Literature DB >> 25935725

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.

Nikolaos Koutsouleris1, Eva M Meisenzahl2, Stefan Borgwardt3, Anita Riecher-Rössler3, Thomas Frodl4, Joseph Kambeitz2, Yanis Köhler2, Peter Falkai3, Hans-Jürgen Möller2, Maximilian Reiser5, Christos Davatzikos6.   

Abstract

Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.
© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  at-risk mental states for psychosis; imaging; mood disorders; multivariate pattern classification; schizophrenia

Mesh:

Year:  2015        PMID: 25935725      PMCID: PMC4572486          DOI: 10.1093/brain/awv111

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  87 in total

Review 1.  Schizophrenia and the disconnection hypothesis.

Authors:  K J Friston
Journal:  Acta Psychiatr Scand Suppl       Date:  1999

2.  Voxelwise meta-analysis of gray matter reduction in major depressive disorder.

Authors:  Ming-Ying Du; Qi-Zhu Wu; Qiang Yue; Jun Li; Yi Liao; Wei-Hong Kuang; Xiao-Qi Huang; Raymond C K Chan; Andrea Mechelli; Qi-Yong Gong
Journal:  Prog Neuropsychopharmacol Biol Psychiatry       Date:  2011-10-07       Impact factor: 5.067

3.  Do we have any solid evidence of clinical utility about the pathophysiology of schizophrenia?

Authors:  Stephen M Lawrie; Bayanne Olabi; Jeremy Hall; Andrew M McIntosh
Journal:  World Psychiatry       Date:  2011-02       Impact factor: 49.548

4.  Early and late onset depression in young and middle aged adults: differential symptomatology, characteristics and risk factors?

Authors:  Nicole C M Korten; Hannie C Comijs; Femke Lamers; Brenda W J H Penninx
Journal:  J Affect Disord       Date:  2012-02-26       Impact factor: 4.839

5.  The psychosis threshold in Ultra High Risk (prodromal) research: is it valid?

Authors:  Alison R Yung; Barnaby Nelson; Andrew Thompson; Stephen J Wood
Journal:  Schizophr Res       Date:  2010-04-08       Impact factor: 4.939

6.  Dimensions and classes of psychosis in a population cohort: a four-class, four-dimension model of schizophrenia and affective psychoses.

Authors:  V Murray; I McKee; P M Miller; D Young; W J Muir; A J Pelosi; D H R Blackwood
Journal:  Psychol Med       Date:  2005-04       Impact factor: 7.723

7.  Diagnosing schizophrenia in the initial prodromal phase.

Authors:  J Klosterkötter; M Hellmich; E M Steinmeyer; F Schultze-Lutter
Journal:  Arch Gen Psychiatry       Date:  2001-02

8.  Boundaries of schizoaffective disorder: revisiting Kraepelin.

Authors:  Roman Kotov; Shirley H Leong; Ramin Mojtabai; Ann C Eckardt Erlanger; Laura J Fochtmann; Eduardo Constantino; Gabrielle A Carlson; Evelyn J Bromet
Journal:  JAMA Psychiatry       Date:  2013-12       Impact factor: 21.596

9.  Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index.

Authors:  Christos Davatzikos; Feng Xu; Yang An; Yong Fan; Susan M Resnick
Journal:  Brain       Date:  2009-05-04       Impact factor: 13.501

10.  Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder.

Authors:  Sergi G Costafreda; Cynthia H Y Fu; Marco Picchioni; Timothea Toulopoulou; Colm McDonald; Eugenia Kravariti; Muriel Walshe; Diana Prata; Robin M Murray; Philip K McGuire
Journal:  BMC Psychiatry       Date:  2011-01-28       Impact factor: 3.630

View more
  50 in total

1.  A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Authors:  Jing Shang; Paul Fisher; Josef G Bäuml; Marcel Daamen; Nicole Baumann; Claus Zimmer; Peter Bartmann; Henning Boecker; Dieter Wolke; Christian Sorg; Nikolaos Koutsouleris; Dominic B Dwyer
Journal:  Hum Brain Mapp       Date:  2019-06-22       Impact factor: 5.038

2.  Empathic Care and Distress: Predictive Brain Markers and Dissociable Brain Systems.

Authors:  Yoni K Ashar; Jessica R Andrews-Hanna; Sona Dimidjian; Tor D Wager
Journal:  Neuron       Date:  2017-06-08       Impact factor: 17.173

3.  The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis.

Authors:  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
Journal:  Schizophr Bull       Date:  2021-01-23       Impact factor: 9.306

4.  Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Authors:  Nicolas Honnorat; Aoyan Dong; Eva Meisenzahl-Lechner; Nikolaos Koutsouleris; Christos Davatzikos
Journal:  Schizophr Res       Date:  2017-12-21       Impact factor: 4.939

5.  Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia.

Authors:  Dominic B Dwyer; Carlos Cabral; Lana Kambeitz-Ilankovic; Rachele Sanfelici; Joseph Kambeitz; Vince Calhoun; Peter Falkai; Christos Pantelis; Eva Meisenzahl; Nikolaos Koutsouleris
Journal:  Schizophr Bull       Date:  2018-08-20       Impact factor: 9.306

Review 6.  Bupropion: a systematic review and meta-analysis of effectiveness as an antidepressant.

Authors:  Krisna Patel; Sophie Allen; Mariam N Haque; Ilinca Angelescu; David Baumeister; Derek K Tracy
Journal:  Ther Adv Psychopharmacol       Date:  2016-02-18

Review 7.  The early identification of psychosis: can lessons be learnt from cardiac stress testing?

Authors:  Swapnil Gupta; Mohini Ranganathan; Deepak Cyril D'Souza
Journal:  Psychopharmacology (Berl)       Date:  2015-11-14       Impact factor: 4.530

8.  Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.

Authors:  Nikolaos Koutsouleris; Thomas Wobrock; Birgit Guse; Berthold Langguth; Michael Landgrebe; Peter Eichhammer; Elmar Frank; Joachim Cordes; Wolfgang Wölwer; Francesco Musso; Georg Winterer; Wolfgang Gaebel; Göran Hajak; Christian Ohmann; Pablo E Verde; Marcella Rietschel; Raees Ahmed; William G Honer; Dominic Dwyer; Farhad Ghaseminejad; Peter Dechent; Berend Malchow; Peter M Kreuzer; Tim B Poeppl; Thomas Schneider-Axmann; Peter Falkai; Alkomiet Hasan
Journal:  Schizophr Bull       Date:  2018-08-20       Impact factor: 9.306

9.  Machine learning in neuroimaging: Progress and challenges.

Authors:  Christos Davatzikos
Journal:  Neuroimage       Date:  2018-10-06       Impact factor: 6.556

10.  Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

Authors:  David E Fleck; Nicholas Ernest; Caleb M Adler; Kelly Cohen; James C Eliassen; Matthew Norris; Richard A Komoroski; Wen-Jang Chu; Jeffrey A Welge; Thomas J Blom; Melissa P DelBello; Stephen M Strakowski
Journal:  Bipolar Disord       Date:  2017-06-02       Impact factor: 6.744

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.