Predicting outcome in schizophrenia: Neuroimaging and clinical assessments

Abstract

Schizophrenia is a severe neuropsychiatric disorder accompanied by debilitating cognitive and psychosocial impairments over the course of the disease. As disease trajectories exhibit considerable inter-individual heterogeneity, early clinical and neurobiological predictors of long-term outcomes are desirable for personalized treatment and care strategies. Despite this obvious clinical need, studies examining predictors of long-term outcome in schizophrenia are still scarce, as they meet several obstacles, especially the need to acquire, maintain, and repeatedly assess large cohorts of patients over a longer course of time. This chapter provides an overview of different approaches to identify clinical and neuro-biological markers to predict the course of the disease. It covers studies applying classical statistical analyses as well as research based on machine learning. Although only a few studies so far have yielded robust long-term predictors, the current literature suggests that clinical and neuropsychological parameters at first episode might provide useful markers for predicting long-term disease trajectories. Neuroimaging measures obtained at intake are less helpful. These parameters might have the potential to be directly translatable to a clinical setting to improve prospective care and treatment planning for schizophrenia patients.