The epilepsies are a family of disorders of brain dynamics. Patients are susceptible to seizures (changes in sensation, awareness, or behavior caused by brief electrical disturbances in the brain). Often, the electrical disturbances originate from an """"""""epileptogenic zone"""""""" in part of the brain, from which the disturbances are propagated to other parts of the brain. Epilepsy affects approximately 2.5 million persons in the United States, over 50 million persons worldwide, and 150,000 to 200,000 new cases occur annually in the U.S. Although most epilepsy patients can control their seizures with the use of antiepileptic drugs, about 20% of patients cannot bring their seizures under control using drug therapy. Many patients with pharmacologically intractable seizures can eliminate their disability largely or completely by neurosurgical intervention, which typically involves resection of tissue in the epileptogenic zone to prevent the spread of electrical disturbances. Candidates for epilepsy surgery are generally evaluated with scalp electroencephalography (EEC) telemetry, and, increasingly, with magnetoencephalography (MEG). We propose to develop computer software that will aid clinicians in the diagnosis and treatment of medically intractable epilepsy, using data obtained from structural magnetic resonance imaging (sMRI), MEG, and EEG. Our goal is to develop and validate improved methods for the non-invasive localization of the epileptogenic zone through the automated analysis of spike-like electrical activity recorded in the interval between seizures (interictal spikes). During Phase I, we will improve the sensitivity, specificity, and computational efficiency of our automatic interictal spike identification and classification software. The software will then be evaluated, using MEG and sMRI data obtained from epilepsy surgery patients. During Phase II, we will add additional features for spike propagation analysis, extend the validation to EEG data, and incorporate the resulting algorithms into our commercial EMSE Suite software.
| Ossadtchi, A; Mosher, J C; Sutherling, W W et al. (2005) Hidden Markov modelling of spike propagation from interictal MEG data. Phys Med Biol 50:3447-69 |
