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VIRTUAL BRAIN TWIN

Horizon Europe · 2024 – 2027

VIRTUAL BRAIN TWIN — Virtual Brain Twin for personalised treatment of Psychiatric Disorders

Financement : 10 000 000 €

21 partenaires

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In the EU, about 165 million people are affected each year by mental disorders, and estimates indicate that mental disorders will become the number 1 economic cost factor in medicine in 2030. Schizophrenia alone affects approximately 1% of the world's population. The clinical effectiveness of the antipsychotics on the market remains limited with 30 to 50% of schizophrenic patients showing an insufficient response to treatment. Several factors, from genetic to psychological and social, may lie behind poor treatment outcomes or side effects and varies from patient to patient. Therefore, the central aim of the VIRTUAL BRAIN TWIN project is to create an ecosystem for generating virtual brain twins for psychiatric patients, by leveraging the consortium’s detailed knowledge and expertise in neuronal microcircuit simulation, mathematical analysis, innovative AI tools, and psychiatric care and clinical studies. This ecosystem will guide clinicians to optimise medication type and dosage, and to evaluate alternative treatments, such as brain stimulation and lifestyle changes. Multiscale cause-effect simulations and virtual brain simulations based on fMRI or sMRI data from the individual patient, will bridge the gap between molecules and the patient's brain. At the centre of this ecosystem will be the Virtual Brain Twin platform, which will make use of big data, multiscale modelling, and high-performance computing (HPC) that will be secured by appropriate data safety and protection. The platform will be embedded in the European digital neuroscience research infrastructure EBRAINS and will be initially accessible to neuroscientists, clinical researchers, and mathematical modellers, and in the future, to clinicians, and patients as well. This ground-breaking project will pave the way for personalised treatment of psychiatric disorders, with the potential to significantly improve the quality of life of patients suffering from these conditions.

ebrains
personalised treatment of psychiatric disorders
schizophrenic patients
psychosis
high performance computing
big data
multiscale modelling
cause effect structure

Structures participantes de Université Paris-Saclay

Provenance des données

Sources : scanR. Données consolidées le 27/10/2026. Une erreur ? Elle se corrige dans la source d’origine (HAL, RNSR, annuaire) ; voir comment.