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Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis.

Spase Petkoski1, J Matias Palva2, Viktor K Jirsa1

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Brain connectivity and neural oscillation phase lags are explored using a delay-coupled oscillator model. The study reveals how brain structure influences synchronization patterns and phase relationships between distant regions.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Neural oscillation phase relationships are crucial for brain function but poorly understood.
  • The spatio-temporal structure of the brain is hypothesized to govern these phase lags.

Purpose of the Study:

  • To identify principles by which brain structure dictates phase lags between distant neural oscillations.
  • To model phase relations and their stability in delay-coupled oscillator networks.

Main Methods:

  • Utilized a phenomenological model of delay-coupled oscillators with varying topological complexity.
  • Derived and numerically confirmed phase relations for two oscillators and complex networks with bimodal delays.
  • Analyzed phase statistics using phase locking values (PLV) and scrutinized surrogate data impact.

Main Results:

  • Clustered delays can induce anti-phase synchronization; phase lag sign depends on natural frequencies and network interactions.
  • Faster oscillators phase lead in in-phase synchronization; stronger connected nodes lag during frequency depression.
  • Non-stationary synchronization leads to multimodal phase lags, potentially misleading if averaged.

Conclusions:

  • Brain spatio-temporal connectivity quantitatively impacts synchronization patterns and phase lags between regions.
  • The study uncovers mechanisms by which brain structure shapes phase distributions around 0 and π.
  • Methodological choices, like surrogate data selection, can affect the detection of weaker interhemispheric coherence.