...
首页> 外文期刊>Human brain mapping >Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy
【24h】

Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy

机译:Intertical Spikes EEG-MEG融合源分析的再现性:癫痫预设评价中的相关性

获取原文
获取原文并翻译 | 示例
           

摘要

Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG = 55%, MEG = 71%, fusion = 90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEMfusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.
机译:脑电图(EEG)和磁性脑图(MEG)数据使用最大熵(MEM-Fusion)的融合,利用EEG和MEG之间的互补性来提高本地化精度。仿真研究表明,诸如单秒尖源本地化(SSSL)之类的嘈杂条件下,仿真研究尤其是稳健的。我们的目标是评估SSSL在临床数据上使用MEM-FUSIC的可靠性。我们建议群集SSSL结果找到从重建源中获得最可靠和一致的源映射,所谓的共识映射。从26例患有明确定义的癫痫焦点的患者分析了34种类型的嵌入癫痫发票(IED)。 SSSL在EEG,MEG和Fusion数据上执行,使用分层聚类估计融合数据和共识映射。使用癫痫焦点作为临床参考,进行定性(Spike to-to-Spike再现性率,SSR)和定量(定位误差和空间分散体)评估。 Fusion SSSL提供的结果明显优于EEG或MEG。融合在所有情况下发现至少有一种群体协调。这一协调群集始终是涉及最多峰值的群集。融合产生的再现性最高(SSR EEG = 55%,MEG = 71%,融合= 90%)和最低定位误差。此外,仅使用少数型(21eeg + 272meg或54eeg + 25meg)的少量通道足以达到精确的融合。与共识地图方法的记忆提供了一种目标方法,可以找到最可靠和IED的最令人熟悉的发电机。因此,我们建议使用Mem-Fusion作为癫痫患者的有价值临床工具的SSSL的解决方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号