Authors :
Presenting Author: William Ojemann, BS – University of Pennsylvania
Carlos Aguila, BS – University of Pennsylvania; Lorenzo Caciagli, MD, PhD – University of Pennsylvania; Erin Conrad, MD – University of Pennsylvania; Joshua LaRocque, MD-PhD – University of Pennsylvania; Brian Litt, MD – University of Pennsylvania; Alfredo Lucas, BS – University of Pennsylvania; Sofia Mouchtaris, BS – University of Pennsylvania; Brittany Scheid, PhD – University of Pennsylvania
Rationale:
Longitudinal EEG recorded by implanted devices may improve how we understand and manage epilepsy. Recent research reports patient-specific multi-day cycles in device-detected events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. In this study, we demonstrate that these seizure-correlated cycles are present in background neural activity, exclusive of interictal epileptiform spikes, and that neurostimulation disrupts these cycles.
Methods:
We analyzed regularly scheduled data epochs from 23 patients implanted with the NeuroPace RNS device over periods of two to six years to explore the relationship between cycles in detected events (dIEA) (A), interictal spikes, background EEG features and neurostimulation. From interictal recordings with and without stimulation we calculated band-limited band power and connectivity in four different frequency bands: theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz). We then built multivariate support vector machine models to relate these features to dIEA cycle phase (B).Results:
Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with much higher effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61]) (F). After accounting for circadian variation and spike rate, we observed significant population trends in all band power and connectivity features at the cycle peaks (sign test, p < 0.05) (E). The specific EEG feature that best tracked dIEA cycle phase in each patient varied despite the population trends (C,D), suggesting a patient-specific effect. In the period directly after stimulation we observed a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-.64]) (F).