北京生物医学工程

基于静息态脑电图的偏头痛患者脑网络变化

Brain network changes of patients with migraine based on resting state electroencephalogram

作者：周燕权利阮江海

单位：简阳市人民医院神经内科(四川简阳641400)； 西南医科大学附属医院神经内科(四川泸州 646000) 通信作者：阮江海0 E-mail ：jianghai.man@ swmu.edu.cn

关键词：偏头痛;脑网络;图论分析;默认模式网络;静息态;脑电图

分类号：R318. 04

出版年·卷·期（页码）：2022·41·1（17-23）

摘要：

目的对偏头痛患者静息态脑电进行图论及默认模式网络(default mode network,DMN)连接存在的可能改变进行分析。方法首先获取2016年1月至2019年6月在简阳市人民医院神经内科接受16导常规脑电图(electroencephalogram,EEG)检查已经临床诊断为偏头痛但脑电图结果未见异常的患者73例作为病例组，同时选取73例年龄、性别匹配的健康者作为对照组，截取两组受试者静息态 EEG数据进行分析，然后对数据进行预处理，预处理后通过相位同步分析方法计算锁相值(phase locking value,PLV)构建电极导联连接矩阵，并计算网络属性参数,再运用精确低分辨率电磁断层成像 (exact low resolution brain electromagnetic tomography, eLORETA)方法探索两组受试者 DMN 连接差异。结果与健康者相比，偏头痛组在全频段、delta频段及betal频段,左侧额区与右侧顶区存在大量连接增强的边;在theta频段，左侧额区与左侧顶枕区之间存在少量连接减弱的边。图论分析结果显示，病例组EEG的聚类系数、全局效率和局部效率高于健康组(P<0. 05)。两组受试者DMN网络连接的差异主要表现在alpha2频段:在该频段,偏头痛组患者双侧顶下小叶之间的连接显著增强(P<0. 05)。结论偏头痛伴随着脑网络连接的异常改变。通过对静息态EEG进行网络连接分析，可能识别临床脑电报告中不能发现的潜在异常，这可为今后偏头痛的临床诊治提供新的思路。

Objective To explore the changes of brain network and default mode network ( DMN) in patients with Migraine. Methods We reviewed the subjects who received EEG examination in the department of Neurology in JianYang People' s Hospital from January 2016 to June 2019. 73 patients were diagnosed with Migraine and had normal EEG results were included. 73 age-and sex-matched healthy physical examination controls were included as control group. The resting state EEG signals of the two groups of subjects were intercepted for analysis. Phase locking value ( PLV) was calculated by phase synchronization analysis method to construct electrode lead connection matrix, and calculate network attribute parameters ( clustering coefficient, path length, global efficiency, local efficiency) , and then exact low-resolution brain electromagnetic tomography ( Exact low resolution brain electromagnetic tomography, eLORETA) software was employed to analyzes the difference in DMN connection between the two groups of subjects. Results The results of graph theory analysis showed that the clustering coefficients, global and local efficiencies between the EEG leads of Migraine were higher than those of the healthy group (P<0.05). Compared with healthy patients, patients with Migraine in the full band, delta band and betal band, there are a large number of edges with enhanced connection on the left frontal region and the right parietal region. In theta band, there are a few weakened edges between the left frontal region and the left top pillow region. The eLORETA method was used to analyze the default mode network connection. The results showed that the difference in the DMN network connection between the two groups of subjects was mainly in the alpha2 band: the connection between the bilateral inferior parietal lobule of the patients Migraine was significantly enhanced ( P < 0. 05 ). Conclusions Migraine is accompanied by abnormal changes in brain network. By analyzing the resting state of the EEG through graph theory and functional connection analysis, it is possible to identify potential abnormalities that cannot be found in clinical EEG report, which provides new insight for diagnosis and treatment of Migraine in future.

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