Computing Sources of Epileptic Discharges Using the Novel BMA Approach

Abstract

Electroencephalography (EEG) source localization in epileptology continues to be a challenge for neuroscientists. A number of inverse solution (IS) methodologies have been proposed to solve this problem, and their advantages and limitations have been described. In the present work, a previously developed IS approach called Bayesian model averaging (BMA) is introduced in clinical practice in order to improve the localization accuracy of epileptic discharge sources. For this study, 31 patients with the diagnosis of partial epilepsies were studied: 14 had benign childhood epilepsy with centrotemporal spikes and 17 had temporal lobe epilepsy (TLE). The underlying epileptic sources were localized using the BMA approach, and the results were compared with those expected from the clinical diagnosis. Additional comparisons with results obtained from 3 of the most commonly used distributed IS methods for these purposes (minimum norm [MN], weighted minimum norm [WMN], and low-resolution electromagnetic tomography [LORETA]) were carried out in terms of source localization accuracy and spatial resolutions. The BMA approach estimated discharge sources that were consistent with the clinical diagnosis, and this method outperformed LORETA, MN, and WMN in terms of both localization accuracy and spatial resolution. The BMA was able to localize deeper generators with high accuracy. In conclusion, the BMA methodology has a great potential for the noninvasive accurate localization of epileptic sources, even those located in deeper structures. Therefore, it could be a promising tool for clinical practice in epileptology, although additional studies in other types of epileptic syndromes are necessary.