A growth chart of brain function from infancy to adolescence based on EEGResearch in context
Kartik K. Iyer,
James A. Roberts,
Michaela Waak,
Simon J. Vogrin,
Ajay Kevat,
Jasneek Chawla,
Leena M. Haataja,
Leena Lauronen,
Sampsa Vanhatalo,
Nathan J. Stevenson
Affiliations
Kartik K. Iyer
Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Brisbane, Australia; Corresponding author. Brain Modeling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
James A. Roberts
Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia
Michaela Waak
Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
Simon J. Vogrin
St Vincent's Hospital, Melbourne, Australia
Ajay Kevat
Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
Jasneek Chawla
Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
Leena M. Haataja
Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
Leena Lauronen
Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
Sampsa Vanhatalo
Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
Nathan J. Stevenson
Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Corresponding author.
Summary: 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.
