Automated localization of magnetoencephalographic interictal spikes by adaptive spatial filtering - Kirsch, H.E.; Robinson, S.E.; Mantle, M.; Nagarajan, S. (Clinical neurophysiology, 2006)
Automated adaptive spatial filtering techniques can be applied to magnetoencephalographic (MEG) data collected from people with epilepsy. Source waveforms estimated by these methods have higher signal-to-noise ratio (SNR) than spontaneous MEG data, allowing identification and location of interictal spikes. The software tool SAM( g 2) provides an adaptive spatial filtering algorithm for MEG data that yields source images of excess kurtosis and provides source time-courses in voxels exhibiting high excess kurtosis. The sensitivity and specificity of SAM( g 2) in epilepsy is unknown. Interictal MEG data from 36 patients with intractable epilepsy were analyzed using SAM( g 2), and results compared with equivalent current dipole (ECD) fit procedures. When SNR of interictal spikes was high (compared to background) with a clear single focus, in most cases there was good agreement between ECD and SAM( g 2). With multiple foci, there was typically overlap but imperfect concordance between results of ECD and SAM( g 2). SAM( g 2) may in some cases be equivalent to manual ECD fit for localizing interictal spikes with single locus and good SNR. Further studies are required to validate SAM( g 2) with multiple foci or poor SNR. In some cases, SAM( g 2) might eventually assist or replace manual ECD analysis of MEG data.