Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, Canada.
Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
Pain. 2021 Apr 1;162(4):1188-1200. doi: 10.1097/j.pain.0000000000002104.
Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.
男性和女性可能表现出不同的疼痛敏感性，许多慢性疼痛病症在某个性别中更为普遍。尽管有证据表明大脑存在性别差异，但尚不清楚慢性疼痛患者的大脑大尺度功能网络组织是否存在性别差异。在这里，我们使用了基于静息态功能磁共振成像数据的图论分析和模块分析以及机器学习方法，研究了 220 名参与者：155 名健康对照者和 65 名因强直性脊柱炎导致慢性腰痛的患者。我们发现，主要的大脑内在系统（即默认模式、中央、视觉和感觉运动模块）的图划分有广泛的重叠，但健康人和慢性疼痛患者的网络拓扑特征也存在性别特异性。慢性疼痛患者表现出更高的跨网络连接性，以及性别特异性的节点图属性变化（即枢纽破坏），其中一些与慢性疼痛状况的严重程度有关。女性在中扣带回皮质和前扣带回皮质的中部表现出异常高的功能分离，而与默认模式和额顶叶模块的网络连接性较低，而男性与感觉运动模块的连接性较强。基于节点图度量的分类模型可以以较高的准确率（77%-92%）对个体的性别和是否患有慢性疼痛进行分类。这些发现突出了慢性疼痛患者静息态大脑网络的组织异常，并为考虑性别特异性疼痛治疗提供了一个框架。
