A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.
A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.
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DOI：
10.1152/jn.00093.2016
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发表时间：
2016-08-01
影响因子：
2.5
通讯作者：
Eglen SJ
中科院分区：
文献类型：
--
作者：
Cotterill E;Charlesworth P;Thomas CW;Paulsen O;Eglen SJ
我们提供了一个公正的定量评估现有的八种方法，用于识别神经元尖峰脉冲串的爆发。我们揭示了一些常用的突发检测技术的局限性，并提供建议的最佳实践，准确识别突发使用现有的技术。还提出了使用我们研究中性能最高的爆发检测器，从人类诱导多能干细胞衍生的神经元网络记录的新数据集中爆发活动的个体发育分析。准确识别爆发活动是表征神经元网络活动的基本要素。尽管如此，还没有一种识别尖峰脉冲序列中爆发的技术被广泛采用。相反，已经开发了许多用于分析爆发活动的方法，通常是临时性的。在这里，我们提供了一个公正的评估的有效性，这些方法中的八个在检测突发范围内的尖峰列车。我们建议一个列表的功能，一个理想的突发检测技术应该拥有和使用合成数据来评估这些属性方面的每种方法。我们进一步采用每一种方法重新分析微电极阵列（MEA）记录从小鼠视网膜神经节细胞和检查它们的连贯性与突发由人类观察员检测到的。我们发现，几种常见的突发检测技术在分析具有各种属性的尖峰序列时表现不佳。我们确定了四个有前途的爆发检测技术，然后将其应用于MEA记录的人类诱导多能干细胞衍生的神经元网络，并用于描述在这些网络的爆发活动在几个月的发展的个体发生。我们的结论是，没有目前的方法可以提供“完美的”突发检测结果在一系列的尖峰列车，但是，两个突发检测技术，最大间隔和logISI方法，优于其他方法。我们提供了强大的分析爆裂活动的实验记录，使用目前的技术的建议。
We provide an unbiased quantitative assessment of eight existing methods for identifying bursts in neuronal spike trains. We reveal limitations in a number of commonly used burst detection techniques and provide recommendations for the best practice for accurate identification of bursts using existing techniques. An analysis of the ontogeny of bursting activity in a novel data set of recordings from human induced pluripotent stem cell-derived neuronal networks, using the highest-performing burst detectors from our study, is also presented. Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide “perfect” burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.