Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada.
Department of Electrical Engineering, École de Technologie Supérieure, Montreal, Quebec, Canada.
Epilepsia. 2024 Oct;65(10):3038-3051. doi: 10.1111/epi.18088. Epub 2024 Aug 24.
In addition to the oscillatory brain activity, the nonoscillatory (scale-free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale-free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug-resistant epilepsy.
To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open-access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome.
We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10-16 Hz band), (2) a higher rhythmic gamma power (30-80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients.
This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug-resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes.
除了振荡脑活动外，背景脑电图（EEG）的非振荡（无标度）成分可能提供有关潜在神经元网络复杂性的更多信息。由于癫痫被认为是一种网络疾病，因此这种无标度度量可能有助于描绘癫痫网络。在这里，我们使用多种发作间期特征对 38 名耐药性癫痫患者的睡眠振荡（纺锤波、慢波和节律光谱功率）和非振荡（H 指数）颅内 EEG 进行了分析，以评估它们是否以及如何偏离正常。
为了量化发作间期区域内和外的颅内 EEG 异常，根据来自开放获取的蒙特利尔神经学研究所开放 iEEG 图谱的规范图谱，对患者的值进行了调整。在 29 名接受切除术的患者亚组中，我们估计了这些特征的预测价值，以识别术后结局良好的患者的致痫区。
我们发现，不同的睡眠振荡和非振荡指标在癫痫网络中表现不同，最强的差异表现在（1）纺锤波活动减少（10-16 Hz 频段的纺锤波率和节律西格玛功率），（2）更高的节律伽马功率（30-80 Hz），和（3）更高的 H 指数（更陡峭的 1/f 斜率）。正如预期的那样，癫痫发作也在发作起始区最高。此外，在手术患者中，H 指数在分类术后和非术后通道方面表现出最高的性能（良好结局患者的平衡准确率为.76）。
这项工作表明，颅内 EEG 信号的非振荡成分可能是耐药性癫痫患者癫痫发作性的有前途的发作间期候选者。我们的发现进一步推进了对癫痫作为一种疾病的理解，即睡眠生理学的缺失或丧失可能提供与病理性癫痫过程互补的信息。
