OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility. METHOD: We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients. RESULTS: Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy). CONCLUSION: These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.

Department of Psychiatry Dalhousie University Halifax NS Canada National Institute of Mental Health Klecany Czech Republic

Department of Psychiatry Louis Mourier Hospital AP HP Colombes France INSERM U894 Centre for Psychiatry and Neurosciences Paris France

Department of Radiation Sciences Umeå University Umeå Sweden

Energy Transition Institute VeDeCoM Versailles France

INRIA CEA Parietal team University of Paris Saclay Lille France Laboratoire de Sciences Cognitives et Sciences Affectives CNRS UMR 9193 University of Lille Lille France Pôle de Psychiatrie Unité CURE CHU Lille Lille France

Institut National de la Santé et de la Recherche Médicale Faculté de Médecine de Créteil DHU PePsy Hôpitaux Universitaires Mondor Créteil France

Laboratoire de Sciences Cognitives et Sciences Affectives CNRS UMR 9193 University of Lille Lille France Pôle de Psychiatrie Unité CURE CHU Lille Lille France

National Institute of Mental Health Klecany Czech Republic

NeuroSpin CEA Gif sur Yvette France

NeuroSpin CEA Gif sur Yvette France INRIA CEA Parietal team University of Paris Saclay Lille France

NeuroSpin CEA Gif sur Yvette France Institut National de la Santé et de la Recherche Médicale Faculté de Médecine de Créteil DHU PePsy Hôpitaux Universitaires Mondor Créteil France

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