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Preterm EEG: A Multimodal Neurophysiological Protocol
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Sensory event-related potential morphology predicts age in premature infants

Coen S Zandvoort1, Marianne van der Vaart1, Shellie Robinson1

  • 1Department of Paediatrics, University of Oxford, Oxford, United Kingdom.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|December 8, 2023
PubMed

Insights

Sensory-evoked cortical potentials accurately predict infant age, aiding in the identification of neurodevelopmental deviations. This brain age model correlates with nervous system maturity and long-term outcomes.

Area of Science:

  • Neuroscience
  • Developmental Biology
  • Biomedical Engineering

Background:

  • Estimating infant age is crucial for monitoring neurodevelopment.
  • Sensory-evoked cortical potentials offer a potential non-invasive biomarker for developmental assessment.

Purpose of the Study:

  • To investigate the efficacy of sensory-evoked cortical potentials in estimating infant post-menstrual age (PMA).
  • To develop a predictive model for infant age using neurophysiological responses.
  • To explore the relationship between predicted brain age and neurodevelopmental outcomes.

Main Methods:

  • Infants aged 28-40 weeks post-menstrual age (PMA) received visual and tactile stimuli.
  • Neurodynamic response functions were derived using principal component analysis.
  • A machine learning model was trained and validated to predict infant age from evoked responses.

Main Results:

  • Infant age was accurately predicted from evoked responses (mean absolute error of 1.41 weeks in training, 1.55 weeks in testing).
  • Predicted brain age showed significant correlation with measures of nervous system maturity.
  • Deviations in brain age were linked to long-term neurodevelopmental trajectories.

Conclusions:

  • Sensory-evoked potentials serve as reliable predictors of age in premature infants.
  • Brain age deviations identified through this method reflect meaningful biological and clinical differences in nervous system maturation.
  • The model holds potential for early detection of abnormal infant development and prediction of neurodevelopmental outcomes.
Abstract

Keywords:
ElectroencephalographyMachine learningPretermReflex withdrawalTactileVisual

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