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Detection and localization of hippocampal activity using beamformers with MEG: A detailed investigation using simulations and empirical data

机译：使用MEG波束形成器检测和定位海马活动：使用模拟和经验数据进行详细调查

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The ability to detect neuronal activity emanating from deep brain structures such as the hippocampus using magnetoencephalography has been debated in the literature. While a significant number of recent publications reported activations from deep brain structures, others reported their inability to detect such activity even when other detection modalities confirmed its presence. In this article, we relied on realistic simulations to show that both sides of this debate are correct and that these findings are reconcilable. We show that the ability to detect such activations in evoked responses depends on the signal strength, the amount of brain noise background, the experimental design parameters, and the methodology used to detect them. Furthermore, we show that small signal strengths require contrasts with control conditions to be detected, particularly in the presence of strong brain noise backgrounds. We focus on one localization technique, the adaptive spatial filter (beamformer), and examine its strengths and weaknesses in reconstructing hippocampal activations, in the presence of other strong brain sources such as visual activations, and compare the performance of the vector and scalar beamformers under such conditions. We show that although a weight‐normalized beamformer combined with a multisphere head model is not biased in the presence of uncorrelated random noise, it can be significantly biased in the presence of correlated brain noise. Furthermore, we show that the vector beamformer performs significantly better than the scalar under such conditions. We corroborate our findings empirically using real data and demonstrate our ability to detect and localize such sources. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.
机译：文献中已经讨论了使用脑磁图检测深部大脑结构（例如海马体）发出的神经元活动的能力。尽管最近的大量出版物报道了大脑深部结构的激活，但其他人则报告说，即使其他检测方式证实了其存在，也无法检测到这种活动。在本文中，我们依靠现实的模拟来表明这场辩论的双方都是正确的，并且这些发现是可以调和的。我们证明了在诱发的反应中检测此类激活的能力取决于信号强度，脑噪声背景的数量，实验设计参数以及用于检测它们的方法。此外，我们显示出较小的信号强度需要与要检测到的控制条件形成对比，特别是在存在强烈的脑噪声背景的情况下。我们专注于一种定位技术，即自适应空间滤波器（波束形成器），并在存在其他强大的脑源（例如视觉激活）的情况下，研究其在重建海马激活中的优缺点，并比较矢量和标量波束形成器在以下情况下的性能这样的条件。我们表明，尽管权重归一化的波束形成器与多球头模型相结合在不相关的随机噪声存在下没有偏见，但在相关的脑噪声存在下却有明显的偏见。此外，我们表明，在这种条件下，矢量波束成形器的性能明显优于标量。我们使用真实数据从经验上证实了我们的发现，并证明了我们检测和定位此类资源的能力。嗡嗡的脑图，2011年。©2010 Wiley-Liss，Inc.

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