Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization - Cai, Chang; Chen, Jessie; Findlay, Anne M.; Mizuiri, Danielle; Sekihara, Kensuke; Kirsch, Heidi E.; Nagarajan, Srikantan S. (Frontiers in Human Neuroscience, 2021)
Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is both subjective and challenging. Automatic localization of epileptiform activity from spontaneous MEG data has been an elusive goal. Recently, we introduced the Champagne algorithm with noise learning, a novel Bayesian inference algorithm that has shown tremendous success in MEG source reconstruction, especially for focal brain sources. In this study, we localized the sources of MEG interictal epileptiform activity using the Champagne algorithm and tested the usefulness of these reconstructions for determining the epileptogenic zone in a cohort of 14 presurgical patients collected in two consecutive series. The reliability of this approach was compared to the performance of equivalent current dipole (ECD) modeling, a conventional source localization technique that is used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for epileptiform activity localization and other clinical applications of MEG.