Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer
Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer
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DOI：
10.1016/j.neuroimage.2011.03.042
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发表时间：
2011-06-15
期刊：
影响因子：
5.7
通讯作者：
Huang, Ming-Xiong
中科院分区：
文献类型：
--
作者：
Diwakar, Mithun;Tal, Omer;Huang, Ming-Xiong
波束形成空间滤波器通常用于在低信噪比（SNR）下探测脑磁图（MEG）记录下的活动神经元源。传统的波束形成技术在低信噪比条件下成功地定位了不相关的神经元源。然而，当源相关时，传统波束形成器重建的时空特征会受到影响，这是真实神经网络的一个常见而重要的特性。最初由Brookes等人开发的双波束形成技术可以成功地定位高相关源并确定其方向和权重，但在低相关情况下，其性能会下降。它们也缺乏产生单个时间过程的能力，因此不能量化源相关性。在本文中，我们提出了先前双核波束形成（DCBF）方法的增强公式，该方法可以重建单个源时间过程及其相关性。通过计算机模拟，我们发现增强的DCBF （eDCBF）无论相关强度如何，都一致且准确地模拟了双源活动。仿真还表明，eDCBF的多核扩展可以有效地处理附加相关源的存在。在人类听觉任务中，我们进一步证明了eDCBF准确地重建了左右听觉时间反应及其相关性。讨论了eDCBF框架内不同措施对应的空间分辨率和源定位策略。综上所述，eDCBF能够准确重建源的时空行为，为表征复杂神经元网络及其通信提供了一种手段。Elsevier Inc.出版。
Beamformer spatial filters are commonly used to explore the active neuronal sources underlying magnetoencephalography (MEG) recordings at low signal-to-noise ratio (SNR). Conventional beamformer techniques are successful in localizing uncorrelated neuronal sources under poor SNR conditions. However, the spatial and temporal features from conventional beamformer reconstructions suffer when sources are correlated, which is a common and important property of real neuronal networks. Dual-beamformer techniques, originally developed by Brookes et al. to deal with this limitation, successfully localize highly-correlated sources and determine their orientations and weightings, but their performance degrades at low correlations. They also lack the capability to produce individual time courses and therefore cannot quantify source correlation. In this paper, we present an enhanced formulation of our earlier dual-core beamformer (DCBF) approach that reconstructs individual source time courses and their correlations. Through computer simulations, we show that the enhanced DCBF (eDCBF) consistently and accurately models dual-source activity regardless of the correlation strength. Simulations also show that a multi-core extension of eDCBF effectively handles the presence of additional correlated sources. In a human auditory task, we further demonstrate that eDCBF accurately reconstructs left and right auditory temporal responses and their correlations. Spatial resolution and source localization strategies corresponding to different measures within the eDCBF framework are also discussed. In summary, eDCBF accurately reconstructs source spatio-temporal behavior, providing a means for characterizing complex neuronal networks and their communication. Published by Elsevier Inc.