Refining Schizophrenia Risk Assessment: Machine Learning Delineates a Brain Signature of Cognitive Basic Symptoms

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Abstract
Biological risk signatures could aid the early detection of schizophrenia, but their precision likely depends on the clinical risk definitions they are derived from. Using machine learning, we analyzed structural MRI data from 1,425 patients and 907 healthy individuals in a multi-site and multi-diagnostic database to detect and validate signatures of different risk syndromes―Cognitive Disturbances (COGDIS), Ultra-High-Risk (UHR) or COGDIS + UHR―compared to schizophrenia. Patients with COGDIS but not UHR-related syndromes were detectable using MRI. COGDIS and schizophrenia brain signatures were highly correlated based on shared prefronto-parieto-perisylvian volume reductions. The expressions of these brain signatures intensified from healthy individuals through affective disorders and non-schizophrenic psychoses to patients with schizophrenia, and they could be predicted with up to 21% variance explained by genetic, neurocognitive, and phenotypic risk factors. COGDIS and schizophrenia signature expressions predicted poor functional outcomes after two years. These results emphasize the need to refine early detection tools by integrating cognitive basic symptoms and their neural correlates into schizophrenia risk assessment.

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