EEG-based brain age prediction in infants-toddlers: Implications for early detection of neurodevelopmental disorders.

Abstract

The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R² of 0.83 and a mean absolute error of 91.7 days. Feature importance analysis that combined hierarchical clustering and Shapley values identified two feature clusters describing periodic alpha and low beta activity as key predictors of age. Application of the model to EEG data from infants later diagnosed with autism or Down syndrome revealed significant underestimations of chronological age, supporting its potential as a clinical tool for early identification of alterations in brain development.