BAYESIAN SOURCE IMAGING OF PEDIATRIC EPILEPSY

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Background: Epilepsy affects over 2.5 million Americans and has an estimated total annual health care cost close to $12.5 billion per year. Medications for epilepsy often have significant side effects and currently fail to halt seizures in up to 20% of patients. Approximately 75% of epilepsy patients have their first seizure in childhood. Many only become candidates for surgical intervention after a long period of partially-effective medication that can have debilitating educational and sociological side effects. The overall objective of this DBP is to develop quantitative analysis algorithms to dramatically improve our capacity to detect and localize epileptogenic foci, in order to enable curative surgery for more patients. Rationale: Technological innovations, as described in this proposal, offer the prospect of improving foci localization and thereby dramatically improving patient outcomes. If foci are considered to be away from eloquent cortex, rather than in or nearby critical fiber pathways, they may be successfully treated, for example, by resection or transection of related fibers. These assessments rely on knowing where the seizure foci are located and their relationship to surrounding structures. With poor localization, some patients may be considered inoperable. If more accurate foci localization can be developed, more patients will be considered candidates for surgery. Questions: Today, the use of sophisticated physics and realistic forward models has been made practical by the exponentially increasing compute capacity of modern workstations. Furthermore, the use of computationally expensive methods can dramatically improve the reliability of the inverse solution, enabling more realistic dipole models and allowing better noise and artifact rejection. Unfortunately, these technologies have not yet reached clinical practice. Design & Methods: Each patient who is identified by the clinical epilepsy service for evaluation as a potential candidate for surgery will be approached and given the opportunity to participate in this study. If a patient chooses to participate in the study, additional data acquisition and data analysis will be carried out. All patients will receive the standard clinical care, which involves independent assessment of each modality. Experts blinded to the clinical interpretation of the data will review the multi-modality data fusion and determine if the patient is a candidate for surgical intervention based on the presence of a focal seizure zone.