Spectral entropy based neuronal network synchronization analysis based on microelectrode array measurements
Kapucu, F. E., Vornanen, I., Mikkonen, J., Leone, C., Lenk, K., Tanskanen, J. M., & Hyttinen, J. (2016). Spectral entropy based neuronal network synchronization analysis based on microelectrode array measurements. Frontiers in Computational Neuroscience, 10, Article 112. https://doi.org/10.3389/fncom.2016.00112
Published inFrontiers in Computational Neuroscience
DisciplinePsykologiaMonitieteinen aivotutkimuskeskusPsychologyCentre for Interdisciplinary Brain Research
© 2016 Kapucu, Välkki, Mikkonen, Leone, Lenk, Tanskanen and Hyttinen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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. ...
PublisherFrontiers Research Foundation
ISSN Search the Publication Forum1662-5188
Publication in research information system
MetadataShow full item record
Except where otherwise noted, this item's license is described as © 2016 Kapucu, Välkki, Mikkonen, Leone, Lenk, Tanskanen and Hyttinen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Showing items with similar title or keywords.
Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics Kapucu, Fikret E.; Tanskanen, Jarno M. A.; Mikkonen, Jarno; Ylä-Outinen, Laura; Narkilahti, Susanna; Hyttinen, Jari A. K. (Frontiers Research Foundation, 2012)In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally ...
Effect of low-frequency stimulation on the maturation of neuronal networks in vitro Kärnä, Paula (2016)Solunsiirtohoitoja voidaan tulevaisuudessa mahdollisesti käyttää neurorappeumasairauksien, kuten Parkinsonin ja Alzheimerin tautien, hoitoon. Ihmisen kantasolut ovat suuren erilaistumiskykynsä vuoksi yksi lupaava hoitomuoto. ...
Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy Välkki, Inkeri A.; Lenk, Kerstin; Mikkonen, Jarno; Kapucu, Fikret E.; Hyttinen, Jari A. K. (Frontiers Research Foundation, 2017)Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying ...
Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain Zhang, Chi; Cong, Fengyu; Kujala, Tuomo; Liu, Wenya; Liu, Jia; Parviainen, Tiina; Ristaniemi, Tapani (MDPI, 2018)Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the ...
Synchronization to metrical levels in music depends on low-frequency spectral components and tempo Burger, Birgitta; London, Justin; Thompson, Marc; Toiviainen, Petri (Springer, 2018)Previous studies have found relationships between music-induced movement and musical characteristics on more general levels, such as tempo or pulse clarity. This study focused on synchronization abilities to music of ...