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Mapping neurodevelopment with sleep macro- and micro-architecture across multiple pediatric populations
N Kozhemiako1, A W Buckley2, R D Chervin3
1Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
Insights
Sleep patterns and electroencephalogram (EEG) activity can track brain maturation in children. Researchers developed an accurate EEG-based model to predict chronological age, revealing neurodevelopmental differences in children with disorders.
Area of Science:
- Neuroscience
- Developmental Biology
- Biomarkers
Background:
- Sleep patterns and electroencephalogram (EEG) activity are crucial indicators of brain maturation during childhood and adolescence.
- These neurodevelopmental changes support cognitive and behavioral development and may serve as markers for typical and atypical neurodevelopment.
Purpose of the Study:
- To develop and validate a quantitative, sleep-based metric for assessing brain maturation.
- To evaluate the potential of electroencephalogram (EEG)-derived sleep metrics as biomarkers for neurodevelopment.
Main Methods:
- Utilized whole-night polysomnography data from two large cohorts (N=4,013, ages 2.5-17.5 years): the Childhood Adenotonsillectomy Trial (CHAT) and Nationwide Children's Hospital (NCH) Sleep Databank.
- Analyzed electroencephalogram (EEG) metrics during non-rapid eye movement (NREM) sleep, including sleep spindles and slow oscillations, for age-related changes.
- Constructed and validated a predictive model using NCH data to estimate chronological age based on EEG sleep metrics, with independent replication.
Main Results:
- Robust age-related changes in sleep metrics were observed in children without neurodevelopmental disorders (NDD) across datasets.
- The EEG-based age prediction model demonstrated high accuracy (r=0.93 in NCH, r=0.85 in PATS replication).
- Children with NDD exhibited greater variability in predicted age. Those with Down syndrome or intellectual disability showed significantly younger brain age predictions compared to controls.
Conclusions:
- Sleep architecture provides a sensitive measure of brain maturation.
- Objective, sleep-based biomarkers derived from EEG show promise for quantifying neurodevelopment.
- This approach has the potential for scalable application in tracking typical and atypical neurodevelopment.
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 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.
