Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK.
Neuroimage. 2010 Jan 1;49(1):525-38. doi: 10.1016/j.neuroimage.2009.07.043. Epub 2009 Jul 25.
This study shows that the spatial specificity of MEG beamformer estimates of electrical activity can be affected significantly by the way in which covariance estimates are calculated. We define spatial specificity as the ability to extract independent timecourse estimates of electrical brain activity from two separate brain locations in close proximity. Previous analytical and simulated results have shown that beamformer estimates are affected by narrowing the time frequency window in which covariance estimates are made. Here we build on this by both experimental validation of previous results, and investigating the effect of data averaging prior to covariance estimation. In appropriate circumstances, we show that averaging has a marked effect on spatial specificity. However the averaging process results in ill-conditioned covariance matrices, thus necessitating a suitable matrix regularisation strategy, an example of which is described. We apply our findings to an MEG retinotopic mapping paradigm. A moving visual stimulus is used to elicit brain activation at different retinotopic locations in the visual cortex. This gives the impression of a moving electrical dipolar source in the brain. We show that if appropriate beamformer optimisation is applied, the moving source can be tracked in the cortex. In addition to spatial reconstruction of the moving source, we show that timecourse estimates can be extracted from neighbouring locations of interest in the visual cortex. If appropriate methodology is employed, the sequential activation of separate retinotopic locations can be observed. The retinotopic paradigm represents an ideal platform to test the spatial specificity of source localisation strategies. We suggest that future comparisons of MEG source localisation techniques (e.g. beamformer, minimum norm, Bayesian) could be made using this retinotopic mapping paradigm.
这项研究表明，MEG 波束形成器对电活动的空间特异性估计可能会受到协方差估计方式的显著影响。我们将空间特异性定义为从两个靠近的不同脑区提取独立的电脑活动时间序列估计的能力。先前的分析和模拟结果表明，波束形成器的估计受到在制作协方差估计时缩小时间频率窗口的影响。在这里，我们通过实验验证以前的结果，并研究在协方差估计之前进行数据平均的影响，来进一步研究这个问题。在适当的情况下，我们表明平均化对空间特异性有显著影响。但是，平均化过程会导致协方差矩阵条件不良，因此需要适当的矩阵正则化策略，我们描述了其中一个示例。我们将我们的发现应用于 MEG 视网膜映射范式。使用移动视觉刺激在视觉皮层的不同视网膜位置引发大脑激活。这给人一种大脑中移动电偶极源的印象。我们表明，如果应用适当的波束形成器优化，移动源可以在皮层中进行跟踪。除了对移动源进行空间重建外，我们还表明可以从视觉皮层中感兴趣的相邻位置提取时间序列估计值。如果采用适当的方法，就可以观察到单独的视网膜位置的顺序激活。视网膜映射范式是测试源定位策略空间特异性的理想平台。我们建议，未来可以使用这种视网膜映射范式来比较 MEG 源定位技术（例如波束形成器、最小范数、贝叶斯）。
