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Electroencephalographic functional connectivity in extreme prematurity: a pilot study based on graph theory.

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Electroencephalogram (EEG) connectivity analysis using graph theory reveals distinct patterns in extremely preterm infants compared to late-preterm infants at 35 weeks post-conception, suggesting potential developmental differences.

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Area of Science:

  • Neonatal neuroscience
  • Brain development
  • Computational neuroscience

Background:

  • Functional MRI (fMRI) offers insights into neonatal brain development but is not clinically feasible at the bedside.
  • Electroencephalogram (EEG) connectivity, especially using graph theory, is less explored in neonates.
  • Previous research has not extensively utilized graph theory for EEG connectivity analysis in early neonatal development.

Purpose of the Study:

  • To explore functional EEG connectivity using graph theory in extremely premature (ELGA) and late-preterm infants.
  • To assess EEG connectivity at 35 weeks' post-conception in healthy neonates.
  • To identify potential differences in brain network organization between ELGA and late-preterm infants.

Main Methods:

  • Recruited 16 neonates (8 ELGA, 8 late-preterm) with multichannel EEG recordings at 35 weeks' post-conception.
  • Calculated global (small-worldness) and local (clustering, strength) connectivity measures.
  • Analyzed EEG data using a single-subject connectivity matrix and graph theory.

Main Results:

  • Both ELGA and late-preterm infants exhibited small-worldness organization at 35 weeks' post-conception.
  • Extremely low gestational age (ELGA) infants showed hemispheric asymmetry in theta band strength (right < left), absent in late-preterm infants.
  • Late-preterm infants had significantly greater mean strength in the right hemisphere compared to ELGA infants.

Conclusions:

  • EEG connectivity measures may serve as an index of left-to-right maturation.
  • These measures could indicate developmental disadvantage in extremely preterm infants.
  • Graph theory analysis of EEG provides novel insights into neonatal brain network development.