BACKGROUND: The clinical effects of deep brain stimulation for neurological conditions manifest across multiple timescales, spanning seconds to months, and involve direct electrical modulation, neuroplasticity, and network reorganization. In epilepsy, the delayed effects of deep brain stimulation on seizures limit optimization. Single pulse electrical stimulation and the resulting pulse evoked potentials offer a measure network effective connectivity and excitability. This study leverages single pulse and high frequency thalamic stimulation during stereotactic electroencephalography to assess seizure network engagement, modulate network activity, and track changes in excitability and epileptiform abnormalities. METHODS: Ten individuals with drug resistant epilepsy undergoing clinical stereotactic electroencephalography were enrolled in this retrospective cohort study. Each underwent a trial of high frequency (145 Hz) thalamic stimulation. Pulse evoked potentials were acquired before and after high frequency stimulation. Baseline evoked potential root-mean-square amplitude assessed seizure network engagement, and modulation of amplitude (post high frequency stimulation versus baseline
Cohen's RESULTS: Thalamic stimulation delivered for >
1.5 hours significantly reduced pulse evoked potential amplitudes in connected areas compared to baseline, with the degree of modulation correlated with baseline connectivity strength. Shorter stimulation durations did not induce reliable changes. High frequency stimulation immediately suppressed interictal epileptiform discharge rates in seizure networks with strong baseline thalamocortical connectivity. Pulse evoked potentials delineated the anatomical distribution of network engagement, revealing distinct patterns across thalamic subfields. CONCLUSION: Pulse evoked potentials and thalamic stimulation during stereotactic electroencephalography provide novel network biomarkers to evaluate target engagement and modulation of large-scale networks across acute and subacute timescales. This approach demonstrates potential for efficient, data-driven neuromodulation optimization, and a new paradigm for personalized deep brain stimulation in epilepsy.