目的:探讨高频振荡(HFO)与癫痫类型之间的关联,并提高源定位的准确性。
方法:检测63例耐药癫痫患者的脑磁图(MEG)波纹。涟漪率,分布,空间复杂性,并将波纹通道的聚类系数用于颞叶外侧癫痫(LTLE)的初步分类,内侧颞叶癫痫(MTLE),和非颞叶癫痫(NTLE),主要是额叶癫痫(FLE)。此外,使用TuckerLCMV方法和来源水平介数中心性改善了癫痫发作部位识别。
结果:MTLE的纹波率明显高于LTLE和NTLE(p<0.05)。LTLE和MTLE主要分布在颞叶,接着是顶叶,枕叶,和额叶,而MTLE波纹主要分布在额叶,然后是顶叶和枕叶。然而,NTLE波纹主要在额叶,部分在枕叶(p<0.05)。同时,NTLE的空间复杂度明显高于LTLE和MTLE,MTLE最低(p<0.01)。然而,与空间复杂度相比,标准化聚类系数的趋势相反(p<0.01)。最后,当增加介数中心性时,tucker算法显示手术部位的波纹百分比更高(p<0.01)。
结论:这项研究表明,HFO率,分布,空间复杂性,三种癫痫类型之间的波纹通道聚类系数差异很大。此外,TuckerMEG估计与基于源级别功能连通性的纹波率相结合是一种有前途的术前癫痫评估方法。
To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.
Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality.
Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01).
This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.