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Abstract：
The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.
摘要：
静息状态网络（RSN）的电生理基础仍在争论中。特别是,尚未确定能够同样很好地解释所有RSN的原则性机制。虽然脑磁图（MEG）和脑电图是确定RSN电生理基础的首选方法，还没有RSN的标准分析管道。在这篇文章中,我们比较了从MEG数据中提取RSN的两种主要的现有数据驱动分析策略，并介绍了第三种方法。第一种方法使用相位-振幅耦合来确定RSN。第二种方法通过对不同频段的希尔伯特包络进行独立分量分析来提取RSN，而第三种新方法使用奇异值分解代替。为了评估这些方法，我们将MEG-RSN与来自相同受试者的功能磁共振成像（fMRI）-RSN进行了比较。总的来说,可以使用所有三种技术用MEG提取RSN，与特定组的fMRI-RSN匹配。有趣的是，与两种现有方法相比，基于SVD的新方法与七个fMRI-RSN中的五个产生了显着更高的对应关系。重要的是,用这种方法，除视觉网络外,所有网络在一个频带内与fMRI网络的对应度最高.因此，我们提供了对fMRI-RSN的电生理基础的进一步见解。这些知识对于电生理连接体的分析将是重要的。
