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Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value

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Spectral coherence often inaccurately measures neural synchronization. A phase-locking value (PLV) method, using Hilbert Transform and Singular Spectrum Decomposition, offers a more reliable alternative for quantifying neural network information flow.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Neuronal synchronization is crucial for information coordination in cortical networks.
  • Spectral coherence is a common but potentially flawed method for assessing neural phase-locking.
  • Understanding synchronization dynamics is key to deciphering neural communication.

Purpose of the Study:

  • To systematically evaluate the accuracy of spectral coherence for quantifying neural synchronization.
  • To identify limitations of spectral coherence, especially in partially synchronized states.
  • To explore alternative methods for robustly measuring neural synchronization and information flow.

Main Methods:

  • Simulated coupled oscillatory signals using phase-oscillator and spiking neural network models.
  • Assessed spectral coherence against expected phase-locking across various parameters and signal-to-noise ratios.
  • Investigated spectral coherence's correlation with transfer entropy (TE) for information flow.
  • Explored the phase-locking value (PLV) method utilizing Hilbert Transform (HT) and Singular Spectrum Decomposition (SSD) for instantaneous phase reconstruction.

Main Results:

  • Spectral coherence significantly deviated from true phase-locking in a wide parameter range.
  • Increasing signal-to-noise ratio did not improve spectral coherence accuracy.
  • Spectral coherence failed to accurately capture synchronization in partially synchronized states due to rapid signal changes.
  • Spectral coherence did not consistently reflect synchrony-mediated information flow measured by TE.
  • PLV method demonstrated accurate synchronization estimation and better tracking of information flow.

Conclusions:

  • Spectral coherence is unreliable for quantifying neural synchronization, particularly in intermittent states.
  • The PLV method, employing HT and SSD, provides a more accurate and versatile approach for analyzing neural synchronization.
  • PLV is suitable for non-stationary signals and offers improved insights into synchronization-mediated information transfer between neural networks.