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Lalousis PA, Wood SJ, Schmaal L, et al. Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophr Bull. 2021;47(4):1130-1140doi: 10.1093/schbul/sbaa185.
Lalousis, P. A., Wood, S. J., Schmaal, L., Chisholm, K., Griffiths, S. L., Reniers, R. L. E. P., Bertolino, A., Borgwardt, S., Brambilla, P., Kambeitz, J., Lencer, R., Pantelis, C., Ruhrmann, S., Salokangas, R. K. R., Schultze-Lutter, F., Bonivento, C., Dwyer, D., Ferro, A., Haidl, T., Rosen, M., Schmidt, A., Meisenzahl, E., Koutsouleris, N., Upthegrove, R. (2021). Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophrenia bulletin, 47(4), 1130-1140. https://doi.org/10.1093/schbul/sbaa185
Lalousis, Paris Alexandros, et al. "Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach." Schizophrenia bulletin vol. 47,4 (2021): 1130-1140. doi: https://doi.org/10.1093/schbul/sbaa185
Lalousis PA, Wood SJ, Schmaal L, Chisholm K, Griffiths SL, Reniers RLEP, Bertolino A, Borgwardt S, Brambilla P, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Bonivento C, Dwyer D, Ferro A, Haidl T, Rosen M, Schmidt A, Meisenzahl E, Koutsouleris N, Upthegrove R. Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophr Bull. 2021 Jul 08;47(4):1130-1140. doi: 10.1093/schbul/sbaa185. PMID: 33543752; PMCID: PMC8266654.
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Schizophr Bull. 2021 Jul 08;47(4):1130-1140. doi: 10.1093/schbul/sbaa185.

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach.

Schizophrenia bulletin

Paris Alexandros Lalousis, Stephen J Wood, Lianne Schmaal, Katharine Chisholm, Sian Lowri Griffiths, Renate L E P Reniers, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Joseph Kambeitz, Rebekka Lencer, Christos Pantelis, Stephan Ruhrmann, Raimo K R Salokangas, Frauke Schultze-Lutter, Carolina Bonivento, Dominic Dwyer, Adele Ferro, Theresa Haidl, Marlene Rosen, Andre Schmidt, Eva Meisenzahl, Nikolaos Koutsouleris, Rachel Upthegrove,

Affiliations

  1. Institute for Mental Health, University of Birmingham, Birmingham, UK.
  2. Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
  3. Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.
  4. Centre for Youth Mental Health, The University of Melbourne, Parkville, Australia.
  5. Department of Psychology, Aston University, Birmingham, UK.
  6. Institute of Clinical Sciences, University of Birmingham, Birmingham, UK.
  7. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
  8. Department of Psychiatry, University of Basel, Basel, Switzerland.
  9. Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism, University of Lübeck, Germany.
  10. Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
  11. Department of Neurosciences and Mental Health, IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
  12. Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany.
  13. Department of Psychiatry, University of Münster, Münster, Germany.
  14. Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Australia.
  15. Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
  16. Department of Psychiatry, University of Turku, Turku, Finland.
  17. Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
  18. Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia.
  19. University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
  20. IRCCS "E. Medea" Scientific Institute, San Vito al Tagliamento (Pn), Italy.
  21. Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.

PMID: 33543752 PMCID: PMC8266654 DOI: 10.1093/schbul/sbaa185

Abstract

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.

© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: [email protected].

Keywords: MRI; comorbidity; depression; gray matter volume; machine learning; psychosis; transdiagnostic

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