Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
School of Psychology, South China Normal University, Guangzhou, China.
Eur J Pain. 2021 Oct;25(9):1912-1924. doi: 10.1002/ejp.1808. Epub 2021 Jun 6.
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.
Thirty-five women with PDM and 38 healthy women without PDM were included. A dynamic brain functional network was constructed using the slide-window 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.
Compared with healthy controls, women with PDM had not only lower TP-Stab in global DTP, which included cluster clustering coefficient (C ), characteristic path length (L ), global efficiency (E ) and local efficiency (E ), but also lower TP-Stab and higher TP-Var in nodal DTP (nodal efficiency, E ), 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%.
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.
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.
原发性痛经（PDM）已知会改变大脑的静息功能活动。本研究旨在探讨无痛期 PD 女性的动态脑功能网络的动态拓扑特性（DTP），并评估其在区分无痛期 PD 与健康对照组方面的性能。
纳入 35 名 PD 女性和 38 名无 PD 的健康女性。使用滑动窗口方法构建动态脑功能网络。使用图论方法计算动态功能网络的 DTP 的稳定性（TP-Stab）和可变性（TP-Var）。使用支持向量机（SVM）评估 DTP 在识别无痛期 PD 中的性能。
与健康对照组相比，PD 女性不仅在全局 DTP 中的聚类系数（C）、特征路径长度（L）、全局效率（E）和局部效率（E）等方面的 TP-Stab 较低，而且在节点 DTP（节点效率，E）中的 TP-Stab 较低，TP-Var 较高，主要涉及前额叶皮质、前扣带皮质、海马旁回和岛叶。TP-Stab 和 TP-Var 与心理变量，即积极情绪、控制感和有意义的存在显著相关。SVM 分析表明，DTP 可以以 79.31%的准确率、82.61%的灵敏度和 76%的特异性从健康对照组中识别无痛期 PD。
无痛期 PD 女性的全局 DTP 和节点 DTP 发生改变，主要涉及与疼痛相关的神经回路。高变异性的脑网络有助于识别无痛期 PD。
本研究表明，无痛期原发性痛经（PDM）女性的动态网络拓扑特性（DTP）稳定性降低，DTP 可变性增加。改变的 DTP 可用于识别无痛期 PDM。这些发现表明，整个网络存在不稳定特征，疼痛相关神经回路中断，这可能作为无痛期 PDM 的潜在分类器。本研究提高了我们对 PDM 脑机制的认识。
