Biomems Lab, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany
ISI Foundation, 10126 Turin, Italy
Neural Comput. 2020 May;32(5):887-911. doi: 10.1162/neco_a_01277. Epub 2020 Mar 18.
As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains.
由于同步活动与基本的大脑功能和病理状态有关，因此尖峰串同步已成为分析实验神经元数据的重要指标。已经提出了许多尖峰串同步度量标准，但没有黄金标准可以比较来自不同实验的结果。本工作旨在为量化致癫痫诱导物质（例如，荷包牡丹碱，BIC）对体外神经元尖峰串数据的影响，提供最佳同步度量标准的指导。记录的尖峰串数据可能会受到错误尖峰检测的影响，例如错过尖峰（假阴性）或噪声（假阳性）。因此，在不同的时间尺度上，对依赖（互相关、互信息、尖峰时间平铺系数）和独立于时间尺度的（尖峰对比度、相位同步（PS）、A-尖峰同步（A-SPIKE-synchronization）、A-ISI 距离、ARI-SPIKE 距离）同步度量标准进行了比较，以比较其对错误尖峰串的稳健性。为此，通过从实验数据中随机添加（假阳性）或删除（假阴性）尖峰（模拟操纵数据）来生成错误尖峰串。此外，还使用不同的尖峰检测阈值因子来分析实验数据，以确认同步度量标准的稳健性。所有的实验数据都是从微电极阵列芯片上的皮质神经元网络上记录下来的，这些网络显示出由物质 BIC 诱导的癫痫样活动。由于模拟操纵数据，尖峰对比度是唯一对假阴性和假阳性尖峰都稳健的度量标准。分析实验数据集表明，所有的度量标准都能够以统计学上显著的方式捕获 BIC 的作用，即使在低尖峰检测阈值下，尖峰对比度也显示出最高的统计显著性。总之，我们建议使用尖峰对比度来补充现有的同步度量标准，因为它是独立于时间尺度的，并且对错误尖峰串稳健。
