A growth chart of brain function from infancy to adolescence based on electroencephalography

Abstract

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 physiologically reliable measures of brain function. Here, we develop a novel measure of functional brain age (FBA) using a residual neural network based interpretation of the pediatric EEG. We show that the FBA from a 10 to 15 minute segment of 18-channel EEG during light sleep (stages 1 and 2) in typically developing children and adolescents was strongly associated with chronological age (R2 = 0.96, 95%CI: 0.94 - 0.96, n = 1062, age range: 1 month to 18 years). The mean absolute error (MAE) between FBA and age was 0.6 years (n = 1062), with an MAE of 2.1 years following validation on an independent set of EEG recordings (n = 723). The FBA detected group level maturational delays in a small cohort of children with abnormal neurodevelopment (p = 0.00053, n = 40). Our work offers a practical, scalable and powerful automated tool for tracking maturation of brain function throughout childhood with an accuracy comparable to that of widely used physical growth charts.