中文摘要：

本文旨在介绍神经元放电序列与节律性场电位间的相位分析方法。多通道在体记录技术能同时记录群体神经元和局部场电位的活动信号。神经元的放电活动一般表征为放电时间序列;而在局部场电位信号中,则包含有不同频率成分的周期性节律振荡。相位分析主要考察神经元放电时刻与周期性节律场电位相位间的相互关系。具体分析时,先运用Hilbert变换计算出某一频段节律场电位信号的瞬时相位值,然后再计算某一神经元放电序列中每个动作电位相对于该节律场电位的放电相位,最后通过考察这些放电相位的分布特性,来判断该神经元与该节律场电位相位间的放电相位关系。如一神经元放电序列对某种节律场电位的相位分布经统计检验不是随机的,则表明该神经元对这种节律场电位有放电锁相关系。Theta相位进动则是一种特殊的神经元放电与周期性节律场电位间的相位关系,也是海马位置细胞放电的基本特性之一。海马位置细胞在位置野内一般呈theta节律簇状放电模式,而相位进动是指每一theta波内放电的theta相位,相对上一theta波会逐渐提前。这一现象可通过对位置细胞放电的theta相位和动物实时位置使用线性模型来描述;并运用圆周线性相关分析法,计算它们之间的相关系数,从而研究位置细胞在位置野中的放电相对于theta相位的进动情况。通过相位分析,可以帮助我们了解神经元放电与节律性场电位信号间的时间信息编码特性。

英文摘要：

The purpose of this article is to introduce the measurements of phase coupling between spikes and rhythmic oscillations of local field potentials（LFPs）. Multi-channel in vivo recording techniques allow us to record ensemble neuronal activity and LFPs simultaneously from the same sites in the brain. Neuronal activity is generally characterized by temporal spike sequences, while LFPs contain oscillatory rhythms in different frequency ranges. Phase coupling analysis can reveal the temporal relationships between neuronal firing and LFP rhythms. As the first step, the instantaneous phase of LFP rhythms can be calculated using Hilbert transform, and then for each time-stamped spike occurred during an oscillatory epoch, we marked instantaneous phase of the LFP at that time stamp. Finally, the phase relationships between the neuronal firing and LFP rhythms were determined by examining the distribution of the firing phase. Phase-locked spikes are revealed by the non-random distribution of spike phase. Theta phase precession is a unique phase relationship between neuronal firing and LFPs, which is one of the basic features of hippocampal place cells. Place cells show rhythmic burst firing following theta oscillation within a place field. And phase precession refers to that rhythmic burst firing shifted in a systematic way during traversal of the field, moving progressively forward on each theta cycle. This relation between phase and position can be described by a linear model, and phase precession is commonly quantified with a circular-linear coefficient. Phase coupling analysis helps us to better understand the temporal information coding between neuronal firing and LFPs.