Schizophrenia is a complex psychiatric disorder whose pathophysiology spans multiple spatial and temporal scales. Structural and functional neuroimaging studies have identified a broad range of disease-associated markers encompassing cortical atrophy, white matter disruptions, and aberrant functional connectivity patterns. Their application to personalized diagnosis and treatment selection has remained elusive. Here, we introduce the first Virtual Brain Twin (VBT) pipeline that integrates individual connectome-based network models with multimodal neuroimaging data, incorporating patient-specific structural connectivity, cortical thickness, and resting-state fMRI features to construct personalised whole-brain dynamical models. Dopaminergic and serotonergic signaling pathways are embedded within a mean-field framework, and simulation-based inference (SBI) is used to recover key pathophysiological parameters from individual patient data. The validity of this inference is first established using synthetic patients with known ground truth parameters, confirming that the pipeline can accurately identify underlying neurochemical states from simulated functional data. Applied to a cohort of 33 subjects in three clinical centers, the framework identifies personalized pathophysiological parameter regimes consistent with current neurobiological hypotheses of schizophrenia, including reduced cortical dopaminergic drive and elevated subcortical dopaminergic drive relative to healthy controls. Simulated pharmacological interventions within the VBT generate individualized medication effect trajectories that align retrospectively with known treatment outcomes (66.6\% accuracy), demonstrating the framework's capacity to capture patient-specific pharmacological responses. These results establish a principled and extensible computational foundation for neuroimaging-guided personalized medicine in psychiatry, with direct implications for two prospective clinical trials conducted in Marseille and Munich as part of the Virtual Brain Twin project, designed to evaluate VBT-guided individualised antipsychotic treatment selection.

The authors have declared no competing interest.

This study was funded by the European Union's Horizon Europe Programme

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Ethics committee/IRB of Aix-Marseille University waived ethical approval for this work.

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All data produced in the present study are available upon reasonable request to the authors