Sztuczna inteligencja w precyzyjnej stymulacji mózgu: od obrazowania mózgu do modeli fundamentalnych i wirtualnych mózgów

Precision brain stimulation is an emerging paradigm in clinical neuroscience, offering the potential to modulate dysfunctional circuits with individualized protocols for disorders ranging from neurological to psychiatric disorders. The convergence of large-scale neuroimaging datasets, physics-based computational models, and artificial intelligence (AI) is rapidly accelerating this vision. This review integrates developments across four domains. First, we examine the availability and harmonisation of population-level neuroimaging repositories such as the UK Biobank, Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD) study, and ENIGMA consortium, which provide unprecedented insight into structural and functional variability relevant to stimulation targeting biomarkers. Second, we trace the evolution of physics-based and computational head models that simulate electric, magnetic, and acoustic field distributions across non-invasive modalities—including transcranial electrical stimulation (tES), temporal interference stimulation (TIS), transcranial magnetic stimulation (TMS), and transcranial focused ultrasound (tFUS)—as well as invasive techniques such as cortical and deep brain stimulation. Third, we highlight the emergence of AI-driven brain imaging foundation models, such as NeuroSTORM, which leverage large-scale pre-training to enable predictive modeling of brain states, treatment response, and individualized stimulation optimization. Fourth, we present the concept of individualized digital twin brains—computational avatars that integrate neuroimaging, physics-based simulations, and AI foundation models to simulate and test stimulation strategies tailored to each patient’s connectivity and activation profile. Finally, we illustrate translational potential through two clinical use-case scenarios: precision targeting for seizure suppression in epilepsy and individualized stimulation for craving reduction and relapse prevention in addiction. By bridging databases, physics, and AI, this review outlines a roadmap for precision brain stimulation, emphasizing not only technological advances but also the clinical, ethical, and translational implications for real-world practice.