Predicting Adolescent Intelligence Using Hierarchical Bayesian Regression

Understanding typical brain development and its relationship to cognitive abilities is crucial for developmental neuroscience. Here, I applied normative modeling to a large dataset of adolescent brain imaging (N > 10,000, aged 9-15 years) to characterize developmental trajectories of brain structure, function, and white matter microstructure. Using these normative models, I quantified how individuals deviate from typical developmental trajectories and investigated whether these deviations predict cognitive abilities. I found that deviation scores from typical brain development significantly predicted intelligence and learning ability, outperforming predictions based on raw brain measures. Multiple brain features contributed to the prediction, including the volumes of memory-related structures, functional connectivity between attention and cognitive control networks, and the temporal lobe's white matter microstructure. My findings demonstrate that normative modeling can capture meaningful individual differences in brain development and their relationship to cognitive abilities. This approach provides a framework for understanding typical brain development and identifying clinically relevant developmental variations.