Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland.
Haskins Laboratories, New Haven, Connecticut, USA.
J Headache Pain. 2021 Mar 3;22(1):8. doi: 10.1186/s10194-021-01216-8.
Migraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network.
19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters.
Network based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients' group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients' group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs.
We found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain.
偏头痛是一种原发性头痛障碍，可分为发作性（EM）和慢性形式（CM）。基于连通模式的图论框架内的网络分析提供了一种观察大规模结构完整性的方法。我们检验了偏头痛患者具有隔离网络的假设。
纳入 19 名健康对照者（HC）、17 名 EM 患者和 12 名 CM 患者。计算皮质厚度和皮质下体积，并使用图论分析框架和基于网络的统计学方法分析拓扑结构。我们进一步使用支持向量机回归（SVR）来确定这些网络测量是否能够预测临床参数。
基于网络的统计学分析显示，与 HC 相比，EM 和 CM 患者的额颞、顶叶和视觉等解剖区域之间的区域间连通性强度明显降低。两个患者组的聚类系数均较高，CM 患者的模块性较高，EM 患者的传递性较高。对于皮质下网络，两个患者组的聚类系数和传递性均较高，CM 患者的模块性较高。SVR 表明，网络测量可以可靠地预测偏头痛患者的临床参数。
我们发现 EM 和 CM 患者的全局网络中断，与 HC 相比，偏头痛患者的网络呈现高度隔离状态。CM 中较高的模块性但较低的聚类系数表明，与 EM 相比，该组的隔离程度更高。隔离网络的存在可能是与头痛相关的大脑回路适应性重组的标志，导致偏头痛发作或疼痛的继发性改变。
