Authors
Emanuel Schwarz, Nhat Trung Doan, Giulio Pergola, Lars T Westlye, Tobias Kaufmann, Thomas Wolfers, Ralph Brecheisen, Tiziana Quarto, Alex J Ing, Pasquale Di Carlo, Tiril P Gurholt, Robbert L Harms, Quentin Noirhomme, Torgeir Moberget, Ingrid Agartz, Ole A Andreassen, Marcella Bellani, Alessandro Bertolino, Giuseppe Blasi, Paolo Brambilla, Jan K Buitelaar, Simon Cervenka, Lena Flyckt, Sophia Frangou, Barbara Franke, Jeremy Hall, Dirk J Heslenfeld, Peter Kirsch, Andrew M McIntosh, Markus M Nöthen, Andreas Papassotiropoulos, Dominique JF de Quervain, Marcella Rietschel, Gunter Schumann, Heike Tost, Stephanie H Witt, Mathias Zink, Andreas Meyer-Lindenberg, IMAGEMEND Consortium, Karolinska Schizophrenia Project (KaSP) Consortium
Publication date
2019/1/17
Journal
Translational psychiatry
Volume
9
Issue
1
Pages
12
Publisher
Nature Publishing Group UK
Description
Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be …
Scholar articles
E Schwarz, NT Doan, G Pergola, LT Westlye… - Translational psychiatry, 2019