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Large-scale biophysical models of neuronal circuits to study brain function and disease Understanding brain function and disease requires studying interactions across multiple scales, from molecular and cellular mechanisms to circuit-wide dynamics and behavior. Biophysically detailed brain circuit models provide a powerful tool to integrate diverse experimental data across these scales, allowing researchers to simulate and analyze brain activity in a mechanistic and predictive manner. Leveraging AI and high-performance computing, we have developed large-scale, biologically realistic models of various thalamocortical circuits, including motor, somatosensory, and auditory regions, each comprising ~15,000 neurons and ~30 million synapses. These models incorporate experimentally derived neuronal morphologies, electrophysiological properties, and connectivity, enabling them to reproduce cell-type and layer-specific electrical activity patterns observed in vivo across scales, from membrane voltages and action potentials to local field potentials and electroencephalogram signals. Our models have been instrumental in elucidating the cellular and circuit mechanisms underlying neurological disease and psychiatric disorders such as schizophrenia, epilepsy, dystonia, Alzheimer's and Parkinson’s disease. They also offer insights into therapeutic targets to restore healthy neural dynamics through novel pharmacological and neurostimulation treatments, such as transcranial magnetic stimulation (TMS). These mechanistic models provide the foundation for constructing AI-driven Brain Digital Twins, virtual replicas of an individual’s brain state that integrate multimodal patient data to simulate brain function, predict disease progression, and optimize personalized treatment strategies. By bridging mechanistic modeling of brain circuits and AI, Brain Digital Twins have the potential to transform diagnosis, prevention and treatment of neurological and psychiatric conditions, addressing key challenges in brain research and clinical care.