Major depressive disorder (MDD) and alcohol use disorders (AUDs) are among the most prevalent psychiatric disorders and are frequently co-morbid. However, some component of this co-morbidity may be artifactual and arise from the influence of current mental state on self-reports of AUD.

This study examined whether past-year MDD is associated with differential criterion functioning (DCF) in reports of AUD symptomatology in male and female participants in the National Epidemiological Survey on Alcohol and Related Conditions (NEASRC).

Reports of past-year AUD symptomatology were adequately summarized by a single-factor model in which each of the 11 abuse and dependence criteria had high factor loadings (0.71–0.93) and did not vary between men and women after allowing for threshold differences. Co-morbid MDD was associated with higher AUD mean scores. There was some evidence for DCF with past-year MDD being associated with a lower endorsement of use in hazardous situations among men whereas women with MDD were more likely to endorse both social/interpersonal problems and emotional/physical problems.

Several items assessing AUD display DCF in the presence of MDD. While these findings highlight the need to consider the possibility that mental state can influence reporting of psychiatric symptoms and potentially inflate estimates of co-morbidity, they suggest that only a negligible component of the co-morbidity between MDD and AUDs can be attributed to over-reporting of alcohol symptomatology conditional on current MDD.

Impairments in self-recognition (i.e. recognition of own thoughts and actions) have been repeatedly shown in individuals with schizophrenia. According to classical clinical characterizations, schizophrenia is included in a continuum encompassing a large range of genetic statuses, psychotic states and symptoms. The current meta-analysis aims to determine whether self-recognition is affected by individuals within the psychosis continuum.

Three populations were considered: people with an at-risk mental state for psychosis (ARMS), hallucination-prone individuals and unaffected relatives of patients with schizophrenia. Eleven studies contrasted self-recognition between these three populations (n = 386) and healthy controls (n = 315) and four studies used correlational analysis to estimate comparable effects (n = 629). Eligible studies used experimental paradigms including source-monitoring and self-monitoring.

We observed significantly reduced self-recognition accuracy in these populations [g = −0.44 (−0.71 to −0.17), p = 0.002] compared to controls. No influence of the type of population, experimental paradigm or study design was observed.

The present analysis argues for self-recognition deficits in populations with no full-blown psychotic symptoms represented across the continuum of psychosis.

Tourette disorder (TD), hallmarks of which are motor and vocal tics, has been related to functional abnormalities in large-scale brain networks. Using a fully data driven approach in a prospective, case–control study, we tested the hypothesis that functional connectivity of these networks carries a neural signature of TD. Our aim was to investigate (i) the brain networks that distinguish adult patients with TD from controls, and (ii) the effects of antipsychotic medication on these networks.

Using a multivariate analysis based on support vector machine (SVM), we developed a predictive model of resting state functional connectivity in 48 patients and 51 controls, and identified brain networks that were most affected by disease and pharmacological treatments. We also performed standard univariate analyses to identify differences in specific connections across groups.

SVM was able to identify TD with 67% accuracy (p = 0.004), based on the connectivity in widespread networks involving the striatum, fronto-parietal cortical areas and the cerebellum. Medicated and unmedicated patients were discriminated with 69% accuracy (p = 0.019), based on the connectivity among striatum, insular and cerebellar networks. Univariate approaches revealed differences in functional connectivity within the striatum in patients v. controls, and between the caudate and insular cortex in medicated v. unmedicated TD.

SVM was able to identify a neuronal network that distinguishes patients with TD from control, as well as medicated and unmedicated patients with TD, holding a promise to identify imaging-based biomarkers of TD for clinical use and evaluation of the effects of treatment.

Problematic anger is frequently reported by soldiers who have deployed to combat zones. However, evidence is lacking with respect to how anger changes over a deployment cycle, and which factors prospectively influence change in anger among combat-deployed soldiers.

Reports of problematic anger were obtained from 7298 US Army soldiers who deployed to Afghanistan in 2012. A series of mixed-effects growth models estimated linear trajectories of anger over a period of 1–2 months before deployment to 9 months post-deployment, and evaluated the effects of pre-deployment factors (prior deployments and perceived resilience) on average levels and growth of problematic anger.

A model with random intercepts and slopes provided the best fit, indicating heterogeneity in soldiers' levels and trajectories of anger. First-time deployers reported the lowest anger overall, but the most growth in anger over time. Soldiers with multiple prior deployments displayed the highest anger overall, which remained relatively stable over time. Higher pre-deployment resilience was associated with lower reports of anger, but its protective effect diminished over time. First- and second-time deployers reporting low resilience displayed different anger trajectories (stable v. decreasing, respectively).

Change in anger from pre- to post-deployment varies based on pre-deployment factors. The observed differences in anger trajectories suggest that efforts to detect and reduce problematic anger should be tailored for first-time v. repeat deployers. Ongoing screening is needed even for soldiers reporting high resilience before deployment, as the protective effect of pre-deployment resilience on anger erodes over time.

Neuroprogressive models of the trajectory of cognitive dysfunction in patients with bipolar disorder (BD) have been proposed. However, few studies have explored the relationships among clinical characteristics of BD, cognitive dysfunction, and aging.

We conducted a cross-sectional analysis in euthymic participants with the MATRICS Cognitive Consensus Battery, the Trail Making Test B, the Stroop Test, and the Wechsler Test of Adult Reading. Age- and gender-equated control participants without a mental disorder [‘Healthy Controls’ – HC)] were assessed similarly. We compared cognitive performance both globally and in seven domains in four groups: younger BD (age ⩽49 years; n = 70), older BD (age ⩾50 years; n = 48), younger HC (n = 153), and older HC (n = 44). We also compared the BD and HC groups using age as a continuous measure. We controlled for relevant covariates and applied a Bonferroni correction.

Our results support both an early impairment (‘early hit’) model and an accelerated aging model: impairment in attention/vigilance, processing speed, and executive function/working memory were congruent with the accelerated aging hypothesis whereas impairment in verbal memory was congruent with an early impairment model. BD and HC participants exhibited similar age-related decline in reasoning/problem solving and visuospatial memory. There were no age- or diagnosis-related differences in social cognition.

Our findings support that different cognitive domains are affected differently by BD and aging. Longitudinal studies are needed to explore trajectories of cognitive performance in BD across the lifespan.

Despite unclear evidence to support the long-term use of antipsychotics to treat challenging (problem) behaviours in people with autism in the absence of a psychiatric disorder, this practice is common.

We conducted a systematic review and meta-analysis of all randomised controlled trials (RCTs) involving antipsychotics for people with autism of all ages, irrespective of the outcomes assessed. We searched seven databases and hand-searched ten relevant journals. Two authors independently screened titles, abstracts and full papers and extracted data using the Cochrane Handbook template. We conducted meta-analyses of outcomes and the rate of adverse events.

We included 39 papers based on 21 primary RCTs that recruited 1482 people with autism. No RCT assessed any psychiatric disorder outcome, such as psychoses or bipolar disorder. A meta-analysis of ten placebo-controlled RCTs showed a significantly improved Aberrant Behaviour Checklist-Irritability score in the antipsychotic group with an effect size of −6.45 [95% confidence interval (CI) −8.13 to −4.77] (low certainty). Pooled Clinical Global Impression data on 11 placebo-controlled RCTs showed an overall effect size of 0.84 (95% CI 0.48 to 1.21) (moderate certainty). There was a significantly higher risk of overall adverse effects (p = 0.003) and also weight gain (p < 0.00001), sedation (p < 0.00001) and increased appetite (p = 0.001) in the antipsychotic group.

There is some evidence for risperidone and preliminary evidence for aripiprazole to significantly improve scores on some outcome measures among children with autism but not adults or for any other antipsychotics. There is a definite increased risk of antipsychotic-related different adverse effects.

Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders.

We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20–0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses.

We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19–0.54]; for ESMA: 0.23 [0.09–0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49–0.63) or placebo-controlled (0.12–0.38) trials than in trials comparing active treatments (0.07–0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = −0.06, p ⩽ 0.001).

Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature.

Evidence supporting the continuous latent structure of mood phenomena has not been incorporated into psychiatric diagnostic systems, in part because the evidence has been incomplete. For example, no studies have investigated the boundary between ‘sick’ and ‘well’ periods in individuals with bipolar disorder, despite agreement that characterization of mood disorders as having a discrete episodic course is inaccurate. The present study examined the validity of mood episode symptom thresholds in out-patients with bipolar disorder using multiple methodologies: taxometrics and information-theoretic latent distribution modeling (ITLDM), to evaluate the continuity/discontinuity of mood symptoms; and structural equation mixture modeling (SEMM), to evaluate the continuity/discontinuity of associations between mood symptoms and general functioning.

A total of 3721 out-patients with bipolar disorder from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) were available for analysis. Data were collected at participants’ baseline STEP-BD visit. Taxometric [maximum covariance/means above minus below a cut (MAXCOV/MAMBAC) with simulated comparison data], ITLDM and SEMM methods were applied twice, once to the Montgomery–Åsberg Depression Rating Scale and again to the Young Mania Rating Scale.

Taxometric results unequivocally supported a continuous interpretation of the data. ITLDM results favored many valued ‘discrete metrical’ models, suggesting that mood symptoms have continuous, but potentially non-normally distributed, latent structures in out-patients with bipolar disorder. Finally, SEMM results demonstrated that latent associations between mood symptoms and general functioning were linear.

Results from the present study argue against the validity of DSM mood episode thresholds and argue for a graded continuum of care of bipolar symptom management.

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.