Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age ( p value < 0.001) within and between datasets. Models’ performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score.

In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers.

Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI).

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