Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in...

Area of Science:

• Neuroscience
• Complexity Science
• Information Theory

Background:

• Scale-free brain activity is crucial for cognition, learning, and mental model formation, often linked to metastable brain dynamics.
• The spectral slope of neural activity has been explored as a marker for sleep stages and anesthetic effects.
• Sex-based differences in brain function exist but are not always clear in standard electroencephalography (EEG).

Purpose of the Study:

• To define and explore the Rényi entropy-complexity causality space for classical dynamical systems.
• To assess the ability of this space to discriminate between time series exhibiting scale-free activity.
• To apply this framework to intracranial electroencephalography (iEEG) data for detecting dynamic brain features.

Main Methods:

• Utilized the Bandt and Pompe method to analyze ordinal patterns in time series, creating probability distributions.
• Computed causal measures of Rényi entropy and complexity based on the parameter q.
• Developed a novel visual representation: the Rényi entropy-complexity causality space.

Main Results:

• The framework effectively discriminated simulated scale-free time series and correlated noise.
• Analysis of iEEG data revealed significant sex-based differences in the Rapid Eye Movement (REM) sleep stage.
• The supramarginal gyrus showed the most variability across different analytical modes.

Conclusions:

• The Rényi entropy-complexity causality space is a promising tool for analyzing scale-free brain dynamics.
• This framework can uncover subtle dynamic features and sex-based variations in brain activity.
• Findings may advance understanding of cognition, neurological disorders, and sex differences in brain function.