Dynamic network topological properties for classifying primary dysmenorrhoea in the pain‐free phase

Abstract

Background: Primary dysmenorrhoea (PDM) is known to alter brain static functional activity. This study aimed to explore the dynamic topological properties (DTP) of dynamic brain functional network in women with PDM in the pain-free phase and their performance in distinguishing PDM in the pain-free phase from healthy controls. Methods: Thirty-five women with PDM and 38 healthy women without PDM were included. A dynamic brain functional network was constructed using the slidewindow approach. The stability (TP-Stab) and variability (TP-Var) of the DTP of the dynamic functional network were computed using the graph-theory method. A support vector machine (SVM) was used to evaluate the performance of DTP in identifying PDM in the pain-free phase. Results: Compared with healthy controls, women with PDM had not only lower TP-Stab in global DTP, which included cluster clustering coefficient (C p ), characteristic path length (L p ), global efficiency (E g ) and local efficiency (E loc ), but also lower TP-Stab and higher TP-Var in nodal DTP (nodal efficiency, E nod ), mainly in the prefrontal cortex, anterior cingulate cortex, parahippocampal regions and insula. The TP-Stab and TP-Var were significantly correlated with psychological variables, that is positive emotions, sense of control and meaningful existence. SVM analysis showed that the DTP could identify PDM in the pain-free phase from healthy controls with an accuracy of 79.31%, sensitivity of 82.61% and specificity of 76%. Conclusions: Women with PDM in the pain-free phase have altered global DTP and nodal DTP, mainly involving pain-related neurocircuits. The highly variable brain network is helpful for identifying PDM in the pain-free phase. Significance: This study shows that women with primary dysmenorrhoea (PDM) have decreased stability of dynamic network topological properties (DTP) and increased DTP variability in the pain-free phase. The altered DTP can be used to identify PDM in the pain-free phase. These findings demonstrate the presence of unstable characteristics in the whole network and disrupted pain-related neurocircuits, which might be used as potential classifiers for PDM in the pain-free phase. This study improves our knowledge of the brain mechanisms underlying PDM.

Altered brain activities in mesocorticolimbic pathway in primary dysmenorrhea patients of long-term menstrual pain

“…Previous studies on PDM have also reported activity and structural changes in the mesocorticolimbic pathway. For instance, changes in network stability and variability of PFC, ACC, and Hip were found during pain-free periods, and these traits are associated with positive emotions and a sense of control (Wu et al, 2021a ). In the pain-free stage, the ALFF of PFC and ACC increased in patients with PDM, and the ALFF of PFC was correlated with the course of the disease.…”

Altered Brain Functional Network Dynamics in Classic Trigeminal Neuralgia: A Resting-state Functional Magnetic Resonance Imaging Study

“…The present results showed lower dynamics of E g , E loc , σ, γ and L in CTN patients, which suggested a broken temporal variability of the overall functional brain networks. Similarly, Wu et al explored the dynamic topological properties about primary dysmenorrhea and found altered temporal stability of global parameters [25], which suggested brain network reorganization. The ndings in our study implied a less e cient information transfer throughout the whole brain and may con rm the vulnerability of the resting state network in CTN.…”

“…Whereas dynamic functional network connectivity (dFNC) analysis can not only provide time-varying information of FC between resting-state connectivity networks (RSNs) [13], but also capture reproducible connectivity states and calculate temporal properties. By applied in chronic pain studies, such as migraine [22,23], low back pain [24] and primary dysmenorrhea [25], dFNC has been indicated useful as potential biomarkers of pain and effective to provide insightful viewpoints of pathogenesis [26]. However, the exploration of changes of whole-brain dFNC pattern in CTN is limited.…”