Volk Denis, Dubinin Igor, Myasnikova Alexandra, Gutkin Boris, Nikulin Vadim V
Interdisciplinary Scientific Center J.-V. Poncelet (CNRS UMI 2615), Moscow, Russia.
Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia.
Front Neuroinform. 2018 Oct 18;12:72. doi: 10.3389/fninf.2018.00072. eCollection 2018.
Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any and whenever : is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP).
感知、运动和认知过程基于人类大脑中远距离区域之间丰富的相互作用。这种相互作用可以通过振荡信号的相位同步来实现。神经元同步主要是在相同频率范围内进行研究的，例如在阿尔法或贝塔频段内。然而，最近的研究表明，神经元群体也可以在不同频率范围之间表现出相位同步。然而，从脑电图/脑磁图记录中提取这种跨频率相互作用在方法上仍然具有挑战性。在这里，我们提出了一种用于稳健提取跨频率相位到相位同步成分的新方法。广义跨频率分解（GCFD）重建了同步神经元成分的时间进程、它们的空间滤波器和模式。我们的方法将先前的技术水平——跨频率分解（CFD）扩展到了整个频率范围：只要 是有理数，它就适用于任何 和 。GCFD基于跨频率相位耦合对非线性相互作用的神经元源进行了简洁的描述。我们在模拟中成功验证了该新方法，并使用包括静息态数据和稳态视觉诱发电位（SSVEP）在内的真实脑电图记录对其进行了测试。
