Kapucu Fikret Emre, Mäkinen Meeri E-L, Tanskanen Jarno M A, Ylä-Outinen Laura, Narkilahti Susanna, Hyttinen Jari A K
Tampere University of Technology, Department of Electronics and Communications Engineering, Computational Biophysics and Imaging Group, BioMediTech, Biokatu 6, FI-33520 Tampere, Finland.
University of Tampere, NeuroGroup, BioMediTech, Biokatu 12, FI-33014 Tampere, Finland.
J Neurosci Methods. 2016 Feb 1;259:143-155. doi: 10.1016/j.jneumeth.2015.11.022. Epub 2015 Dec 7.
Neuronal networks are routinely assessed based on extracellular electrophysiological microelectrode array (MEA) measurements by spike sorting, and spike and burst statistics. We propose to jointly analyze sorted spikes and detected bursts, and hypothesize that the obtained spike type compositions of the bursts can provide new information on the functional networks.
Spikes are detected and sorted to obtain spike types and bursts are detected. In the proposed joint analysis, each burst spike is associated with a spike type, and the spike type compositions of the bursts are assessed.
The proposed method was tested with simulations and MEA measurements of in vitro human stem cell derived neuronal networks under different pharmacological treatments. The results show that the treatments altered the spike type compositions of the bursts. For example, 6-cyano-7-nitroquinoxaline-2,3-dione almost completely abolished two types of spikes which had composed the bursts in the baseline, while bursts of spikes of two other types appeared more frequently. This phenomenon was not observable by spike sorting or burst analysis alone, but was revealed by the proposed joint analysis.
The existing methods do not provide the information obtainable with the proposed method: for the first time, the spike type compositions of bursts are analyzed.
We showed that the proposed method provides useful and novel information, including the possible changes in the spike type compositions of the bursts due to external factors. Our method can be employed on any data exhibiting sortable action potential waveforms and detectable bursts.
神经元网络通常基于细胞外电生理微电极阵列(MEA)测量,通过尖峰分类以及尖峰和爆发统计进行评估。我们建议联合分析分类后的尖峰和检测到的爆发,并假设所获得的爆发的尖峰类型组成可以提供有关功能网络的新信息。
检测并分类尖峰以获得尖峰类型,并检测爆发。在所提出的联合分析中,每个爆发尖峰都与一种尖峰类型相关联,并评估爆发的尖峰类型组成。
使用模拟以及在不同药物处理下体外人干细胞衍生神经元网络的MEA测量对所提出的方法进行了测试。结果表明,处理改变了爆发的尖峰类型组成。例如,6-氰基-7-硝基喹喔啉-2,3-二酮几乎完全消除了在基线时构成爆发的两种尖峰类型,而另外两种类型的尖峰爆发出现得更频繁。这种现象单独通过尖峰分类或爆发分析无法观察到,但通过所提出的联合分析得以揭示。
现有方法无法提供通过所提出的方法可获得的信息:首次对爆发的尖峰类型组成进行了分析。
我们表明,所提出的方法提供了有用且新颖的信息,包括由于外部因素导致的爆发的尖峰类型组成的可能变化。我们的方法可应用于任何呈现可分类动作电位波形和可检测爆发的数据。