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Background The social restrictions occurring during the pandemic contributed to loss of many sources of reward, which contributes to poor mental health.Objective This trial evaluated a brief positive affect training programme to reduce anxiety, depression and suicidality during the pandemic.Methods In this single-blind, parallel, randomised controlled trial, adults who screened positive for COVID-19-related psychological distress across Australia were randomly allocated to either a 6-session group-based programme based on positive affect training (n=87) or enhanced usual care (EUC, n=87). Primary outcome was total score on the Hospital Anxiety and Depression Scale—anxiety and depression subscales assessed at baseline, 1-week post-treatment, 3 months (primary outcome time point) as well as secondary outcome measures of suicidality, generalised anxiety disorder, sleep impairment, positive and negative mood and COVID-19-related stress.Findings Between 20 September 2020 and 16 September 2021, 174 participants were enrolled into the trial. Relative to EUC, at 3-month follow-up the intervention led to greater reduction on depression (mean difference 1.2 (95% CI 0.4 to 1.9)), p=0.003), with a moderate effect size (0.5 (95% CI 0.2 to 0.9)). There were also greater reduction of suicidality and improvement in quality of life. There were no differences in anxiety, generalised anxiety, anhedonia, sleep impairment, positive or negative mood or COVID-19 concerns.Conclusions This intervention was able to reduce depression and suicidality during adverse experiences when rewarding events were diminished, such as pandemics.Clinical implications Strategies to improve positive affect may be useful to reduce mental health issues.Trial registration number ACTRN12620000811909.

Abstract The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10−20), thalamus (p = 7.46 × 10−10), caudate (p = 1.97 × 10−18), putamen (p = 1.7 × 10−12), and nucleus accumbens (p = 1.99 × 10−7). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = −0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.