Statistical analysis
psychotic symptoms, psychotic depression, depressive symptoms
REGRESSION, MRS
Baseline MRS with water-scaled data were obtained at CAMH and UMass. For the cross-sectional comparison between brain metabolite levels in patients with remitted psychotic depression and controls, we used an analysis of covariance with group (patient versus control) as the independent variable, metabolite levels as the dependent variable, and age, sex, and years of education as covariates. For the longitudinal comparison of MRS measures over time in the olanzapine versus placebo groups, a linear mixed-model regression was used, given that the interval between scans varied among patients. Time (in days) was included, and a treatment-group x time interaction was modeled, with age and sex as covariates. A fixed intercept was included, along with random intercepts to account for within-subject variability. The data were analyzed from CAMH and UMass separately and then harmonized meta-analytically. For multiple comparison corrections, the Bonferroni method was applied. Thus, a significance threshold of p < 0.0025 (= 0.05/20) was applied for metabolite level comparison at each site, given that 5 metabolites × 2 ROIs × 2 sites were examined. Then, we averaged the effect sizes of the dACC data at the two sites by meta-analyzing them as a standardized mean difference (SMD) with a significance threshold of p < 0.01 considering 5 metabolites. The L-DLPFC data were not meta-analyzed due to the different voxel sizes between sites. Pearson correlation coefficients (two-tailed) were evaluated to examine the associations between 1) baseline metabolite levels and treatment outcome/symptom changes, and 2) change in metabolite levels and symptom changes in the whole patient sample, as assessed with the change in HDRS-17 total score for depressive symptoms and the SADS delusion score (Spitzer & Endicott 1979) for psychotic symptoms (Bonferroni correction applied). A detailed description of the statistical analysis is available in the Supplemental Methods.