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Updated: Jan 10, 2026
Cortical Source Analysis of High-Density EEG Recordings in Children
Published on: June 30, 2014
Decoding brain age predictions from sleep electroencephalography across infancy to adolescence
Kartik K Iyer1,2, Sally Staton3, Andrew Collaro3,4
1QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Australia. Kartik.Iyer@qimrberghofer.edu.au.
Insights
We developed a Functional Brain Age (FBA) using electroencephalography (EEG) to track brain maturation in children. This non-invasive tool accurately estimates neurodevelopmental health from sleep patterns.
Area of Science:
- Neuroscience
- Developmental Biology
- Biomedical Engineering
Background:
- Childhood sleep electroencephalography (EEG) patterns reflect brain maturation.
- Age-aligned EEG provides insights into neurodevelopment.
- Objective markers of neurodevelopmental health are needed.
Purpose of the Study:
- To predict chronological age from overnight EEG in children using supervised neural networks.
- To derive a Functional Brain Age (FBA) across different sleep stages.
- To assess FBA accuracy and reliability as a marker of neurodevelopmental health.
Main Methods:
- Utilized supervised neural networks to analyze overnight EEG data from 814 children.
- Calculated FBA across wake, NREM (N1-N3), and REM sleep stages.
- Evaluated prediction accuracy, influencing factors (EEG channels, sleep segments, data quality), and explainability.
Main Results:
- Achieved an overall Mean Absolute Error (MAE) of 0.96 years for FBA prediction.
- Prediction accuracy varied developmentally (infancy MAE: 0.78 years, childhood MAE: 0.87 years, adolescence MAE: 1.55 years).
- FBA accuracy was highest during N2, N3, and REM sleep stages, correlating with known developmental EEG changes.
Conclusions:
- Functional Brain Age derived from sleep EEG is a reliable, non-invasive tool for tracking childhood brain maturation.
- FBA can serve as an objective marker for neurodevelopmental health.
- Scalable EEG-based tools can enhance monitoring of child brain development.
Abstract:
Childhood sleep electroencephalography (EEG) reveals brain maturation patterns aligned with age, offering a window into development at the bedside. Leveraging this, we used supervised neural networks to predict age from overnight EEG and derive a Functional Brain Age (FBA) across wake, NREM (N1-N3), and REM sleep in 814 children with clinically normal sleep studies. We evaluated how FBA varies across sleep architecture and assessed the accuracy of neural networks, EEG channels, sleep segments, data quality, quantitative EEG features, and explainability methods on prediction performance. Prediction accuracy varied developmentally, with a mean absolute error (MAE) of 0.78 years in infancy (0-2 years), 0.87 years in childhood (2-12 years), and 1.55 years in adolescence (12-18 years), yielding an overall MAE of 0.96 years (95CI 0.90-1.01). FBA fell within ± 25% of chronological age in over 95% of children, with highest accuracy (< 1 year MAE) during N2, N3, and REM stages that reflect well-defined developmental EEG changes such as delta power and spindles. FBA reliability was shaped by signal quality and stable, age-specific patterns across sleep stages. Explainability analyses showed that network activations aligned with quantitative EEG features, supporting the biological validity of FBA. These findings support scalable, non-invasive tools that use sleep EEG to track brain maturation as an objective marker of neurodevelopmental health.
