Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

Area of Science:

• Neuroscience
• Complex Systems Analysis
• Signal Processing

Background:

• Functional connectivity (FC) is typically studied in a scale-dependent manner.
• Neural processes may exhibit broadband dynamics with power-law scaling, indicating scale-free interdependence.
• Existing methods may not fully capture these scale-independent coupled dynamics.

Purpose of the Study:

• Introduce bivariate focus-based multifractal (BFMF) analysis as a tool for scale-free functional connectivity.
• Characterize broadband FC in resting-state electroencephalography (EEG) data.
• Investigate the scale-independent coupled dynamics of neural processes.

Main Methods:

• Applied BFMF analysis to resting-state EEG recordings from 12 subjects.
• Characterized broadband FC between 62 cortical regions.
• Analyzed within- and between- resting-state networks (RSNs) connectivity.

Main Results:

• Most connections exhibited true bivariate multifractality, suggesting genuine scale-free neural coupling.
• A consistent topological pattern of bivariate multifractality was observed across subjects.
• Within-RSN connections showed higher long-term autocorrelation, while between-RSN connections had stronger multifractality.

Conclusions:

• Provides statistical evidence for the bivariate multifractal nature of functional brain coupling.
• Validates BFMF analysis as a robust method for capturing scale-independent coupled neural dynamics.
• Highlights the importance of considering scale-free dynamics in brain connectivity research.