Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Brain Imaging

Background:

• Resting-state functional connectivity (rsFC) is crucial for understanding brain development.
• Estimating rsFC in infants is challenging due to motion artifacts and data limitations.
• Previous studies often lack individual-level precision in infant brain network analysis.

Purpose of the Study:

• To characterize individual variability in brain functional network organization in a large cohort of term-born infants.
• To develop and validate a novel data-driven Bayesian framework for infant rsFC analysis.
• To investigate the relationship between age and functional connectivity strength across different brain networks.

Main Methods:

• Utilized resting-state functional magnetic resonance imaging (fMRI) data from the developing Human Connectome Project (dHCP) cohort (N=289).
• Employed a novel data-driven Bayesian framework for network estimation.
• Conducted analysis exclusively on the cortical surface using surface-based registration guided by neonatal atlases for enhanced cross-subject alignment.

Main Results:

• Successfully estimated subject-level functional connectivity maps for fourteen brain networks/subnetworks.
• Generated individual functional parcellation maps, highlighting significant inter-subject differences.
• Identified a significant positive relationship between infant age and mean connectivity strength across all brain regions, including higher-order networks.

Conclusions:

• The novel Bayesian framework and surface-based analysis effectively capture individual variability in infant brain networks.
• The findings provide valuable insights into the developmental trajectory of functional brain organization in early life.
• This approach offers enhanced precision for studying brain development in young populations using fMRI data.