Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Machine Learning Group, Technical University of Berlin, Berlin, Germany; International Max Planck Research School NeuroCom, Leipzig, Germany.
Machine Learning Group, Technical University of Berlin, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, Republic of Korea; Max Planck Institute for Informatics, Stuhlsatzenhausweg, Saarbrücken, Germany.
Neuroimage. 2020 May 1;211:116599. doi: 10.1016/j.neuroimage.2020.116599. Epub 2020 Feb 5.
Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.
跨频率耦合（CFC）反映了大脑中空间和频谱分布信息的整合。在这里，我们提出了一种新的框架，用于在脑磁图和脑电图（MEG/EEG）中检测这种相互作用，我们称之为非线性相互作用分解（NID）。与以前所有用于分离大脑中跨频率（CF）源的方法不同，我们提出可以基于它们的线性混合物的统计特性来提取非线性相互作用的振荡。NID 的主要思想是，非线性耦合的脑振荡可以以这样的方式混合，即产生的线性混合物具有非高斯分布。我们针对相位-相位或幅度-幅度 CF 耦合的调幅窄带振荡进行了分析评估。我们使用现实的头部建模获得的模拟 EEG 对 NID 进行了广泛验证。该方法甚至在信噪比低至-15dB 时也能可靠地提取非线性相互作用的成分。此外，我们将 NID 应用于 81 名受试者的静息态 EEG 中，以描述 alpha 和 beta 振荡之间的 CF 相位-相位耦合。提取的源位于颞叶、顶叶和额叶区域，证明了静息态 EEG 数据中存在多种局部和远距离的非线性相互作用。所有代码都可以通过 GitHub 公开获得。
