Continuous exposure to stressors can lead to vulnerability and, in some cases, resilience. This study examined the variation in its psychological impact across the first four waves of COVID-19 in Hong Kong.

Transcripts from Open Up, an online text-based counseling service, between January 2019 and January 2021 were analyzed (N = 60 775). We identified COVID-19 mentioned sessions using keywords and further categorized them into those that also mentioned symptoms of common mental disorders (CMDs) and those that did not. Autoregressive integrated moving average models were used to analyze the associations between the severity of the outbreak and the mention of COVID-19 and CMDs.

Results revealed that the pandemic led to increased psychological distress. Compared to prior to its advent, more people sought help in the initial months of the outbreak. Furthermore, associations were found between the severity of the outbreak and the number of help-seeker mentioning the pandemic, as well as between the outbreak severity and the number of help-seekers disclosing psychological distress. However, these relationships were not uniform across the four waves of outbreaks; a dissociation between outbreak severity and help-seekers' concern was found in the fourth wave.

As the pandemic waxes and wanes, people may become habituated to its psychological toll. This may be interpreted as a form of resilience. Instead of worsening with time, the psychological impact of COVID-19 may reduce with repeated exposure.

Prior investigation of adult patients with obsessive compulsive disorder (OCD) has found greater functional connectivity within orbitofrontal–striatal–thalamic (OST) circuitry, as well as altered connectivity within and between large-scale brain networks such as the cingulo-opercular network (CON) and default mode network (DMN), relative to controls. However, as adult OCD patients often have high rates of co-morbid anxiety and long durations of illness, little is known about the functional connectivity of these networks in relation to OCD specifically, or in young patients near illness onset.

In this study, unmedicated female patients with OCD (ages 8–21 years, n = 23) were compared to age-matched female patients with anxiety disorders (n = 26), and healthy female youth (n = 44). Resting-state functional connectivity was used to determine the strength of functional connectivity within and between OST, CON, and DMN.

Functional connectivity within the CON was significantly greater in the OCD group as compared to the anxiety and healthy control groups. Additionally, the OCD group displayed greater functional connectivity between OST and CON compared to the other two groups, which did not differ significantly from each other.

Our findings indicate that previously noted network connectivity differences in pediatric patients with OCD were likely not attributable to co-morbid anxiety disorders. Moreover, these results suggest that specific patterns of hyperconnectivity within CON and between CON and OST circuitry may characterize OCD relative to non-OCD anxiety disorders in youth. This study improves understanding of network dysfunction underlying pediatric OCD as compared to pediatric anxiety.

Predictors of new-onset bipolar disorder (BD) or psychotic disorder (PD) have been proposed on the basis of retrospective or prospective studies of ‘at-risk’ cohorts. Few studies have compared concurrently or longitudinally factors associated with the onset of BD or PDs in youth presenting to early intervention services. We aimed to identify clinical predictors of the onset of full-threshold (FT) BD or PD in this population.

Multi-state Markov modelling was used to assess the relationships between baseline characteristics and the likelihood of the onset of FT BD or PD in youth (aged 12–30) presenting to mental health services.

Of 2330 individuals assessed longitudinally, 4.3% (n = 100) met criteria for new-onset FT BD and 2.2% (n = 51) met criteria for a new-onset FT PD. The emergence of FT BD was associated with older age, lower social and occupational functioning, mania-like experiences (MLE), suicide attempts, reduced incidence of physical illness, childhood-onset depression, and childhood-onset anxiety. The emergence of a PD was associated with older age, male sex, psychosis-like experiences (PLE), suicide attempts, stimulant use, and childhood-onset depression.

Identifying risk factors for the onset of either BD or PDs in young people presenting to early intervention services is assisted not only by the increased focus on MLE and PLE, but also by recognising the predictive significance of poorer social function, childhood-onset anxiety and mood disorders, and suicide attempts prior to the time of entry to services. Secondary prevention may be enhanced by greater attention to those risk factors that are modifiable or shared by both illness trajectories.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Individuals diagnosed with borderline personality disorder (BPD) often describe their lives as stressful and unpredictable. However, it is unclear whether the adversity faced by those with BPD is a product of stress reactivity or stress generation. Here, we examined the dynamic, prospective associations between BPD and stressful life events over 3 years. Given the heterogeneity present in BPD, we sought to understand which empirically derived dimensions of this heterogeneous disorder explain stress reactivity v. stress generation.

Participants included 355 individuals diagnosed with BPD and followed longitudinally at three annual assessments. Auto-regressive cross-lagged panel models were used to examine prospective associations between stressful life events and three latent dimensions implicated in BPD: negative affect, disinhibition, and antagonism.

Antagonism and disinhibition, but not negative affect, prospectively predicted dependent stressful life events (events the individual may have some role in). Evidence for decompensation under stress was more tenuous, with independent stressful life events (those presumably outside the individual's control) predicting increases in negative affect.

Our longitudinal study of a well-characterized clinical sample found more evidence for stress generation than for stress-induced decompensation in BPD. Stress generation in BPD is driven by externalizing dimensions: antagonism and disinhibition. These results highlight the utility of empirically derived dimensions for parsing heterogeneity present in BPD, leading to improvements in diagnostic evaluation, clinical prediction, and individualized approaches to treatment planning.

Previous findings revealed that the acquisition of new semantic concepts' labels was impaired in uncomplicated alcoholic patients. The use of errorless learning may therefore allow them to improve learning performance. However, the flexibility of the new knowledge and the memory processes involved in errorless learning remain unclear.

New concepts' labels acquisition was examined in 15 alcoholic patients and 15 control participants in an errorless learning condition compared with 19 alcoholic patients and 19 control subjects in a trial-and-error learning condition. The flexibility of the new information was evaluated using different photographs from those used in the learning sessions but representing the same concepts. All of the participants carried out an additional explicit memory task and an implicit memory task was also performed by subjects in the errorless learning condition.

The alcoholic group in the errorless condition differed significantly from the alcoholic group in the trial-and-error condition but did not differ from the two control groups. There was no significant difference between results in the learning test and the flexibility task. Finally, in the alcoholic group, the naming score in the learning test was correlated with the explicit memory score but not with the implicit memory score.

Using errorless learning, alcoholics improved their abilities to learn new concepts' labels. Moreover, new knowledge acquired with errorless learning was flexible. The errorless learning advantage may rely on explicit rather than implicit memory processes in these alcohol-dependent patients presenting only mild to moderate deficits of explicit memory capacities.

The findings of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study and the Cost Utility of the Latest Antipsychotic Drugs in Schizophrenia Study (CUtLASS) called previous trials of antipsychotics into question, including pre-licensing trials. Concerns regarding methodological robustness and quality of reporting increased. This systematic review aimed to examine the quality of reporting of phase II and III trials for new antipsychotics in the aftermath of the CATIE and CUtLASS studies.

Electronic searches were conducted in EMBASE, Medline and Cochrane databases and also ClinicalTrials.gov for antipsychotic trials (published between January 2006 and February 2012). Phase II and III randomized controlled trials (RCTs) for iloperidone, asenapine, paliperidone, olanzapine, lurasidone and pomaglumetad methionil were selected for schizophrenia and schizoaffective disorder. The reporting of the methodology was evaluated in accordance with Consolidated Standards of Reporting Trials (CONSORT) guidelines.

Thirty-one articles regarding 32 studies were included. There was insufficient reporting of design in 47% of studies and only 13% explicitly stated a primary hypothesis. Exclusion criteria were poorly reported for diagnosis in 22% of studies. Detail regarding comparators, particularly placebos, was suboptimal for 56% of studies, and permitted concomitant medication was often not reported (19%). Randomization methods were poorly described in 56% of studies and reporting on blinding was insufficient in 84% of studies. Sample size calculations were insufficiently reported in 59% of studies.

The quality of reporting of phase II and III trials for new antipsychotics does not reach the standards outlined in the CONSORT guidelines. Authors often fail to adequately report design and methodological processes, potentially impeding the progress of research on antipsychotic efficacy. Both policymakers and clinicians require high quality reporting before decisions are made regarding licensing and prescribing of new antipsychotics.

Cigarette smoking is associated with worse cognition and decreased cortical volume and thickness in healthy cohorts. Chronic cigarette smoking is prevalent in schizophrenia spectrum disorders (SSD), but the effects of smoking status on the brain and cognition in SSD are not clear. This study aimed to understand whether cognitive performance and brain morphology differed between smoking and non-smoking individuals with SSD compared to healthy controls.

Data were obtained from the Australian Schizophrenia Research Bank. Cognitive functioning was measured in 299 controls and 455 SSD patients. Cortical volume, thickness and surface area data were analysed from T1-weighted structural scans obtained in a subset of the sample (n = 82 controls, n = 201 SSD). Associations between smoking status (cigarette smoker/non-smoker), cognition and brain morphology were tested using analyses of covariance, including diagnosis as a moderator.

No smoking by diagnosis interactions were evident, and no significant differences were revealed between smokers and non-smokers across any of the variables measured, with the exception of a significantly thinner left posterior cingulate in smokers compared to non-smokers. Several main effects of smoking in the cognitive, volume and thickness analyses were initially significant but did not survive false discovery rate (FDR) correction.

Despite the general absence of significant FDR-corrected findings, trend-level effects suggest the possibility that subtle smoking-related effects exist but were not uncovered due to low statistical power. An investigation of this topic is encouraged to confirm and expand on our findings.

Existing evidence for gene × environment interaction (G × E) in neuroticism largely relies on candidate gene studies, although neuroticism is highly polygenic. This study aimed to investigate the long-term associations between polygenic risk scores for neuroticism (PRSN), objective childhood adversity and their interplay on emotional health aspects such as neuroticism itself, depressive symptoms, anxiety symptoms, loneliness and life satisfaction.

The sample consisted of reared-apart (TRA) and reared-together (TRT) middle- and old age twins (N = 699; median age at separation = 2). PRSN were created under nine p value cut-off thresholds (pT-s) and the pT with the highest degree of neuroticism variance explained was chosen for subsequent analyses. Linear regressions were used to assess the associations between PRSN, childhood adversity (being reared apart) and emotional health. G × E was further investigated using a discordant twin design.

PRSN explained up to 1.7% (pT < 0.01) of phenotypic neuroticism in the total sample. Analyses across two separation groups revealed substantial heterogeneity in the variance explained by PRSN; 4.3% was explained in TRT, but almost no effect was observed in TRA. Similarly, PRSN explained 4% and 1.7% of the variance in depressive symptoms and loneliness, respectively, only in TRT. A significant G × E interaction was identified for depressive symptoms.

By taking advantage of a unique sample of adopted twins, we demonstrated the presence of G × E in neuroticism and emotional health using PRSN and childhood adversity. Our results may indicate that genome-wide association studies are detecting genetic main effects associated with neuroticism, but not those susceptible to early environmental influences.

It has recently been proposed that major depression disorder (MDD) may, in a heterogeneous population-based cohort, be interpreted in terms of a random-mood model. Mood fluctuations are thought to result from stressors that occur randomly in time. We have investigated whether this concept also holds for more homogeneous groups, defined by known determinants for MDD, and whether the model's parameters, susceptibility (Z) and relaxation time (T), may be evaluated and used to differentiate between subcohorts.

From a large epidemiological survey, the Netherlands Mental Health Survey and Incidence Study (NEMESIS), data on the duration of MDD were obtained for subcohorts, based on gender, severity of depression, recurrence and co-morbidity with dysthymia, anxiety and somatic disorder, and were compared with random-mood simulation calculations.

Susceptibility, Z, is empirically found to be proportional to incidence and may be identified with a risk ratio. A second scaling rule states the proportionality of mean duration with the product of Z and T. This Z–T classification proves to be more sensitive than conventional significance tests. Notably for men/women and for co-morbid anxiety, differences are seen that have previously gone unnoticed.

Depression may be conceptualized as a disorder resulting from random-mood fluctuations, the response to which is influenced by a large variety of determinants or risk factors. The model's parameters can be evaluated and may be used in differentiating between risk factor-defined subgroups.

Maternal stress during pregnancy may increase the risk of preterm delivery (PD), but the associations between stress and subtypes of PD are not clear. We investigated maternal loss of a close relative and risks of very and moderately PD (<32 and 32–36 weeks, respectively) and spontaneous and medically indicated PD.

We studied 4 940 764 live singleton births in Denmark (1978–2008) and Sweden (1973–2006). We retrieved information on death of women's family members (children, partner, siblings, parents), birth outcomes and maternal characteristics from nationwide registries.

Overall, the death of a close family member the year before pregnancy or in the first 36 weeks of pregnancy was associated with a 7% increased risk of PD [95% confidence interval (CI) 1.04–1.10]. The highest hazard ratios (HR) for PD were found for death of an older child [HR (95% CI) 1.20 (1.10–1.31)] and for death of a partner [HR (95% CI) 1.31 (1.03–1.66)]. These losses were associated with higher risks of very preterm [HR (95% CI) 1.61 (1.29–2.01) and 2.07 (1.15–3.74), respectively] than of moderately preterm [HR (95% CI) 1.14 (1.03–1.26) and 1.22 (0.94–1.58), respectively] delivery. There were no substantial differences in the association between death of a child or partner and the risk of spontaneous v. medically indicated PD.

Death of a close family member the year before or during pregnancy was associated with an increased risk of PD, especially very PD. Possible mechanisms include both spontaneous and medically indicated preterm birth.