Abstract :
The combination of a nonlinear time series analysis technique, Recurrence Quantification Analysis (RQA) based on RecurrencePlots (RPs), and traditional statistical analysis for neuronal electrophysiology is proposed in this paper as an innovativeparadigm for studying the variation of spontaneous electrophysiological activity of in vitro Neuronal Networks (NNs) coupledto Multielectrode Array (MEA) chips. Recurrence, determinism, entropy, distance of activity patterns, and correlation incorrespondence 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 networkdevelopment and maturation. The results show the similarities/differences between several channels and time periods as well asthe evolution of the spontaneous activity in the MEA chip. RPs could be used for graphically exploring possible neuronal dynamicbreaking/changing points, whereas RQA parameters are suited for locating them. The combination of RQA with traditionalapproaches improves the identification, description, and predic tion of electrophysiological changes and it will be used to allowintercomparison between results obtained from di fferent MEA chips. Results suggest the proposed processing paradigm as avaluable tool to analyze neuronal activity for screening purposes (e.g., toxicology, neurodevelopmental toxicology).
