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The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis

  • Rachel Upthegrove*
  • , Paris Lalousis
  • , Pavan Mallikarjun
  • , Katharine Chisholm
  • , Sian Lowri Griffiths
  • , Mariam Iqbal
  • , Mirabel Pelton
  • , Renate Reniers
  • , Alexandra Stainton
  • , Marlene Rosen
  • , Anne Ruef
  • , Dominic B Dwyer
  • , Marian Surman
  • , Theresa Haidl
  • , Nora Penzel
  • , Lana Kambeitz-llankovic
  • , Alessandro Bertolino
  • , Paolo Brambilla
  • , Stefan Borgwardt
  • , Joseph Kambeitz
  • Rebekka Lencer, Christos Pantelis, Stephan Ruhrmann, Frauke Schultze-lutter, Raimo K R Salokangas, Eva Meisenzahl, Stephen J Wood, Nikolaos Koutsouleris
*Corresponding author for this work
  • Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
  • Institute of Clinical Sciences, University of Birmingham, Birmingham B15 2TT, UK. [email protected].
  • Centre for Youth Mental Health, University of Melbourne, Parkville, Australia
  • Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
  • Department of Mental Health, University of Münster, Münster, Germany
  • Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
  • Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro,” Bari, Italy
  • Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
  • Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
  • Department of Psychiatry and Psychotherapy, University Lübeck, Germany
  • Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
  • Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
  • Department of Psychiatry, University of Turku, Turku, Finland
  • Department of Psychology, School of Life & Health Sciences, Aston University, Aston Triangle, Birmingham B47ET, UK.
  • Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia
  • Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy

Research output: Contribution to journalArticlepeer-review

Abstract

Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP−D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP−D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.
Original languageEnglish
Pages (from-to)249–258
Number of pages10
JournalSchizophrenia Bulletin
Volume47
Issue number1
Early online date7 Jul 2020
DOIs
Publication statusPublished - 23 Jan 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • depression
  • gray matter volume
  • machine learning
  • psychopathology
  • psychosis
  • schizophrenia
  • Humans
  • Male
  • Depression/classification
  • Gray Matter/diagnostic imaging
  • Young Adult
  • Magnetic Resonance Imaging
  • Adolescent
  • Adult
  • Female
  • Schizophrenia/classification
  • Psychotic Disorders/classification
  • Supervised Machine Learning
  • Principal Component Analysis

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