Instantaneous frequency and amplitude modulation of EEG in the hippocampus reveals state dependent temporal structure.
Instantaneous frequency and amplitude modulation of EEG in the hippocampus reveals state dependent temporal structure.
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海马体脑电图的瞬时频率和幅度调制揭示了状态依赖的时间结构。
DOI：
10.1109/iembs.2008.4649506
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Brown,EmeryN
中科院分区：
文献类型：
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作者：
Nguyen,DavidP;Barbieri,Riccardo;Wilson,MatthewA;Brown,EmeryN
脑电图和LFP活动反映了神经系统的动态和有组织的相互作用；因此，有可能利用脑节律的特征来确定神经网络的计算状态。当神经元网络被激活时，物理原理预测，场电位的频率内容本身应该反映网络状态，从而反映状态转换。一种描述大脑状态的新方法是量化调幅和调频活动的时间结构（振幅和频率随时间的变化）。在定量意义上，调幅和调频的概念在系统神经科学中几乎没有被探索过。考虑到调频活动的估计需要精细的时间和精确的瞬时频率估计，这并不奇怪。对于AM活动，希尔伯特变换的绝对值是足够的。在这里，我们概述了一种实用的极点跟踪算法，该算法对单变量AR过程使用卡尔曼滤波器来估计瞬时频率。我们使用模拟的啁啾和从大鼠海马记录的真实EEG/LFP数据来演示滤波器的性能；脑电图/LFP的AM/FM活动具有时间结构，并依赖于行为和认知状态。该算法有潜力成为表征电生理数据基本结构和对大脑计算状态进行分类的实用工具。
EEG and LFP activity reflect the dynamic and organized interactions of neural ensembles; therefore, it may be possible to use the features of brain rhythms to determine the computational state of a neuronal network. When neuronal networks are activated, physical principles predict that the frequency content of the field potential should reflect the network state, per se, and ergo the state transition. A novel way for characterizing brain states is by quantifying the temporal structure of AM and FM activity (change in amplitude and frequency over time) for brain rhythms of interest. The concept of AM and FM, in the quantitative sense, is virtually unexplored in systems neuroscience. This is not surprising considering estimation of FM activity requires fine temporal and precise estimation of instantaneous frequency. For AM activity, the absolute value of the Hilbert transform is sufficient. Here, we outline a practical pole tracking algorithm which uses a Kalman filter for univariate AR processes to estimate instantaneous frequency. We demonstrate the filter performance using simulated chirp and real EEG/LFP data recorded from the rat hippocampus; and show that AM/FM activity in EEG/LFP is temporally structured and dependent on behavioral and cognitive state. This algorithm has the potential to be a practical tool for characterizing fundamental structure in electrophysiology data and classifying computational states in the brain.