Weighted phase lag index and graph analysis: preliminary investigation of functional connectivity during resting...

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Brain Network Analysis

Background:

• Understanding resting-state functional connectivity in children is crucial for developmental neuroscience.
• Previous studies often used different connectivity metrics, necessitating a comparison with advanced methods.

Purpose of the Study:

• To investigate resting-state functional brain networks in 9-10-year-old children using Magnetoencephalography (MEG).
• To compare Weighted Phase Lag Index (WPLI) with coherence for connectivity estimation.
• To analyze network properties like modularity and centrality using graph analysis.

Main Methods:

• MEG data from 29 children (9-10 years old) were analyzed.
• Weighted Phase Lag Index (WPLI) was used to measure functional connectivity, compared against coherence.
• Graph theory metrics, including clustering coefficient (C), betweenness centrality (BC), and characteristic path length (L), were calculated.

Main Results:

• WPLI provided a distinct connectivity map compared to power and coherence.
• Graph analysis indicated high connectedness in the occipital area (BC, modularity) and identified isolated regions (C).
• A globally interconnected network with a short characteristic path length (L) was observed in the alpha band.

Conclusions:

• The findings suggest that resting-state functional brain networks in children aged 9-10 years resemble those in adults, particularly in the alpha band.
• The combination of WPLI and graph analysis offers a robust method for characterizing brain networks in children.
• This approach enhances the description of resting-state networks and their developmental trajectories.