Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants
Xiaowan Wang1, Eline R de Groot1, Maria Luisa Tataranno1,2
1Department of Neonatology, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht 3584 EA, The Netherlands.
Insights
Active sleep in preterm infants is linked to better white matter development. Preserving sleep in the neonatal intensive care unit (NICU) may support brain plasticity and growth in vulnerable newborns.
Area of Science:
- Neonatal Neuroscience
- Developmental Biology
- Computational Biology
Background:
- White matter dysmaturation is a common issue in preterm infants in the neonatal intensive care unit (NICU).
- Active sleep, crucial for brain plasticity in animal models, has potential as a biomarker for early brain development.
Purpose of the Study:
- To investigate the association between active sleep percentage in preterm infants and subsequent white matter volume.
- To determine if active sleep can serve as an early predictor of white matter development in preterm neonates.
Main Methods:
- Developed a machine learning-based automated sleep stage classifier using routine neonatal intensive care unit (NICU) cardiorespiratory monitoring.
- Applied the classifier to 58 preterm infants to quantify active sleep percentage between 29-32 weeks postmenstrual age.
- Correlated active sleep percentage with total white matter volume measured by MRI at term-equivalent age using multiple linear regression.
Main Results:
- The automated sleep classifier demonstrated high performance (AUROC = 0.87).
- A higher percentage of active sleep during the preterm period was significantly associated with increased white matter volume at term-equivalent age (β = 0.31, FDR-adjusted p = 0.021).
Conclusions:
- Active sleep percentage is a significant predictor of white matter volume in preterm infants.
- Findings support the importance of sleep for brain development and suggest sleep preservation strategies in the NICU setting may benefit preterm infants.
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 [β = 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.
