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

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Article number12
Number of pages13
JournalTranslational Psychiatry
Volume9
Issue number1
DOIs
Publication statusPublished - 17 Jan 2019

Keywords

  • LIKELIHOOD ESTIMATION
  • MRI SCANS
  • METAANALYSIS
  • 1ST-EPISODE
  • RISK
  • CLASSIFICATION
  • SEGMENTATION
  • DEFICITS
  • VOLUME

Cite this

@article{9db5ec1978c24f31b64bf09a9af58cf7,
title = "Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder",
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.",
keywords = "LIKELIHOOD ESTIMATION, MRI SCANS, METAANALYSIS, 1ST-EPISODE, RISK, CLASSIFICATION, SEGMENTATION, DEFICITS, VOLUME",
author = "Emanuel Schwarz and Doan, {Nhat Trung} and Giulio Pergola and Westlye, {Lars T} and Tobias Kaufmann and Thomas Wolfers and Ralph Brecheisen and Tiziana Quarto and Ing, {Alex J} and Carlo, {Pasquale Di} and Gurholt, {Tiril P} and Harms, {Robbert L} and Quentin Noirhomme and Torgeir Moberget and Ingrid Agartz and Andreassen, {Ole A} and Marcella Bellani and Alessandro Bertolino and Giuseppe Blasi and Paolo Brambilla and Buitelaar, {Jan K} and Simon Cervenka and Lena Flyckt and Sophia Frangou and Barbara Franke and Jeremy Hall and Heslenfeld, {Dirk J} and Peter Kirsch and McIntosh, {Andrew M} and N{\"o}then, {Markus M} and Andreas Papassotiropoulos and {de Quervain}, {Dominique J-F} and Marcella Rietschel and Gunter Schumann and Heike Tost and Witt, {Stephanie H} and Mathias Zink and Andreas Meyer-Lindenberg",
year = "2019",
month = "1",
day = "17",
doi = "10.1038/s41398-018-0225-4",
language = "English",
volume = "9",
journal = "Translational Psychiatry",
issn = "2158-3188",
publisher = "Nature Publishing Group",
number = "1",

}

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

In: Translational Psychiatry, Vol. 9, No. 1, 12, 17.01.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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 - Westlye, Lars T

AU - Kaufmann, Tobias

AU - Wolfers, Thomas

AU - Brecheisen, Ralph

AU - Quarto, Tiziana

AU - Ing, Alex J

AU - Carlo, Pasquale Di

AU - Gurholt, Tiril P

AU - Harms, Robbert L

AU - Noirhomme, Quentin

AU - Moberget, Torgeir

AU - Agartz, Ingrid

AU - Andreassen, Ole A

AU - Bellani, Marcella

AU - Bertolino, Alessandro

AU - Blasi, Giuseppe

AU - Brambilla, Paolo

AU - Buitelaar, Jan K

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 - Nöthen, Markus M

AU - Papassotiropoulos, Andreas

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

PY - 2019/1/17

Y1 - 2019/1/17

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.

KW - LIKELIHOOD ESTIMATION

KW - MRI SCANS

KW - METAANALYSIS

KW - 1ST-EPISODE

KW - RISK

KW - CLASSIFICATION

KW - SEGMENTATION

KW - DEFICITS

KW - VOLUME

U2 - 10.1038/s41398-018-0225-4

DO - 10.1038/s41398-018-0225-4

M3 - Article

C2 - 30664633

VL - 9

JO - Translational Psychiatry

JF - Translational Psychiatry

SN - 2158-3188

IS - 1

M1 - 12

ER -