Mareček Radek, Lamoš Martin, Labounek René, Bartoň Marek, Slavíček Tomáš, Mikl Michal, Rektor Ivan, Brázdil Milan
Central European Institute of Technology, Masaryk University, 62500 Brno, Czech Republic
Central European Institute of Technology, Masaryk University, 62500 Brno, Czech Republic, and Brno University of Technology, 60190 Brno, Czech Republic
Neural Comput. 2017 Apr;29(4):968-989. doi: 10.1162/NECO_a_00933. Epub 2017 Jan 17.
Multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method, parallel factor analysis (PARAFAC). We focused on patterns' stability over time and in population and divided the complete data set containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time, as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large-scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, the common way of dealing with EEG data. Altogether, our results suggest that PARAFAC is a suitable method for research in the field of large-scale brain networks and their manifestation in EEG signal.
多路阵列分解方法已被证明是用于识别脑电图（EEG）频谱中神经活动的很有前景的统计工具。它们通过考虑在不同频率和传感器处采集的信号之间的内在关系，将EEG频谱盲目地分解为时空谱模式。我们的研究评估了由一种特定方法——平行因子分析（PARAFAC）得出的时空谱模式的稳定性。我们关注模式随时间和在群体中的稳定性，并将包含50名健康受试者数据的完整数据集分成几个子集。我们的结果表明，这些模式在时间上以及在不同受试者亚组之间都高度稳定。此外，我们通过同时采集的功能磁共振成像（fMRI）数据表明，某些模式的功率波动与大规模脑网络中的血液动力学波动具有稳定的对应关系。而对于处理EEG数据的常用方式——标准频段的功率波动，我们并未发现这种对应关系。总体而言，我们的结果表明PARAFAC是研究大规模脑网络及其在EEG信号中表现的合适方法。
