Identifying individual brain development using multimodality brain network.

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Brain Network Analysis

Background:

• Cortical development follows a hierarchical pattern, establishing large-scale functional brain hierarchies.
• Interindividual variability in brain development complicates understanding spatiotemporal network features related to mental health.

Purpose of the Study:

• To investigate how spatiotemporal features of brain networks change during development.
• To determine if multimodal brain network properties can predict brain age and identify mental disorders.

Main Methods:

• Collected resting-state electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data.
• Analyzed dynamic patterns of brain states and network shifts during growth.
• Assessed the robustness of multimodal brain networks for age prediction and disorder identification.

Main Results:

• During brain growth, global dynamic brain states become more active.
• Dominant brain networks shift from sensory to higher-level cognitive networks.
• Individual functional network patterns increasingly resemble adult patterns with stable spatial coupling.
• Multimodal brain network properties accurately identify healthy brain age and specific mental disorders.

Conclusions:

• Multimodal brain networks offer novel insights into functional brain development.
• These networks provide a robust approach for age prediction and individual diagnosis of mental health conditions.