The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis

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.

Keywords: depression; gray matter volume; machine learning; psychopathology; psychosis; schizophrenia.

Fig. 1.

Classification performance of Neurominer support vector machine (SVM) learning with (a) SVM classification model of recent onset psychosis with depression (ROP+D) vs recent onset depression (ROD) using Beck Depression Inventory-II (BDI-II) individual scores: balanced accuracy 51.5%, sensitivity 76.7%, specificity 26.3%, area under the curve (AUC) 0.54, (b) visualization of feature weights of BDI-II classification, (c) SVM classification model of ROP+D vs ROP without depression (ROP−D) using neuroanatomical variables: balanced accuracy 50.1%, sensitivity 67.4%, specificity 34.6%, AUC 0.58, (d) neuroanatomical variables entered in to (c) as derived from ROD vs healthy controls (HC).