The current longitudinal study examined the developmental patterns of marijuana use and their relationship with subsequent psychological adjustment in a community-based sample of urban African American and Puerto Rican women.

Participants were interviewed five times over a period ranging from adolescence (mean age 14.0 years) to adulthood (mean age 32.5 years). Outcome measures included depressive symptoms, anger/hostility and the presence of a substance use disorder (abuse/dependence).

Three distinct trajectories of marijuana use were identified: non-users, increasers and quitters. Increasers reported higher levels of depressive symptoms and anger/hostility than did non-users and were more likely to meet criteria for a substance use disorder at age 32.5 years.

Our findings indicate that early-starting long-term use of marijuana is associated with psychological maladjustment among women. Prevention efforts should emphasize the long-term cost associated with marijuana use, and that the best psychological health is reported by those who abstain from the drug.

Schizophrenia (SZ) and bipolar disorder (BD) have substantial negative impact on the quality of human life. Both, microRNA (miRNA) expression profiling in SZ and BD postmortem brains [and genome-wide association studies (GWAS)] have implicated miRNAs in disease etiology. Here, we aim to determine whether significant GWAS signals observed in the Psychiatric Genetic Consortium (PGC) are enriched for miRNAs.

A two-stage approach was used to determine whether association signals from PGC affect miRNAs: (i) statistical assessment of enrichment using a Simes test and sum of squares test (SST) and (ii) biological evidence that quantitative trait loci (eQTL) mapping to known miRNA genes affect their expression in an independent sample of 78 postmortem brains from the Stanley Medical Research Institute.

A total of 2567 independent single nucleotide polymorphisms (SNPs) (R2 > 0.8) were mapped locally, within 1 Mb, to all known miRNAs (miRBase v. 21). We show robust enrichment for SZ- and BD-related SNPs with miRNAs using Simes (SZ: p ≤ 0.0023, BD: p ≤ 0.038), which remained significant after adjusting for background inflation in SZ (empirical p = 0.018) and approached significance in BD (empirical p = 0.07). At a false discovery rate of 10%, we identified a total of 32 eQTLs to influence miRNA expression; 11 of these overlapped with BD.

Our approach of integrating PGC findings with eQTL results can be used to generate specific hypotheses regarding the role of miRNAs in SZ and BD.

Multiple lines of evidence suggest the presence of altered neuroimmune processes in patients with schizophrenia (Sz) and severe mood disorders. Recent studies using a novel free water diffusion tensor imaging (FW DTI) approach, proposed as a putative biomarker of neuroinflammation, atrophy, or edema, have shown significantly increased FW in patients with Sz. However no studies to date have investigated the longitudinal stability of FW alterations during the early course of psychosis, nor have studies focused separately on FE psychosis patients with Sz or bipolar disorder (BD) with psychotic features.

The current study included 188 participants who underwent diffusion magnetic resonance imaging scanning at baseline. Sixty-four participants underwent follow-up rescanning after 12 months. DTI-based alterations in patients were calculated using voxelwise tract-based spatial statistics and region of interest analyses.

Patients with FE psychosis, both Sz and BD, exhibited increased FW at illness onset which remained unchanged over the 12-month follow-up period. Preliminary analyses suggested that antipsychotic medication exposure was associated with higher FW in gray matter that reached significance in the BD group. Higher FW in white matter correlated with negative symptom severity.

Our results support the presence of elevated FW at the onset of psychosis in both Sz and BD, which remains stable during the early course of the illness, with no evidence of either progression or remission.

The neurocognitive deficits and other correlates of problem gambling are also observable in individuals with lower cognitive abilities, suggesting that a low IQ may be a determinant of problem gambling. There has been very little research into this possibility. This study aimed to investigate the characteristics associated with problem gambling in a large population-based study in England, with a particular focus on IQ.

The Adult Psychiatric Morbidity Survey (APMS) 2007 comprised detailed interviews with 7403 individuals living in private households in England. Problem gambling was ascertained using a questionnaire based on DSM-IV criteria. Verbal IQ was estimated using the National Adult Reading Test (NART). Confounders included socio-economic and demographic factors, common mental disorders, impulsivity, smoking, and hazardous drug and alcohol use.

More than two-thirds of the population reported engaging in some form of gambling in the previous year, but problem gambling was rare [prevalence 0.7%, 95% confidence interval (CI) 0.5–1.0]. The odds of problem gambling doubled with each standard deviation drop in estimated verbal IQ [adjusted odds ratio (OR) 2.1, 95% CI 1.3–3.4, p = 0.003], after adjusting for other characteristics associated with problem gambling including age, sex, socio-economic factors, drug and alcohol dependence, smoking, impulsivity and common mental disorders. There was no strong relationship observed between IQ and non-problem gambling.

People with lower IQs may be at a higher risk of problem gambling. Further work is required to replicate and study the mechanisms behind these findings, and may aid the understanding of problem gambling and inform preventative measures and interventions.

The COVID-19 pandemic has been associated with increased levels of depression and anxiety with implications for the use of antidepressant medications.

The incident rate of antidepressant fills before and during the COVID-19 pandemic were compared using interrupted time-series analysis followed by comprehensive sensitivity analyses on data derived from electronic medical records from a large health management organization providing nationwide services to 14% of the Israeli population. The dataset covered the period from 1 January 2013 to 1 February 2021, with 1 March 2020 onwards defined as the period of the COVID-19 pandemic. Forecasting analysis was implemented to test the effect of the vaccine roll-out and easing of social restrictions on antidepressant use.

The sample consisted of 852 233 persons with a total antidepressant incident fill count of 139 535.4 (total cumulative rate per 100 000 = 16 372.91, 95% CI 16 287.19–16 459.01). We calculated the proportion of antidepressant prescription fills for the COVID-19 period, and the counterfactual proportion for the same period, assuming COVID-19 had not occurred. The difference in these proportions was significant [Cohen's h = 10−3 (0.16), 95% CI 10−3 ( − 0.71 to 1.03)]. The pandemic was associated with a significant increase in the slope of the incident rate of antidepressant fills (slope change = 0.01, 95% CI 0.00–0.03; p = 0.04) and a monthly increase of 2% compared to the counterfactual (the estimated rate assuming no pandemic occurred). The increased rate was more pronounced in women, and was not modified by lockdown on/off periods, socioeconomic or SARS-CoV-2 status. The rate of observed antidepressant fills was similar to that forecasted under the assumption of ongoing COVID-19 distress.

These findings underscore the toll of the pandemic on mental health and inform mental health policy and service delivery during and after implementing COVID-19 attenuation strategies.

Combat exposure is associated with elevated risk for post-traumatic stress disorder (PTSD). Despite considerable research on PTSD symptom clustering, it remains unknown how symptoms of PTSD re-organize following combat. Network analysis provides a powerful tool to examine such changes.

A network analysis approach was taken to examine how symptom networks change from pre- to post-combat using longitudinal prospective data from a cohort of infantry male soldiers (Mage = 18.8 years). PTSD symptoms measured using the PTSD Checklist (PCL) were assessed after 6 months of combat training but before deployment and again after 6 months of combat (Ns = 910 and 725 at pre-deployment and post-combat, respectively)

Stronger connectivity between PTSD symptoms was observed post-combat relative to pre-deployment (global strength values of the networks were 7.54 pre v. 7.92 post; S = .38, p < 0.05). Both the re-experiencing symptoms cluster (1.92 v. 2.12; S = .20, p < 0.03) and the avoidance symptoms cluster (2.61 v. 2.96; S = .35, p < 0.005) became more strongly inter-correlated post-combat. Centrality estimation analyses revealed that psychological reaction to triggers was central and linked the intrusion and avoidance sub-clusters at post-combat. The strength of associations between the arousal and reactivity symptoms cluster remained stable over time (1.85 v. 1.83; S = .02, p = .92).

Following combat, PTSD symptoms and particularly the re-experiencing and avoidance clusters become more strongly inter-correlated, indicating high centrality of trigger-reactivity symptoms.

Few studies have examined the latent construct of psychotic symptoms or distinguished between the latent construct and its manifest indicators. The current study aimed to investigate the latent structure of psychotic symptoms using factor mixture modeling (FMM) and to use the best-fitting model to examine its sociodemographic and clinical correlates.

The Singapore Mental Health Study (SMHS) was based on an adult representative sample of the Singapore population. Psychotic symptoms were assessed by using the Psychosis Screen section of the Composite International Diagnostic Interview version 3.0 (CIDI 3.0). FMM analyses were applied to determine the latent construct of psychotic symptoms. Sociodemographic and clinical correlates of the latent structure of psychosis symptoms were examined using multiple linear and logistic regression analyses.

The overall weighted lifetime prevalence of any psychotic experience was 3.8% in the SMHS after excluding subthreshold experiences. The FMM analysis clearly supported the dimensional model of the latent structure of psychotic symptoms. On deriving the total score for ‘psychosis symptoms’ in accordance with the one latent trait model, and correlating it with sociodemographic factors, we found that female gender, vocational education, current and past smokers were positively associated with the ‘psychosis’ total score.

There is a need for an increased understanding of, and research into, this intermediate state of ‘psychosis symptoms’ that do not meet diagnostic criteria for psychosis. It is also important to learn more about the group of individuals in the community who may have preserved functioning to elucidate the protective factors that prevent transition to psychosis.

Evidence shows that cognitive deficits and white matter (WM) dysconnectivity can independently be associated with clinical manifestations in schizophrenia. It is important to explore this triadic relationship in order to investigate whether the triplet could serve as potential extended endophenotypes of schizophrenia.

Diffusion tensor images and clinical performances were evaluated in 122 individuals with first-episode schizophrenia and 122 age- and gender-matched controls. In addition, 65 of 122 of the patient group and 40 of 122 controls were measured using intelligence quotient (IQ) testing.

The schizophrenia group showed lower fractional anisotropy (FA) values than controls in the right cerebral frontal lobar sub-gyral (RFSG) WM. The schizophrenia group also showed a significant positive correlation between FA in the RFSG and performance IQ (PIQ); in turn, their PIQ score showed a significant negative correlation with negative syndromes.

Overall, these findings support the hypothesis that WM deficits may be a core deficit that contributes to cognitive deficits as well as to negative symptoms.

Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not take into account clinical and sociodemographic characteristics that could have played a relevant role in cognitive variability. This study aims to identify empirical clusters based on cognitive, clinical and sociodemographic variables in a sample of acute MDD patients.

In a sample of 174 patients with an acute depressive episode, a two-step clustering analysis was applied considering potentially relevant cognitive, clinical and sociodemographic variables as indicators for grouping.

Treatment resistance was the most important factor for clustering, closely followed by cognitive performance. Three empirical subgroups were obtained: cluster 1 was characterized by a sample of non-resistant patients with preserved cognitive functioning (n = 68, 39%); cluster 2 was formed by treatment-resistant patients with selective cognitive deficits (n = 66, 38%) and cluster 3 consisted of resistant (n = 23, 58%) and non-resistant (n = 17, 42%) acute patients with significant deficits in all neurocognitive domains (n = 40, 23%).

The findings provide evidence upon the existence of cognitive heterogeneity across patients in an acute depressive episode. Therefore, assessing cognition becomes an evident necessity for all patients diagnosed with MDD, and although treatment resistant is associated with greater cognitive dysfunction, non-resistant patients can also show significant cognitive deficits. By targeting not only mood but also cognition, patients are more likely to achieve full recovery and prevent new relapses.

Sleep and circadian timing shifts later during adolescence, conflicting with early school start times, and resulting in circadian misalignment. Although circadian misalignment has been linked to depression, substance use, and altered reward function, a paucity of experimental studies precludes the determination of causality. Here we tested, for the first time, whether experimentally-imposed circadian misalignment alters the neural response to monetary reward and/or response inhibition.

Healthy adolescents (n = 25, ages 13–17) completed two in-lab sleep schedules in counterbalanced order: An ‘aligned’ condition based on typical summer sleep-wake times (0000–0930) and a ‘misaligned’ condition mimicking earlier school year sleep-wake times (2000–0530). Participants completed morning and afternoon functional magnetic resonance imaging scans during each condition, including monetary reward (morning only) and response inhibition (morning and afternoon) tasks. Total sleep time and circadian phase were assessed via actigraphy and salivary melatonin, respectively.

Bilateral ventral striatal (VS) activation during reward outcome was lower during the Misaligned condition after accounting for the prior night's total sleep time. Bilateral VS activation during reward anticipation was lower during the Misaligned condition, including after accounting for covariates, but did not survive correction for multiple comparisons. Right inferior frontal gyrus activation during response inhibition was lower during the Misaligned condition, before and after accounting for total sleep time and vigilant attention, but only during the morning scan.

Our findings provide novel experimental evidence that circadian misalignment analogous to that resulting from school schedules may have measurable impacts on healthy adolescents' reward processing and inhibition of prepotent responses.