PDF(Pubmed)
Abstract：
Objective. Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron\'s spiking, one might attempt to seek peaks in the spike train\'s power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period (\'RP\', the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established \'shuffling\' procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach. In a novel \'residuals\' method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the precedingnrmilliseconds. Finally, we compute the PSD of the model\'s residuals.Main results. We compared the residuals and shuffling methods\' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey-in which alpha-beta oscillations (8-30 Hz) were anticipated-the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance. These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.
摘要：
Objective.振荡作为神经系统疾病的标志和神经调节目标而突出。为了检测神经元尖峰中的振荡，人们可能试图寻找峰值序列的功率谱密度（PSD）超过一个平坦的基线。然而对于一个非振荡的神经元，PSD不平坦:恢复期(\"RP\",尖峰概率的尖峰后下降，从不应期开始)引入全局频谱失真。已建立的“混洗”程序通过去除由尖峰间间隔（ISI）分布解释的频谱分量来校正RP失真。然而,此过程牺牲了ISI中存在的振荡相关信息，因此在PSD中。我们询问点过程模型(PPM)是否可以实现更有选择性的RP失真去除，从而能够改进振荡检测。方法。在一种新颖的“残差”方法中，我们首先从ISI分布估计RP持续时间(nr)。然后，我们将尖峰序列与PPM拟合，该PPM根据自先前nrms内的任何尖峰中的最新尖峰所经过的时间来预测尖峰可能性。最后,我们计算模型残差的PSD。主要结果。我们比较了残差和混洗方法的能力，以使准确的振荡检测与平坦的基线假设测试。在合成数据上，残差方法在真实与假阳性振荡功率的分类中通常优于混洗方法，主要是由于稀疏尖峰序列的灵敏度增强。在来自帕金森猴的内部苍白球（GPi）和腹外侧前丘脑（VLa）的单单位数据中-预期会出现α-β振荡（8-30Hz）-残差方法报告了最大的发生率显着的α-β功率，低触发率预测残差-选择性振荡检测。意义。这些结果鼓励残差方法的持续发展，支持更精确的振荡检测。改进的振荡识别可以促进改进的疾病模型和治疗技术。
