{
  "abstract": "Reliably estimating cerebral maturation in preterm newborns is crucial for early identification of atypical neurodevelopment. We developed a deep-learning framework using a convolutional neural network (CNN) to predict postmenstrual age (PMA) from spontaneous neural activity in EEG recordings of 165 healthy preterm infants (28–38 weeks PMA). The model achieved a mean absolute error of 0.75 ± 0.60 weeks, with 73.9% of prediction errors within one week. To identify key neurophysiological drivers, we systematically manipulated signal components. Results showed that removing low-frequency activity (<4 Hz) significantly degraded performance and disrupting phase-amplitude coupling selectively impaired prediction accuracy before 34 weeks PMA, while high-frequency activity (>12 Hz) had minimal impact. Our results demonstrate that maturation signatures are primarily driven by slow-wave dynamics and their hierarchical coupling with faster oscillations, which are fundamental hallmarks of early neurodevelopment.",
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  "affiliations": [],
  "article_type": "Article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Alban Gallard",
      "orcid": "0000-0003-1813-6902"
    },
    {
      "name": "Alexander Billiot"
    },
    {
      "name": "Émilie Bourel-Ponchel"
    },
    {
      "name": "Pauline Brunel"
    },
    {
      "name": "Christèle Chazal"
    },
    {
      "name": "Guy Kongolo"
    },
    {
      "name": "Ghida Ghostine"
    },
    {
      "name": "Fabrice Wallois"
    },
    {
      "name": "Laura Routier"
    },
    {
      "name": "Sahar Moghimi",
      "orcid": "0009-0008-5677-5944"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/753hq1cpah6gy.bib",
  "cited_by": 0,
  "doi": "10.1016/j.isci.2026.118052",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734788/",
  "id": "753hq1cpah6gy",
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  "landing_url": "https://www.cell.com/iscience/fulltext/S2589-0042(26)03435-8",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
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      "heading",
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    [
      "heading",
      "Cognitive neuroscience"
    ],
    [
      "heading",
      "Systems neuroscience"
    ],
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      "openalex",
      "2805|Neural dynamics and brain function"
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  "pdf_url": "https://www.cell.com/iscience/pdf/S2589-0042(26)03435-8.pdf",
  "pmcid": "PMC13734788",
  "pmid": "42990601",
  "published": "2026-10-26",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/753hq1cpah6gy.ris",
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      "source": "crossref",
      "url": "https://linkinghub.elsevier.com/retrieve/pii/S2589004226034358"
    },
    {
      "first_seen": "2026-10-30T12:18:03Z",
      "native_id": "hal-05845430",
      "source": "hal",
      "url": "https://u-picardie.hal.science/hal-05845430v1"
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      "native_id": "PMC13734788",
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      "url": "https://doi.org/10.1016/j.isci.2026.118052"
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  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.neu",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "hal:2.sdv.mhep.ped",
      "subfield": "reproductive-child"
    }
  ],
  "title": "EEG-based deep learning estimates preterm infants’ cerebral age: Contribution of slow-wave and cross-frequency coupling",
  "title_text": "EEG-based deep learning estimates preterm infants’ cerebral age: Contribution of slow-wave and cross-frequency coupling",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:20:37Z",
  "url": "https://research.draconitic.com/paper/753hq1cpah6gy",
  "venue": "iScience",
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}