Cotterill Ellese, Charlesworth Paul, Thomas Christopher W, Paulsen Ole, Eglen Stephen J
Cambridge Computational Biology Institute, University of Cambridge, Cambridge, United Kingdom; and
Department of Physiology, Development and Neuroscience, Physiological Laboratory, University of Cambridge, Cambridge, United Kingdom.
J Neurophysiol. 2016 Aug 1;116(2):306-21. doi: 10.1152/jn.00093.2016. Epub 2016 Apr 20.
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.
准确识别爆发活动是表征神经网络活动的一个基本要素。尽管如此，尚未有一种用于识别尖峰序列中爆发的技术被广泛采用。相反，人们开发了许多方法来分析爆发活动，这些方法往往是临时制定的。在此，我们对其中八种方法在一系列尖峰序列中检测爆发的有效性进行了无偏评估。我们提出了理想的爆发检测技术应具备的一系列特征，并使用合成数据来评估每种方法在这些特性方面的表现。我们进一步运用每种方法重新分析来自小鼠视网膜神经节细胞的微电极阵列（MEA）记录，并检验它们与人类观察者检测到的爆发的一致性。我们表明，几种常见的爆发检测技术在分析具有各种特性的尖峰序列时表现不佳。我们确定了四种有前景的爆发检测技术，然后将其应用于人类诱导多能干细胞衍生神经元网络的MEA记录，并用于描述这些网络在数月发育过程中爆发活动的个体发生。我们得出结论，目前没有一种方法能够在一系列尖峰序列中提供“完美”的爆发检测结果；然而，与其他方法相比，MaxInterval和logISI这两种爆发检测技术表现更优。我们为使用当前技术对实验记录中的爆发活动进行稳健分析提供了建议。
