Kirsch H E, Robinson S E, Mantle M, Nagarajan S
UCSF Epilepsy Center, Department of Neurology, University of California, San Francisco, USA.
Clin Neurophysiol. 2006 Oct;117(10):2264-71. doi: 10.1016/j.clinph.2006.06.708. Epub 2006 Aug 7.
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.
自动自适应空间滤波技术可应用于从癫痫患者收集的脑磁图（MEG）数据。通过这些方法估计的源波形比自发MEG数据具有更高的信噪比（SNR），从而能够识别和定位发作间期棘波。软件工具SAM(g(2))为MEG数据提供了一种自适应空间滤波算法，该算法可生成峰度异常的源图像，并在表现出高异常峰度的体素中提供源时间历程。SAM(g(2))在癫痫中的敏感性和特异性尚不清楚。
使用SAM(g(2))分析了36例难治性癫痫患者的发作间期MEG数据，并将结果与等效电流偶极子（ECD）拟合程序进行比较。
当发作间期棘波的SNR较高（与背景相比）且有明确的单一病灶时，在大多数情况下，ECD和SAM(g(2))之间有良好的一致性。对于多个病灶，ECD和SAM(g(2))的结果通常有重叠但不完全一致。
在某些情况下，SAM(g(2))可能等同于手动ECD拟合，用于定位具有单一阵位和良好SNR的发作间期棘波。需要进一步研究以验证SAM(g(2))在多个病灶或低SNR情况下的有效性。
在某些情况下，SAM(g(2))最终可能有助于或取代对MEG数据的手动ECD分析。
