Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra‐hemispheric networks of healthy controls (HCs) and patients with left‐ or right‐hemispheric temporal lobe or temporal plus extra‐temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra‐hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve‐Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network‐based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra‐hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra‐hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.

中文翻译：

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使用基于静息状态 MEG 数据的半球内脑网络对癫痫进行侧向化。

局灶性癫痫起源于一个半球的网络。然而，之前的研究已经调查了整个大脑的网络拓扑。在这项研究中，脑磁图 (MEG) 用于研究健康对照 (HC) 和左侧或右侧半球颞叶或颞叶加颞叶外癫痫患者的功能性半球内网络。招募了 22 名 HC、25 名左侧患者 (LP) 和 16 名右侧患者 (RP)。去偏置加权相位滞后指数用于计算六个频段 246 个大脑区域之间的功能连接。计算左右半球内网络的全局效率、特征路径长度和传递性。θ 波段中的正确全局图测量 (GGM) 显着不同 ( p< .005) 在 RP 与 LP 和 HC 之间。HCs和患者之间较高频段的右侧和左侧 GGM 显着不同 ( p < .05)。右 GGM 被用作朴素贝叶斯分类器的输入特征，以对 LP 和 RP（准确率 78.0%）以及所有三组（准确率 75.5%）进行分类。使用基于网络的统计 (NBS) 和聚类系数 (CC)、节点效率 (NE)、介数中心性 (BC) 和特征向量中心性 (EVC) 比较 LP 和 RP 之间的完整 theta 波段大脑网络。NBS 确定了一个主要由右侧半球内连接组成的子网。显着不同（p< .05) 节点主要位于 CC 和 NE 的右半球，主要位于 BC 和 EVC 的左半球。这些结果表明，半球内 MEG 网络可用于局灶性癫痫的诊断和偏侧化。