The prevalence of non-suicidal self-injury (NSSI) among adolescents underscores the importance of understanding the complex factors that drive this behaviour. Framed within broader constructs of emotional regulation theories, alexithymia and peer victimisation are thought to interact to influence NSSI behaviours.

This research addresses whether alexithymia and peer victimisation serve as risk factors for NSSI and, if so, how these factors interact with each other.

This quantitative study analysed data from 605 adolescents, using a range of validated self-report measures including the Toronto Alexithymia Scale. Statistical analyses including one-way analysis of variance, multiple regression and structural equation modelling were employed to scrutinise the relationships among the variables.

Alexithymia and peer victimisation significantly predicted NSSI behaviours. Specifically, the ‘difficulty in identifying feelings’ subscale of alexithymia emerged as a noteworthy predictor of NSSI (P < 0.001). Peer victimisation mediated the relationship between alexithymia and NSSI, explaining approximately 24.50% of alexithymia's total effect on NSSI. In addition, age was a significant predictor of NSSI, but gender and education years were not (P > 0.05). These relationships were found to be invariant across genders.

This study enriches our understanding of the interplay between alexithymia, peer victimisation and NSSI, particularly within the Chinese context. Its findings have significant implications for a rethinking of alexithymia's theoretical construct and interventions targeting emotional literacy and peer dynamics among adolescents. Future research could benefit from a longitudinal design to establish causality.

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Adolescence is a pivotal stage vulnerable to mental health problems such as anxiety and depression. Although self-acceptance and social comparison are known to affect adolescent mental health, their interactive and moderating roles are not fully understood.

To explore the role of self-acceptance, social comparison and attributional style in predicting these mental health outcomes among adolescents in clinical settings.

A cross-sectional study was conducted on a sample of 242 adolescents. Participants completed measures assessing self-acceptance, social comparison, attributional style and mental health outcomes (depression and anxiety). Mediation models and multi-group analysis were used to examine the relationships among these variables.

Our findings demonstrated a significant relationship between self-acceptance, social comparison, depression and anxiety (rs = 0.32–0.88). Specifically, lower self-acceptance and higher social comparison were associated with higher levels of depression and anxiety. Additionally, individuals with external attributional tendencies reported higher depression (Cohen's d = 0.61) and anxiety (d = 0.58) compared with those with internal tendencies. Mediation modelling showed that social comparison is a mediator between self-acceptance and depression (effect size −0.04, 95% CI −0.08 to −0.01) and anxiety (effect size −0.06, 95% CI −0.10 to −0.02). Crucially, multi-group analysis showed that the impact of social comparison on mental health outcomes varied significantly based on attributional style.

These findings underscore the importance of considering self-acceptance, social comparison and attributional style in understanding and addressing mental health challenges during adolescence. This could inform the development of targeted interventions to promote mental health and well-being among adolescents. However, further research is needed to confirm these findings in diverse populations and to explore the underlying mechanisms in greater detail.

Many studies suggest that both psychotherapy and drug therapy are effective in the treatment of bipolar disorders (BDs). However, the pathophysiology of both types of intervention has not been established definitively.

An activation likelihood estimation meta-analysis was performed to identify the distinct brain activity alterations between psychotherapy and drug therapy for the treatment of BDs. Articles were identified by searching databases including PubMed, Embase, Cochrane Library, and Web of Science databases. Eligible studies on BDs were published up until 10 June 2021.

21 studies were included and we conducted a meta-analysis for different therapies and imaging tasks. After receiving psychotherapy, BD patients showed increased activation in the inferior frontal gyrus (IFG) and superior temporal gyrus. While after taking drug therapy, BD patients displayed increased activation in the anterior cingulate cortex, medial frontal gyrus, IFG, and decreased activation in the posterior cingulate cortex. The regions of brain activity changes caused by psychotherapy were mostly focused on the frontal areas, while drug therapy mainly impacted on the limbic areas. Different type of tasks also affected brain regions which were activated.

Our comprehensive meta-analysis indicates that these two treatments might have effect on BD in their own therapeutic modes. Psychotherapy might have a top-down effect, while drug therapy might have a bottom-up effect. This study may contribute to differential diagnosis of BDs and would be helpful to finding more accurate neuroimaging biomarkers for BD treatment.

COVID-19 has long-term impacts on public mental health, while few research studies incorporate multidimensional methods to thoroughly characterise the psychological profile of general population and little detailed guidance exists for mental health management during the pandemic. This research aims to capture long-term psychological profile of general population following COVID-19 by integrating trajectory modelling approaches, latent trajectory pattern identification and network analyses.

Longitudinal data were collected from a nationwide sample of 18 804 adults in 12 months after COVID-19 outbreak in China. Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Insomnia Severity Index were used to measure depression, anxiety and insomnia, respectively. The unconditional and conditional latent growth curve models were fitted to investigate trajectories and long-term predictors for psychological symptoms. We employed latent growth mixture model to identify the major psychological symptom trajectory patterns, and ran sparse Gaussian graphical models with graphical lasso to explore the evolution of psychopathological network.

At 12 months after COVID-19 outbreak, psychological symptoms generally alleviated, and five psychological symptom trajectories with different demographics were identified: normal stable (63.4%), mild stable (15.3%), mild-increase to decrease (11.7%), mild-decrease to increase (4.0%) and moderate/severe stable (5.5%). The finding indicated that there were still about 5% individuals showing consistently severe distress and approximately 16% following fluctuating psychological trajectories, who should be continuously monitored. For individuals with persistently severe trajectories and those with fluctuating trajectories, central or bridge symptoms in the network were mainly ‘motor abnormality’ and ‘sad mood’, respectively. Compared with initial peak and late COVID-19 phase, aftermath of initial peak might be a psychologically vulnerable period with highest network connectivity. The central and bridge symptoms for aftermath of initial peak (‘appetite change’ and ‘trouble of relaxing’) were totally different from those at other pandemic phases (‘sad mood’).

This research identified the overall growing trend, long-term predictors, trajectory classes and evolutionary pattern of psychopathological network of psychological symptoms in 12 months after COVID-19 outbreak. It provides a multidimensional long-term psychological profile of the general population after COVID-19 outbreak, and accentuates the essentiality of continuous psychological monitoring, as well as population- and time-specific psychological management after COVID-19. We believe our findings can offer reference for long-term psychological management after pandemics.

Cerebrovascular disease is the most common cause of death in China, and the incidence of ischemic stroke (240 per 100,000 people) is higher than that of hemorrhagic stroke (82 per 100,000 people). More than 80 percent of strokes can be prevented by early control of risk factors. Therefore, identifying and managing high-risk groups is a top priority in preventing stroke. The CHA2DS2-VASc score is a key prediction tool for stratifying stroke risk in individuals with atrial fibrillation (AF) as follows: zero score is low risk; one is intermediate risk; and two is high risk. The present study was undertaken to evaluate the accuracy of the CHA2DS2-VASc scoring system for stratifying ischemic stroke risk in the non-AF population.

We searched PubMed, EMBASE, and the Cochrane Library in June 2018 for relevant diagnostic studies. Study selection, data extraction, and quality assessment (using the QUADAS-2 criteria) were performed independently by two authors. Methodological variation across the selected studies precluded meta-analysis, so the results were synthesized narratively.

Seven prospective studies involving 50,652 patients (6,760 with ischemic stroke) were included. The treatment threshold ranged from two to four across the studies. Three studies reported diagnostic accuracy at a threshold of two, with a sensitivity above 0.8 and a specificity ranging from 0.32 to 0.68. The diagnostic odds ratio was greater than two (seven studies). The two studies using a treatment threshold of four reported a sensitivity of 0.59 to 0.76 and a specificity of 0.43 to 0.69. One study used a threshold of three, with a sensitivity of 0.79 and a specificity of 0.39.

The CHA2DS2-VASc score may be used to predict ischemic stroke in the non-atrial fibrillation population. Treatment thresholds greater than two provide more optimal diagnostic accuracy, although the predictive performance of the CHA2DS2-VASc score may be better in patients with chronic obstructive pulmonary disease but not AF.