Abstract
The combination of a nonlinear time series analysis technique, Recurrence Quantification Analysis (RQA) based on Recurrence Plots (RPs), and traditional statistical analysis for neuronal electrophysiology is proposed in this paper as an innovative paradigm for studying the variation of spontaneous electrophysiological activity of in vitro Neuronal Networks (NNs) coupled to Multielectrode Array (MEA) chips. Recurrence, determinism, entropy, distance of activity patterns, and correlation in correspondence to spike and burst parameters (e.g., mean spiking rate, mean bursting rate, burst duration, spike in burst, etc.) have been computed to characterize and assess the daily changes of the neuronal electrophysiology during neuronal network development and maturation. The results show the similarities/differences between several channels and time periods as well as the evolution of the spontaneous activity in the MEA chip. RPs could be used for graphically exploring possible neuronal dynamic breaking/changing points, whereas RQA parameters are suited for locating them. The combination of RQA with traditional approaches improves the identification, description, and prediction of electrophysiological changes and it will be used to allow intercomparison between results obtained from different MEA chips. Results suggest the proposed processing paradigm as a valuable tool to analyze neuronal activity for screening purposes (e.g., toxicology, neurodevelopmental toxicology).
Highlights
In vitro neuronal networks are a simplified and accessible model of the central nervous system (CNS)
In particular we extracted the Mean Firing Rate (MFR) and Mean Burst Rate (MBR), and we studied the number of spikes per burst (i.e., Burst Amplitude (BA)), Burst Duration (BD) and Interburst Interval (IBI)
In vitro neuronal networks of cortical cells grown on Multielectrode Array (MEA) chips are becoming a widely used means to investigate basic neuronal properties [4,5,6,7,8], higher properties [2, 9], and electrophysiological response to pharmacological manipulation [13, 17, 18]
Summary
In vitro neuronal networks are a simplified and accessible model of the central nervous system (CNS). They exhibit morphological and physiological properties [1] and activity-dependent path-specific synaptic modification similar to the in vivo tissue [2, 3]. The spike trains can be accurately extracted from MEA recordings, for example, [21, 22], and neuronal activity is translated into time series of discrete events. The ensembles of spike trains simultaneously recorded from many channels represent multidimensional nonstationary pointprocess time series which makes the data analysis highly challenging [23]. Identification, description, and prediction of the changes of such dynamic during the neuronal network development are even more complex and challenging
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