Region-specific Brain Age Prediction Models for Children and Adolescents Derived by Machine Learning

Abstract EEG biomarkers can reveal significant and actionable differences in brain development between normal children and those with developmental disorders. Frontal slow frequency EEG is one common differentiator between normal and abnormal brain function. The present study sought to establish models, based on machine learning, to predict brain age in children and adolescents. Four brain regions were studied: left anterior, right anterior, left posterior, and right posterior, based on the different functions characteristic of each region. Importantly, differences were also considered in the construction of the models. All models yielded promising r2 values for the prediction of brain age, with values of 0.80 or higher. Our technique employed a tree-based feature selection algorithm, allowing selection of a minimum number of features while still preserving predictive power. These prediction models can be used to quantify deviations between estimated and biological brain age, and so serve as valuable tools in efforts to assess and intervene early in several profound developmental disorders.