Decoding Epileptogenesis in a Reduced State Space

TL;DR

This study develops a biomarker using reduced state space analysis and hidden Markov models to decode and predict epileptogenesis stages from hippocampal evoked potentials in an animal model.

Contribution

It introduces a novel low-dimensional configuration approach combined with hidden Markov modeling to monitor and predict epilepsy development.

Findings

Biomarker reliably decodes epileptogenesis stages

Predicts recovery or seizure development

Identifies universal low-dimensional patterns

Abstract

We describe here the recent results of a multidisciplinary effort to design a biomarker that can actively and continuously decode the progressive changes in neuronal organization leading to epilepsy, a process known as epileptogenesis. Using an animal model of acquired epilepsy, wechronically record hippocampal evoked potentials elicited by an auditory stimulus. Using a set of reduced coordinates, our algorithm can identify universal smooth low-dimensional configurations of the auditory evoked potentials that correspond to distinct stages of epileptogenesis. We use a hidden Markov model to learn the dynamics of the evoked potential, as it evolves along these smooth low-dimensional subsets. We provide experimental evidence that the biomarker is able to exploit subtle changes in the evoked potential to reliably decode the stage of epileptogenesis and predict whether an animal will eventually recover from the injury, or develop spontaneous seizures.