Automated interictal spike detection and source localization in MEG using ICA and spatial-temporal clustering

MEG dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most existing methods are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal generators within the brain that produce interictal spike activity. We first use an ICA (Independent Component Analysis) method to decompose the multichannel MEG data and identify those components that exhibit spikelike characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources and determine the foci of equivalent current dipoles and their associated time courses. Finally we perform a clustering of the sources based on distance metrics that takes into consideration both their locations and time courses. Tight clusters of equivalent current dipoles with a fit of greater than 95% are considered to be the reliably determined sources and are the final output of our detection scheme.