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

Fikret E Kapucu1, Inkeri Välkki2, Jarno E Mikkonen3

  • 1Department 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.

Frontiers in Computational Neuroscience
|November 3, 2016
PubMed
Summary

We introduce Correlated Spectral Entropy (CorSE), a new method to analyze neuronal synchronization. CorSE effectively reveals network connectivity and functional relationships in neuronal populations using complex frequency analysis.

Keywords:
MEAcorrelationdeveloping neuronal networksmicroelectrode arraymouse cortical cellsrat cortical cellsspectral entropysynchronization

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Neuronal synchrony and asynchrony are critical for understanding interconnected neuronal networks.
  • Traditional methods for analyzing neuronal synchronization, such as time-domain analysis or frequency spectrum analysis, have limitations.
  • Spectral Entropy (SE) quantifies time series complexity by assessing frequency spectrum distribution uniformity, previously used in EEG analysis.

Purpose of the Study:

  • To revisit and provide evidence for the justification, usability, and benefits of the Correlated Spectral Entropy (CorSE) method.
  • To demonstrate CorSE's capability in identifying synchronized neuronal populations and analyzing network development.
  • To validate CorSE's effectiveness on both simulated and real experimental data.

Main Methods:

  • Correlated Spectral Entropy (CorSE) method, which analyzes temporal changes in the complexity of frequency signals by correlating time-varying spectral entropies.
  • Simulations including a tailored toy model and integrate-and-fire computational neuronal networks.
  • Analysis of in vitro microelectrode array (MEA) data from rat cortical cell cultures, compared with established event-based synchronization measures.
  • Tracking network development in dissociated mouse cortical cell cultures.

Main Results:

  • CorSE successfully identified synchronized neuronal populations in simulated data.
  • Analysis of in vitro MEA data yielded biologically plausible results, consistent with known synchronization measures.
  • CorSE effectively tracked the developmental trajectory of neuronal networks.
  • The method's reliance on continuous data analysis bypasses issues related to poor spike or event detection.

Conclusions:

  • Temporal correlations in frequency spectrum distributions, as captured by CorSE, reflect neuronal population network relationships.
  • CorSE is a robust method for revealing neuronal network synchronization using in vitro MEA field potential measurements.
  • The method is anticipated to be equally applicable to in vivo and ex vivo neuronal data analysis.