Abstract
High frequency oscillations have been extensively investigated as interictal biomarkers of epilepsy. Yet, their value is largely debated due to the presence of physiological oscillations, which complicate distinguishing between normal versus abnormal events. So far, this debate has been addressed using intracranial EEG data from patients with drug-resistant epilepsy. Yet, this approach suffers from inability to record control data from healthy subjects and lack of whole brain coverage. Here, we aim to differentiate physiological from pathological high frequency oscillations using non-invasive whole brain electrophysiological recordings from children with drug-resistant epilepsy and typically developing controls. We recorded high-density EEG and magnetoencephalography data from 47 controls (median age: 11 years; 25 females) and 54 children with drug-resistant epilepsy (median age: 14 years, 33 females). We detected high frequency oscillations (in ripple frequency band) semi-automatically and localized their cortical generators through electric or magnetic source imaging. From each ripple, we extracted a set of temporal, morphological, spectral and spatial features. We then compared the features between ripples recorded from the epileptic brain (further distinguished into those from epileptogenic and non-epileptogenic regions) and those recorded from the control group (normal brain). We used these features to cross-validate a Naïve-Bayes algorithm for classifying each ripple recorded from children with epilepsy as coming from an epileptogenic region or not. We observed more high frequency oscillations on EEG than magnetoencephalography recordings (P < 0.001) both in the epilepsy and control groups. Physiological high frequency oscillations (recorded from controls) showed lower power, shorter duration and less variability (in both amplitude and duration) than those recorded from the epilepsy group (P < 0.001). Inter-channel latency of physiological ripples was longer compared to ripples from the epileptogenic regions (P < 0.01), while it was similar to the ripples from non-epileptogenic regions (P > 0.05). Ripples from epileptogenic regions showed larger extent than those from non-epileptogenic regions or from the control group (P < 0.001). The classification model showed an accuracy of 73%, with negative and positive predictive values of 73% and 70% (P < 0.0001), respectively, in classifying high frequency oscillations from the drug-resistant epilepsy group (as either epileptogenic or not). Our study indicates that physiological high frequency oscillations, recorded from the healthy brain, have distinct temporal, morphological, spectral and spatial features compared to those generated by the epileptic brain. The differentiation of pathological from physiological high frequency oscillations through non-invasive full-head techniques may augment the presurgical evaluation process of children with drug-resistant epilepsy and lead to better postsurgical seizure outcomes.