A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development.

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Neuroimaging

Background:

• Most neurodevelopmental studies use single imaging modalities, limiting understanding of brain network architecture changes.
• Developmental changes in structural and functional brain connectomes during childhood and adolescence remain poorly understood.

Purpose of the Study:

• To characterize developmental changes in structural and functional brain connectomes using multimodal neuroimaging.
• To develop and implement a multimodal multilevel model (MMM) for investigating brain maturation.

Main Methods:

• Utilized diffusion MRI (dMRI) and resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC).
• Developed a multimodal multilevel model (MMM) to infer connection states, model structural-functional interplay, incorporate covariates, and enable scalable whole-brain analysis.
• MMM addresses challenges in multimodal connectivity analysis, including noisy measurements and network heterogeneity.

Main Results:

• Revealed that most white fiber connectivity growth during adolescence occurs in cognitive networks.
• Identified a significant increase (15%) in structural connections between the default mode and executive control networks.
• Uncovered that functional connectome development is primarily driven by global functional integration, not solely direct anatomical connections.

Conclusions:

• The study provides novel insights into adolescent neurodevelopment using multimodal connectomics.
• Findings highlight the importance of integrated structural and functional network analysis for understanding brain maturation.
• The developed MMM offers a scalable and robust framework for future neurodevelopmental research.