ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2024
Sensory event-related potential morphology predicts age in premature infants.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Article
- Brain activity as a candidate biomarker for personalised caffeine treatment in premature neonates.Frontiers in pediatrics · 2026Article
- Music-based research in neonatal intensive care units requires guidelines grounded in developmental neurology and responsive to the individual needs of preterm infants.Frontiers in psychology · 2026Article
- [Research progress in electroencephalogram-based brain age prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Pharmacological treatment for apnoea of prematurity-the need for an individualised approach.Frontiers in pediatrics · 2025Review
- Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
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Abstract
objectiveWe investigated whether sensory-evoked cortical potentials could be used to estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment.
methodsInfants aged between 28- and 40-weeks post-menstrual age (PMA) (166 recording sessions in 96 infants) received trains of visual and tactile stimuli. Neurodynamic response functions for each stimulus were derived using principal component analysis and a machine learning model trained and validated to predict infant age.
resultsPMA could be predicted accurately from the magnitude of the evoked responses (training set mean absolute error and 95% confidence intervals: 1.41 [1.14; 1.74] weeks,p = 0.0001; test set mean absolute error: 1.55 [1.21; 1.95] weeks,p = 0.0002). Moreover, we show that their predicted age (their brain age) is correlated with a measure known to relate to maturity of the nervous system and is linked to long-term neurodevelopment.
conclusionsSensory-evoked potentials are predictive of age in premature infants and brain age deviations are related to biologically and clinically meaningful individual differences in nervous system maturation. SIGNIFICANCE: This model could be used to detect abnormal development of infants' response to sensory stimuli in their environment and may be predictive of neurodevelopmental outcome.
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