Department of Neurology, University South Alabama, 307 University Blvd, Mobile, AL 36688, USA; Division of System Neurophysiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki 444-8585, Japan.
Division of System Neurophysiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki 444-8585, Japan; Department of Physiological Sciences, SOKENDAI, 38 Nishigonaka, Myodaiji, Okazaki 444-8585, Japan.
J Neurosci Methods. 2022 Apr 15;372:109532. doi: 10.1016/j.jneumeth.2022.109532. Epub 2022 Feb 17.
Spike trains are series of interspike intervals in a specific order that can be characterized by their probability distributions and order in time which refer to the concepts of rate and spike timing features. Periodic structure in the spike train can be reflected in oscillatory activities. Thus, there is a direct link between oscillator activities and the spike train. The proposed methods are to investigate the dependency of emerging oscillatory activities to the rate and the spike timing features.
First, the circular statistics methods were compared to Fast Fourier Transform for best estimation of spectra. Second, two statistical tests were introduced to help make decisions regarding the dependency of spectrum, or individual frequencies, onto rate and spike timing. Third, the methodology is applied to in-vivo recordings of basal ganglia neurons in mouse, primate, and human. Finally, this novel framework is shown to allow the investigation of subsets of spikes contributing to individual oscillators.
Use of circular statistical methods, in comparison to FFT, minimizes spectral leakage. Using virtual spike trains, the Rate versus Timing Dependency Spectrum Test (or RTDs-Test) permits identifying spectral spike trains solely dependent on the rate feature from those that are also dependent on the spike timing feature. Similarly, the Rate versus Timing Dependency Frequency Test (or RTDf-Test), allows to identify individual oscillators with partial dependency on spike timing. Dependency on spike timing was found for all in-vivo recordings but only in few frequencies. The mapping in frequency and time of dependencies showed a dynamical process that may be organizing the basal ganglia function.
The methodology may improve our understanding of the emergence of oscillatory activities and, possibly, the relation between oscillatory activities and circuitry functions.
尖峰序列是特定顺序的尖峰间间隔序列，可以通过它们的概率分布和时间顺序来特征化，这两个特征分别指的是率和尖峰时间特征。尖峰序列中的周期性结构可以反映在振荡活动中。因此，振荡器活动与尖峰序列之间存在直接联系。提出的方法是研究新兴的振荡活动与率和尖峰时间特征之间的依赖关系。
首先，比较了循环统计方法和快速傅里叶变换，以获得最佳的频谱估计。其次，引入了两种统计检验方法，以帮助决定频谱或单个频率是否依赖于率和尖峰时间。第三，该方法应用于在体记录的小鼠、灵长类和人类基底神经节神经元。最后，该新框架被证明允许研究对个体振荡器有贡献的子脉冲集。
与 FFT 相比，循环统计方法的使用可最大程度地减少频谱泄漏。使用虚拟尖峰序列，率与时间依赖谱测试（或 RTDs-Test）允许从仅依赖于率特征的频谱尖峰序列中识别出也依赖于尖峰时间特征的频谱尖峰序列。类似地，率与时间依赖频率测试（或 RTDf-Test）允许识别出部分依赖于尖峰时间的个体振荡器。所有在体记录都发现了对尖峰时间的依赖，但只在少数频率下发现。依赖性在频率和时间上的映射显示了一个可能组织基底神经节功能的动态过程。
该方法可以提高我们对振荡活动出现的理解，并且可能有助于理解振荡活动与电路功能之间的关系。
