Projects

Schizophrenia (SZ) and major depressive disorder (MDD) are common and severe mental disorders that constitute an extraordinarily high public health burden. To facilitate the development of effective, individualized therapeutic interventions, robust biomarkers are needed that index specific functional deficits relevant for a given patient. Among these, psychomotor abnormalities represent a core clinical feature with transdiagnostic importance in SZ and MDD, but their relevance for individualizing therapeutic options remains unexplored. Psychomotor functioning is defined through the interaction of primary sensorimotor function (e.g., the dopaminergic-based subcortical-cortical motor circuit) and non-motor function, including cognition and emotion, and changes in the underlying neural processes are known to cross diagnostic boundaries of mental illness. This project will provide the basis for characterizing psychomotor mechanisms in SZ and MDD and their downstream translation into clinically useful predictors of therapeutic response. For this, we will integrate a harmonized battery of behavioural assessments targeted at psychomotor functioning with a multimodal neuroimaging approach, in order to provide a detailed characterization of psychomotor alterations in SZ and MDD, and to generate a data backbone tailored towards application of machine learning. Using such multimodal machine learning, we will identify neurobehavioral signatures predictive of treatment response in psychomotor domains 12 weeks after an acute illness episode within and across SZ and MDD. Through such neural and behavioral characterization of psychomotor mechanisms, this study will contribute to the dimensional dissection of severe mental illness and provide preliminary markers for individualization of therapy in the psychomotor domain.

At the beginning of the 20th century, many authors proposed that a considerable number of schizophrenia patients experience genuine motor abnormalities. Recent evidence suggests that the association between psychotic symptoms and motor abnormalities in schizophrenia reflects neuronal dysfunction in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) as conceptualized in the model of “cognitive dysmetria”. If this dysfunction develops earlier than prodromal symptoms of the disorder, it might give rise to subtle motor abnormalities. In this context, subtle motor deficits have been suggested to be an external marker of underlying neuronal dysfunction that is linked with an elevated risk for developing schizophrenia. Neurological soft signs (NSS) are frequently found in psychiatric disorders of significant neurodevelopmental origin, e.g. in patients with schizophrenia, borderline and autism. Previous neuroimaging studies in patients with schizophrenia have shown that NSS are associated with abnormal cortical and subcortical structure and function. Yet, it still remains unclear whether these findings are associated with neuropathological processes underlying the disease or if they are confounded by antipsychotic treatment. Some of these issues could be addressed in individuals at clinical ultra-high risk (UHR) for developing psychosis or healthy individuals. Given that NSS are present in schizophrenia patients, UHR individuals and healthy individuals, they might suggest a neurodevelopmental signature of CCTCC dysfunction, probably as a continuum between health and disease. This project will test several important and innovative hypotheses while focusing on three study samples. So far, there has been no previous research combining this diverse set of novel neuroimaging methods with advanced data analysis techniques in the context of NSS. This project will add significantly to the previous evidence in this area by focusing on contributions of structural and functional connectivity (e.g. alterations of white matter tracts and aberrant neural activity in the CCTCC) to NSS expression in three study groups. Furthermore, we seek to combine dMRI and fMRI to examine whether regions with altered patterns of white matter connectivity correspond to areas of aberrant functional connectivity. This approach will provide substantial information on a “motor phenotype” within the schizophrenia spectrum in terms of behaviour, brain structure and brain function. To conclude, following a dimensional and multimodal approach we argue that there are distinct structural and functional correlates of NSS that, if precisely defined in terms of their clinical associations, have potential for use early in the stratification of patients presenting with psychotic disorders. Finally, we expect that the results from this project will provide an important data-based framework for future longitudinal studies on NSS within the schizophrenia spectrum and beyond.