Abstract
Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP−D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP−D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.
| Original language | English |
|---|---|
| Pages (from-to) | 249–258 |
| Number of pages | 10 |
| Journal | Schizophrenia Bulletin |
| Volume | 47 |
| Issue number | 1 |
| Early online date | 7 Jul 2020 |
| DOIs | |
| Publication status | Published - 23 Jan 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- depression
- gray matter volume
- machine learning
- psychopathology
- psychosis
- schizophrenia
- Humans
- Male
- Depression/classification
- Gray Matter/diagnostic imaging
- Young Adult
- Magnetic Resonance Imaging
- Adolescent
- Adult
- Female
- Schizophrenia/classification
- Psychotic Disorders/classification
- Supervised Machine Learning
- Principal Component Analysis
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