Beyond rates: Time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Abstract

High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection (Frauscher et al., 2017). However, previous research has shown that the number of HFOs per minute (i.e. the HFO "rate") is not stable over the duration of intracranial recordings. The rate of HFOs increases during periods of slow-wave sleep (von Ellenrieder et al., 2017), and HFOs that are predictive of epileptic tissue may occur in oscillatory patterns (Motoi et al., 2018). We sought to further understand how between-seizure (i.e. "interictal") HFO dynamics predict the seizure onset zone (SOZ). Using long-term intracranial EEG from 16 subjects, we fit Poisson and Negative Binomial mixture models that describe HFO dynamics and include the ability to switch between two discrete brain states. Oscillatory dynamics of HFO occurrences were found to be predictive of SOZ and were more consistently predictive than HFO rate. Using concurrent scalp-EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM (NREM) sleep and (2) awake and rapid eye movement (REM) sleep. This work suggests that unsupervised approaches for classification of epileptic tissue without sleep-staging can be developed using mixture modeling of HFO dynamics.