TY - JOUR
T1 - Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
AU - Schwarz, Emanuel
AU - Doan, Nhat Trung
AU - Pergola, Giulio
AU - Kaufmann, Tobias
AU - Wolfers, Thomas
AU - Brecheisen, Ralph
AU - Quarto, Tiziana
AU - Ing, Alex J
AU - Di Carlo, Pasquale
AU - Gurholt, Tiril P.
AU - Harms, Robert L.
AU - Noirhomme, Quentin
AU - Moberget, Torgeir
AU - Agartz, Ingrid
AU - Andreassen, Ole
AU - Westlye, Lars T.
AU - Bellani, Marcella
AU - Bertolino, Alessandro
AU - Blasi, Giuseppe
AU - Brambilla, Paolo
AU - Buitelaar, Jan
AU - Cervenka, Simon
AU - Flyckt, Lena
AU - Frangou, Sophia
AU - Franke, Barbara
AU - Hall, Jeremy
AU - Heslenfeld, Dirk J
AU - Kirsch, Peter
AU - McIntosh, Andrew M
AU - Noethen, Markus M.
AU - Papassotiropoulos, A
AU - de Quervain, Dominique J. -F.
AU - Rietschel, Marcella
AU - Schumann, Gunter
AU - Tost, Heike
AU - Witt, Stephanie H.
AU - Zink, Mathias
AU - Meyer-Lindenberg, Andreas
AU - The IMAGEMEND Consortium, Karolinska Schizophrenia Project (KaSP) Consortium
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060226635&partnerID=8YFLogxK
U2 - 10.1038/s41398-018-0225-4
DO - 10.1038/s41398-018-0225-4
M3 - Article
SN - 2158-3188
VL - 9
JO - Translational psychiatry
JF - Translational psychiatry
IS - 1
M1 - 12
ER -
