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

Diego Derman1, Damon D Pham2, Amanda F Mejia2

  • 1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States.

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Researchers mapped individual brain networks in infants using advanced fMRI analysis. This study reveals age-related changes in brain connectivity, highlighting differences between infants.

Keywords:
Bayesian modelingbrain developmentfunctional connectivityresting-state networksrs-fMRIstatistical methods

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Neuroimaging

Background:

  • Resting-state functional connectivity (rsFC) is crucial for understanding brain development.
  • Estimating rsFC in infants is challenging due to data acquisition complexities.
  • Previous methods struggled to capture individual variability in infant brain networks.

Purpose of the Study:

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

Main Methods:

  • Utilized resting-state fMRI data from 289 infants from the developing Human Connectome Project (dHCP) database.
  • Employed a novel data-driven Bayesian framework for network estimation.
  • Conducted analysis on the cortical surface using surface-based registration with neonatal atlases for enhanced alignment.

Main Results:

  • Successfully estimated subject-level maps for eight distinct brain networks.
  • Generated individual functional parcellation maps revealing 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 study demonstrates the efficacy of surface-based methods and Bayesian approaches for analyzing infant fMRI data.
  • The findings provide novel insights into individual variability and age-related changes in early brain network organization.
  • This framework advances the study of neurodevelopmental trajectories in very young populations.