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Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants.
Wang, Xiaowan; de Groot, Eline R; Tataranno, Maria Luisa; van Baar, Anneloes; Lammertink, Femke; Alderliesten, Thomas; Long, Xi; Benders, Manon J N L; Dudink, Jeroen.
Afiliación
  • Wang X; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
  • de Groot ER; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
  • Tataranno ML; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
  • van Baar A; Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht 3584 CX, The Netherlands.
  • Lammertink F; Child and Adolescent Studies, Utrecht University, Utrecht 3584 CS, The Netherlands.
  • Alderliesten T; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
  • Long X; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
  • Benders MJNL; Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht 3584 CX, The Netherlands.
  • Dudink J; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612 AZ, The Netherlands.
J Neurosci ; 44(5)2024 01 31.
Article en En | MEDLINE | ID: mdl-38124010
ABSTRACT
White matter dysmaturation is commonly seen in preterm infants admitted to the neonatal intensive care unit (NICU). Animal research has shown that active sleep is essential for early brain plasticity. This study aimed to determine the potential of active sleep as an early predictor for subsequent white matter development in preterm infants. Using heart and respiratory rates routinely monitored in the NICU, we developed a machine learning-based automated sleep stage classifier in a cohort of 25 preterm infants (12 females). The automated classifier was subsequently applied to a study cohort of 58 preterm infants (31 females) to extract active sleep percentage over 5-7 consecutive days during 29-32 weeks of postmenstrual age. Each of the 58 infants underwent high-quality T2-weighted magnetic resonance brain imaging at term-equivalent age, which was used to measure the total white matter volume. The association between active sleep percentage and white matter volume was examined using a multiple linear regression model adjusted for potential confounders. Using the automated classifier with a superior sleep classification performance [mean area under the receiver operating characteristic curve (AUROC) = 0.87, 95% CI 0.83-0.92], we found that a higher active sleep percentage during the preterm period was significantly associated with an increased white matter volume at term-equivalent age [ß = 0.31, 95% CI 0.09-0.53, false discovery rate (FDR)-adjusted p-value = 0.021]. Our results extend the positive association between active sleep and early brain development found in animal research to human preterm infants and emphasize the potential benefit of sleep preservation in the NICU setting.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Recien Nacido Prematuro / Sustancia Blanca Límite: Female / Humans / Infant / Newborn Idioma: En Revista: J Neurosci Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Recien Nacido Prematuro / Sustancia Blanca Límite: Female / Humans / Infant / Newborn Idioma: En Revista: J Neurosci Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos