Pathway to Adaptive Neuromodulation: Modeling Epileptogenic Brain States Using SEEG

Area of Science:

• Neurology
• Neuroscience
• Biomedical Engineering

Background:

• Neuromodulation is a growing treatment for drug-resistant epilepsy in non-surgical candidates.
• Responsive neurostimulation reduces seizure frequency, but optimal stimulation signals and parameters are not fully understood.
• Personalized treatment requires understanding neural signals and biological rhythms.

Purpose of the Study:

• To review current insights into biological rhythms of epilepsy for personalized neuromodulation.
• To explore the potential of machine learning in optimizing epilepsy neuromodulation.
• To guide clinical teams in selecting optimal sites and stimulation programs.

Main Methods:

• Literature review of recent advancements in epilepsy neuromodulation.
• Analysis of biological rhythms and their role in seizure generation.
• Discussion of machine learning applications for treatment personalization.

Main Results:

• Epilepsy's biological rhythms offer potential for triggered neuromodulation.
• Personalization protocols can enhance therapeutic efficacy.
• Machine learning can provide actionable feedback for clinical decision-making.

Conclusions:

• Integrating biological rhythms and machine learning can advance personalized epilepsy neuromodulation.
• Further research is needed to systematically explore the parameter space for optimal outcomes.
• Future directions focus on data-driven approaches for improved patient care.