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Related Experiment Video

Updated: Jun 19, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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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, USA.

Biorxiv : the Preprint Server for Biology
|August 16, 2024
PubMed
Summary

Researchers developed a novel Bayesian framework to map individual brain networks in infants using functional magnetic resonance imaging (fMRI). This method successfully identified unique brain connectivity patterns in 289 infants, revealing age-related changes in network strength.

Keywords:
Bayesian modelingRSFCbrain developmentfMRIfunctional connectivityneonatologyresting-state networksstatistical methods

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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.