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神经元振荡幅度与动态功能连接之间的关系。

Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity.

机构信息

Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.

School of Mathematical Sciences, University of Nottingham, Nottingham, UK.

出版信息

Cereb Cortex. 2019 Jun 1;29(6):2668-2681. doi: 10.1093/cercor/bhy136.

Abstract

Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.

摘要

事件相关的神经振荡幅度波动在认知处理的背景下被广泛报道,并通常被解释为大脑“活动”的标志。然而,这些效应的确切性质仍不清楚; 特别是,这些波动是否反映了局部动态、区域之间的整合,或者两者兼而有之,尚不清楚。在这里,我们使用脑磁图(MEG)表明运动诱导的振荡调制与感觉运动区域之间的瞬态连接有关。此外,在静息状态数据中,我们证明了振荡调制与动态连接之间存在显著的关联。对于这种经验测量存在一个混淆因素,即增加的幅度必然意味着增加了信噪比(SNR):这意味着幅度和连接是否真正耦合的问题,或者增加的连接是否仅仅由于增加的 SNR 而观察到的问题,尚未得到解答。在这里,我们通过类比具有全局网络耦合和局部多稳定性的计算模型来解决这个问题,这些模型表明,在存在全局网络耦合和局部多稳定性的情况下,振荡调制和长程连接之间的联系是神经网络的自然结果。我们的结果为连接是由神经振荡介导的观点提供了证据,并表明时频谱图不仅是局部同步性的描述,也反映了长程连接的波动。

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