Mapping Typical and Altered Neurodevelopment with Sleep Macro- and Micro-Architecture

Abstract

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 NCH, a pediatric sleep clinic cohort. Among children without developmental disorders, sleep metrics derived from the electroencephalogram (EEG) displayed robust age-related changes consistently across datasets. Prominent stage-, band- and channel-specific developmental trajectories in spectral power were found. During non-rapid eye movement (NR) 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 individuals chronological age. The model performed with high accuracy (r = 0.95 in the held-out NCH testing sample and r = 0.88 in a second independent replication sample (PATS) with a broadly comparable age range). EEG-based age predictions reflected clinically meaningful neurodevelopmental differences; for example, compared to typically developing children, those with neurodevelopmental diagnoses (NDD) showed greater variability in predicted age, and children with Down syndrome or intellectual disability had significantly younger brain age predictions (respectively, 2.2 and 0.59 years less than their chronological age) compared to age-matched non-NDD children. Overall, our results indicate that sleep architecture offers a sensitive window for characterizing brain maturation, suggesting the potential for scalable, objective sleep-based biomarkers to measure typical and atypical neurodevelopment.