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Updated: Jun 29, 2025
Cortical Source Analysis of High-Density EEG Recordings in Children
Published on: June 30, 2014
A growth chart of brain function from infancy to adolescence based on EEG
Kartik K Iyer1, James A Roberts2, Michaela Waak3
1Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Insights
A new functional brain age (FBA) tool uses electroencephalography (EEG) to accurately track brain development in children. This scalable method provides objective neurodevelopmental insights, comparable to physical growth charts.
Area of Science:
- Neuroscience
- Developmental Pediatrics
- Biomedical Engineering
Background:
- Objective, quantitative tools for assessing pediatric neurodevelopment are limited and not easily scalable for clinical use.
- Electroencephalography (EEG) offers a reliable method for measuring brain function through direct cortical activity recordings.
Purpose of the Study:
- To develop and validate a novel measure of functional brain age (FBA) for children.
- To assess the scalability and accuracy of an automated EEG-based tool for tracking neurodevelopment.
Main Methods:
- A residual neural network was employed to interpret pediatric EEG data for FBA calculation.
- The study included 1056 typically developing children aged 1 month to 18 years.
- Analysis involved 10- to 15-minute EEG segments recorded during light sleep.
Main Results:
- The developed FBA demonstrated a weighted mean absolute error (wMAE) of 0.85 years.
- A simplified two-channel FBA version showed a wMAE of 1.51 years, validated on an independent dataset (wMAE = 2.27 years).
- The tool detected group-level maturational delays in children with Trisomy 21.
Conclusions:
- Functional brain age (FBA) derived from EEG is an accurate, practical, and scalable automated tool.
- This method effectively tracks brain function maturation throughout childhood.
- The accuracy of the FBA tool is comparable to established physical growth charts.
Background:
In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function.
Methods:
We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states).
Findings:
The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028).
Interpretation:
A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts.
Funding:
This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.
