Data from: Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that inter-areal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at meso-scale resolution from stereo-electroencephalography (SEEG) and at macro-scale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from non-sinusoidal signals ubiquitous in neuronal activity. We show that genuine inter-areal CFC is present in human resting-state activity in both MEG and SEEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for inter-areal CFS and PAC being two distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.特定频段内的神经元振荡相位同步，可协调解剖学分布各异的神经元信息处理与通信活动。通常，不同频率的神经元振荡与同步现象会同时出现，各自在认知功能中承担独立的计算角色。尽管同频相位同步已被广泛研究，但针对跨频率且跨脑区的神经元信息处理机制，目前仍所知甚少。此类跨频率的信息整合可通过跨频率耦合（cross-frequency coupling, CFC）实现，具体包括相位振幅耦合（phase-amplitude coupling, PAC）或n:m跨频率相位同步（n:m-cross-frequency phase synchrony, CFS）两种路径。 迄今为止，相关研究大多聚焦于单个脑区内的局部CFC，而脑区间CFC的存在及其功能组织模式仍在很大程度上尚不明确。我们提出假设：脑区间CFC或许是神经元活动大规模协调的核心机制，并在此探究静息态人脑活动中是否存在真实的CFC网络。 为评估CFC网络的功能组织模式，我们分别基于立体脑电图（stereo-electroencephalography, SEEG）的中尺度分辨率数据，以及经源重构的脑磁图（magnetoencephalography, MEG）的大尺度分辨率数据，识别出全脑范围的CFC网络。我们开发了一种全新的图论方法，用于区分真实CFC与由神经元活动中普遍存在的非正弦信号所产生的伪CFC。 研究结果表明，无论是MEG还是SEEG数据，人脑静息态活动中均存在真实的脑区间CFC。CFS与PAC两类网络均会耦合θ振荡与α振荡及其更高频率的成分，形成连接脑区前后部位的大规模网络。两类网络具有截然不同的频谱特征，且低频与高频网络枢纽的分布模式截然相反，这意味着二者属于不同的CFC机制。此外，CFS网络的强度还可独立预测另一项神经心理学评估中的认知表现。 综上，本研究结果证实，脑区间CFS与PAC是两种截然不同的跨频率振荡耦合机制，可实现大规模脑网络内的频率间信息交互。