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Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks.

Alec G Sheffield1, Sachira Denagamage1,2, Mitchell P Morton1,3

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This study introduces Continuous-Time weighted Dynamic Bayesian Networks (CTwDBN) to map dynamic information flow in neural systems. CTwDBN reveals smoothly time-varying dependencies, outperforming traditional methods for analyzing complex brain activity.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Understanding neural information flow is vital but challenging due to the dynamic nature of brain networks.
  • Traditional methods often assume static networks, failing to capture real-time interactions.
  • Existing models struggle with the smoothly time-varying dynamics of neural connectivity.

Purpose of the Study:

  • Introduce Continuous-Time weighted Dynamic Bayesian Networks (CTwDBN) as a novel framework.
  • Develop a non-stationary graphical model to uncover time-varying conditional dependencies in neural data.
  • Analyze dynamic information flow in both synthetic and real electrophysiological recordings.

Main Methods:

  • Developed the Continuous-Time weighted Dynamic Bayesian Networks (CTwDBN) framework.
  • Validated CTwDBN on synthetic datasets to assess its accuracy in recovering ground-truth information flow.
  • Applied CTwDBN to electrophysiological recordings from a guided saccade task and resting-state fMRI data.

Main Results:

  • CTwDBN accurately recovered the structure and dynamics of information flow in synthetic data.
  • Analysis of electrophysiological recordings revealed temporal fluctuations in cortical network dependencies lasting longer than receptive field dynamics.
  • CTwDBN identified persistent fluctuations in resting-state cortical networks within a low-dimensional dependency space.

Conclusions:

  • CTwDBN is a versatile tool for analyzing dynamic information flow in neural systems.
  • The framework captures smoothly time-varying conditional dependencies missed by traditional static models.
  • CTwDBN has broad applicability for complex biological and artificial systems requiring dynamic network analysis.