Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression

TL;DR

This study develops a computational framework to identify patient-specific neurostimulation patterns that effectively prevent seizures, demonstrating success in both open-loop and closed-loop paradigms using a neuronal network model.

Contribution

The paper introduces a novel algorithmic approach using simulated annealing to optimize neurostimulation patterns tailored to individual patients, improving seizure suppression.

Findings

Optimal stimulation patterns abated 92% of seizures in simulations.

Common periodic stimulation trains failed to achieve permanent seizure control.

Closed-loop stimulation effectively prevented seizure onset in the model.

Abstract

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in-silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern which successfully abated 92% of seizures. Finally, in a fully responsive, or "closed-loop" neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.