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

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.