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Dynamic causal modelling of phase-amplitude interactions.

Erik D Fagerholm1, Rosalyn J Moran1, Inês R Violante2

  • 1Centre for Neuroimaging Sciences, Department of Neuroimaging, IoPPN, King's College London, United Kingdom.

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This study introduces phase-amplitude models for neuroimaging, enhancing dynamic causal modeling beyond phase-only approaches. These new models better capture neural dynamics in strongly coupled systems and brain activity.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Coupled phase oscillator models are widely used in neuroimaging to understand neural interactions.
  • Current phase-only models have limitations in explaining complex dynamics due to their restrictive nature.

Purpose of the Study:

  • To generalize dynamic causal modeling by incorporating both phase and amplitude information.
  • To enable separate quantification of phase and amplitude contributions to neural connectivity.

Main Methods:

  • Developed a generalized dynamic causal modeling framework incorporating phase and amplitude.
  • Validated the model using simulated data from coupled pendula and model-generated data.
  • Evaluated model performance against common neuroimaging metrics: Kuramoto order parameter, cross-correlation, phase-lag index, and spectral entropy.

Main Results:

  • Phase-amplitude models provide a more effective description of strongly coupled systems compared to phase-only models.
  • The phase-amplitude model captured four key neuroimaging metrics more effectively than phase-only models, except for spectral entropy.
  • Neural amplitude dynamics were shown to be crucial for describing activity in anesthetized rodent and macaque monkey brains using LFP and fMRI data, respectively.

Conclusions:

  • Incorporating amplitude alongside phase significantly enhances the explanatory power of oscillator models in neuroimaging.
  • Phase-amplitude models offer a more comprehensive approach to quantifying neural connectivity and dynamics.
  • This framework has implications for understanding brain states and neural interactions in various neuroimaging applications.