Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
Brain. 2022 Nov 21;145(11):3886-3900. doi: 10.1093/brain/awac214.
Successful outcomes in epilepsy surgery rely on the accurate localization of the seizure onset zone. Localizing the seizure onset zone is often a costly and time-consuming process wherein a patient undergoes intracranial EEG monitoring, and a team of clinicians wait for seizures to occur. Clinicians then analyse the intracranial EEG before each seizure onset to identify the seizure onset zone and localization accuracy increases when more seizures are captured. In this study, we develop a new approach to guide clinicians to actively elicit seizures with electrical stimulation. We propose that a brain region belongs to the seizure onset zone if a periodic stimulation at a particular frequency produces large amplitude oscillations in the intracranial EEG network that propagate seizure activity. Such responses occur when there is 'resonance' in the intracranial EEG network, and the resonant frequency can be detected by observing a sharp peak in the magnitude versus frequency response curve, called a Bode plot. To test our hypothesis, we analysed single-pulse electrical stimulation response data in 32 epilepsy patients undergoing intracranial EEG monitoring. For each patient and each stimulated brain region, we constructed a Bode plot by estimating a transfer function model from the intracranial EEG 'impulse' or single-pulse electrical stimulation response. The Bode plots were then analysed for evidence of resonance. First, we showed that when Bode plot features were used as a marker of the seizure onset zone, it distinguished successful from failed surgical outcomes with an area under the curve of 0.83, an accuracy that surpassed current methods of analysis with cortico-cortical evoked potential amplitude and cortico-cortical spectral responses. Then, we retrospectively showed that three out of five native seizures accidentally triggered in four patients during routine periodic stimulation at a given frequency corresponded to a resonant peak in the Bode plot. Last, we prospectively stimulated peak resonant frequencies gleaned from the Bode plots to elicit seizures in six patients, and this resulted in an induction of three seizures and three auras in these patients. These findings suggest neural resonance as a new biomarker of the seizure onset zone that can guide clinicians in eliciting native seizures to more quickly and accurately localize the seizure onset zone.
癫痫手术的成功结果依赖于对癫痫起始区的准确定位。定位癫痫起始区通常是一个代价高昂且耗时的过程，在此过程中，患者需要接受颅内脑电图监测，并且一个临床医生团队等待癫痫发作。然后，临床医生在每次癫痫发作前分析颅内脑电图，以确定癫痫起始区，并且捕获的癫痫发作越多，定位准确性就越高。在这项研究中，我们开发了一种新方法来指导临床医生主动用电刺激引发癫痫。我们提出，如果特定频率的周期性刺激在颅内 EEG 网络中产生大振幅振荡，从而传播癫痫活动，则该脑区属于癫痫起始区。这种反应发生在颅内 EEG 网络中存在“共振”时，并且可以通过观察幅度与频率响应曲线中的尖锐峰值（称为波特图）来检测共振频率。为了验证我们的假设，我们分析了 32 名接受颅内 EEG 监测的癫痫患者的单脉冲电刺激反应数据。对于每个患者和每个刺激的脑区，我们通过从颅内 EEG“脉冲”或单脉冲电刺激响应中估计传递函数模型来构建波特图。然后分析波特图是否存在共振的证据。首先，我们表明，当使用波特图特征作为癫痫起始区的标志物时，它能够以 0.83 的曲线下面积区分手术成功与失败，其准确性超过了皮质-皮质诱发电位幅度和皮质-皮质频谱响应的当前分析方法。然后，我们回顾性地表明，在四名患者中，在给定频率的常规周期性刺激期间意外触发的五例原发性癫痫中的三例对应于波特图中的共振峰。最后，我们前瞻性地刺激从波特图中得出的峰值共振频率，以诱发六名患者的癫痫发作，这导致这六名患者中的三名癫痫发作和三名癫痫先兆。这些发现表明神经共振作为癫痫起始区的新生物标志物，可以指导临床医生引发原发性癫痫，从而更快、更准确地定位癫痫起始区。
