Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization

• Published in: Frontiers in Human Neuroscience Vol. 15; p. 642819
• Main Authors: Cai, Chang, Chen, Jessie, Findlay, Anne M., Mizuiri, Danielle, Sekihara, Kensuke, Kirsch, Heidi E., Nagarajan, Srikantan S.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media SA 20.05.2021; Frontiers Research Foundation; Frontiers Media S.A
• Subjects: 2.1 Biological and endogenous factors; Aetiology; Algorithms; Bayesian analysis; Biological psychology; Biomedical and Clinical Sciences; Biomedical Imaging; Brain Disorders; Brain research; brain source imaging; brain source localization; Clinical Research; Cognitive and computational psychology; Cognitive Sciences; Convulsions & seizures; Epilepsy; Experimental Psychology; Human Neuroscience; Localization; Magnetic fields; Magnetic resonance imaging; Magnetoencephalography; Neurodegenerative; Neurological; Neurosciences; Neurosciences. Biological psychiatry. Neuropsychiatry; Noise; Performance evaluation; Psychology; RC321-571; Seizures; source imaging analysis; source localization; spike analysis; Canada; epilepsy; brain source imaging; source imaging analysis; source localization; spike analysis; brain source localization; magnetoencephalography
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2021.642819

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 challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.