Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.
Hum Brain Mapp. 2010 Dec;31(12):1907-27. doi: 10.1002/hbm.20986. Epub 2010 May 24.
Beamforming approaches have recently been developed for the field of electroencephalography (EEG) and magnetoencephalography (MEG) source analysis and opened up new applications within various fields of neuroscience. While the number of beamformer applications thus increases fast-paced, fundamental methodological considerations, especially the dependence of beamformer performance on leadfield accuracy, is still quite unclear. In this article, we present a systematic study on the influence of improper volume conductor modeling on the source reconstruction performance of an EEG-data based synthetic aperture magnetometry (SAM) beamforming approach. A finite element model of a human head is derived from multimodal MR images and serves as a realistic volume conductor model. By means of a theoretical analysis followed by a series of computer simulations insight is gained into beamformer performance with respect to reconstruction errors in peak location, peak amplitude, and peak width resulting from geometry and anisotropy volume conductor misspecifications, sensor noise, and insufficient sensor coverage. We conclude that depending on source position, sensor coverage, and accuracy of the volume conductor model, localization errors up to several centimeters must be expected. As we could show that the beamformer tries to find the best fitting leadfield (least squares) with respect to its scanning space, this result can be generalized to other localization methods. More specific, amplitude, and width of the beamformer peaks significantly depend on the interaction between noise and accuracy of the volume conductor model. The beamformer can strongly profit from a high signal-to-noise ratio, but this requires a sufficiently realistic volume conductor model.
波束形成方法最近已被开发用于脑电图 (EEG) 和脑磁图 (MEG) 源分析领域，并在神经科学的各个领域开辟了新的应用。虽然波束形成器的应用数量增长迅速，但基本的方法考虑因素，特别是波束形成器性能对导联场准确性的依赖性，仍然相当不清楚。在本文中，我们对基于 EEG 数据的综合孔径磁测 (SAM) 波束形成方法中不当体积导体建模对源重建性能的影响进行了系统研究。从多模态磁共振图像中导出了一个人类头部的有限元模型，并用作现实的体积导体模型。通过理论分析和一系列计算机模拟，深入了解了由于几何形状和各向异性体积导体模型不准确、传感器噪声和传感器覆盖不足而导致的峰值位置、峰值幅度和峰值宽度的重建误差对波束形成器性能的影响。我们得出的结论是，根据源位置、传感器覆盖范围和体积导体模型的准确性，可能会出现高达几厘米的定位误差。由于我们可以证明波束形成器试图根据其扫描空间找到最佳拟合导联场（最小二乘法），因此可以将该结果推广到其他定位方法。更具体地说，波束形成器峰值的幅度和宽度明显取决于噪声和体积导体模型准确性之间的相互作用。波束形成器可以从高信噪比中受益匪浅，但这需要一个足够现实的体积导体模型。
