Gärtner Matthias, Duvarci Sevil, Roeper Jochen, Schneider Gaby
Institute of Mathematics, Johann Wolfgang Goethe University, 60325 Frankfurt (Main), Germany.
Institute of Neurophysiology, Johann Wolfgang Goethe University, 60590 Frankfurt (Main), Germany.
J Neurosci Methods. 2017 Jun 15;285:69-81. doi: 10.1016/j.jneumeth.2017.05.008. Epub 2017 May 8.
Transient periods with reduced neuronal discharge - called 'pauses' - have recently gained increasing attention. In dopamine neurons, pauses are considered important teaching signals, encoding negative reward prediction errors. Particularly simultaneous pauses are likely to have increased impact on information processing.
Available methods for detecting joint pausing analyze temporal overlap of pauses across spike trains. Such techniques are threshold dependent and can fail to identify joint pauses that are easily detectable by eye, particularly in spike trains with different firing rates.
We introduce a new statistic called pausiness that measures the degree of synchronous pausing in spike train pairs and avoids threshold-dependent identification of specific pauses. A new graphic termed the cross-pauseogram compares the joint pausiness of two spike trains with its time shifted analogue, such that a (pausiness) peak indicates joint pausing. When assessing significance of pausiness peaks, we use a stochastic model with synchronous spikes to disentangle joint pausiness arising from synchronous spikes from additional 'joint excess pausiness' (JEP). Parameter estimates are obtained from auto- and cross-correlograms, and statistical significance is assessed by comparison to simulated cross-pauseograms.
Our new method was applied to dopamine neuron pairs recorded in the ventral tegmental area of awake behaving mice. Significant JEP was detected in about 20% of the pairs.
Given the neurophysiological importance of pauses and the fact that neurons integrate multiple inputs, our findings suggest that the analysis of JEP can reveal interesting aspects in the activity of simultaneously recorded neurons.
神经元放电减少的短暂时期——称为“停顿”——最近受到越来越多的关注。在多巴胺能神经元中，停顿被认为是重要的教学信号，编码负奖励预测误差。特别是同时出现的停顿可能对信息处理有更大的影响。
现有的检测联合停顿的方法分析了跨尖峰序列的停顿时间重叠。这些技术依赖于阈值，可能无法识别肉眼容易检测到的联合停顿，特别是在具有不同放电率的尖峰序列中。
我们引入了一种名为“停顿度”的新统计量，用于测量尖峰序列对中的同步停顿程度，并避免依赖阈值来识别特定的停顿。一种名为交叉停顿图的新图形将两个尖峰序列的联合停顿度与其时间移位的类似物进行比较，这样一个（停顿度）峰值表示联合停顿。在评估停顿度峰值的显著性时，我们使用一个具有同步尖峰的随机模型，以区分由同步尖峰产生的联合停顿度与额外的“联合过度停顿度”（JEP）。参数估计是从自相关图和交叉相关图中获得的，统计显著性是通过与模拟的交叉停顿图进行比较来评估的。
我们的新方法应用于在清醒行为小鼠的腹侧被盖区记录的多巴胺能神经元对。在大约20%的神经元对中检测到了显著的JEP。
鉴于停顿在神经生理学上的重要性以及神经元整合多个输入的事实，我们的研究结果表明，对JEP的分析可以揭示同时记录的神经元活动中有趣的方面。
