TY - JOUR
T1 - The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis
AU - Upthegrove, Rachel
AU - Lalousis, Paris
AU - Mallikarjun, Pavan
AU - Chisholm, Katharine
AU - Griffiths, Sian Lowri
AU - Iqbal, Mariam
AU - Pelton, Mirabel
AU - Reniers, Renate
AU - Stainton, Alexandra
AU - Rosen, Marlene
AU - Ruef, Anne
AU - Dwyer, Dominic B
AU - Surman, Marian
AU - Haidl, Theresa
AU - Penzel, Nora
AU - Kambeitz-llankovic, Lana
AU - Bertolino, Alessandro
AU - Brambilla, Paolo
AU - Borgwardt, Stefan
AU - Kambeitz, Joseph
AU - Lencer, Rebekka
AU - Pantelis, Christos
AU - Ruhrmann, Stephan
AU - Schultze-lutter, Frauke
AU - Salokangas, Raimo K R
AU - Meisenzahl, Eva
AU - Wood, Stephen J
AU - Koutsouleris, Nikolaos
PY - 2021/1/23
Y1 - 2021/1/23
N2 - 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.
AB - 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.
KW - depression
KW - gray matter volume
KW - machine learning
KW - psychopathology
KW - psychosis
KW - schizophrenia
KW - Humans
KW - Male
KW - Depression/classification
KW - Gray Matter/diagnostic imaging
KW - Young Adult
KW - Magnetic Resonance Imaging
KW - Adolescent
KW - Adult
KW - Female
KW - Schizophrenia/classification
KW - Psychotic Disorders/classification
KW - Supervised Machine Learning
KW - Principal Component Analysis
UR - https://academic.oup.com/schizophreniabulletin/advance-article/doi/10.1093/schbul/sbaa094/5868437
UR - http://www.scopus.com/inward/record.url?scp=85096770480&partnerID=8YFLogxK
U2 - 10.1093/schbul/sbaa094
DO - 10.1093/schbul/sbaa094
M3 - Article
C2 - 32634220
SN - 0586-7614
VL - 47
SP - 249
EP - 258
JO - Schizophrenia Bulletin
JF - Schizophrenia Bulletin
IS - 1
ER -