Kuroda Naoto, Sonoda Masaki, Miyakoshi Makoto, Nariai Hiroki, Jeong Jeong-Won, Motoi Hirotaka, Luat Aimee F, Sood Sandeep, Asano Eishi
Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.
Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan.
Brain Commun. 2021 Mar 14;3(2):fcab042. doi: 10.1093/braincomms/fcab042. eCollection 2021.
Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving post-operative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3-4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across non-epileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the non-epileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of post-operative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.
研究人员一直在寻找能够快速且客观测量的电生理生物标志物,以准确地定位致痫区。有前景的候选标志物包括发作间期高频振荡和相位-振幅耦合。研究人员已独立创建了用于计算高频振荡率和相位-振幅耦合严重程度的工具箱。这项针对135名患者的研究确定了哪些工具箱和分析方法能最佳地对术后实现癫痫发作控制的患者进行分类。四个不同的检测工具箱计算了颅内脑电图通道处≥80Hz的高频振荡率。另一个工具箱计算了反映3-4Hz高频振荡与慢波之间相位-振幅耦合强度的调制指数。我们将发作间期异常区域的切除完整性定义为所有保留部位的高频振荡率(或调制指数)平均值与所有切除部位的平均值之差。我们仅考虑临床、发作期颅内脑电图和神经影像学变量,计算了基于逻辑回归的标准模型的结果分类准确性。然后我们确定纳入高频振荡/调制指数能在多大程度上改善上述标准模型。为了评估非癫痫部位的解剖变异性,我们生成了特定检测方法的高频振荡和调制指数的标准图谱。每个图谱使我们能够计算高频振荡/调制指数与非癫痫平均值的统计偏差。我们确定纳入绝对或标准化的高频振荡/调制指数作为评估发作间期异常区域的生物标志物是否会提高模型准确性。我们最终确定通过选择性纳入经证实具有不可归因于高通滤波效应的高频振荡成分的高频振荡是否会提高模型准确性。95名患者实现了成功的癫痫发作控制,定义为国际抗癫痫联盟1级结果。多因素逻辑回归分析表明,发作间期异常区域的完全切除会增加成功的几率。通过纳入z分数标准化的高频振荡/调制指数或选择性纳入经证实的高频振荡,模型准确性进一步提高。标准模型的分类准确性为0.75。纳入标准化的高频振荡/调制指数或经证实的高频振荡将分类准确性提高到了0.82。这些结果预测模型在交叉验证过程中表现良好,并在个体层面上证明了基于模型的成功可能性与观察到的成功之间的一致性。发作间期高频振荡和调制指数在癫痫术前评估中具有相当的附加效用。我们的经验数据支持这样一种理论观点,即通过同时考虑发作间期和发作期异常情况,可以优化术后癫痫发作结果的预测。