Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model
Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model
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DOI：
10.1016/j.neuroimage.2009.12.040
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发表时间：
2010-09-01
期刊：
影响因子：
5.7
通讯作者：
Panzeri, Stefano
中科院分区：
文献类型：
--
作者：
Mazzoni, Alberto;Whittingstall, Kevin;Panzeri, Stefano
尽管脑电图广泛用于测量人脑的大规模动态，但人们对脑电图动态与皮层神经元潜在尖峰率之间的关系知之甚少。然而，最近的神经生理学实验取得了进展，报告称脑电图 delta 带相位和 gamma 带幅度可靠地预测了视觉皮层神经元尖峰时间过程的一些互补方面。为了阐明这些发现背后的机制，我们假设脑电图δ相位反映了由于感觉刺激的夹带或持续活动的波动而引起的网络输入缓慢波动引起的局部皮质兴奋性的变化，并且由此产生的局部兴奋性波动调节尖峰速率和产生伽马带振荡的兴奋性抑制环路的参与。我们通过模拟由动态输入刺激的兴奋性和抑制性神经元的循环网络来定量测试这些假设，该网络呈现与自然视觉刺激和自发活动期间丘脑反应相似的时间规律。该网络模型详细再现了尖峰率和脑电图之间的实验关系，并表明从脑电图δ相位或伽马振幅获得的尖峰率预测的互补性源于兴奋抑制环路参与的非线性和网络输入振幅的时间调制，这分别限制了仅从伽马振幅或δ相位获得的尖峰率的可预测性。该模型还提出了改进和扩展当前脑电图峰值速率在线预测算法的方法。 (C) 2009 Elsevier Inc. 保留所有权利。
Despite the widespread use of EEGs to measure the large-scale dynamics of the human brain, little is known on how the dynamics of EEGs relates to that of the underlying spike rates of cortical neurons. However, progress was made by recent neurophysiological experiments reporting that EEG delta-band phase and gamma-band amplitude reliably predict some complementary aspects of the time course of spikes of visual cortical neurons. To elucidate the mechanisms behind these findings, here we hypothesize that the EEG delta phase reflects shifts of local cortical excitability arising from slow fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations in ongoing activity, and that the resulting local excitability fluctuations modulate both the spike rate and the engagement of excitatory-inhibitory loops producing gamma-band oscillations. We quantitatively tested these hypotheses by simulating a recurrent network of excitatory and inhibitory neurons stimulated with dynamic inputs presenting temporal regularities similar to that of thalamic responses during naturalistic visual stimulation and during spontaneous activity. The network model reproduced in detail the experimental relationships between spike rate and EEGs, and suggested that the complementariness of the prediction of spike rates obtained from EEG delta phase or gamma amplitude arises from nonlinearities in the engagement of excitatory-inhibitory loops and from temporal modulations in the amplitude of the network input, which respectively limit the predictability of spike rates from gamma amplitude or delta phase alone. The model suggested also ways to improve and extend current algorithms for online prediction of spike rates from EEGs. (C) 2009 Elsevier Inc. All rights reserved.