Lalousis, Paris Alexandros; Schmaal, Lianne; Wood, Stephen J; Reniers, Renate L E P; Barnes, Nicholas M; Chisholm, Katharine; Griffiths, Sian Lowri; Stainton, Alexandra; Wen, Junhao; Hwang, Gyujoon; Davatzikos, Christos; Wenzel, Julian; Kambeitz-Ilankovic, Lana; Andreou, Christina; Bonivento, Carolina; Dannlowski, Udo; Ferro, Adele; Lichtenstein, Theresa; Riecher-Rössler, Anita; Romer, Georg; ... (2022). Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biological psychiatry, 92(7), pp. 552-562. Elsevier 10.1016/j.biopsych.2022.03.021
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy 04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy > Research Division |
UniBE Contributor: |
Schultze-Lutter, Frauke |
ISSN: |
1873-2402 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
22 Jun 2022 12:02 |
Last Modified: |
05 Dec 2022 16:21 |
Publisher DOI: |
10.1016/j.biopsych.2022.03.021 |
PubMed ID: |
35717212 |
Uncontrolled Keywords: |
Clustering Depression Machine learning Nosology Psychosis Transdiagnostic |
BORIS DOI: |
10.48350/170827 |
URI: |
https://boris.unibe.ch/id/eprint/170827 |
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