  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.
