Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 201306, China.
Comput Math Methods Med. 2021 Apr 17;2021:6614520. doi: 10.1155/2021/6614520. eCollection 2021.
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.
偏头痛由于其反复发作和对环境的高度敏感，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未完全阐明。本研究采用具有两次聚类的自动力功能连接组模型（A-DFCM）来解决这一问题，以比较偏头痛患者和正常对照者静息态功能磁共振成像数据的动态功能连接组模式（DFCP）。我们使用自动定位段点来提高模型的效率，并分析组间差异和网络指标，以确定偏头痛的神经机制。使用 A-DFCM 模型，我们根据组间差异确定了 17 种 DFCP，包括 1 种特异性和 16 种普遍性。特异性 DFCP 与偏头痛中的神经元功能障碍密切相关，而普遍性 DFCP 则表明两组具有相似的功能拓扑结构以及大脑静息状态的差异。网络指标分析揭示了特异性 DFCP 中的关键脑区；这些脑区不仅分布在与疼痛相关的脑区，如布罗德曼区 1/2/3、基底神经节和丘脑，还位于与偏头痛症状相关的脑区，如枕叶。两组间一般 DFCP 的差异分析确定了 6 个属于所谓的疼痛矩阵的脑区。我们的研究结果为偏头痛的神经机制提供了深入的了解，同时也确定了有助于偏头痛患者诊断或监测的神经影像学生物标志物。
