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Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals

机译:双谱对成对交互源分析,可从脑电图或磁脑电图信号中识别出跨频交互脑源系统

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摘要

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis ( PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.
机译:大脑的认知功能是通过几个大脑区域的协调活动而产生的,这些大脑区域实际上形成了以多个频率运行的复杂动力系统。这些系统通常由相互作用的子系统组成,其子系统的表征对于全面了解大脑的相互作用过程非常重要。为了解决这个问题,我们提出了一种技术,即双光谱成对交互源分析(biPISA),用于在多通道脑电图(EEG)或磁脑电图(MEG)数据可用时分析跨频交互脑源系统。具体而言,基于相互作用是成对的假设,biPISA可以通过分解EEG或MEG信号之间的交叉双谱的反对称分量,来识别交叉频率交互源的一个或多个子系统。由于交叉双谱图的反对称成分的特性,biPISA还可抵抗由混合伪影(即体积传导或场扩展)引起的虚假相互作用,这些伪影始终会影响EEG或MEG功能连接性估计。此方法是成对交互源分析(PISA)的扩展,它最初是为研究相同频率的交互作用而引入的,用于跨频交互的研究。这种方法的有效性在多达三个交互源对的仿真中以及对自发性大脑活动的真实MEG记录中得到了证明。仿真表明,biPISA在估计相互作用源之间的相位差方面的性能受噪声水平的提高而不是受到相互作用子系统数量的影响。实际MEG数据的分析揭示了中央对mu和β节律的两对来源之间的相互作用,位于左右中央沟附近。

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