TY - JOUR
T1 - Heterogeneity and Classification of Recent Onset Psychosis and Depression
T2 - A Multimodal Machine Learning Approach
AU - Lalousis, Paris Alexandros
AU - Wood, Stephen J.
AU - Schmaal, Lianne
AU - Chisholm, Katharine
AU - Griffiths, Sian Lowri
AU - Reniers, Renate L.E.P.
AU - Bertolino, Alessandro
AU - Borgwardt, Stefan
AU - Brambilla, Paolo
AU - Kambeitz, Joseph
AU - Lencer, Rebekka
AU - Pantelis, Christos
AU - Ruhrmann, Stephan
AU - Salokangas, Raimo K.R.
AU - Schultze-Lutter, Frauke
AU - Bonivento, Carolina
AU - Dwyer, Dominic
AU - Ferro, Adele
AU - Haidl, Theresa
AU - Rosen, Marlene
AU - Schmidt, Andre
AU - Meisenzahl, Eva
AU - Koutsouleris, Nikolaos
AU - Upthegrove, Rachel
N1 - Publisher Copyright:
© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - 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: 2 = 14.874; P <.001; GMV model: 2 = 4.933; P =.026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: 2 = 1.956; P = 0.162; GMV model: 2 = 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.
AB - 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: 2 = 14.874; P <.001; GMV model: 2 = 4.933; P =.026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: 2 = 1.956; P = 0.162; GMV model: 2 = 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.
UR - http://www.scopus.com/inward/record.url?scp=85111078924&partnerID=8YFLogxK
U2 - 10.1093/schbul/sbaa185
DO - 10.1093/schbul/sbaa185
M3 - Journal articles
C2 - 33543752
AN - SCOPUS:85111078924
SN - 0586-7614
VL - 47
SP - 1130
EP - 1140
JO - Schizophrenia bulletin
JF - Schizophrenia bulletin
IS - 4
ER -