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Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schwarz, Emanuel and Doan, Nhat Trung and Pergola, Giulio and Westlye, Lars T. and Kaufmann, Tobias and Wolfers, Thomas and Brecheisen, Ralph and Quarto, Tiziana and Ing, Alex J. and Di Carlo, Pasquale and Gurholt, Tiril P. and Harms, Robbert L. and Noirhomme, Quentin and Moberget, Torgeir and Agartz, Ingrid and Andreassen, Ole A. and Bellani, Marcella and Bertolino, Alessandro and Blasi, Giuseppe and Brambilla, Paolo and Buitelaar, Jan K. and Cervenka, Simon and Flyckt, Lena and Frangou, Sophia and Franke, Barbara and Hall, Jeremy and Heslenfeld, Dirk J. and Kirsch, Peter and McIntosh, Andrew M. and Nöthen, Markus M. and Papassotiropoulos, Andreas and de Quervain, Dominique J.-F. and Rietschel, Marcella and Schumann, Gunter and Tost, Heike and Witt, Stephanie H. and Zink, Mathias and Meyer-Lindenberg, Andreas and Imagemend Consortium, Karolinska Schizophrenia Project Consortium. (2019) Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Translational Psychiatry, 9 (1). p. 12.

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Official URL: https://edoc.unibas.ch/73741/

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Abstract

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 represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
Faculties and Departments:03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Molekulare Neurowissenschaften (Papassotiropoulos)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Molekulare Neurowissenschaften (Papassotiropoulos)
05 Faculty of Science > Departement Biozentrum > Services Biozentrum > Life Sciences Training Facility (Papassotiropoulos)
07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Molecular Psychology (Papassotiropoulos)
03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Kognitive Neurowissenschaften (de Quervain)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Kognitive Neurowissenschaften (de Quervain)
07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Cognitive Neuroscience (de Quervain)
UniBasel Contributors:Papassotiropoulos, Andreas and de Quervain, Dominique J.-F.
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature Publishing Group
e-ISSN:2158-3188
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:28 Sep 2020 08:39
Deposited On:13 Jul 2020 13:33

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