Predicting attention-deficit/hyperactivity disorder (ADHD) from neuroimaging remains challenging due to heterogeneous brain morphology. In this study, we proposed an end-to-end framework using Vision Transformer (ViT) models to directly learn discriminative features from individual-space T1-weighted MRI. We evaluated two anatomical coverage patterns to assess the impact of data reduction and spatial granularity: (1) whole-brain (WB) axial slices and (2) 11 representative slices (R11). Our…
