There is compelling evidence that epigenetic factors contribute to the manifestation of depression, in which microRNA132 (miR-132) is suggested to play a pivotal role in the pathogenesis and neuronal mechanisms underlying the symptoms of depression. Additionally, several depression-associated genes [MECP2, ARHGAP32 (p250GAP), CREB, and period genes] were experimentally validated as miR-132 targets. However, most studies regarding miR-132 in major depressive disorder are based on post-mortem, animal models or genetic comparisons. This work will be the first attempt to investigate how miR-132 dysregulation may impact covariation of multimodal brain imaging data in 81 unmedicated major depressive patients and 123 demographically-matched healthy controls, as well as in a medication-naïve subset of major depressive patients. MiR-132 values in blood (patients > controls) was used as a prior reference to guide fusion of three MRI features: fractional amplitude of low frequency fluctuations, grey matter volume, and fractional anisotropy. The multimodal components correlated with miR-132 also show significant group difference in loadings. Results indicate that (i) higher miR-132 levels in major depressive disorder are associated with both lower fractional amplitude of low frequency fluctuations and lower grey matter volume in fronto-limbic network; and (ii) the identified brain regions linked with increased miR-132 levels were also associated with poorer cognitive performance in attention and executive function. Using a data-driven, supervised-learning method, we determined that miR-132 dysregulation in major depressive disorder is associated with multi-facets of brain function and structure in fronto-limbic network (the key network for emotional regulation and memory), which deepens our understanding of how miR-132 dysregulation in major depressive disorders contribute to the loss of specific brain areas and is linked to relevant cognitive impairments.

Background: Internet addiction has become a major global concern and a burden on mental health. However, there is a lack of consensus on its link to mental health outcomes. Objective: The aim of this study was to investigate the associations between internet addiction severity and adverse mental health outcomes. Methods: First-year undergraduates enrolled at Sichuan University during September 2015, 2016, 2017, and 2018 were invited to participate in the current study survey, 85.13% (31,659/37,187) of whom fully responded. Young’s 20-item Internet Addiction Test, Patient Health Questionnaire-15, Patient Health Questionnaire-9, Symptom Checklist 90, Six-Item Kessler Psychological Distress Scale, and Suicidal Behaviors Questionnaire-Revised were used to evaluate internet addiction, four psychopathologies (high somatic symptom severity, clinically significant depression, psychoticism, and paranoia), serious mental illness, and lifetime suicidality. Results: The prevalence of students with mild, moderate, and severe internet addiction was 37.93% (12,009/31,659), 6.33% (2003/31,659), and 0.20% (63/31,659), respectively. The prevalence rates of high somatic symptom severity, clinically significant depression, psychoticism, paranoid ideation, and serious mental illness were 6.54% (2072/31,659), 4.09% (1294/31,659), 0.51% (160/31,659), 0.52% (165/31,659), and 1.88% (594/31,659), respectively, and the lifetime prevalence rates of suicidal ideation, suicidal plan, and suicidal attempt were 36.31% (11,495/31,659), 5.13% (1624/31,659), and 1.00% (315/31,659), respectively. The prevalence rates and odds ratios (ORs) of the four psychopathologies and their comorbidities, screened serious mental illness, and suicidalities in the group without internet addiction were much lower than the average levels of the surveyed population. Most of these metrics in the group with mild internet addiction were similar to or slightly higher than the average rates; however, these rates sharply increased in the moderate and severe internet addiction groups. Among the four psychopathologies, clinically significant depression was most strongly associated with internet addiction after adjusting for the confounding effects of demographics and other psychopathologies, and its prevalence increased from 1.01% (178/17,584) in the students with no addiction to 4.85% (582/12,009), 24.81% (497/2,003), and 58.73% (37/63) in the students with mild, moderate, and severe internet addiction, respectively. The proportions of those with any of the four psychopathologies increased from 4.05% (713/17,584) to 11.72% (1408/12,009), 36.89% (739/2003), and 68.25% (43/63); those with lifetime suicidal ideation increased from 24.92% (4382/17,584) to 47.56% (5711/12,009), 67.70% (1356/2003), and 73.02% (46/63); those with a suicidal plan increased from 2.59% (456/17,584) to 6.77% (813/12,009), 16.72% (335/2003), and 31.75% (20/63); and those with a suicidal attempt increased from 0.50% (88/17,584) to 1.23% (148/12,009), 3.54% (71/2003), and 12.70% (8/63), respectively. Conclusions: Moderate and severe internet addiction were strongly associated with a broad group of adverse mental health outcomes, including somatic symptoms that are the core features of many medical illnesses, although clinically significant depression showed the strongest association. This finding supports the illness validity of moderate and severe internet addiction in contrast to mild internet addiction. These results are important for informing health policymakers and service suppliers from the perspective of resolving the overall human health burden in the current era of “Internet Plus” and artificial intelligence.

Mental disorders among adolescents and young adults (ages 10–24) are a significant public health challenge, contributing to long–term morbidity and substantial societal impact. This study analyzes the prevalence, incidence, and years lived with disability (YLDs) of mental disorders in this age group from 1990–2021, with particular attention to the effects of the COVID–19 pandemic during 2019–2021. Using data from the Global Burden of Disease Study 2021, we estimated the prevalence, incidence, and YLDs, along with age–standardized rates (ASR) and 95% uncertainty intervals (95% UIs) for 2021, stratified by sex and age group. Joinpoint regression was employed to calculate annual percentage change (APC) and average annual percentage change (AAPC), along with their corresponding 95% confidence intervals (CIs). In 2021, the global prevalence of mental disorders among adolescents and young adults was 278.98 million (95% UI: 248.61–312.81), with an ASR of 14,764.94 (95% UI: 2804.87–16,908.09). From 2019–2021, there were significant increases in the prevalence, incidence, and YLDs of mental disorders, especially for depressive and anxiety disorders. High–income regions, including North America, Western Europe, and the Asia Pacific, experienced the highest burdens. Globally, the prevalence of mental disorders was higher in males than females among adolescents aged 10–14, but higher in females among those aged 15–24. Joinpoint regression analysis from 1990–2021 revealed an increased burden in depressive disorders, anxiety disorders, bipolar disorder, eating disorders, autism spectrum disorder, conduct disorder, and idiopathic developmental intellectual disability, while decreases were observed in schizophrenia, ADHD, and other mental disorders. This study highlights the significant impact of COVID–19 on the mental health of adolescents and young adults, revealing disparities by region, age, sex, time period, and cohort. It underscores the need for targeted interventions to address the rising mental health burden in this group.

This study was to explore the sex differences in clinical characteristics and brain gray matter volume (GMV) alterations in 29 male patients with major depressive disorder (MDDm), 53 female patients with MDD (MDDf), and in 29 male and 53 female matched healthy controls. Maps of GMV were constructed using magnetic resonance imaging data and compared between groups. We evaluated clinical symptoms using the Hamilton Rating Scale for Depression and obtained a total score and five syndrome scores. A two-factor ANCOVA model was specified using SPM8, with sex and diagnosis as the between-subject factors. We found that: (1) significant GMV increase in the left cerebellum and GMV reduction in the bilateral middle temporal gyrus and left ventral medial prefrontal gyrus occurred selectively in male patients, while the GMV reduction in the left lingual gyrus and dorsal medial prefrontal gyrus occurred selectively in female patients; (2) MDDf may have experienced more severe sleep disturbance than MDDm; and (3) the severity of sleep symptom could be predicted by the sex specific brain structural alterations in depressions. These findings suggest that sex specific anatomical alterations existed in MDD, and these alterations were associated with the clinical symptoms.