Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare - Saeideh Davoudi; Gabriela Lopez Arango; Florence Deguire; Inga Sophie Knoth; Fanny Thebault-Dagher; Rebecca Reh; Laurel Trainor; Janet Werker; Sarah Lippé (NeuroImage)
Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and after. Early assessment of brain maturation using electroencephalography (EEG) is crucial for clinical intervention and care planning. We developed a reliable methodology using conventional machine learning (ML) and novel deep learning (DL) networks to efficiently quantify the difference between chronological and biological age, so-called brain age gap (BAG) as a marker of accelerated/decelerated biological brain development. In this cross-sectional study, EEG from 219 typically-developing infants aged from three to 14-months was used. For DL networks, the input samples were increased to 2628 recordings. We further validated the BAG tool in a population at clinical risk with abnormal brain growth (macrocephaly) to capture deviation from normal aging. Our results indicate that DL networks outperform conventional ML models, capturing complex non-monotonic EEG characteristics and predicting the biological age with a mean absolute error of only one month (MAE = 1 month, 95 %CI:0.88–1.15, r = 0.82, 95 %CI:0.78–0.85). Additionally, the developing brain follows a trajectory characterized by increased non-linearity and complexity in which alpha rhythm plays an important role. BAG could detect group-level maturational delays between typically-developing and macrocephaly (pvalue=0.009). In macrocephaly, BAG negatively correlated with the general adaptive composite of the ABAS-II (pvalue=0.04) at 18-months and the information processing speed scale of the WPSSI-IV at age four (pvalue=0.006). The EEG-based BAG score offers a reliable non-invasive measure of brain maturation, with significant advantages and implications for developmental neuroscience and clinical practice.