Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.

Department of Applied Neurosciences and Brain Imaging National Institute of Mental Health Klecany Czechia

Department of Clinical and Behavioural Neurology IRCCS Santa Lucia Foundation Rome Italy

Department of Education Psychology Communication University of Bari Aldo Moro Bari Italy

Department of Neurosciences and Mental Health Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico via F Sforza 35 20122 Milan Italy

Department of Neurosciences Biomedicine and Movement Sciences Section of Psychiatry University of Verona Italy

Department of Pathophysiology and Transplantation University of Milan Milan Italy

Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany

Department of Psychiatry and Psychotherapy Ludwig Maximilian University Munich Germany

Department of Psychiatry and Psychotherapy University of Lübeck Germany

Department of Psychiatry University Hospital Marqués de Valdecilla School of Medicine University of Cantabria IDIVAL Santander Spain

Department of Psychiatry University of Basel Basel Switzerland

Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London UK

Department of Radiology Marqués de Valdecilla University Hospital Valdecilla Biomedical Research Institute IDIVAL Spain

Department of Radiology University Hospital Halle Germany

International Max Planck Research School for Translational Psychiatry Munich Germany

Max Planck School of Cognition Stephanstrasse 1a Leipzig Germany

Medical Physics Group Department of Diagnostic and Interventional Radiology Jena University Hospital Jena Germany

MoMiLab Research Unit IMT School for Advanced Studies Lucca Lucca Italy

University Hospital Virgen del Rocio Department of Psychiatry School of Medicine University of Sevilla IBiS CIBERSAM Sevilla Spain

UOC of Psychiatry Azienda Ospedaliera Universitaria Integrata of Verona Italy

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