​The purpose of our research is to provide a scientific basis for new treatment and preventive strategies directed at the pathogenetic and pathophysiological disturbances in individual patients with schizophrenia. In the long run, we aim at developing clinically useful predictors for the effect of specific interventions and the course of the disease in individual patients.

In CINS II, the research has been divided into three work packages (WPs).


Work package 1: Pathophysiology

This WP aims at stratifying patients within the schizophrenia spectrum based on multimodal variables. Further aims are to predict the treatment response and short-term outcome (2 years) for individual patients based on this stratification.

The research is based on the following CINS cohorts that are extended in this work package:


Work package 2: Course of illness
The overall aim of this WP is to investigate the long-term stability of clinical, functional and neurobiological markers for schizophrenia, and how these markers relate to societal outcome measures.

Data from this WP will allow us to answer key questions regarding mechanisms behind progressive brain changes and loss of functions - among others the relations between these and the disease process, psychotic episodes and relapses, and/or environmental factors including antipsychotic treatment and other interventions.

WP2 reexamines participants from the following cohorts:

Work package 3: Subgrouping and prediction
With data from the abovementioned cohorts, this WP aims to identify subgroups of patients and predict outcome based on patterns of several endophenotypes.

We use unsupervised machine learning with the aim of identifying objectively measurable, biologically valid, and clinically meaningful subgroups of antipsychotic-naïve, first-episode patients with schizophrenia.

With supervised machine learning and long-term outcome data, we further aim to predict clinical, biological, functional and societal outcome for future first-episode patients and UHR individuals.



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