Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada.
Neuroimage. 2010 Feb 1;49(3):2387-400. doi: 10.1016/j.neuroimage.2009.10.012. Epub 2009 Oct 19.
Adaptive spatial filters (beamformers) have gained popularity as an effective method for the localization of brain activity from magnetoencephalography (MEG) data. Among the attractive features of some beamforming methods are high spatial resolution and no localization bias even in the presence of random noise. A drawback common to all beamforming methods, however, is significant degradation in performance in the presence of sources with high temporal correlations. Using numerical simulations and examples of auditory and visual evoked field responses, we demonstrate that, at typical signal-to-noise levels, the complete attenuation of fully correlated brain activity is less likely to occur, although significant localization and amplitude biases may occur. We compared various methods for correcting these biases and found the coherent source suppression model (CSSM) (Dalal et al., 2006) to be the most effective, with small biases for widely separated sources (e.g., bilateral auditory areas), however, amplitude biases increased systematically as distance between the sources was decreased. We assessed the performance and systematic biases that may result from the use of this model, and confirmed our findings with real examples of correlated brain activity in bilateral occipital and inferior temporal areas evoked by visually presented faces in a group of 21 adults. We demonstrated the ability to localize source activity in both regions, including correlated sources that are in close proximity ( approximately 3 cm) in bilateral primary visual cortex when using a priori information regarding source location. We conclude that CSSM, when carefully applied, can significantly improve localization accuracy, although amplitude biases may remain.
自适应空间滤波器（波束形成器）作为一种从脑磁图（MEG）数据定位大脑活动的有效方法，已经越来越受欢迎。一些波束形成方法的吸引人之处在于，即使存在随机噪声，它们也具有高空间分辨率和无定位偏差。然而，所有波束形成方法都存在一个共同的缺点，即在存在具有高时间相关性的源时，性能会显著下降。我们使用数值模拟和听觉和视觉诱发电场响应的示例，证明在典型的信噪比水平下，完全相关的大脑活动不太可能完全衰减，尽管可能会出现显著的定位和幅度偏差。我们比较了各种校正这些偏差的方法，发现相干源抑制模型（CSSM）（Dalal 等人，2006）最有效，对于相隔较远的源（例如双侧听觉区域），偏差较小，但是随着源之间距离的减小，幅度偏差会系统增加。我们评估了使用这种模型可能导致的性能和系统偏差，并使用 21 名成年人在视觉呈现面孔时双侧枕叶和下颞区的相关大脑活动的真实示例证实了我们的发现。我们证明了在使用关于源位置的先验信息时，即使在双侧初级视觉皮层中非常接近（约 3 厘米）的相关源也能够定位源活动的能力。我们得出结论，CSSM 可以显著提高定位准确性，尽管幅度偏差可能仍然存在。
