Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Finland.
Doctoral Program Brain & Mind, University of Helsinki, Finland.
PLoS Biol. 2020 May 6;18(5):e3000685. doi: 10.1371/journal.pbio.3000685. eCollection 2020 May.
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.
神经元振荡在特定频带中的相位同步协调了解剖分布的神经元处理和通讯。通常，在许多不同的频率中，振荡和同步同时发生，它们在认知功能中发挥着不同的计算作用。虽然在频内相位同步已经被广泛研究，但对于控制跨频率和脑区分布的神经元处理的机制知之甚少。这种跨频率处理的整合可以通过跨频率耦合（CFC）来实现，要么是通过相位-幅度耦合（PAC），要么是通过 n:m-交叉频率相位同步（CFS）。到目前为止，研究主要集中在单个脑区的局部 CFC 上，而脑区之间的 CFC 的存在和功能组织仍然很大程度上未知。我们假设脑区间的 CFC 对于神经元活动的大规模协调可能是必不可少的，并在这里研究在人类静息状态（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，我们在立体脑电图（SEEG）中以中尺度分辨率和源重建脑磁图（MEG）数据中以宏观尺度分辨率识别了全脑 CFC 网络。我们开发了一种新颖的、据我们所知的图论方法，以区分真正的 CFC 与可能源于神经元活动中普遍存在的非正弦信号的虚假 CFC。我们表明，真正的脑区间 CFC 存在于 SEEG 和 MEG 数据中的人类 RS 活动中。CFS 和 PAC 网络都在连接前脑和后脑区域的大尺度网络中，将 theta 和 alpha 振荡与更高频率耦合。CFS 和 PAC 网络具有不同的频谱模式和低频和高频网络枢纽的相反分布，这意味着它们构成了不同的 CFC 机制。CFS 网络的强度也可以预测独立神经心理学评估中的认知表现。总之，这些结果为脑区间 CFS 和 PAC 是两种不同的机制提供了证据，用于在大尺度脑网络中耦合跨频率的振荡。
