Accumulating evidence shows that a propensity towards a pro-inflammatory status in the brain plays an important role in schizophrenia. Anti-inflammatory drugs might compensate this propensity. This study provides an update regarding the efficacy of agents with some anti-inflammatory actions for schizophrenia symptoms tested in randomized controlled trials (RCTs).

PubMed, Embase, the National Institutes of Health website (http://www.clinicaltrials.gov), and the Cochrane Database of Systematic Reviews were systematically searched for RCTs that investigated clinical outcomes.

Our search yielded 56 studies that provided information on the efficacy of the following components on symptom severity: aspirin, bexarotene, celecoxib, davunetide, dextromethorphan, estrogens, fatty acids, melatonin, minocycline, N-acetylcysteine (NAC), pioglitazone, piracetam, pregnenolone, statins, varenicline, and withania somnifera extract. The results of aspirin [mean weighted effect size (ES): 0.30; n = 270; 95% CI (CI) 0.06–0.54], estrogens (ES: 0.78; n = 723; CI 0.36–1.19), minocycline (ES: 0.40; n = 946; CI 0.11–0.68), and NAC (ES: 1.00; n = 442; CI 0.60–1.41) were significant in meta-analysis of at least two studies. Subgroup analysis yielded larger positive effects for first-episode psychosis (FEP) or early-phase schizophrenia studies. Bexarotene, celecoxib, davunetide, dextromethorphan, fatty acids, pregnenolone, statins, and varenicline showed no significant effect.

Some, but not all agents with anti-inflammatory properties showed efficacy. Effective agents were aspirin, estrogens, minocycline, and NAC. We observed greater beneficial results on symptom severity in FEP or early-phase schizophrenia.

Reappraisal, an adaptive emotion regulation strategy, is associated with frontal engagement. In internalizing psychopathologies (IPs) such as anxiety and depression frontal activity is atypically reduced suggesting impaired regulation capacity. Yet, successful reappraisal is often demonstrated at the behavioral level. A data-driven approach was used to clarify brain and behavioral relationships in IPs.

During functional magnetic resonance imaging, anxious [general anxiety disorder (n = 43), social anxiety disorder (n = 72)] and depressed (n = 47) patients reappraised negative images to reduce negative affect (‘ReappNeg’) and viewed negative images (‘LookNeg’). After each trial, the affective state was reported. A cut-point (i.e. values <0 based on ΔReappNeg-LookNeg) demarcated successful reappraisers. Neural activity for ReappNeg-LookNeg, derived from 37 regions of interest, was submitted to Principal Component Analysis (PCA) to identify unique components of reappraisal-related brain response. PCA factors, symptom severity, and self-reported habitual reappraisal were submitted to discriminant function analysis and linear regression to examine whether these data predicted successful reappraisal (yes/no) and variance in reappraisal ability.

Most patients (63%) were successful reappraisers according to the behavioral criterion (values<0; ΔReappNeg-LookNeg). Discriminant function analysis was not significant for PCA factors, symptoms, or habitual reappraisal. For regression, more activation in a factor with high loadings for frontal regions predicted better reappraisal facility. Results were not significant for other variables.

At the individual level, more activation in a ‘frontal’ factor corresponded with better reappraisal facility. However, neither brain nor behavioral variables classified successful reappraisal (yes/no). Findings suggest individual differences in regions strongly implicated in reappraisal play a role in on-line reappraisal capability.

Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.

An ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8–15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI −0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9–19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules.

A number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

Non-motor features of Parkinson's disease (PD) and dementia with Lewy bodies (DLB), such as auditory hallucinations (AH), contribute to disease burden but are not well understood.

Systematic review and random-effects meta-analyses of studies reporting AH associated with PD or DLB. Prevalence of visual hallucinations (VH) in identified studies meeting eligibility criteria were included in meta-analyses, facilitating comparison with AH. Synthesis of qualitative descriptions of AH was performed. PubMed, Web of Science and Scopus databases were searched for primary journal articles, written in English, published from 1970 to 2017. Studies reporting AH prevalence in PD or DLB were screened using PRISMA methods.

Searches identified 4542 unique studies for consideration, of which, 26 met inclusion criteria. AH pooled prevalence in PD was estimated to be 8.9% [95% confidence interval (CI) 5.3–14.5], while in DLB was estimated to be 30.8% (±23.4 to 39.3). Verbal hallucinations, perceived as originating outside the head, were the most common form of AH. Non-verbal AH were also common while musical AH were rare. VH were more prevalent, with an estimated pooled prevalence in PD of 28.2% (±19.1 to 39.5), while in DLB they were estimated to be 61.8% (±49.1 to 73.0). Meta-regression determined that the use of validated methodologies to identify hallucinations produced higher prevalence estimates.

AH and VH present in a substantial proportion of PD and DLB cases, with VH reported more frequently in both conditions. Both AH and VH are more prevalent in DLB than PD. There is a need for standardised use of validated methods to detect and monitor hallucinations.

We assessed whether the risk of various psychotic disorders and non-psychotic bipolar disorder (including mania) varied by migrant status, a region of origin, or age-at-migration, hypothesizing that risk would only be elevated for psychotic disorders.

We established a prospective cohort of 1 796 257 Swedish residents born between 1982 and 1996, followed from their 15th birthday, or immigration to Sweden after age 15, until diagnosis, emigration, death, or end of 2011. Cox proportional hazards models were used to model hazard ratios by migration-related factors, adjusted for covariates.

All psychotic disorders were elevated among migrants and their children compared with Swedish-born individuals, including schizophrenia and schizoaffective disorder (adjusted hazard ratio [aHR]migrants: 2.20, 95% CI 1.96–2.47; aHRchildren : 2.00, 95% CI 1.79–2.25), affective psychotic disorders (aHRmigrant1.42, 95% CI 1.25–1.63; aHRchildren: 1.22 95% CI 1.07–1.40), and other non-affective psychotic disorders (aHRmigrant: 1.97, 95% CI 1.81–2.14; aHRchildren: 1.68, 95% CI 1.54–1.83). For all psychotic disorders, risks were generally highest in migrants from Africa (i.e. aHRschizophrenia: 5.24, 95% CI 4.26–6.45) and elevated at most ages-of-migration. By contrast, risk of non-psychotic bipolar disorders was lower for migrants (aHR: 0.58, 95% CI 0.52–0.64) overall, and across all ages-of-migration except infancy (aHR: 1.20; 95% CI 1.01–1.42), while risk for their children was similar to the Swedish-born population (aHR: 1.00, 95% CI 0.93–1.08).

Increased risk of psychiatric disorders associated with migration and minority status may be specific to psychotic disorders, with exact risk dependent on the region of origin.

The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is associated with response to antidepressant drugs in depressed patients and with metabolic side effects after antipsychotic treatment. This study aims to assess the association between this polymorphism and insulin resistance after antidepressant treatment in depressed patients.

One hundred forty-eight Caucasian patients with a current unipolar major depressive episode (DSM IV-TR) were genotyped for the BDNF Val66Met polymorphism and assessed at baseline and after 3 and 6 months of antidepressant treatment for the ‘Homoeostasis model assessment of insulin resistance’ (HOMA-IR) index, a valid measure of insulin resistance based on fasting plasma insulinaemia and glycaemia. Because validity assumptions were fulfilled, data were analysed using analysis of variance for repeated measures.

The 52 (35%) Met carriers and 96 (65%) Val/Val patients were not different at baseline for clinical characteristics and HOMA-IR. A significant Val66Met × time interaction (p = 0.02), a significant time effect (p = 0.03) and a significant Val66Met effect (p = 0.0497) were shown for HOMA-IR. A significant Val66Met × time interaction (p = 0.01) and a significant time effect (p = 0.003) were shown for fasting glycaemia. HOMA-IR and fasting glycaemia changes after antidepressant treatment were significantly higher in Met carrier than in Val/Val patients (HOMA-IR changes: Met: 0.71 ± 3.29 v. Val/Val: −0.16 ± 1.34, t = 2.3, df = 146, p = 0.02, glycaemia changes: Met: 0.09 ± 0.30 v. Val/Val: 0.02 ± 0.16, t = −2.0, df = 146, p = 0.045).

The Met allele of the Val66Met BDNF polymorphism confers to depressed patients a higher risk of insulin-resistance after antidepressant treatment. These patients could benefit from specific monitoring of metabolism and preventive measures.

Identifying characteristics of individuals at greatest risk for prolonged grief disorder (PGD) can improve its detection and elucidate the etiology of the disorder. The Safe Passage Study, a study of women at high risk for sudden infant death syndrome (SIDS), prospectively examined the psychosocial functioning of women while monitoring their healthy pregnancies. Mothers whose infants died of SIDS were followed in bereavement.

Pre-loss data were collected from 12 000 pregnant mothers and analyzed for their associations with grief symptoms and PGD in 50 mothers whose infants died from SIDS, from 2 to 48 months after their infant's death, focusing on pre-loss risk factors of anxiety, depression, alcohol use, maternal age, the presence of other living children in the home, and previous child loss.

The presence of any four risk factors significantly predicted PGD for 24 months post-loss (p < 0.003); 2–3 risk factors predicted PGD for 12 months (p = 0.02). PGD rates increased in the second post-loss year, converging in all groups to approximately 40% by 3 years. Pre-loss depressive symptoms were significantly associated with PGD. Higher alcohol intake and older maternal age were consistently positively associated with PGD. Predicted risk scores showed good discrimination between PGD and no PGD 6–24 months after loss (C-statistic = 0.83).

A combination of personal risk factors predicted PGD in 2 years of bereavement. There is a convergence of risk groups to high rates at 2–3 years, marked by increased PGD rates in mothers at low risk. The risk factors showed different effects on PGD.

Previous research has shown prospectively that religiosity/spirituality protects against depression, but these findings are commonly critiqued on two grounds, namely: (1) apparent religiosity/spirituality reflects merely an original absence of depression or elevated mood and (2) religiosity/spirituality too often is measured as a global construct. The current study investigates the relationship between depression and religiosity/spirituality by examining its multidimensional structural integrity.

Confirmatory factor analyses with a previously observed cross-cultural factor structure of religiosity/spirituality variables were conducted on an independent sample, diagnostic and familial risk subgroups from this sample, and a subsample of the original cross-cultural sample. Linear regressions onto a previous diagnosis of major depressive disorder (MDD) 5 years prior to assess the potential attenuating impact of a previous depression was explored.

Across familial risk groups and clinical subgroups, each of the previously validated religiosity/spirituality domains was confirmed, namely: religious/spiritual commitment, contemplative practice, sense of interconnectedness, the experience of love, and altruistic engagement. Previous MDD diagnosis was associated with a lower religious/spiritual commitment among high-risk individuals, higher contemplation among low-risk individuals, and lower importance of religion or spirituality regardless of risk group.

Structural integrity was found across familial risk groups and diagnostic history for a multidimensional structure of religiosity/spirituality. Differential associations between a previous diagnosis of MDD and level of religiosity/spirituality across domains suggest a complex and interactive relation between depression, familial risk, and religiosity/spirituality. Accounting for an empirically valid, multidimensional understanding of religiosity/spirituality may advance research on mechanisms underlying the relationship between religiosity/spirituality and mental health.

An increasing importance is being placed on mental health and wellbeing at individual and population levels. While there are several interventions that have been proposed to improve wellbeing, more evidence is needed to understand which aspects of wellbeing are most influential. This study aimed to identify key items that signal improvement of mental health and wellbeing.

Using network analysis, we identified the most central items in the graph network estimated from the well-established Warwick-Edinburgh Mental Well-being Scale (WEMWBS). Results were compared across four major UK cohorts comprising a total of 47,578 individuals: the Neuroscience in Psychiatry Network, the Scottish Schools Adolescent Lifestyle and Substance Use Survey, the Northern Ireland Health Survey, and the National Child Development Study.

Regardless of gender, the three items most central in the network were related to positive self-perception and mood: ‘I have been feeling good about myself’; ‘I have been feeling confident’; and ‘I have been feeling cheerful’. Results were consistent across all four cohorts.

Positive self-perception and positive mood are central to psychological wellbeing. Psychotherapeutic and public mental health interventions might best promote psychological wellbeing by prioritising the improvement of self-esteem, self-confidence and cheerfulness. However, empirical testing of interventions using these key targets is needed.

Bipolar disorder is a highly heritable mental illness that transmits intergeneratively. Previous studies supported that first-degree relatives (FDRs), such as parents, offspring, and siblings, of patients with bipolar disorder, had a higher risk of bipolar disorder. However, whether FDRs of bipolar patients have an increased risk of schizophrenia, major depressive disorder (MDD), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) remains unclear.

Among the entire population in Taiwan, 87 639 patients with bipolar disorder and 188 290 FDRs of patients with bipolar disorder were identified in our study. The relative risks (RRs) of major psychiatric disorders were assessed among FDRs of patients with bipolar disorder.

FDRs of patients with bipolar disorder were more likely to have a higher risk of major psychiatric disorders, including bipolar disorder (RR 6.12, 95% confidence interval (CI) 5.95–6.30), MDD (RR 2.89, 95% CI 2.82–2.96), schizophrenia (RR 2.64, 95% CI 2.55–2.73), ADHD (RR 2.21, 95% CI 2.13–2.30), and ASD (RR 2.10, 95% CI 1.92–2.29), than the total population did. These increased risks for major psychiatric disorders were consistent across different familial kinships, such as parents, offspring, siblings, and twins. A dose-dependent relationship was also found between risk of each major psychiatric disorder and numbers of bipolar patients.

Our study was the first study to support the familial coaggregation of bipolar disorder with other major psychiatric disorders, including schizophrenia, MDD, ADHD, and ASD, in a Taiwanese (non-Caucasian) population. Given the elevated risks of major psychiatric disorders, the public health government should pay more attention to the mental health of FDRs of patients with bipolar disorder.

Depression is increasingly recognized as a chronic and relapsing disorder. However, an important minority of patients who start treatment for their major depressive episode recover to euthymia. It is clinically important to be able to predict such individuals.

The study is a secondary analysis of a recently completed pragmatic megatrial examining first- and second-line treatments for hitherto untreated episodes of non-psychotic unipolar major depression (n = 2011). Using the first half of the cohort as the derivation set, we applied multiply-imputed stepwise logistic regression with backward selection to build a prediction model to predict remission, defined as scoring 4 or less on the Patient Health Quetionnaire-9 at week 9. We used three successively richer sets of predictors at baseline only, up to week 1, and up to week 3. We examined the external validity of the derived prediction models with the second half of the cohort.

In total, 37.0% (95% confidence interval 34.8–39.1%) were in remission at week 9. Only the models using data up to week 1 or 3 showed reasonable performance. Age, education, length of episode and depression severity remained in the multivariable prediction models. In the validation set, the discrimination of the prediction model was satisfactory with the area under the curve of 0.73 (0.70–0.77) and 0.82 (0.79–0.85), while the calibration was excellent with non-significant goodness-of-fit χ2 values (p = 0.41 and p = 0.29), respectively.

Patients and clinicians can use these prediction models to estimate their predicted probability of achieving remission after acute antidepressant therapy.

Patient expectancy is an important source of placebo effects in antidepressant clinical trials, but all prior studies measured expectancy prior to the initiation of medication treatment. Little is known about how expectancy changes during the course of treatment and how such changes influence clinical outcome. Consequently, we undertook the first analysis to date of in-treatment expectancy during antidepressant treatment to identify its clinical and demographic correlates, typical trajectories, and associations with treatment outcome.

Data were combined from two randomized controlled trials of antidepressant medication for major depressive disorder in which baseline and in-treatment expectancy assessments were available. Machine learning methods were used to identify pre-treatment clinical and demographic predictors of expectancy. Multilevel models were implemented to test the effects of expectancy on subsequent treatment outcome, disentangling within- and between-patient effects.

Random forest analyses demonstrated that whereas more severe depressive symptoms predicted lower pre-treatment expectancy, in-treatment expectancy was unrelated to symptom severity. At each measurement point, increased in-treatment patient expectancy significantly predicted decreased depressive symptoms at the following measurement (B = −0.45, t = −3.04, p = 0.003). The greater the gap between expected treatment outcomes and actual depressive severity, the greater the subsequent symptom reductions were (B = 0.49, t = 2.33, p = 0.02).

Greater in-treatment patient expectancy is associated with greater subsequent depressive symptom reduction. These findings suggest that clinicians may benefit from monitoring and optimizing patient expectancy during antidepressant treatment. Expectancy may represent another treatment parameter, similar to medication compliance and side effects, to be regularly monitored during antidepressant clinical management.

To better understand the associations of childhood trauma and childhood disorder with past-year suicidality (thoughts, plans or attempts), we compared male military and civilian populations aged 18–60 years old.

Data derived from the 2010 Australian Defence Force (ADF) Mental Health Prevalence and Wellbeing Study and the 2007 Australian Bureau of Statistics Australian National Survey of Mental Health and Wellbeing were compared using logistic regression and Generalized Structural Equation Modelling (GSEM).

A greater proportion of the ADF experienced suicidality than civilians. Those who experienced childhood trauma that was not interpersonal in nature were not at increased odds of suicidality, in either population. A higher proportion of the ADF experienced three or more types of trauma in childhood and first experienced three or more types of trauma in adulthood. Both were associated with suicidality in the ADF and civilians. Childhood anxiety had a strong and independent association with suicidality in the ADF (controlling for demographics and childhood trauma, adult trauma and adult onset disorder). Childhood anxiety fully mediated the relationship between childhood trauma and suicidality in the ADF, but not in civilians.

These data highlight the need to take a whole life approach to understanding suicidality, and the importance of categorizing the nature of childhood trauma exposure. Importantly, childhood anxiety was not only associated with suicidality, it fully mediated the relationship between childhood trauma and suicidality in the more trauma exposed (military) population only. These findings have the potential to inform the development of strategies for suicide prevention.

Randomized control trials (RCTs) comparing attention control training (ACT) and attention bias modification (ABM) in posttraumatic stress disorder (PTSD) have shown mixed results. The current RCT extends the extant literature by comparing the efficacy of ACT and a novel bias-contingent-ABM (BC-ABM), in which direction of training is contingent upon the direction of pre-treatment attention bias (AB), in a sample of civilian patients with PTSD.

Fifty treatment-seeking civilian patients with PTSD were randomly assigned to either ACT or BC-ABM. Clinician and self-report measures of PTSD and depression, as well as AB and attention bias variability (ABV), were acquired pre- and post-treatment.

ACT yielded greater reductions in PTSD and depressive symptoms on both clinician-rated and self-reported measures compared with BC-ABM. The BC-ABM condition successfully shifted ABs in the intended training direction. In the ACT group, there was no significant change in ABV or AB from pre- to post-treatment.

The current RCT extends previous results in being the first to apply ABM that is contingent upon AB at pre-treatment. This personalized BC-ABM approach is associated with significant reductions in symptoms. However, ACT produces even greater reductions, thereby emerging as a promising treatment for PTSD.

Depression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed – but non-help-seeking and unmedicated – individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.

Individuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.

Significant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = −0.017, p = 0.006) and putamen (b = −0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).

Striatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.

While previous studies have identified relationships between hippocampal volumes and memory performance in schizophrenia, these relationships are not apparent in healthy individuals. Further, few studies have examined the role of hippocampal subfields in illness-related memory deficits, and no study has examined potential differences across varying illness stages. The current study aimed to investigate whether individuals with early and established psychosis exhibited differential relationships between visuospatial associative memory and hippocampal subfield volumes.

Measurements of visuospatial associative memory performance and grey matter volume were obtained from 52 individuals with a chronic schizophrenia-spectrum disorder, 28 youth with recent-onset psychosis, 52 older healthy controls, and 28 younger healthy controls.

Both chronic and recent-onset patients had impaired visuospatial associative memory performance, however, only chronic patients showed hippocampal subfield volume loss. Both chronic and recent-onset patients demonstrated relationships between visuospatial associative memory performance and hippocampal subfield volumes in the CA4/dentate gyrus and the stratum that were not observed in older healthy controls. There were no group by volume interactions when chronic and recent-onset patients were compared.

The current study extends the findings of previous studies by identifying particular hippocampal subfields, including the hippocampal stratum layers and the dentate gyrus, that appear to be related to visuospatial associative memory ability in individuals with both chronic and first-episode psychosis.