Background Post-stroke depression (PSD) is a prevalent neuropsychiatric complication after stroke that severely impairs neurological recovery and quality of life. Increasing evidence suggests that persistent neuroinflammation, microenvironmental dysregulation, and impaired neuroplasticity are critically involved in PSD progression; however, effective therapeutic strategies remain limited. Urine-derived stem cells (USCs), a noninvasively accessible subtype of mesenchymal stem cells, possess advantages including convenient collection, robust proliferative capacity, low immunogenicity, and potential for autologous transplantation, highlighting their translational potential. This study aimed to evaluate the therapeutic effects of USCs on PSD and to investigate the underlying mechanisms. Methods A rat PSD model was established by combining middle cerebral artery occlusion (MCAO) with chronic unpredictable mild stress (CUMS) to mimic ischemic brain injury and depression-like behaviors after stroke. Behavioral assessments, histological analysis, immunofluorescence staining, and molecular assays were performed to evaluate the therapeutic effects of USCs in vivo . To further explore the mechanisms involved, a lipopolysaccharide (LPS)-stimulated microglial inflammatory model was established in vitro . immunofluorescence staining, reactive oxygen species detection, flow cytometry, and RT-qPCR were conducted to assess inflammatory signaling and microglial polarization. Results USCs treatment significantly alleviated depression-like behaviors, preserved brain tissue integrity, improved hippocampal neuronal status, and reduced apoptosis in the ischemic penumbra of PSD rats. In addition, USCs markedly attenuated both central and peripheral inflammatory responses. In vitro studies demonstrated that USCs inhibited activation of the NF-κB signaling pathway by suppressing NF-κB phosphorylation, reduced intracellular ROS accumulation, and decreased the expression of pro-inflammatory cytokines, including IL-6 and TNF-α. Furthermore, USCs promoted microglial polarization toward the anti-inflammatory M2 phenotype, thereby reprogramming the neuroinflammatory microenvironment and enhancing neuroprotection. Conclusions USCs ameliorated PSD-related pathological and behavioral alterations through paracrine-mediated anti-inflammatory and neuroprotective effects associated with inhibition of the NF-κB signaling pathway. These findings provide mechanistic evidence supporting USCs as a promising stem cell-based therapeutic strategy for post-stroke neuropsychiatric complications.

BACKGROUND:Biological aging may contribute to the pathogenesis of major depressive disorder (MDD). However, whether and how peripheral transcriptomic aging increases the risk of MDD onset remains unclear. METHODS:Transcriptomic age was estimated using peripheral blood RNA sequencing data from 141 individuals with MDD and 134 healthy controls. The residuals of transcriptomic age regressed on chronological age were calculated to indicate transcriptomic aging acceleration. Enrichment analysis was performed to explore potential biological mechanisms underlying aging- and MDD-associated transcriptomic alterations. Associations between transcriptomic aging and clinical, neurocognitive, environmental, genetic, and neuroimaging phenotypes were examined. RESULTS:Participants with MDD exhibited significantly accelerated transcriptomic aging both before (t = 2.06, P = 0.040) and after adjusting for chronological age and sex (t = 3.72, P < 0.001). Enrichment analysis revealed shared terms in innate immune-related inflammation, ribosome biogenesis, and mitochondrial energy metabolism, while telomere length maintenance was specifically enriched in aging but not in MDD. No significant associations were found between transcriptomic aging and clinical symptoms, neurocognitive functions, childhood trauma exposure, or polygenic risk score. Neuroimaging analyses demonstrated that transcriptomic aging was associated with structural (t = -3.30, P = 0.001) and functional (t = 2.64, P = 0.009) alterations in the right insular cortex. Further analyses indicated that insular abnormalities partially mediated the impact of transcriptomic aging on MDD vulnerability. CONCLUSIONS:Transcriptomic aging may represent a novel risk factor for MDD. Disruption in the insular cortex may serve as a critical neural substrate through which accelerated transcriptomic aging increases vulnerability to MDD.

BACKGROUND:Major depressive disorder (MDD) is often accompanied by cognitive impairment; however, the cognitive heterogeneity of MDD and its neurobiological context remain poorly understood. METHODS:A total of 198 participants with MDD and 275 HCs underwent multi-domain cognitive assessments and multi-modal MRI acquisition. A semi-supervised approach was applied to identify cognitive dimensions of MDD, and individual-level structural-enriched functional networks (SFNs) were constructed. Network-based statistics were applied to characterize network-level associations between structural-functional coupling deviation and cognitive dimensions. Furthermore, the correlations between the spatial pattern of SFN deviation and meta-analytic neurocognitive terms, cortical transcriptome, and neurotransmitter density distribution maps were detected. RESULTS:In the MDD group, 103 individuals were assigned to Cluster 1, presenting widespread cognitive impairments, whereas 95 were assigned to Cluster 2, presenting cognitive preservations. An abnormally enhanced SFN subnetwork (PPerm = 0.042) was identified, which showed significant spatial correlation with meta-analytic neurocognitive maps (r = 0.181, P < 0.001). The SFN deviation pattern was spatially associated with 2458 genes enriched primarily in neuronal and synaptic function, and these genes also showed enrichment for pathways annotated to neurodegenerative diseases (PFDR < 0.05). In addition, SFN deviation was spatially associated with three neurotransmitter maps, including N-methyl-D-aspartate receptor, cannabinoid type-1 receptor, and norepinephrine transporter (PFDR = 0.028). CONCLUSIONS:The study provides a data-driven characterization of cognitive heterogeneity in MDD, identifying two cognitive dimensions spanning from relative preservation to widespread impairment. By integrating structural-functional coupling deviations with transcriptomic and neurotransmitter maps, these findings provide preliminary biological context for interpreting cognitive heterogeneity in MDD.

AIMS:Perivascular water diffusivity, as assessed by diffusion tensor imaging along the perivascular space (DTI-ALPS) index, serves as an indirect imaging marker potentially reflecting glymphatic function. This study investigated its associations with childhood trauma and major depressive disorder (MDD) and explored the related biological mechanisms. METHODS:We enrolled 86 patients with MDD and 74 matched healthy controls (HCs). Childhood trauma, cognitive performance, and peripheral blood transcriptomic profiles were evaluated. Regression and exploratory mediation analyses were performed. RESULTS:MDD patients exhibited lower DTI-ALPS index (F = 15.716, p < 0.001), and poor attention, processing speed, visual memory and executive function performance (q < 0.05) than HCs. In the MDD group, the DTI-ALPS index was negatively correlated with CTQ score (r = -0.251, p = 0.02), spatial working memory strategy (SWM_Stra; r = -0.272, p = 0.013) and rapid visual information processing mean latency (RVP_ML; r = -0.232, p = 0.036). The yellow, turquoise, and blue transcriptomic modules were associated with MDD and enriched in immune-inflammatory, intracellular trafficking, and mitochondrial metabolic pathways, whereas the magenta module was associated with the DTI-ALPS index (r = -0.25, p = 0.022) and enriched in hemostasis, platelet activation, and vascular integrity pathway. Mediation analyses revealed that the DTI-ALPS index partially mediated the relationships between childhood trauma and the diagnosis of MDD (indirect effect: coefficient = 0.0001, p = 0.0372). CONCLUSIONS:Altered DTI-ALPS may reflect links between childhood trauma, cognitive dysfunction, and vascular integrity, but as an indirect glymphatic marker, these findings require cautious interpretation.

BACKGROUND AND HYPOTHESIS:Shared clinical features and genetic factors in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) have led to the hypothesis of common pathophysiological mechanisms. This study aims to elucidate aberrant transdiagnostic structural covariance patterns across these disorders employing a multivariate analytical approach. STUDY DESIGN:Structural magnetic resonance imaging data were acquired from a sample of 704 subjects, comprising 244 healthy controls, 119 first-episode treatment-naïve SCZ individuals, 159 BD individuals, and 182 treatment-naïve MDD individuals. Seed-based partial least squares correlation analysis was applied to construct structural covariance networks (SCNs) across 6 predefined functional networks: the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), somatomotor network (SMN), ventral attention network (VAN), and visual network. Network seeds were selected based on functional network definitions. Spatial distributions of SCNs were calculated, and individual network integrity indices were derived as measures of SCN strength. Group comparisons of network integrity were performed using multiple t-tests to identify network-specific alterations across the diagnostic groups. STUDY RESULTS:Structural covariance patterns exhibited spatial distributions akin to those of functional networks. Network integrity showed common reductions across all 3 disorders in DMN, DAN, and FPCN, while BD showed specific reductions in the SMN, and both BD and MDD showed reductions in the VAN. Furthermore, there was a significant correlation between individualized network integrity and clinical and cognitive manifestations. CONCLUSIONS:Our results highlight the potential of the integrity of SCNs as transdiagnostic biomarkers.

Oxylipins are bioactive lipid metabolites that may bridge immune dysregulation and depressive symptomatology. However, systematic alterations of oxylipin networks in MDD and their therapeutic implications remain unclear. In this study, a plasma oxylipidomics analysis was performed in 154 patients with MDD and 134 healthy controls (HC). Findings were validated in a chronic restraint stress (CRS) mouse model of depression, followed by pharmacological inhibition of lipoxygenase (LOX) pathways using ML-355 and zileuton. Behavioral tests and immunofluorescence analysis of microglial activation were conducted to assess therapeutic effects. MDD patients exhibited significantly elevated plasma levels of 22 oxylipins compared to HCs, including oxylipins derived from lipoxygenase (LOX), cytochrome P450 (CYP450), cyclooxygenase (COX), and non-enzymatic pathways. Notably, non-responders to antidepressant treatment displayed higher baseline levels of 20 oxylipins than both responders and HCs, and baseline oxylipin levels negatively correlated with Hamilton Depression Rating Scale (HAMD) score reduction rates. In CRS mice, the LOX pathway was activated, as evidenced by increased LOX levels in the blood and brain, as well as elevated plasma levels of LOX-derived oxylipins. Pharmacological inhibition of 12-lipoxygenase (12-LOX) with ML-355 significantly alleviated depressive-like and anxiety-like behaviors and reversed stress-induced microglial activation in the hippocampus and medial prefrontal cortex. The 5-LOX inhibitor zileuton reduced microglial activation in a region-dependent manner but did not significantly improve behavioral outcomes. These findings reveal elevated LOX-derived oxylipins as potential biomarkers predicting poor antidepressant response in MDD. Targeting the LOX pathway, particularly 12-LOX, represents a promising therapeutic strategy for depression by ameliorating neuroinflammatory processes.

Suicidal ideation (SI) in major depressive disorder (MDD) presents a serious clinical concern, yet its underlying biological mechanisms remain poorly understood. Using an exploratory, multimodal design, this study included 98 MDD patients with SI (MDD_SI), 61 without SI (MDD_nSI), and 233 healthy controls (HC), and collected functional MRI and peripheral blood transcriptomic data. Network-based statistics of resting-state functional connectivity (FC) identified brain network differences among the three groups, followed by graph-theoretical analysis of derived hub regions. Differential module connectivity (MDC) analyses of blood transcriptomic modules were performed to identify group-level differences, and exploratory correlation analyses were performed to examine associations between brain network topology and transcriptomic alterations. A functional network centered on the caudal temporal thalamus (cTtha) showed significant group differences (MDD_SI > MDD_nSI >HC; p < 0.001), with increased nodal efficiency of the left cTtha in both the MDD_SI (p = 0.043) and MDD_nSI (p < 0.001) groups compared with the HC group. Modules differentiating MDD_SI from MDD_nSI were enriched in antiviral immune responses and epigenetic regulation (darkred, MDC = 1.508, p = 0.04; yellow, MDC = 0.93, p = 0.01), while SI-specific alterations involved mitochondrial energy dysregulation and nSI-specific alterations involved vascular and translational pathways. Notably, one immune-related gene module was significantly correlated with left cTtha betweenness centrality (r = 0.274, p = 0.021), degree centrality (r = 0.262, p = 0.027), and nodal efficiency (r = 0.235, p = 0.048) in the MDD_SI group. This study indicates that MDD and SI are associated with cTtha-centered functional network alterations and peripheral transcriptomic dysregulation, and that, within MDD, SI may show a potential link between cTtha-centered brain network disruption and immune transcriptomic dysregulation. These results should be interpreted cautiously given the exploratory design, binary SI assessment, and modest effect sizes.

Sleep disturbances are prevalent in bipolar disorder (BD) patients, and the circadian locomotor output cycles kaput (Clock) gene plays a significant role in this process. The role of microglia (the brain-resident immune cells) in mediating this process remains uncertain. In this study, our findings showed that sleep loss induces mania-like behavior, microglial loss, and time-dependent gene expression changes. Moreover, diurnal oscillations in circadian rhythm-associated and inflammation-related gene expression in the mouse prefrontal cortex (PFC) were altered following sleep deprivation (SD). Further correlative analysis revealed correlations in gene expression between marker genes for microglia and Clock genes. In addition, the Clock mutation induces mania-like behavior, inhibition of neural activity, and microglial loss. Transcriptomic analysis revealed significant alterations in inflammatory pathways, circadian rhythm-related pathways, and the major histocompatibility protein complex in ClockΔ19 mice. Subsequent correlative analysis demonstrated significant correlations in gene expression among inflammation-, circadian rhythm-, and synapse-related genes within the PFC and hypothalamus of both male and female ClockΔ19 mice. In conclusion, our findings demonstrated behavioral, cellular, and molecular changes in SD-induced mice and Clock-mutant mice models. Microglia and CLOCK were associated with mania-like behaviors. Future research will likely focus on microglia-targeted approaches for the diagnosis and treatment of BD.

Psychiatric nurses represent a high-stress occupational group that experiences elevated levels of suicidal ideation (SI), emphasizing the need for focused mental health interventions. The main purpose of this study was to examine the prevalence of SI among psychiatric nurses and to identify the psychological and occupational factors associated with SI. A total of 1,835 psychiatric nurses completed questionnaires on depressive symptoms (PHQ-9), SI, quality of work-related life (QWL), and burnout. Multivariate logistic regression and phenotypic network analyses were conducted to identify factors associated with SI and the potential pathways linking depressive symptoms, burnout, and QWL to SI. The results indicated that 11.33% of the participants had SI in the past two weeks. Multivariate logistic regression revealed that emotional exhaustion, depersonalization, personal accomplishment, stress at work, general well-being, and the home-work interface were significant predictors of SI. Network analysis further revealed that psychomotor changes, guilt, sad mood, low energy, and appetite changes were the symptoms most directly associated with SI. In addition, sad mood, general well-being, and work-home interface were linked to job and career satisfaction, whereas sad mood and low energy were associated with emotional exhaustion and SI. These findings contribute valuable large-scale evidence on the mental health challenges faced by psychiatric nurses and highlight the importance of addressing mood disturbances, energy loss, and work-related stress in SI prevention efforts for this vulnerable group.

Background: Childhood maltreatment (CM) is a significant risk factor for major depressive disorder (MDD), yet the underlying biological mechanisms remain unclear. This study aimed to investigate brain functional networks and peripheral transcriptomics in patients with MDD who have a history of CM. Methods: Functional imaging data were collected and network-based statistics were used to identify differences in functional networks among MDD patients with CM (MDD_CM, n = 78), MDD patients without CM (MDD_nCM, n = 61), and healthy controls (HC, n = 126). Additionally, blood transcriptional data were clustered into co-expression modules, and module differential connectivity analysis was utilized to assess variations in gene co-expression network modules among the groups. Results: The results revealed a significant difference in an inferior occipital gyrus-centered functional network among the three groups. Furthermore, eight gene co-expression modules differed among the groups and were enriched in multiple branches related to immune responses or metabolic processes. Notably, a module enriched in type I interferon-related signaling pathways demonstrated a significant correlation with the disrupted network in the MDD_nCM group. Moreover, multiple immune-related gene modules were found to be significantly correlated with sleep disturbances in MDD_CM patients. Conclusions: Dysregulation of an inferior occipital gyrus-centered functional network and immune-related transcriptomic alterations significantly associate with the pathophysiology of MDD_CM.

Cognitive dysfunction is common but heterogeneous in patients with Major depressive disorder (MDD). This study aimed to validate MDD subtypes based on IQ trajectories and to elucidate their cognitive and multimodal neuroimaging characteristics. Premorbid IQ was estimated using a validated Wechsler Adult Intelligence Scale-based algorithm and compared to current IQ to classify patients. Neuropsychological assessments were conducted, and multimodal neuroimaging analyses included measurements of gray matter volume and low-frequency fluctuation amplitude. A total of 164 MDD patients with preserved IQ (PIQ), 67 MDD patients with deteriorated IQ (DIQ), and 353 healthy controls (HCs) participated in the study. The DIQ group exhibited poorer performance on logical memory and executive function tasks compared to the PIQ group. Patients with IQ decline exhibited greater cognitive impairment. Neuroimaging results revealed reduced gray matter volume and increased amplitude of low-frequency fluctuations, with distinct patterns observed between PIQ and DIQ groups. Using K-nearest neighbors (KNNs), we achieved an accuracy of 0.6442 and an area under the curve of 0.8023 for predicting cognitive changes. These findings confirm the cognitive heterogeneity in depression, highlighting the potential for personalized treatment strategies.

The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD_SI), 82 MDD patients without SI (MDD_nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD_SI from MDD_nSI (p = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD_SI from HC (p = 0.004). Additionally, three modules showed differences between MDD_nSI and HC: magenta (p = 0.009), brown (related to innate immunity and mitochondrial metabolism; p = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD.

Sleep loss is a key trigger for a manic episode of bipolar disorder (BD), but the underlying microglial and molecular mechanisms remain unclear. Sleep loss induces microglial and inflammatory responses. Microglia, resident macrophages in the central nervous system, regulate synaptic pruning by engulfing dendritic spines. Here, we introduce a modified paradoxical sleep deprivation (SD) paradigm as a BD mouse model. After intermittent 16-h daily SD for 4 days, the mice showed mania-like behavior, reduced cytokine/chemokine production, mitochondrial damage, microglial loss, decreased synaptic engulfment by microglia, and synaptic gain. Single-nucleus RNA sequencing (snRNA-seq) revealed cell-type-specific inflammation- and synapse-related gene expression profiles in the prefrontal cortex (PFC) and hippocampus of SD-treated male mice. Interestingly, much more differentially expressed genes were observed in SD-treated female versus male mouse brain, especially in the PFC. Pharmacological depletion of microglia by colony stimulating factor-1 receptor (CSF1R) inhibitor PLX3397 blocked SD-induced inflammation-related and senescence-associated abnormalities in a sex-specific manner. Microglial elimination reversed SD-induced synapse gain and mania-like behavior in males but not in females. However, microglial inhibition by minocycline had no effect on SD-induced behaviors in a sex-independent manner. These findings demonstrate that microglia-mediated neuroinflammation and synaptic pruning contribute to SD-induced mania-like behavior in a mouse model of BD in a sex-specific manner.

BACKGROUND:This study aims to apply a semi-supervised machine learning approach for classifying major depressive disorder (MDD) patients into more homogeneous cognitive subtypes based on multidimensional cognitive profiles, and to perform multimodal neuroimaging to identify subtype-specific neural signatures. METHODS:A total of 147 MDD patients and 222 healthy controls (HCs) completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) and magnetic resonance imaging (MRI) scans. Cognitive subtypes were derived based on neurocognitive profiles using heterogeneity through discriminative analysis (HYDRA). General linear models (GLMs) were employed to assess differences across groups in neurocognitive indexes and neuroimaging data followed by Tukey's post-hoc test for pairwise comparisons between the groups. RESULTS:Based on cognitive profiles, MDD patients were classified into cognitive deficit (CD, N = 75) and cognitive preservation (CP, N = 72) subtypes. Voxel-based morphometry (VBM) revealed reduced grey matter volume (GMV) in the left fusiform gyrus and left cerebellum in MDD patients when compared to HCs, with CD patients showing greater atrophy than patients in CP subtype. Meanwhile, the amplitude of low-frequency fluctuations (ALFF) in the temporal lobe of both MDD subtypes was decreased when compared to that of HCs, showing no inter-subtype differences. CONCLUSIONS:A subtype of MDD characterized by comprehensive cognitive deficits is associated with structural atrophy in the left fusiform gyrus and cerebellum, suggesting these regions as potential biomarkers for the cognitive deficit subtype of MDD. However, no significant differences in ALFF were observed between the two cognitive subgroups.

Gastrointestinal adverse effects are the most commonly reported adverse effects associated with the use of antidepressants. While existing studies on the gastrointestinal effects of antidepressant medications offer valuable insights, there are still opportunities to enhance the evidence base. We included double-blind randomized controlled trials of major depressive disorder (MDD). Eligible studies must focus on comparing the use of 21 commonly used antidepressants in patients with MDD and reporting data on treatment-emergent gastrointestinal SEs. We selected 196 studies that reported specific numbers of individuals with gastrointestinal adverse effects, involving a total of 57,162 patients. A network and dose‒response meta-analysis was conducted. Compared with placebo, 16 antidepressants had higher odds ratios (ORs) for nausea and vomiting, 15 antidepressants had higher ORs for constipation, 8 antidepressants had higher ORs for diarrhoea, 8 antidepressants had higher ORs for anorexia, 12 antidepressants had higher ORs for dry mouth, and 3 antidepressants had higher ORs for dyspepsia. Commonly used antidepressants have different gastrointestinal effects. Duloxetine, levomilnacipran, and vilazodone carry a higher risk of inducing nausea and vomiting, whereas trazodone, amitriptyline, agomelatine, and mirtazapine tend to be better tolerated. Amitriptyline, clomipramine, and reboxetine are more prone to induce constipation. Diarrhoea is more commonly associated with vilazodone, fluvoxamine, and sertraline. Amitriptyline, reboxetine, and duloxetine are more likely to cause anorexia. Amitriptyline, reboxetine, and trazodone are related to causing dry mouth. Compared with the placebo, amitriptyline, fluoxetine, and paroxetine were associated with a greater incidence of dyspepsia.

Background:Adolescent MDD has become a significant public health issue, yet its underlying mechanisms remain unclear. Multimodal brain imaging techniques offer a powerful method for exploring complex mental disorders. However, evidence focusing on the multimodal brain networks and structural-functional coupling in adolescent depression is still limited. Methods:Participants with major depressive disorder (MDD) were Han Chinese individuals aged 13 to 18 who had been unmedicated for at least two weeks. We conducted multimodal MRI acquisitions, including structural MRI (sMRI), resting-state functional MRI (rsfMRI), and Diffusion Tensor Imaging (DTI). The cortex was parceled into 360 regions using the HCP-MMP atlas. Functional connectivity and deterministic structural connectivity matrices were constructed, and structural-functional coupling coefficients were calculated. Differences in connectivity and coupling coefficients between the MDD and healthy controls (HCs) groups were identified. Results:A total of 25 adolescents with MDD (mean age: 15.68 years, standard deviation [SD]: 1.18; Female: 21 (84.00%)) and 27 hCs (mean age: 14.30 years, standard deviation [SD]: 1.51; Female: 13 (48.15%)) were included in the analysis. There were 9 structural connections and 122 functional connections that differed between the two groups, involving multiple cortical regions. Additionally, we identified structural-functional coupling differences in three brain areas, specifically the posterior cingulate cortex and the ventral visual cortex. Conclusion:Adolescent MDD involves disruptions in brain structural networks, functional networks, and structural-functional coupling. These differing indicators may serve as potential biomarkers for adolescent MDD.

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.

BACKGROUND:Childhood trauma is strongly linked to anxiety and depression, significantly increasing the risk of negative outcomes in adulthood. This study employed network analysis to investigate the complex interplay of anxiety and depression symptoms among Chinese college students, focusing on identifying the core symptoms most directly affected by childhood trauma and those exerting the greatest influence on others. METHODS:Data were collected from December 2020 to January 2021 from 2,266 college students at 16 institutions in southwestern and eastern coastal China. Depression, anxiety, and childhood trauma were assessed using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Childhood Trauma Questionnaire-28, respectively. Separate symptom networks were constructed for participants with and without childhood trauma experiences. Central indices were employed to identify the central symptom within each network. The accuracy and stability of the networks were then evaluated. Finally, a network comparison test was used to analyze differences in network properties between the trauma and non-trauma groups. RESULTS:Loss of Energy and Worry too much were the central symptoms in the non-trauma group, while anhedonia and nervousness were the central symptoms in the trauma group. There was a significant difference in the global strength of the network between the trauma group and the non-trauma group (pFDR< 0.01), but no significant difference in the distribution of edge weights between the two networks (pFDR =0.14). Anhedonia, Suicide ideation and Feeling afraid in the trauma group showed increased network centrality compared with the non-trauma group. CONCLUSIONS:This study demonstrates the profound impact of childhood trauma on the central symptoms of anxiety and depression in college students. Further research is warranted to investigate the specific pathways through which these symptoms develop, with the goal of developing targeted interventions for this vulnerable population.

Childhood trauma is strongly linked to emotional distress. However, few studies have explored the impact of sense of coherence (SOC) on the relationship between childhood trauma and emotional distress in college students. This study aimed to explore its impact on the relationship between childhood trauma and emotional distress. Analyzing data from 2307 Chinese college students, we found that SOC moderated the association between childhood trauma and anxiety/depression levels. Females showed higher SOC and lower anxiety/depression despite experiencing more childhood trauma. Multiple linear regression revealed that anxiety was negatively associated with SOC(P < 0.001) and grade(P = 0.027), and positively with childhood trauma(P < 0.001) and male gender(P = 0.004). Similarly, the depression exhibited similar associations. SOC moderated negatively the relationship between CTQ and anxiety, as well as between CTQ and depression. Childhood trauma is associated with increased emotional distress risk among college students, but a strong SOC can reduce this risk.

Background Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients’ IQ scores at the individual level using a prediction model. Methods We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients’ IQ scores, including Verbal IQ (VIQ), Performance IQ (PIQ), and Full-Scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains. Results The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance. Conclusions The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.