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A brain-age model for preterm infants based on functional connectivity.

M Lavanga1,2, O De Wel1,2, A Caicedo1,2

  • 1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Box 2446, 3001, Leuven, Belgium.

Physiological Measurement
|March 30, 2018
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Summary
This summary is machine-generated.

This study developed a predictive age model for premature infants using electroencephalogram (EEG) functional connectivity. Brain network features accurately predict post-menstrual age, aiding in understanding infant neurodevelopment.

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Area of Science:

  • Neuroscience
  • Developmental Biology
  • Computational Biology

Background:

  • Understanding early brain development in premature infants is crucial for timely interventions.
  • Electroencephalogram (EEG) functional connectivity offers insights into developing neural networks.
  • Existing predictive models for infant age have limitations.

Purpose of the Study:

  • To investigate the development of EEG functional connectivity in premature infants.
  • To create a predictive age model for premature infants based on EEG data.
  • To characterize the maturation of brain network topology.

Main Methods:

  • Assessed functional connectivity using coherency (ImCoh, MSC), phase locking value, and Hilbert-Schimdt dependence (HSD).
  • Analyzed EEG data from 30 infants with post-menstrual ages ranging from 27 to 42 weeks.
  • Employed graph-theory indices to investigate EEG coupling topology.

Main Results:

  • Observed significant decreases in ImCoh (θ, α bands) and MSC (β band) with maturation.
  • Found modest positive correlations between PMA and HSD, [Formula: see text], and MSC (γ band).
  • Achieved a mean absolute error of 1.51 weeks for PMA prediction with an adjusted R² of 0.8.

Conclusions:

  • Neonatal brain development is characterized by a segregation of cortex connectivity.
  • Lagged-interaction network features effectively describe neonates' brain development.
  • The developed model shows promise for accurate age prediction in premature infants.