Functional network estimation using multigraph learning with application to brain maturation study.

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Cognitive Neuroscience

Background:

• While perinatal development is well-studied, adolescence and early adulthood are critical periods for significant neurodevelopment.
• Understanding brain maturation during these stages is essential for identifying developmental trajectories.

Purpose of the Study:

• To explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood.
• To introduce a novel multigraph learning model for constructing FCNs from multi-paradigm functional magnetic resonance imaging (fMRI) data.

Main Methods:

• Utilized multi-paradigm task-based fMRI measurements to capture brain activity.
• Developed a multigraph learning model to jointly estimate FCNs from multiple fMRI time series, identifying shared graph structures.
• Applied graph Fourier transform (GFT) and eigen-analysis on low-frequency components to identify brain hub regions indicative of maturity.

Main Results:

• The proposed method effectively extracted informative brain connectivity patterns from both synthetic and real data.
• Identified 14 hub regions in the child group and 12 hub regions in the young adult group.
• Detected age-specific patterns in hub regions, suggesting significant changes in brain organization and maturity.

Conclusions:

• The novel multigraph learning approach accurately extracts brain connectivity networks by leveraging latent common structures across different fMRI paradigms.
• This method enhances understanding of brain development and aids in differentiating age groups based on brain network characteristics.