z


The 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 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 primary dysmenorrhea 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 primary dysmenorrhea and 57 healthy controls at different frequency bands. Compared to healthy controls, patients with primary dysmenorrhea exhibit aberrant brain dynamics, with more significant differences in the slow-4 frequency band. Patients with primary dysmenorrhea show more indirect neural transition times due to an unstable intermediate state, whereas neurotypical brain activity frequently transitions between two major states. This data-driven approach further revealed that the brains of individuals with primary dysmenorrhea have more abnormal brain dynamics than healthy controls. Our results suggested that unstable brain dynamics were associated with the strength of brain functional segregation and the Pain Catastrophizing Scale (PCS) 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 primary dysmenorrhea(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.