Digital Twin Brain Generating Multitask Behavior from Individual Connectome for Psychiatric Treatment Simulation

Personalized psychiatry demands digital twins that translate individual brain connectomes into predictions of multidomain functions (e.g., emotion, cognition) and treatment responses. We present a digital twin brain framework where a Hypernetwork, using resting-state connectomes, generates parameters for a Main recurrent network. This enables the Main network to jointly simulate behavioral and blood-oxygen-level-dependent (BOLD) time series across multiple tasks. Validated in a transdiagnostic cohort (n=228), our model demonstrated high-fidelity prediction of behavioral choices (>90% accuracy), reaction times (r>0.85), and distributed BOLD activation (r=0.84) during affective and cognitive tasks, capturing individual neurobehavioral dynamics. Leveraging the model's differentiability, we identified gradient-based connectome manipulation targets (e.g., limbic, parietal, prefrontal) to selectively modulate affective or cognitive functions. Crucially, in-silico trials simulating these manipulations reproduced realistic inter-individual variability in treatment effects (effect size range >2 SD). This establishes our digital twin brain as a quantitative, mechanistic platform for simulating and designing personalized psychiatric interventions.