Sensitivity of Beamformer Source Analysis to Deficiencies in Forward Modeling
Sensitivity of Beamformer Source Analysis to Deficiencies in Forward Modeling
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DOI：
10.1002/hbm.20986
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发表时间：
2010-12-01
影响因子：
4.8
通讯作者：
Wolters, Carsten H.
中科院分区：
文献类型：
--
作者：
Steinstraeter, Olaf;Sillekens, Stephanie;Wolters, Carsten H.
波束形成方法最近被开发用于脑电(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因此增加，但基本的方法论考虑，特别是波束形成器性能对Leadfield精度的依赖，仍然非常不清楚。本文系统地研究了基于脑电信号的合成孔径磁测(SAM)波束形成方法中，不正确的体导体建模对源重构性能的影响。人头的有限元模型是从多模磁共振图像中得到的，并用作逼真的体导体模型。通过理论分析和一系列计算机模拟，深入了解了波束形成器的性能，这些误差是由几何和各向异性体积导体错误规范、传感器噪声和传感器覆盖不足引起的峰位置、峰幅度和峰宽度的重建误差。我们的结论是，根据震源位置、传感器覆盖范围和体积导体模型的精度，定位误差必须达到几厘米。由于波束形成器试图寻找关于其扫描空间的最佳匹配前场(最小二乘)，这一结果可以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显著取决于噪声和体积导体模型的准确性之间的相互作用。波束形成器可以从高信噪比中获得很大的好处，但这需要一个足够逼真的体导体模型。《哼唱大脑地图》31：1907-1927,2010。(C)2010年Wiley-Liss公司
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. Hum Brain Mapp 31:1907-1927, 2010. (C) 2010 Wiley-Liss, Inc.