Epilepsy is one of the most common neurological diseases, afflicting over 3 million Americans. The recurrent seizures that characterize epilepsy can sometimes be prevented with pharmaceutical treatment; however, over a third of all epilepsy patients cannot be sufficiently helped by antiepileptic drugs or other currently available therapies. The objective of the research described here is to build upon this research team's significant successes in using real-time dynamical analysis of endogenous neural synchrony at multiple sites in the brain and computational analysis of brain circuitry to close the feedback loop in order to produce stimulation protocols with significantly better efficacy than is currently available. Most current approaches that use deep brain stimulation (DBS) to treat epilepsy involve a priori selection of stimulation patterns that bear little relationship to the underlying brain dynamics and have produced unpredictable results overall. Simple application of electrical stimulation to the brain without regard to the timely dynamic state of brain electrophysiology provides a hit-or-miss form of treatment that greatly reduces the probability that DBS can be successful. The research described here seeks to provide a much clearer and more efficacious set of stimulation protocols that would permit targeted therapeutic modulation of activity within the brain. This is especially important since the dynamics by which seizures evolve in human patients can greatly vary. The ultimate goal of the research described in this proposal is to further develop this novel and potentially much more effective treatment for drug-refractory epilepsy patients. The primary focus of this research proposal is that a major electrophysiological effect of DBS lay in its ability to modulate neuronal synchrony throughout the brain. Experiments will use complex nonlinear mathematical analysis to derive critical dynamical information in real-time that will be used to construct control algorithms for delivering novel multisite electrical stimulation through chronic implanted electrodes in epileptic rats to disrupt seizure progression in the brain. Computational modeling of the relevant brain circuitry will be used to assist in deciphering the mechanisms behind the anti- seizure effects of DBS. Furthermore, preliminary analysis of human seizure dynamics by this team has shown evidence that such behavior is also present in human epileptic patients. Recent advances in understanding complex nonlinear processes and in controlling such activity have provided an invaluable opportunity to further apply these principles toward manipulation of diseased electrical activity in the brain. These techniques have the potential to provide an effective treatment for intractable epilepsy that would be adaptable to different epilepsy patients even if underlying seizure dynamics and etiologies differ. Such a treatment could provide huge benefits to the more than one million people in the United States who currently have no satisfactory therapy to treat these disruptive and sometimes dangerous pathological brain states.
Epilepsy is the most common neurological disease after stroke but over one-third of all epilepsy patients cannot be sufficiently helped by conventional therapies. The research described in this proposal seeks to use biomedical engineering, brain electrophysiology, computer modeling, and complex mathematics to devise new effective strategies based on the brain's own rhythms as seizures appear in order to apply deep brain stimulation to terminate seizures from occurring in patients. Such a treatment could potentially benefit over one million people in the Unites States alone which would yield great benefits to public health as well as overall cost savings to society.
|Farahmand, Sina; Sobayo, Tiwalade; Mogul, David J (2018) EMD-Based, Mean-Phase Coherence Analysis to Assess Instantaneous Phase-Synchrony Dynamics in Epilepsy Patients. Conf Proc IEEE Eng Med Biol Soc 2018:2406-2409|
|Farahmand, Sina; Sobayo, Tiwalade; Mogul, David J (2018) Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients. IEEE Trans Neural Syst Rehabil Eng 26:2270-2279|
|Farahmand, Sina; Sobayo, Tiwalade; Mogul, David J (2018) Using Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery. Conf Proc IEEE Eng Med Biol Soc 2018:2264-2267|
|Sobayo, Tiwalade; Mogul, David J (2016) Should stimulation parameters be individualized to stop seizures: Evidence in support of this approach. Epilepsia 57:131-40|