Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.
Cereb Cortex. 2013 Feb;23(2):389-98. doi: 10.1093/cercor/bhs031. Epub 2012 Feb 17.
Oscillations are pervasive in encephalographic signals and supposedly reflect cognitive processes and sensory representations. While the relation between oscillation amplitude (power) and sensory-cognitive variables has been extensively studied, recent work reveals that the dynamic oscillation signature (phase pattern) can carry information about such processes to a greater degree than amplitude. To elucidate the neural correlates of oscillatory phase patterns, we compared the stimulus selectivity of neural firing rates and auditory-driven electroencephalogram (EEG) oscillations. We employed the same naturalistic sound stimuli in 2 experiments, one recording scalp EEGs in humans and one recording intracortical local field potentials (LFPs) and single neurons in macaque auditory cortex. Using stimulus decoding techniques, we show that stimulus selective firing patterns imprint on the phase rather than the amplitude of slow (theta band) oscillations in LFPs and EEG. In particular, we find that stimuli which can be discriminated by firing rates can also be discriminated by phase patterns but not by oscillation amplitude and that stimulus-specific phase patterns also persist in the absence of increases of oscillation power. These findings support a neural basis for stimulus selective and entrained EEG phase patterns and reveal a level of interrelation between encephalographic signals and neural firing beyond simple amplitude covariations in both signals.
振荡在脑电图信号中普遍存在,据推测反映了认知过程和感觉表现。虽然振荡幅度(功率)与感觉认知变量之间的关系已经得到广泛研究,但最近的研究表明,动态振荡特征(相位模式)可以比幅度更大程度地携带有关这些过程的信息。为了阐明振荡相位模式的神经相关性,我们比较了神经发放率和听觉驱动的脑电图(EEG)振荡的刺激选择性。我们在两个实验中使用了相同的自然声音刺激,一个在人类中记录头皮 EEG,另一个在猕猴听觉皮层中记录皮质内局部场电位(LFP)和单个神经元。使用刺激解码技术,我们表明刺激选择性发放模式会在 LFP 和 EEG 的慢(theta 频段)振荡的相位上而不是幅度上留下印记。具体而言,我们发现可以通过发放率区分的刺激也可以通过相位模式区分,但不能通过振荡幅度区分,并且刺激特异性相位模式在没有增加振荡功率的情况下也会持续存在。这些发现支持了刺激选择性和同步 EEG 相位模式的神经基础,并揭示了脑电图信号和神经发放之间的相互关系超出了两个信号中简单的幅度变化。