Decoding Epileptogenesis in a Reduced State Space
This work addresses the challenge of predicting epilepsy progression in animal models, which is incremental as it applies existing methods to a specific biomedical domain.
The researchers tackled the problem of decoding epileptogenesis by designing a biomarker that uses reduced coordinates and a hidden Markov model to identify stages from auditory evoked potentials in an animal model, achieving reliable decoding and prediction of recovery or seizure development.
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.