Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.
Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations andin vitromicroelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied onin vitroMEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the realin vitroMEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based onin vitroMEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of correspondingin vivoandex vivodata analysis.
Department of Pervasive Computing, Tampere University of TechnologyTampere, Finland; Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of TechnologyTampere, Finland.
Fikret E
FE
Kapucu
Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland.
Inkeri
I
Välkki
Department of Psychology, Center for Interdisciplinary Brain Research, University of Jyväskylä Jyväskylä, Finland.
Jarno E
JE
Mikkonen
Department of Management and Production Engineering, Politecnico di Torino Torino, Italy.
Chiara
C
Leone
Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland.
Kerstin
K
Lenk
Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland.
Jarno M A
JM
Tanskanen
Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland.
Jari A K
JA
Hyttinen
MEA; correlation; developing neuronal networks; microelectrode array; mouse cortical cells; rat cortical cells; spectral entropy; synchronization