Decoding Epileptogenesis: A Dynamical System Approach
Event details
| Date | 08.02.2016 |
| Hour | 12:00 › 13:00 |
| Speaker | Professor Francois G Meyer, University of Colorado at Boulder |
| Location | |
| Category | Conferences - Seminars |
Epilepsy is the most common chronic neurological disorder, affecting over 65 million people worldwide. This work addresses the design of a biomarker of epileptogenesis. A reliable biomarker can improve the treatment of the disease, reduce health-care costs, and help develop new drugs.
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, we chronically 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. This is work in collaboration with Daniel Barth, Alexander Benison, and Zachariah Smith.
Please note this Seminar is videotranfered to EPLF at SV 3715
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, we chronically 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. This is work in collaboration with Daniel Barth, Alexander Benison, and Zachariah Smith.
Please note this Seminar is videotranfered to EPLF at SV 3715
Practical information
- Informed public
- Free
- This event is internal
Organizer
- Center for Neuroprosthetics, Prof. Dimitri Van de Ville
Contact
- Catherine Wannier