Learning brain dynamics across distinct scaling regimes reveals psychiatric signatures

Understanding how the brain's nonlinear dynamics give rise to cognition remains a central challenge in neuroscience. Conventional neuroimaging methods assume linearity and stationarity, failing to capture frequency-specific neural computations. We introduce Multi-Band Brain Net (MBBN), a transformer-based framework that models frequency-specific spatiotemporal brain dynamics from fMRI. MBBN integrates biologically grounded frequency decomposition with multi-band self-attention, enabling discovery of frequency-dependent network interactions. Trained on 49,673 individuals across three large-scale cohorts (UK Biobank, Adolescent Brain Cognitive Development Study (ABCD), Autism Brain Imaging Data Exchange (ABIDE)), MBBN achieves state-of-the-art performance in predicting psychiatric and cognitive outcomes-including major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD)-with AUROC improvements of up to 41.36% alongside strong cognitive intelligence prediction. Frequency-resolved analyses reveal disorder-specific signatures: in ADHD, high-frequency fronto-sensorimotor connectivity is attenuated and opercular somatosensory nodes emerge as dynamic hubs; in ASD, orbitofrontal-somatosensory circuits show focal high-frequency disruption alongside enhanced ultra-low-frequency coupling between the temporo-parietal junction and prefrontal cortex. By combining biologically informed frequency decomposition with transformer architecture, MBBN delivers interpretable biomarkers and improved prediction of psychiatric and cognitive traits.