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癫痫术前评估中棘波的 EEG-MEG 融合源分析可重复性:相关性。

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

机构信息

Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.

San Camillo Hospital IRCCS, 80 Via Alberoni, Venice, 30126, Italy.

出版信息

Hum Brain Mapp. 2018 Feb;39(2):880-901. doi: 10.1002/hbm.23889. Epub 2017 Nov 21.

Abstract

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. MEM-fusion 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.

摘要

脑电图 (EEG) 和脑磁图 (MEG) 数据的融合采用均值最大熵方法 (MEM-fusion),利用 EEG 和 MEG 之间的互补性来提高定位准确性。模拟研究表明,MEM-fusion 在噪声条件下(如单峰源定位 (SSSL))非常稳健。我们的目的是使用 MEM-fusion 在临床数据上评估 SSSL 的可靠性。我们提出对 SSSL 结果进行聚类,以从重建源中找到最可靠和一致的源图,即所谓的共识图。从 26 名具有明确致痫灶的患者中分析了 34 种癫痫发作间期放电 (IED)。对 EEG、MEG 和融合数据进行 SSSL,并使用层次聚类估计共识图。使用致痫灶作为临床参考,进行定性(峰峰可重复性率,SSR)和定量(定位误差和空间分散)评估。融合 SSSL 提供的结果明显优于 EEG 或 MEG 单独使用。在所有情况下,融合都找到了至少一个与临床参考一致的簇。这个一致的簇总是包含最多的棘波。融合产生了最高的可重复性(SSR EEG = 55%,MEG = 71%,融合 = 90%)和最低的定位误差。此外,只使用两种模态中的少数通道(21EEG + 272MEG 或 54EEG + 25MEG)就足以实现准确的融合。使用共识图的 MEM-fusion 方法为寻找 IED 最可靠和一致的发生器提供了一种客观的方法。因此,我们建议将 MEM-fusion 用于 SSSL 作为癫痫术前评估的有价值的临床工具。

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