Neurobiologically based stratification of recent onset depression and psychosis: identification of two distinct transdiagnostic phenotypes

PRONIA Consortium, Paris Alexandros Lalousis, Lianne Schmaal, Stephen Wood, Renate Reniers, Nicholas Barnes, Katharine Chisholm, Lowri Griffiths, Alexandra Stainton, Junhao Wen, Gyujoon Hwang, Christos Davatzikos, Julian Wenzel, Lana Kambeitz-Ilankovic, Christina Andreou, Carolina Bonivento, Udo Dannlowski, Adele Ferro, Theresa Liechtenstein, Anita Riecher-RösslerGeorg Romer, Marlene Rosen, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Joseph Kambeitz, Rebekka Lencer, Christos Pantelis, Stephan Ruhrmann, Raimo K.r. Salokangas, Frauke Schultze-Lutter, André Schmidt, Eva M Meisenzahl, Nikolaos Koutsouleris, Dominic Dwyer, Rachel Upthegrove

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. 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 multi-site 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 (COBRE, n=146; MCIC, n=202; MUC, n=470).

Results
The optimal clustering solution was two transdiagnostic clusters (Cluster 1, n=153, 67 ROP, 86 ROD and Cluster 2, n=149, 88 ROP, 61 ROD; ARI=.618). The two clusters contained both ROP and ROD. One cluster had widespread GMV deficits, 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 which are clinically and biologically distinct, challenging current diagnostic boundaries in recent onset mental health disorders. These results may aid understanding of aetiology of poor outcome patients transdiagnostically and improve development of stratified treatments.
Original languageEnglish
JournalBiological Psychiatry
DOIs
Publication statusAccepted/In press - 1 Mar 2022

Bibliographical note

Journal pre-proof currently online. Final version of record not yet available as of 06/06/2022.

Keywords

  • transdiagnostic
  • psychosis
  • depression
  • clustering
  • nosology
  • machine learning

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