Recent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A three-way tensor containing time-frequency phase-coupling between pairs of parcellated brain regions is constructed. Nonnegative CANDECOMP/PARAFAC (CP) decomposition is then applied to extract three interconnected, low-dimensional descriptions of data including temporal, spectral, and spatial connection factors. Musical features are also extracted from stimuli using acoustic feature extraction. Correlation analysis is then conducted between temporal courses of musical features and TCA components to examine the modulation of brain patterns. We derive several brain networks with distinct spectral modes (described by TCA components) significantly modulated by musical features, including higher-order cognitive, sensorimotor, and auditory networks. The results demonstrate that brain networks during music listening in EEG are well characterized by TCA components, with spatial patterns of oscillatory phase-synchronization in specific spectral modes. The proposed method provides evidence for the time-frequency dynamics of brain networks during free music listening through TCA, which allows us to better understand the reorganization of electrophysiological networks.

中文翻译：

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在自由听音乐期间通过张量分量分析得出电生理脑网络连通性

最近的研究表明，电生理功能连接的动态性引起了越来越多的兴趣，因为它被认为比静态网络分析更好地代表了功能性大脑网络。可以相信，具有特定频率模式的动态电生理大脑网络会在连续的任务执行过程中短暂形成并溶解，以支持正在进行的认知功能。在这里，我们提出了一种基于张量分量分析（TCA）的新颖方法，以表征在免费听音乐期间记录的脑电图（EEG）数据中动态电生理脑网络的空间，时间和频谱特征。构造了一个三向张量，其中包含成对的散乱的大脑区域之间的时频相位耦合。然后应用非负CANDECOMP / PARAFAC（CP）分解来提取数据的三个互连的低维描述，包括时间，频谱和空间连接因子。还使用声学特征提取从刺激中提取音乐特征。然后在音乐特征的时间过程和TCA成分之间进行相关分析，以检查大脑模式的调节。我们推导了几个具有不同光谱模式（由TCA成分描述）的大脑网络，这些大脑网络受到音乐特征的显着调节，包括高阶认知，感觉运动和听觉网络。结果表明，在脑电图音乐收听过程中，大脑网络的特征是TCA分量，在特定频谱模式下具有振荡相位同步的空间模式。