von Ellenrieder Nicolás, Frauscher Birgit, Dubeau François, Gotman Jean
Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.
LEICI, CONICET-National University of La Plata, La Plata, Argentina.
Epilepsia. 2016 Jun;57(6):869-78. doi: 10.1111/epi.13380. Epub 2016 May 17.
To characterize the interaction between physiologic and pathologic high-frequency oscillations (HFOs) and slow waves during sleep, and to evaluate the practical significance of these interactions by automatically classifying channels as recording from normal or epileptic brain regions.
We automatically detected HFOs in intracerebral electroencephalography (EEG) recordings of 45 patients. We characterized the interaction between the HFOs and the amplitude and phase of automatically detected slow waves during sleep. We computed features associated with HFOs, and compared classic features such as rate, amplitude, duration, and frequency to novel features related to the interaction between HFOs and slow waves. To quantify the practical significance of the difference in these features we classified the channels as recording from normal/epileptic regions using logistic regression. We assessed the results in different brain regions to study differences in the HFO characteristics at the lobar level.
We found a clear difference in the coupling between the phase of slow waves during sleep and the occurrence of HFOs. In channels recording physiologic activity, the HFOs tend to occur after the peak of the deactivated state of the slow wave, and in channels with epileptic activity, the HFOs occur more often before this peak. This holds for HFOs in the ripple (80-250 Hz) and fast ripple (250-500 Hz) bands, and different regions of the brain. When using this interaction to automatically classify channels as recording from normal/epileptic brain regions, the performance is better than when using other HFO characteristics. We confirmed differences in the HFO characteristics in mesiotemporal structures and in the occipital lobe.
We found the association between slow waves and HFOs to be different in normal and epileptic brain regions, emphasizing their different origin. This is also of practical significance, since it improves the separation between channels recording from normal and epileptic brain regions.
描述睡眠期间生理和病理高频振荡(HFOs)与慢波之间的相互作用,并通过自动将通道分类为记录正常或癫痫脑区的数据来评估这些相互作用的实际意义。
我们自动检测了45例患者的脑内脑电图(EEG)记录中的HFOs。我们描述了睡眠期间HFOs与自动检测到的慢波的幅度和相位之间的相互作用。我们计算了与HFOs相关的特征,并将诸如速率、幅度、持续时间和频率等经典特征与与HFOs和慢波之间相互作用相关的新特征进行了比较。为了量化这些特征差异的实际意义,我们使用逻辑回归将通道分类为记录正常/癫痫区域的数据。我们在不同脑区评估结果,以研究叶水平上HFO特征的差异。
我们发现睡眠期间慢波相位与HFOs发生之间的耦合存在明显差异。在记录生理活动的通道中,HFOs倾向于在慢波失活状态的峰值之后出现,而在有癫痫活动的通道中,HFOs更常在该峰值之前出现。这适用于涟漪(80 - 250Hz)和快速涟漪(250 - 500Hz)频段以及大脑的不同区域中的HFOs。当使用这种相互作用自动将通道分类为记录正常/癫痫脑区的数据时,性能优于使用其他HFO特征时。我们证实了颞中结构和枕叶中HFO特征的差异。
我们发现正常和癫痫脑区中慢波与HFOs之间的关联不同,强调了它们不同的起源。这也具有实际意义,因为它改善了记录正常和癫痫脑区数据的通道之间的区分。