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Brain. 2015 Jul;138(Pt 7):2059-73. doi: 10.1093/brain/awv111. Epub 2015 May 1.

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

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1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany.
2 Department of Psychiatry, University of Basel, Switzerland.
1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany 3 Department of Psychiatry and Psychotherapy, University of Regensburg, Germany 4 Department of Psychiatry, University Dublin, Trinity College Dublin, Ireland.
5 Department of Radiology, Ludwig-Maximilian-University, Munich, Germany.
6 Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, USA.


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.


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

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