Complex intermittency blurred by noise: theory and application to neural dynamics

Area of Science:

• Neuroscience
• Computational Neuroscience
• Complex Systems

Background:

• Understanding the dynamics of metastable brain states is crucial for cognitive neuroscience.
• Rapid transitions in brain activity, detectable via electroencephalography (EEG), serve as critical change points.
• Existing models may not fully capture the complexity of these state transitions.

Purpose of the Study:

• To propose a novel mathematical model for transitions between metastable mental states.
• To investigate the role of complex brain dynamics and rapid EEG signal changes in these transitions.
• To provide an analytical solution for the waiting-time distribution of the proposed model.

Main Methods:

• Utilizing a superimposed process model: a non-Poissonian intermittent process combined with a Poisson process.
• Analyzing simultaneous EEG signals from different cortical areas to identify transition points.
• Deriving an analytical solution for the waiting-time distribution of the proposed dynamical model.

Main Results:

• The proposed model successfully reproduces behaviors observed in physiological data, specifically the waiting-time distribution.
• The non-Poissonian component effectively signals brain complexity during state transitions.
• The model accounts for various reported brain dynamics, even those appearing contradictory in literature.

Conclusions:

• The developed model offers a robust framework for understanding metastable brain state dynamics.
• The model's ability to fit EEG data suggests its potential for analyzing cognitive processes.
• Further research is needed to elucidate the specific role of the superimposed Poisson process in brain function.