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Background <p>As 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.</p> Methods <p>We 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.</p> Results <p>Calculating 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.</p> Conclusion <p>These 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.</p> Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>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.</p> </ItemContent> <ItemContent> <p>We developed a predictive model of cerebral age integrating spontaneous and stimulus-driven EEG activity in premature newborns.</p> </ItemContent> <ItemContent> <p>We show that endogenous and exogenous neural markers offer complementary insights, enhancing evaluation of cerebral maturation and early detection of at-risk infants.</p> </ItemContent> </UnorderedList></p>

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Brain complexity in response to auditory stimulation improves evaluation of cerebral maturation in premature newborns

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Bahar Saadatmehr,
  • Alban Gallard,
  • Mohammadreza Edalati,
  • Guy Kongolo,
  • Ghida Ghostine,
  • Christelle Chazal,
  • Pauline Brunel,
  • Olivier David,
  • Fabrice Wallois,
  • Sahar Moghimi

摘要

Background

As 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.

Methods

We 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.

Results

Calculating 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.

Conclusion

These 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.