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K复合波、纺锤波和事件相关电位作为冲动反应:通过神经场理论实现统一

K-complexes, spindles, and ERPs as impulse responses: unification via neural field theory.

作者信息

Zobaer M S, Anderson R M, Kerr C C, Robinson P A, Wong K K H, D'Rozario A L

机构信息

School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.

Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia.

出版信息

Biol Cybern. 2017 Apr;111(2):149-164. doi: 10.1007/s00422-017-0713-2. Epub 2017 Mar 1.

Abstract

To interrelate K-complexes, spindles, evoked response potentials (ERPs), and spontaneous electroencephalography (EEG) using neural field theory (NFT), physiology-based NFT of the corticothalamic system is used to model cortical excitatory and inhibitory populations and thalamic relay and reticular nuclei. The impulse response function of the model is used to predict the responses to impulses, which are compared with transient waveforms in sleep studies. Fits to empirical data then allow underlying brain physiology to be inferred and compared with other waves. Spontaneous K-complexes, spindles, and other transient waveforms can be reproduced using NFT by treating them as evoked responses to impulsive stimuli with brain parameters appropriate to spontaneous EEG in sleep stage 2. Using this approach, spontaneous K-complexes and sleep spindles can be analyzed using the same single theory as previously been used to account for waking ERPs and other EEG phenomena. As a result, NFT can explain a wide variety of transient waveforms that have only been phenomenologically classified to date. This enables noninvasive fitting to be used to infer underlying physiological parameters. This physiology-based model reproduces the time series of different transient EEG waveforms; it has previously reproduced experimental EEG spectra, and waking ERPs, and many other observations, thereby unifying transient sleep waveforms with these phenomena.

摘要

为了运用神经场理论(NFT)来关联K复合波、纺锤波、诱发反应电位(ERP)和自发脑电图(EEG),基于生理学的皮质丘脑系统NFT被用于对皮质兴奋性和抑制性群体以及丘脑中继核和网状核进行建模。该模型的脉冲响应函数用于预测对脉冲的反应,并与睡眠研究中的瞬态波形进行比较。对经验数据的拟合随后允许推断潜在的脑生理学,并与其他波进行比较。通过将自发K复合波、纺锤波和其他瞬态波形视为对睡眠第2阶段自发EEG适当的脑参数的脉冲刺激的诱发反应,可以使用NFT来再现它们。使用这种方法,可以使用与先前用于解释清醒ERP和其他EEG现象相同的单一理论来分析自发K复合波和睡眠纺锤波。因此,NFT可以解释迄今为止仅在现象学上进行分类的各种瞬态波形。这使得可以使用无创拟合来推断潜在的生理参数。这种基于生理学的模型再现了不同瞬态EEG波形的时间序列;它先前已经再现了实验EEG频谱、清醒ERP以及许多其他观察结果,从而将瞬态睡眠波形与这些现象统一起来。

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