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Recurrence network analysis of wide band oscillations of local field potentials from the primary motor cortex reveals rich dynamics.

机译:对来自初级运动皮层的局部场电势的宽带振荡进行的递归网络分析显示出丰富的动力学特性。

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Aggregate signals that reflect activities of a large number of neurons in the cerebral cortex, local field potentials (LFPs) have been observed to mediate gross functional activities of a relatively small volume of the brain tissues. There are several bands of the oscillations frequencies in LFPs that have been observed across multiple brain areas. The signature oscillation band of the LFPs in the primary motor cortex (MI) is over β range and it has been consistently observed both in human and non-human primates around the time of visual cues and movement onsets. However, its dynamical behavior has not been well characterized. Furthermore, dynamics of β oscillations has been documented based on the phase locking of β oscillations, but not in terms of the inherent dynamics of the oscillations themselves. Here, we used the complexity measure derived from cluster coefficients of a recurrence network and analyzed a pair of wide-band signals, one including β band of the LFPs and the other ranging the low γ band in MI recorded from a non-human primate. We show rather unique temporal profiles of the evoked responses using complexity of the dynamical behavior in both bands of the oscillation, either of which is not simply resembling either the power of the oscillation or the phase locking of β oscillations. Therefore, the current method can reveal a new type of dynamics of the underlying network complexity during the task simply based on event evoked potentials of wide-band oscillatory signals.
机译:反映了大脑皮层中大量神经元活动的聚集信号,已经观察到局部场电势(LFP)介导较小体积的脑组织的总功能活动。在多个大脑区域中观察到的LFP中有几个振荡频率带。 LFP在初级运动皮层(MI)中的特征性振荡带超过β范围,并且在视觉线索和运动发作时间前后,在人类和非人类灵长类动物中都得到了一致的观察。但是,其动力学行为尚未很好地表征。此外,已经基于β振荡的锁相记录了β振荡的动力学,但是没有根据振荡本身的固有动力学来进行记录。在这里,我们使用了从递归网络的簇系数得出的复杂性度量,并分析了一对宽带信号,其中一个包含LFP的β谱带,另一个包含从非人类灵长类动物中记录的MI的低γ谱带。我们使用振荡的两个频带中的动力学行为的复杂性,显示了诱发响应的相当独特的时间分布,其中任何一个都不能简单地类似于振荡的功率或β振荡的锁相。因此,当前的方法可以简单地基于宽带振荡信号的事件诱发电位来揭示任务期间底层网络复杂性的新型动态。

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