Network analysis of resting state EEG in the developing young brain: structure comes with maturation

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Network Science

Background:

• Childhood involves significant brain development and changes in structure and function.
• Graph theory offers a framework to model brain connectivity, identifying small-world properties like high clustering and short path lengths.
• Understanding developmental changes in functional brain networks is crucial for characterizing normal maturation.

Purpose of the Study:

• To investigate changes in functional brain network organization during normal development in young children.
• To apply graph theoretical concepts to electroencephalography (EEG) data to analyze network properties over time.
• To explore potential gender differences in brain network development.

Main Methods:

• Resting-state EEG (14 channels) was recorded from 227 children at ages 5 and 7.
• Synchronization Likelihood (SL) was computed to create SL-weighted graphs representing functional connectivity.
• Graph metrics including clustering coefficient, path length, and weight dispersion were analyzed for changes over time and by gender.

Main Results:

• Mean SL decreased from age 5 to 7 across all frequency bands.
• Clustering coefficient increased in the alpha band, and path length increased in all frequency bands.
• Girls exhibited higher synchronization and mean clustering in alpha and beta bands compared to boys.

Conclusions:

• Functional brain networks transition from random to more organized small-world networks during childhood development.
• This maturation involves decreased overall functional connectivity (SL), increased clustering, and longer path lengths, suggesting increased network efficiency.
• Gender plays a role in early brain network development, with observed differences in synchronization and clustering.