Magnetoencephalography distributed source modeling identifies spatiotemporal propagation patterns of interictal epileptiform discharges

脑磁图分布式源建模识别发作间期癫痫样放电的时空传播模式

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Abstract

OBJECTIVE: Magnetoencephalography (MEG) is an important adjunctive method used to localize interictal epileptiform discharges (IEDs). The equivalent current dipole (ECD) method, the current gold standard modeling approach, identifies a single point source of activity. However, IEDs propagate widely and may be better represented by distributed source modeling approaches. Here, we investigate how areas of maximal IED-related activity estimated using dynamic statistical parametric mapping (dSPM) compare to dipoles and surgical resections. METHODS: We analyzed resting-state MEG recordings from 38 NIH patients. We localized areas of IED-related activity along the IED rising phase and peak using spatial clustering of the top 5% of dSPM activations, comparing localizations to ECD and surgical resection areas in seizure free patients. RESULTS: We identified dominant primary activation clusters in all patients and non-primary clusters in 24/38 patients. Dipoles localized closer to primary than non-primary clusters. In 12 post-operative seizure-free patients, the primary cluster center of mass was significantly closer to the resected area than dipoles and more stable over time. CONCLUSIONS: Distributed source modeling adds to our understanding of IED propagation patterns and may be useful in IED localization for epilepsy surgery planning. SIGNIFICANCE: dSPM enhances pre-surgical planning by refining IED localization, potentially improving surgical outcomes.

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