Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs—including 1 that was specific and 16 that were general—based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

中文翻译：

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偏头痛患者动态功能连接组模式的提取和分析：静息态功能磁共振成像研究

偏头痛因其复发及其引起的对环境的过敏而严重影响患者的身心健康。然而，偏头痛的发病机制和病理生理学尚不完全清楚。我们在本研究中解决了这个问题，使用具有两次聚类的自动功能连接组模型（A-DFCM）来比较来自偏头痛患者和正常对照受试者的静息态功能磁共振成像数据的动态功能连接组模式（DFCP）。我们使用分段点的自动定位来提高模型的效率，并分析组间差异和网络指标来识别偏头痛的神经机制。使用 A-DFCM 模型，我们根据组间差异确定了 17 个 DFCP，其中 1 个是特定的，16 个是一般的。特定的DFCP与偏头痛的神经元功能障碍密切相关，而一般的DFCP显示两组具有相似的功能拓扑以及大脑静息状态的差异。对网络指标的分析揭示了特定 DFCP 中的关键大脑区域；这些不仅分布在与疼痛相关的大脑区域，如布罗德曼区 1/2/3、基底神经节和丘脑，而且还分布在与偏头痛症状有关的区域，如枕叶。对两组之间一般 DFCP 差异的分析确定了属于所谓疼痛矩阵的 6 个大脑区域。我们的研究结果提供了对偏头痛神经机制的深入了解，同时还确定了有助于诊断或监测偏头痛患者的神经影像生物标志物。