Department of Biomedical Engineering, University of Houston, Houston, Texas, USA.
Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey.
Brain. 2018 Mar 1;141(3):713-730. doi: 10.1093/brain/awx374.
High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001.
颅内脑电图记录的局部场电位中的高频振荡被认为是癫痫大脑中癫痫发作起始区的生物标志物。然而,正常和非癫痫性脑结构也可以记录到局部 80-500Hz 振荡。当仅通过速率或频率定义时,生理高频振荡与病理高频振荡无法区分,这限制了它们在癫痫术前规划中的应用。我们假设病理高频振荡以相似的波形形态重复出现,这特别表明了癫痫发作起始区。我们研究了 13 名癫痫患者和 5 名对照者自动检测到的高频振荡的波形模式,每个患者平均记录了 73 个硬膜下和脑内电极。使用无监督机器学习技术的流水线识别重复的振荡波形,然后将其与独立的临床医生定义的癫痫发作起始区相关联。在所有患者中,具有最高波形相似度的典型高频振荡仅局限于癫痫发作起始区,而嵌入随机波形中的产生高频振荡的通道则位于与致痫部位无关的功能区。与尖波相比,快尖波中的重复波形模式更为明显,这表明波形重复与潜在的病理网络之间存在潜在关联。我们的发现为解释病理高频振荡提供了一种新工具,可以有效地用于区分癫痫发作起始区和功能重要区,这是将这些特征事件转化为有效临床生物标志物的关键步骤。