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Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements

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Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements. / Kapucu, Fikret E.; Välkki, Inkeri; Mikkonen, Jarno E.; Leone, Chiara; Lenk, Kerstin; Tanskanen, Jarno M.A.; Hyttinen, Jari A.K.

julkaisussa: Frontiers in Computational Neuroscience, Vuosikerta 10, 112, 18.10.2016.

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Kapucu FE, Välkki I, Mikkonen JE, Leone C, Lenk K, Tanskanen JMA et al. Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements. Frontiers in Computational Neuroscience. 2016 loka 18;10. 112. https://doi.org/10.3389/fncom.2016.00112

Author

Kapucu, Fikret E. ; Välkki, Inkeri ; Mikkonen, Jarno E. ; Leone, Chiara ; Lenk, Kerstin ; Tanskanen, Jarno M.A. ; Hyttinen, Jari A.K. / Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements. Julkaisussa: Frontiers in Computational Neuroscience. 2016 ; Vuosikerta 10.

Bibtex - Lataa

@article{f6765154f73e45ad8842f85b9fac291b,
title = "Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements",
abstract = "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 and in vitro microelectrode 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 on in vitro MEA 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 real in vitro MEA 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 on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.",
author = "Kapucu, {Fikret E.} and Inkeri V{\"a}lkki and Mikkonen, {Jarno E.} and Chiara Leone and Kerstin Lenk and Tanskanen, {Jarno M.A.} and Hyttinen, {Jari A.K.}",
year = "2016",
month = "10",
day = "18",
doi = "10.3389/fncom.2016.00112",
language = "English",
volume = "10",
journal = "Frontiers in Computational Neuroscience",
issn = "1662-5188",
publisher = "Frontiers",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements

AU - Kapucu, Fikret E.

AU - Välkki, Inkeri

AU - Mikkonen, Jarno E.

AU - Leone, Chiara

AU - Lenk, Kerstin

AU - Tanskanen, Jarno M.A.

AU - Hyttinen, Jari A.K.

PY - 2016/10/18

Y1 - 2016/10/18

N2 - 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 and in vitro microelectrode 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 on in vitro MEA 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 real in vitro MEA 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 on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.

AB - 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 and in vitro microelectrode 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 on in vitro MEA 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 real in vitro MEA 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 on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.

U2 - 10.3389/fncom.2016.00112

DO - 10.3389/fncom.2016.00112

M3 - Article

VL - 10

JO - Frontiers in Computational Neuroscience

JF - Frontiers in Computational Neuroscience

SN - 1662-5188

M1 - 112

ER -