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 interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG 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 interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

中文翻译：

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人体静止状态电生理记录中的真正跨频耦合网络。

在特定频带中神经元振荡的相位同步可协调解剖上分布的神经元处理和通信。通常，振荡和同步同时发生在许多不同的频率上，这些频率在认知功能中起着独立的计算作用。尽管对频率内相位同步进行了广泛研究，但对于控制分布在频率和大脑区域的神经元处理的机制知之甚少。可以通过跨频耦合（CFC），通过相幅耦合（PAC）或通过n：m跨频相位同步（CFS）来实现频率之间处理的这种集成。到目前为止，研究主要集中在单个大脑区域的局部CFC，然而，大脑区域之间CFC的存在和功能组织仍然未知。我们假设区域间的CFC对于大规模协调神经元活动可能是必不可少的，并且在这里调查人的静止状态（RS）脑活动中是否存在真正的CFC网络。为了评估CFC网络的功能组织，我们从立体脑电图（SEEG）的中尺度分辨率和从源重构的磁脑电图（MEG）数据的宏观尺度确定了全脑CFC网络。据我们所知，我们开发了一种新颖的图论方法来区分真正的CFC和可能由神经元活动中普遍存在的非正弦信号产生的伪CFC。我们显示，在SEEG和MEG数据中，真正的区域间CFC存在于人类RS活动中。在连接前脑和后脑区域的大规模网络中，CFS和PAC网络都以较高的频率将theta和alpha振荡耦合在一起。CFS和PAC网络具有独特的频谱模式，并且低频和高频网络集线器的分布相反，这意味着它们构成了独特的CFC机制。CFS网络的强度还可以在单​​独的神经心理学评估中预测认知表现。总之，这些结果为区域间CFS和PAC是在大型脑网络中跨频率耦合振荡的2种不同机制提供了证据。CFS和PAC网络具有独特的频谱模式，并且低频和高频网络集线器的分布相反，这意味着它们构成了独特的CFC机制。CFS网络的强度还可以在单​​独的神经心理学评估中预测认知表现。总之，这些结果为区域间CFS和PAC是在大型脑网络中跨频率耦合振荡的2种不同机制提供了证据。CFS和PAC网络具有独特的频谱模式，并且低频和高频网络集线器的分布相反，这意味着它们构成了独特的CFC机制。CFS网络的强度还可以在单​​独的神经心理学评估中预测认知表现。总之，这些结果提供了证据，即区域间CFS和PAC是在大型脑网络中跨频率耦合振荡的两种不同机制。