ArticlePediatric research2026
Brain complexity in response to auditory stimulation improves evaluation of cerebral maturation in premature newborns.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
backgroundAs sensory pathways and neural interactions develop, the encoding of exogenous input becomes increasingly complex, reflecting greater functional integration and differentiation in cortical and subcortical networks. We hypothesize that integrating how the developing brain processes exogenous information improves the evaluation of cerebral maturation.
methodsWe developed a predictive model of cerebral age that integrates two complementary dimensions: the brain's processing of auditory stimulation and endogenous spontaneous neural activity. High-density EEGs were recorded from 74 premature neonates, born between 27 and 35 weeks of gestational age, and tested within their first week of life during rest and auditory stimulation.
resultsCalculating an adapted version of the perturbational complexity index, we demonstrate that the spatiotemporal complexity of neural responses to auditory stimulation increases with increasing gestational age at birth. While models based on the characteristics of spontaneous neural activity (linear slope of the aperiodic component and inter-burst interval) yield acceptable estimates of cerebral age, incorporating measures of neural complexity in response to exogenous stimuli significantly enhances predictive accuracy.
conclusionThese findings establish response complexity to auditory stimulation as a robust and automated biomarker of cerebral maturation. The integrated endogenous-exogenous framework can serve to recognize at-risk premature newborns and improve neurodevelopmental assessment. IMPACT: We designed a complexity index to measure how distributed cortical networks respond to auditory stimulation in premature newborns and showed its systematic modulation with neurodevelopment. We developed a predictive model of cerebral age integrating spontaneous and stimulus-driven EEG activity in premature newborns. We show that endogenous and exogenous neural markers offer complementary insights, enhancing evaluation of cerebral maturation and early detection of at-risk infants.
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