Resting-state background features demonstrate multidien cycles in long-term EEG device recordings

Area of Science:

• Neuroscience
• Epileptology
• Biomedical Engineering

Background:

• Longitudinal electroencephalography (EEG) from implanted devices is crucial for epilepsy understanding and management.
• Patient-specific, multi-day cycles in device-detected epileptiform events correlate with increased seizure likelihood.
• Investigating these cycles may reveal seizure generation mechanisms and improve therapies.

Approach:

• Analyzed seizure-free EEG data from 20 patients with responsive neurostimulation (RNS) devices (≥1.5 years).
• Explored relationships between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated spikes, background EEG features, and neurostimulation.
• Assessed background EEG features' ability to track dIEA cycle phase.

Key Points:

• Background EEG features tracked dIEA cycle phase more effectively than spike rate alone (AUC 0.63 vs. 0.55).
• Elevated theta and beta band power, and theta/alpha connectivity occurred at cycle peaks.
• Post-stimulation, the association between cycle phase and EEG features decreased.

Conclusions:

• Seizure-correlated cycles involve background brain states, not just epileptiform discharges.
• Responsive neurostimulation may disrupt these underlying neural cycles.
• Findings may yield new biomarkers for seizure risk and improve epilepsy monitoring and treatment.