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

Refining Schizophrenia Risk Assessment: Machine Learning Delineates a Brain Signature of Cognitive Basic Symptoms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Refining Schizophrenia Risk Assessment: Machine Learning Delineates a Brain Signature of Cognitive Basic Symptoms Nikolaos Koutsouleris, Madalina Buciuman, Lisa-Maria Neuner, Clara Weyer, and 32 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6278819/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Psychiatric disorders/Schizophrenia Health sciences/Diseases/Psychiatric disorders/Psychosis Biological sciences/Computational biology and bioinformatics/Machine learning Health sciences/Medical research/Biomarkers/Prognostic markers Schizophrenia Psychosis risk syndromes Diagnostic brain markers Machine learning Neuroimaging Biopsychosocial correlations Full Text Additional Declarations There is NO Competing Interest. Supplementary Files PRONIACHRConstructsSupplementNatNeurosci.docx Supplementary Methods and Results, Tables and Figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6278819","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432302709,"identity":"36f122a3-ed61-474a-b7a5-c058a95a0719","order_by":0,"name":"Nikolaos 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