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Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data

Program na podporu zdravotnického aplikovaného výzkumu na léta 2020 - 2026 - MZ NU21-08-00432 [Registered results] 2021 - 2024

Principal Investigator: Ing. Mgr. Jaroslav Hlinka, Ph.D.

Schizophrenia is a chronic, severe and profoundly disabling disorder. For every 100 individuals with schizophrenia, only 1 or 2 individuals per year meet the recovery criteria, and approximately 14% recover over 10 years, with poor functional outcome for 27% of patients. There is an urgent need to develop predictive models of outcome to be applied in the initial stages of illness and thus optimize and intensify intervention programs to avoid an aversive outcome. Functional outcomes are difficult to predict solely on the basis of the clinical features, but Magnetic Resonance Imaging (MRI), particularly multi-modal, holds promise for improved stratification of patients. The aim of this project is to develop tools to predict the functional outcome of schizophrenia from neuroimaging, clinical and cognitive measurement taken early after the disease onset. To overcome the limitations due to high dimensionality of MRI data, we shall apply a combination of robust machine-learning tools, data-driven feature selection as well as theory-based constraint to key brain networks characteristics.