Adolescence is a pivotal stage for brain development and a critical window for the emergence and transition of self-injury thoughts and behaviours (SITBs). However, the genetic and neurobiological mechanisms underlying SITBs transition during this developmental period are poorly understood.

This study investigates associations among genetic predispositions, brain abnormalities and SITBs transition during adolescence, and identifies potential neurobiological and clinical mediators of genetic effects.

This national retrospective cohort study analysed 5-year longitudinal data from the Adolescent Brain and Cognitive DevelopmentSM Study® (N = 11 868 children aged 9–10 years at baseline). Logistic regression models identified genetic susceptibility and neurobiological abnormalities associated with SITBs transition over a 4-year period. Generalised additive models characterised genetic risk trajectories and critical developmental periods. Mediation analyses examined neurobiological and clinical pathways linking genetic susceptibility to SITBs.

Our findings highlight a notable correlation between SITBs transition and genetic susceptibility, including polygenic risk scores for suicide attempt, ever contemplated self-harm and ever self-harm. The analysis indicates that ages 10–15 years may be a critical period during which genetic risk exerts its most pronounced influence. Structural and functional brain imaging detected some alterations, particularly in grey matter volume (GMV) of the left ventral posterior cingulate cortex, alongside disrupted resting-state functional connectivity in the dorsal attention and default mode networks. Mediation analysis suggests that the association between genetic susceptibility and SITBs transition over 4 years may be partially mediated by GMV changes in the left inferior frontal sulcus, altered resting-state connectivity between the auditory and sensorimotor hand networks and the p-factor.

These results may offer insights into integrating genetic, neurobiological and clinical data to enhance the accuracy of suicide risk stratification in adolescents, and inform the development of more nuanced and targeted early intervention strategies.

Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

Nutraceuticals have been taken as an alternative and add-on treatment for depressive disorders. Direct comparisons between different nutraceuticals and between nutraceuticals and placebo or antidepressants are limited. Thus, it is unclear which nutraceuticals are the most efficacious.

We conducted a network meta-analysis to estimate the comparative efficacy and tolerability of nutraceuticals for the treatment of depressive disorder in adults. The primary outcome was the change in depressive symptoms, as measured by the standard mean difference (SMD). Secondary outcomes included response rate, remission rate, and anxiety. Tolerability was defined as all-cause discontinuation and adverse events. Frequentist random-effect NMA was conducted.

Hundred and ninety-two trials involving 17,437 patients and 44 nutraceuticals were eligible for inclusion. Adjunctive nutraceuticals consistently showed better efficacy than antidepressants (ADT) alone in outcomes including SMD, remission, and response. Notable combinations were Eicosapentaenoic acid + Docosahexaenoic Acid plus ADT (EPA + DHA + ADT) (SMD 1.04, 95% confidence interval 0.64–1.44), S-Adenosyl Methionine (SAMe) + ADT (0.99, 0.31–1.68), curcumin + ADT (1.03, 0.55–1.51), Zinc + ADT (1.59, 0.63–2.55), tryptophan + ADT (1.24, 0.32–2.16), and folate + ADT (0.64, 0.17–1.10). Additionally, four nutraceutical monotherapies demonstrated superior efficacy compared to ADT: EPA + DHA (0.6, 0.32–0.88), SAMe (0.52, 0.18–0.87), curcumin (0.62, −0.17 to 1.40) and saffron (0.69, 0.34–1.04). It is noted that EPA + DHA, SAMe, and curcumin showed strong performance as either monotherapies or adjuncts to ADT. Most nutraceuticals showed comparable tolerability to placebo.

This extensive systematic review and NMA of nutraceuticals for treating depressive disorders indicated a number of nutraceuticals that could offer benefits, either as adjuncts or monotherapies.

Studies examining age-stratified risk factors for suicide among individuals with bipolar disorder in different stages of life are scant, possibly because of the insufficient number of suicide cases.

This study investigated suicide mortality rates and risk profiles of suicide mortality stratified by five age groups in individuals with bipolar disorder.

This study identified patients with a diagnosis of bipolar disorder between January 1, 2000, and December 31, 2021, from Taiwan’s National Health Insurance Research Database. The study population comprised 45,211 inpatients diagnosed with bipolar disorder, with 1,370 suicide cases during the study period. We calculated the standardized mortality ratio (SMR) of the bipolar cohort relative to the general population. In the age-stratified nested case–control study, risk set sampling was performed to match 1 suicide case with 10 living controls by age, sex, and the year of first diagnosis. The age-stratified risk associated with demographic characteristics, psychiatric and physical comorbidities was estimated using multivariable conditional logistic regression.

The highest SMR (47.0) for suicide was observed in individuals with bipolar disorder aged <30 years. SMR decreased with age; patients aged >60 years had an SMR of 9.5. Among those younger than 40 years, a higher percentage of unemployment was noted among suicide cases than among controls. A significantly increased risk of the depressive phase of bipolar disorder was noted shortly before suicide mortality among patients with bipolar disorder in all age groups. Drug-induced and alcohol-induced mental disorders were associated with suicide and were highly prevalent in patients aged <30 years. Other forms of heart disease were identified in patients aged <40 years, and pneumonia was detected in the 50–59 years age group.

These findings aid the development of health-care intervention strategies for preventing suicide among patients with bipolar disorder in various stages of life.

Anxiety disorders are among the most common mental disorders worldwide, and most previous studies have focused solely on alcohol drinking or tobacco smoking as risk factors for anxiety.

This study investigated the associations of alcohol drinking and tobacco smoking with anxiety.

The data of 30 836 individuals in the Taiwan Biobank were retrieved and analysed in our study. To investigate the associations of tobacco and alcohol use with anxiety, we analysed Patient Health Questionnaire 4 (specifically scores for the first two questions assessing generalised anxiety disorder) results of the included participants and data on their tobacco and alcohol use, and other covariates.

Participants who used only tobacco and those using both tobacco and alcohol were more likely to experience anxiety than were those who did not use tobacco or alcohol. Among men, the use of alcohol and/or tobacco was associated with a significantly higher risk of anxiety. Among women, the use of both alcohol and tobacco was associated with a significantly higher risk of anxiety. Older age was associated with a lower risk of anxiety.

Tobacco and alcohol use significantly influence the risk of anxiety, particularly in men, and older age also influences this risk. The associations of anxiety with tobacco and alcohol use in women may change because of the increasing prevalence of their use among women in Taiwan in recent years.

Increasing evidence has established a strong association between social anxiety disorder and suicidal behaviours, including suicidal ideation and suicide attempts. However, the association between social anxiety disorder and suicide mortality remains unclear.

This study analysed data from 15,776 patients with social anxiety disorder, extracted from a nationwide Taiwanese cohort between 2003 and 2017. Two unexposed groups without social anxiety disorder, matched by birth year and sex in 1:4 and 1:10 ratios, respectively, were used for comparison. Suicide deaths during the same period were examined. Psychiatric comorbidities commonly associated with social anxiety disorder, including schizophrenia, bipolar disorder, major depression, alcohol use disorder (AUD), substance use disorder (SUD), obsessive-compulsive disorder, autism, and attention deficit hyperactivity disorder, were identified.

Time-dependent Cox regression models, adjusted for demographic factors and psychiatric comorbidities, revealed that individuals with social anxiety disorder had an increased risk of suicide (hazard ratio: 3.49 in the 1:4 matched analysis and 2.84 in the 1:10 matched analysis) compared with those without the disorder. Comorbidities such as schizophrenia, bipolar disorder, major depression, AUD, and SUD further increased the risk of suicide in patients with social anxiety disorder.

Social anxiety disorder is an independent risk factor for suicide death. Additional psychiatric comorbidities, including schizophrenia, major affective disorders, and AUD, further increased social anxiety disorder-related suicide risk. Therefore, mental health officers and clinicians should develop targeted suicide prevention strategies for individuals with social anxiety disorder.

Panic disorder (PD) may increase the likelihood of suicidal ideation and behaviors because of psychiatric comorbidities such as major depressive disorder (MDD). However, research has yet to demonstrate a direct relationship between PD and suicide mortality.

Using data from Taiwan’s National Health Insurance Research Database, we identified 171,737 individuals with PD and 686,948 age- and sex-matched individuals without PD during 2003–2017. We assessed the risk of suicide within the same period. Psychiatric comorbidities such as schizophrenia, bipolar disorder, MDD, obsessive-compulsive disorder (OCD), autism, alcohol use disorder (AUD), and substance use disorder (SUD) were also evaluated. Time-dependent Cox regression models were used to compare the risk of suicide in different groups after adjustment for demographic data and psychiatric comorbidities.

Our Cox regression model revealed that PD was an independent risk factor for suicide (hazard ratio [HR] = 1.85, 95% confidence interval [CI] = 1.59–2.14), regardless of psychiatric comorbidities. Among all comorbidities, MDD with PD was associated with the highest risk of suicide (HR = 6.08, 95% CI = 5.48–6.74), followed by autism (HR = 4.52, 95% CI = 1.66–12.29), schizophrenia (HR = 3.34, 95% CI = 2.7–4.13), bipolar disorder (HR = 3.20, 95% CI = 2.71–3.79), AUD (HR = 2.99, 95% CI = 2.41–3.72), SUD (HR = 2.82, 95% CI = 2.28–3.47), and OCD (HR = 2.10, 95% CI = 1.64–2.67).

PD is an independent risk factor for suicide. Psychiatric comorbidities (i.e. schizophrenia, bipolar disorder, MDD, OCD, AUD, SUD, and autism) with PD increase the risk of suicide.

In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.