Schanze T, Eckhorn R
Department of Physics, Philipps-University, Marburg, Germany.
Int J Psychophysiol. 1997 Jun;26(1-3):171-89. doi: 10.1016/s0167-8760(97)00763-0.
In classical EEG analysis rhythms with different frequencies occurring at separable regions and states of the brain are analysed. Rhythms in different frequency bands have often been assumed to be independent and their occurrence was interpreted as a sign of different functional operations. Independence has scarcely been proved because of conceptual and computational difficulties. It is, on the other hand, probable that different rhythmic brain processes are coupled because of the broad recurrent connectivity among brain structures. We, therefore, set out to find interactions among rhythmic signals at different frequencies. We were particularly interested in interactions between lower frequency bands and gamma-activities (30-90 Hz), because the latter have been analysed in our laboratory in great detail and had properties suggesting their involvement in perceptual feature linking. Fast oscillations occurred synchronized in a stimulus-specific way in the visual cortex of cat and monkey. Their presence was often accompanied by lower frequency components at considerable power. Such multiple spectral peaks are known from many cortical and subcortical structures. Despite their well known occurrence, coupling among different frequencies has not been established, apart from harmonic components. For the present investigation we extended existing analytical tools to detect non-linear correlations among signal pairs at any frequency (including incommensurate ones). These methods were applied to multiple microelectrode recordings from visual cortical areas 17 and 18 of anesthetized cats and V1 of awake monkeys. In particular, we assessed non-linear correlations by means of higher order spectral analysis of multi-unit spike activities (MUA) and local slow wave field potentials (LFP, 1-120 Hz) recorded with microelectrodes. Non-linear correlations among signal components at different frequencies were investigated in the following steps. First, the frequency content of short (approximately 250 ms) sliding window signal epochs was analyzed for simultaneously occurring rhythms of significant power at different frequencies. This was done by a newly developed method derived from the trispectrum using separate averaging of the products of short-epoch power spectra for any possible combination of frequency pairs. Second, non-linear (quadratic) phase coupling between different frequencies was assessed by the methods of bispectrum and bicoherence. We found phase correlations at different frequencies in the visual cortex of the cat and monkey. These couplings were significant in about 60% of the investigated MUA and LFP recordings, including several cases of coupling among incommensurate (i.e. non-harmonic) frequencies. Significant phase correlations were present: (1) within the gamma-frequency range; (2) between gamma- and low frequency ranges (1-30 Hz, including alpha- and beta-rhythms); and (3) within the low frequency range. Phase correlations depended, in most cases, on specific visual stimulation. We discuss the possible functional significance of phase correlations among high and low frequencies by including proposals from previous work about potential roles of single-frequency rhythms of the EEG. Our suggestions include: (1) visual feature linking across different temporal and spatial scales provided by coherent oscillations at high and low frequencies; (2) linking of visual cortical representations (high frequencies) to subcortical centers (low frequencies) like the thalamus and hippocampus; and (3) temporal segmentation of the sustained stream of incoming visual information into separate frames at different temporal resolutions in order to prevent perceptual smearing due to shifting retinal images. These proposals are, at present, merely speculative. However, they can, in principle, be proved by microelectrode recordings from trained behaving animals.
在经典脑电图分析中,会对大脑不同区域和状态下出现的不同频率节律进行分析。不同频带的节律通常被认为是相互独立的,其出现被解释为不同功能运作的标志。由于概念和计算上的困难,独立性几乎没有得到证明。另一方面,由于脑结构之间广泛的递归连接,不同的节律性脑过程很可能是相互耦合的。因此,我们着手寻找不同频率节律信号之间的相互作用。我们特别关注低频带与伽马活动(30 - 90赫兹)之间的相互作用,因为后者在我们实验室已得到详细分析,且其特性表明它们参与了感知特征的关联。快速振荡在猫和猴的视觉皮层中以刺激特异性的方式同步出现。它们的出现常常伴随着相当功率的低频成分。许多皮层和皮层下结构都存在这种多个频谱峰值。尽管它们的出现广为人知,但除了谐波成分外,不同频率之间的耦合尚未得到证实。在本研究中,我们扩展了现有的分析工具,以检测任意频率(包括非 commensurate 频率)信号对之间的非线性相关性。这些方法应用于麻醉猫的视觉皮层区域17和18以及清醒猴的V1的多个微电极记录。特别是,我们通过对用微电极记录的多单元锋电位活动(MUA)和局部慢波场电位(LFP,1 - 120赫兹)进行高阶谱分析来评估非线性相关性。不同频率信号成分之间的非线性相关性按以下步骤进行研究。首先,分析短(约250毫秒)滑动窗口信号片段的频率内容,以确定不同频率下同时出现的具有显著功率的节律。这是通过一种新开发的方法完成的,该方法源自三谱,对任何可能的频率对的短片段功率谱乘积进行单独平均。其次,通过双谱和双相干性方法评估不同频率之间的非线性(二次)相位耦合。我们在猫和猴的视觉皮层中发现了不同频率之间的相位相关性。这些耦合在约60%的研究MUA和LFP记录中是显著的,包括几个非 commensurate(即非谐波)频率之间耦合的情况。存在显著的相位相关性:(1)在伽马频率范围内;(2)在伽马和低频范围(1 - 30赫兹,包括阿尔法和贝塔节律)之间;以及(3)在低频范围内。在大多数情况下,相位相关性取决于特定的视觉刺激。我们通过纳入先前关于脑电图单频节律潜在作用的研究建议,讨论了高低频之间相位相关性的可能功能意义。我们的建议包括:(1)高低频相干振荡在不同时间和空间尺度上提供视觉特征关联;(2)将视觉皮层表征(高频)与丘脑和海马等皮层下中心(低频)联系起来;以及(3)将持续传入的视觉信息流在不同时间分辨率下分割成单独的帧,以防止由于视网膜图像移动导致的感知模糊。目前,这些建议仅仅是推测性的。然而,原则上它们可以通过对经过训练的行为动物进行微电极记录来证明。