Mapping neurodevelopment with sleep macro- and micro-architecture across multiple pediatric populations

• Published in: NeuroImage clinical Vol. 41; p. 103552
• Main Authors: Kozhemiako, N., Buckley, A.W., Chervin, R.D., Redline, S., Purcell, S.M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2024; Elsevier
• Subjects: Adolescent; Brain; Child; Electroencephalography; Humans; Polysomnography; Regular; Sleep; Snoring
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2023.103552

•This study demonstrates the potential of sleep-based features to provide practical markers for tracking neurodevelopment in children across multiple clinical populations.•Using whole-night polysomnography data, we comprehensively describe robust age-related changes in multiple sleep metrics derived from electroencephalogram and demonstrate their utility in predicting an individual's chronological age with high accuracy.•The differences between the predicted age and the chronological age can be indicative of neurodevelopmental alterations.•This study suggests that sleep patterns can provide a sensitive way to understand the process of brain maturation and could lead to the creation of objective sleep-based biomarkers that can be used on a larger scale to measure neurodevelopment. Profiles of sleep duration and timing and corresponding electroencephalographic activity reflect brain changes that support cognitive and behavioral maturation and may provide practical markers for tracking typical and atypical neurodevelopment. To build and evaluate a sleep-based, quantitative metric of brain maturation, we used whole-night polysomnography data, initially from two large National Sleep Research Resource samples, spanning childhood and adolescence (total N = 4,013, aged 2.5 to 17.5 years): the Childhood Adenotonsillectomy Trial (CHAT), a research study of children with snoring without neurodevelopmental delay, and Nationwide Children’s Hospital (NCH) Sleep Databank, a pediatric sleep clinic cohort. Among children without neurodevelopmental disorders (NDD), sleep metrics derived from the electroencephalogram (EEG) displayed robust age-related changes consistently across datasets. During non-rapid eye movement (NREM) sleep, spindles and slow oscillations further exhibited characteristic developmental patterns, with respect to their rate of occurrence, temporal coupling and morphology. Based on these metrics in NCH, we constructed a model to predict an individual's chronological age. The model performed with high accuracy (r = 0.93 in the held-out NCH sample and r = 0.85 in a second independent replication sample – the Pediatric Adenotonsillectomy Trial for Snoring (PATS)). EEG-based age predictions reflected clinically meaningful neurodevelopmental differences; for example, children with NDD showed greater variability in predicted age, and children with Down syndrome or intellectual disability had significantly younger brain age predictions (respectively, 2.1 and 0.8 years less than their chronological age) compared to age-matched non-NDD children. Overall, our results indicate that sleep architectureoffers a sensitive window for characterizing brain maturation, suggesting the potential for scalable, objective sleep-based biomarkers to measure neurodevelopment.