Multifractal dynamics of resting-state functional connectivity in the prefrontal cortex

Area of Science:

• Neuroscience
• Complex Systems
• Network Science

Background:

• Brain function relies on dynamic functional connectivity (FC) between neuronal populations.
• Traditional static FC analysis overlooks the spatio-temporal dynamics of brain networks.
• Understanding dynamic functional connectivity (DFC) is crucial for capturing neural system complexity.

Purpose of the Study:

• To investigate whether dynamic functional connectivity (DFC) in the prefrontal cortex exhibits multifractal dynamics.
• To determine if neural dynamics in the prefrontal cortex display scale-free and multifractal properties.
• To explore the complex temporal structuring of brain activity.

Main Methods:

• Utilized functional near-infrared spectroscopy (fNIRS) to monitor resting-state brain activity in healthy volunteers.
• Applied sliding window correlation (SWC) and graph theory to analyze network topology (Density, Clustering Coefficient, Efficiency).
• Employed multifractal time series analysis to characterize the temporal dynamics of network metrics.

Main Results:

• Confirmed that DFC in the prefrontal cortex displays scale-free, specifically multifractal, dynamics.
• Observed distinct multifractal characteristics across different topological network properties.
• Demonstrated reproducibility of results across SWC window sizes, with notable dependence of multifractal properties on window size.

Conclusions:

• Findings suggest a self-organized critical state underlies resting-state brain activity.
• The analysis of functional brain dynamics offers new perspectives for clinical applications.
• Multifractal analysis provides a robust method for characterizing complex brain network dynamics.