Brain age prediction in children and adolescents: current challenges and future directions

Recent advancements in computational techniques have enhanced our understanding of human brain development, particularly through the analysis of high-dimensional data from magnetic resonance imaging (MRI). One notable approach is the brain-age prediction framework, which predicts biological age based on neuroimaging data and calculates the brain age gap (BAG); a marker indicating deviation from expected (chronological) age. This method has most commonly been used in adult samples but has in recent years increasingly been applied to children and adolescents. However, there are several considerations that must be taken into account when applying brain-age prediction in youth. In this Perspective, we outline important challenges and provide recommendations for researchers as well as future directions for the field.