Structural and functional connectome relationships in early childhood

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Brain Imaging

Background:

• The relationship between brain structure and function is well-established in adults and older children.
• However, how this structure-function coupling develops in early childhood remains largely unknown.
• Understanding this developmental trajectory is crucial for comprehending typical and atypical brain development.

Purpose of the Study:

• To investigate the developmental trajectory of cortical structure-function coupling from infancy to early childhood.
• To compare structure-function relationships in young children with those in adults.
• To develop and validate a novel deep learning model for predicting functional brain connectivity from structural connectivity.

Main Methods:

• Longitudinal neuroimaging data from 360 children scanned at ages 1, 2, 4, and 6 years.
• A comparison dataset of 89 adults.
• Application of a novel graph convolutional neural network (GCNN) deep learning model with a custom loss function.

Main Results:

• Early childhood exhibits regional patterns of structure-function coupling that mirror adult patterns.
• The developed GCNN deep learning model significantly improved the prediction accuracy of individual functional connectivity from structural connectivity.
• The model demonstrated enhanced ability to capture inter-subject variability in brain connectivity.

Conclusions:

• Cortical structure-function coupling is established early in development and shows continuity into adulthood.
• Advanced deep learning approaches can effectively model and predict individual brain connectivity patterns.
• This research provides novel insights into early brain development and offers a powerful tool for future neuroscience research.