College of Medicine, Inha University, Incheon, Republic of Korea.
Department of Data Science, Inha University, Incheon, Republic of Korea.
J Headache Pain. 2024 Jun 11;25(1):99. doi: 10.1186/s10194-024-01806-2.
Migraine is a complex neurological condition characterized by recurrent headaches, which is often accompanied by various neurological symptoms. Magnetic resonance imaging (MRI) is a powerful tool for investigating whole-brain connectivity patterns; however, systematic assessment of structural connectome organization has rarely been performed. In the present study, we aimed to examine the changes in structural connectivity in patients with episodic migraines using diffusion MRI. First, we computed structural connectivity using diffusion MRI tractography, after which we applied dimensionality reduction techniques to the structural connectivity and generated three low-dimensional eigenvectors. We subsequently calculated the manifold eccentricity, defined as the Euclidean distance between each data point and the center of the data in the manifold space. We then compared the manifold eccentricity between patients with migraines and healthy controls, revealing significant between-group differences in the orbitofrontal cortex, temporal pole, and sensory/motor regions. Between-group differences in subcortico-cortical connectivity further revealed significant changes in the amygdala, accumbens, and caudate nuclei. Finally, supervised machine learning effectively classified patients with migraines and healthy controls using cortical and subcortical structural connectivity features, highlighting the importance of the orbitofrontal and sensory cortices, in addition to the caudate, in distinguishing between the groups. Our findings confirmed that episodic migraine is related to the structural connectome changes in the limbic and sensory systems, suggesting its potential utility as a diagnostic marker for migraine.
偏头痛是一种复杂的神经系统疾病,其特征是反复发作的头痛,常伴有各种神经系统症状。磁共振成像(MRI)是研究全脑连接模式的有力工具;然而,对结构连接组组织的系统评估很少进行。在本研究中,我们旨在使用扩散 MRI 研究发作性偏头痛患者的结构连接变化。首先,我们使用扩散 MRI 轨迹计算结构连接,然后将降维技术应用于结构连接,并生成三个低维特征向量。我们随后计算了流形的偏心度,定义为每个数据点与流形空间中数据中心之间的欧几里得距离。然后,我们比较了偏头痛患者和健康对照组之间的流形偏心度,发现在眶额皮质、颞极和感觉/运动区域存在显著的组间差异。皮质下-皮质连接的组间差异进一步揭示了杏仁核、伏隔核和尾状核的显著变化。最后,监督机器学习有效地使用皮质和皮质下结构连接特征对偏头痛患者和健康对照组进行分类,突出了额叶和感觉皮质以及尾状核在区分两组中的重要性。我们的发现证实了发作性偏头痛与边缘和感觉系统的结构连接组变化有关,表明其作为偏头痛诊断标志物的潜在用途。