Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

Abstract

Background: Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. Methods: HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). Results: The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. Conclusions: We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Publication DOI: https://doi.org/10.1016/j.biopsych.2022.03.021
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
College of Health & Life Sciences > Applied Health Research Group
College of Health & Life Sciences
Additional Information: CC BY 4.0
Uncontrolled Keywords: Clustering,Depression,Machine learning,Nosology,Psychosis,Transdiagnostic,Biological Psychiatry
Publication ISSN: 1873-2402
Full Text Link:
Related URLs: https://www.sci ... 1568?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-10-01
Published Online Date: 2022-04-12
Accepted Date: 2022-03-01
Authors: Lalousis, Paris Alexandros
Schmaal, Lianne
Wood, Stephen J.
Reniers, Renate L.E.P.
Barnes, Nicholas M.
Chisholm, Katharine (ORCID Profile 0000-0002-0575-0789)
Griffiths, Sian Lowri
Stainton, Alexandra
Wen, Junhao
Hwang, Gyujoon
Davatzikos, Christos
Wenzel, Julian
Kambeitz-Ilankovic, Lana
Andreou, Christina
Bonivento, Carolina
Dannlowski, Udo
Ferro, Adele
Liechtenstein, Theresa
Riecher-Rössler, Anita
Romer, Georg
Rosen, Marlene
Bertolino, Alessandro
Borgwardt, Stefan
Brambilla, Paolo
Kambeitz, Joseph
Lencer, Rebekka
Pantelis, Christos
Ruhrmann, Stephan
Salokangas, Raimo K.R.
Schultze-Lutter, Frauke
Schmidt, André
Meisenzahl, Eva
Koutsouleris, Nikolaos
Dwyer, Dominic
Upthegrove, Rachel

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