Depression is a major global public health issue. The recurrent episodes of depression and its unpredictable suicide risk have posed a critical medical challenge, highlighting the urgent need for an in-depth investigation into its underlying mechanisms. While the conventional hypothesis of monoamine neurotransmitter depletion is the primary cause of major depressive disorder (MDD), this theory does not entirely explain the complexity and clinical variability of MDD. The presence of treatment-resistant depression (TRD), as a subgroup of MDD, serves as compelling evidence. The immune-inflammation hypothesis has gained increasing attention, with a growing focus on the pivotal role of microglia in this framework. Microglia, the resident immune cells of the central nervous system, sustain homeostasis through self-renewal in adulthood. They are essential for regulating neuron survival, apoptosis, and synaptic plasticity under normal physiological conditions. Under pathological situations, such as traumatic injury, infection, and psychological stress, microglia receive stimulating signals to promptly respond to events. When microglia homeostasis is disrupted, they become over-activated, releasing pro-inflammatory cytokines, activating related signaling pathways, and disrupting neurotransmitter metabolism. This triggers neuroinflammation, oxidative stress, and mitochondrial dysfunction, which hinder neurogenesis, disrupt neural circuits, and ultimately contribute to MDD progression, or even potentially advance it to TRD. This review focuses on the relationship between the biology of microglia and MDD, providing a comprehensive analysis of microglial biological characteristics, factors affecting microglia function, and underlying mechanisms associated with MDD. In conclusion, this review provides a novel perspective on the pathogenesis of MDD from the angle of microglial homeostasis imbalance, aiming to lay a theoretical basis for subsequent investigation of the mechanism. In addition, based on the mechanism of neuroinflammation caused by microglial homeostasis imbalance in MDD, this review aims to offer two new promising therapeutic approaches for MDD and even its progression to the TRD stage.

Objective The literature on large-scale studies of Chinese patients with adolescent-onset bipolar disorder (adolescent-onset BD) was limited. Based on the analysis of the National Bipolar Mania Pathway Survey (BIPAS) Phase II data, we examined the demographic and clinical characteristics of adults with adolescent-onset BD. Methods Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors. Results In the sample, 360 (40%) had adolescent-onset BD and 539 (60%) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third. Conclusions Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.

PURPOSE:To identify clinical predictors of 6-month rehospitalization or suicide events following repetitive transcranial magnetic stimulation (rTMS) in hospitalized patients with depression. PATIENTS AND METHODS:This retrospective cohort study analyzed electronic health records (EHRs) from a tertiary psychiatric hospital in Shanghai, China. Inpatients with depression (ICD-10 codes F31.3-F31.5, F32 or F33) treated with rTMS during hospitalization were included. Missing data were addressed using multiple imputation by chained equations (MICE). Based on univariate analyses of baseline characteristics, further multivariable logistic regression, group least absolute shrinkage and selection operator (LASSO), and random forest analyses were used to identify predictors of rehospitalization or suicide events within 6 months post-discharge. Firth logistic regression was used for bipolar depression (BD) subgroup analyses due to the limited sample size. RESULTS:A total of 275 inpatients were included, including 222 with unipolar depression (UD) and 53 with BD. Among them, 25.5% experienced rehospitalization or suicide-related events. Comorbid substance use disorders (SUDs), previous electroconvulsive therapy (ECT), and use of benzodiazepines were independently associated with higher odds of these outcomes in the overall cohort and the UD subgroup (all p < 0.05). Predictive models in UD showed moderate discrimination and calibration, whereas no significant predictors were identified in BD. These findings reflect observations from a single-center, retrospective cohort. CONCLUSION:Clinically accessible factors were associated with poor long-term outcomes in hospitalized patients with UD receiving rTMS. These findings may help inform patient stratification and support future research aimed at improving risk assessment and personalization of rTMS treatment.

Atypical depression (AD) is a subtype of depression characterized by emotional reactivity, excessive sleep, increased appetite, and interpersonal sensitivity. The prevalence of excessive sleep in AD patients is significantly higher than that in patients with typical depression. Patients have characteristics such as prolonged total sleep time, poor sleep quality, and abnormal sleep structure, which are closely related to prolongation of the disease course, increased risk of recurrence, and poor treatment response. Imbalances in neurotransmitters, elevation of inflammatory factors, and disorders of the hypothalamic-pituitary-adrenal axis are considered core pathological mechanisms related to sleep disturbance in AD. Existing diagnostic tools for AD have deficiencies, thus necessitating the integration of sleep disturbance characteristics, biomarkers, and neuroimaging findings to optimize diagnostic strategies. Given the complexity and heterogeneity of AD-related sleep disturbance in terms of clinical manifestations, pathogenesis, and treatment, in-depth research requires interdisciplinary integration and collaboration. This article reviews the clinical features, pathological mechanisms, and current treatment strategies of sleep disturbances in atypical depression.

BACKGROUND:Differentiating bipolar depression (BPD) from unipolar depression (UPD) is clinically challenging due to symptom overlap. This study explores eye-movement differences between UPD, BPD, and healthy controls (HCs) using a multi-task eye-tracking approach. METHODS:Eye-movement data were collected from 228 participants (60 UPD, 56 BPD, 112 HCs) across four tasks: fixation stability, free-viewing, visual search, and smooth pursuit. A total of 155 eye-movement features were extracted and analyzed using robust analysis of covariance (ANCOVA) and machine learning for classification. RESULTS:Significant differences were identified in 53 features. BPD was characterized by shorter total, average, and first fixation durations across all fixation stability conditions. During free-viewing task, attention to happy images showed a graded decline (HC > UPD > BPD), while BPD exhibited increased fixation allocation to threatening images and reduced saccade velocity and amplitude to negative stimuli. UPD demonstrated reduced efficiency in processing happy faces in the visual search task. Machine learning achieved good discrimination (mean area under the receiver operating characteristic curve [AUC]: 0.78 for HC vs. UPD; 0.88 for HC vs. BPD; 0.87 for UPD vs. BPD), with key contributing features overlapping with those identified as significant in statistical analyses. CONCLUSIONS:Eye-movement patterns reveal both shared and disorder-specific features in UPD and BPD, supporting the potential of eye-tracking as an objective and scalable tool for differentiating mood disorders.

Although deep brain stimulation (DBS) shows potential in treatment-resistant depression (TRD), the efficacy remains controversial. To assess the efficacy of DBS of the bed nucleus of the stria terminalis (BNST) and nucleus accumbens (NAc) for TRD, we conduct a randomized, double-blind, crossover trial after an open-label optimization stage. Stimulation leads to a mean decrease in the Hamilton Depression Scale-17 (HAMD) scores of 10.1 points (p < 0.001) with a 50% response rate at the end of the open-label stage. Anxiety, quality of life, and disability are also improved. The HAMD scores are significantly lower in patients during active DBS than during sham DBS (p < 0.001). Serious adverse events include suicide (1 patient) and seizure (1 patients). With individual stimulation analysis, we identify optimal stimulation sites, fiber tracts, and functional networks (false discovery rate [FDR] p < 0.05). This study demonstrates the efficacy and safety of BNST-NAc DBS for TRD and reveals optimal brain targets for stimulation. This study was registered at ClinicalTrials.gov (NCT04530942).

OBJECTIVES:Phenotypic heterogeneity between suicidal ideation (SI) only and suicide attempt (SA) in major depressive disorder (MDD) suggests distinct suicide risk profiles, yet key clinical and symptomatic factors distinguishing SA from SI remain incompletely defined. METHODS:A total of 799 MDD patients were allocated into SI only (n = 407) and SA (n = 392) groups. Sociodemographic indicators, clinical characteristics and depressive symptoms were compared. Predictors were selected using combined Boruta and LASSO regression, followed by binary logistic regression to identify independent factors associated with SA. A predictive model was constructed and assessed for discrimination, calibration, and clinical utility using decision curve analysis (DCA). RESULTS:Compared with the SI-only group, the SA group had more recurrent depression, treatment-resistant depression (TRD), positive psychiatric family history, hospitalizations, and depressive episodes. For symptoms, the SA group showed higher prevalence of self-harm, rejection sensitivity, weight gain, and mood-congruent psychosis, but lower prevalence of sluggishness and other pain conditions. Independent positive predictors of SA included number of hospitalizations (OR = 1.45, 95%CI: 1.17-1.79), recurrent depression (OR = 1.97, 1.24-3.10), tricyclic antidepressant use (OR = 5.17, 1.16-22.95), self-harm (OR = 31.11, 19.61-49.31), and rejection sensitivity (OR = 1.97, 1.27-3.05). In contrast, unhappiness, agitation, demoralization, and derealization were inversely associated with SA (all P < 0.05). The predictive model showed excellent discrimination (AUC = 0.907,95%CI: 0.887-0.928), satisfactory calibration, and favorable clinical net benefit in DCA. CONCLUSION:Distinct phenotypic differences exist between MDD patients with and without suicide attempts. Self-harm and rejection sensitivity are key risk factors, and the developed predictive model provides reliable individualized risk assessment for clinical practice.

Although deep brain stimulation (DBS) shows potential in treatment-resistant depression (TRD), the efficacy remains controversial. To assess the efficacy of DBS of the bed nucleus of the stria terminalis (BNST) and nucleus accumbens (NAc) for TRD, we conduct a randomized, double-blind, crossover trial after an open-label optimization stage. Stimulation leads to a mean decrease in the Hamilton Depression Scale-17 (HAMD) scores of 10.1 points (p < 0.001) with a 50% response rate at the end of the open-label stage. Anxiety, quality of life, and disability are also improved. The HAMD scores are significantly lower in patients during active DBS than during sham DBS (p < 0.001). Serious adverse events include suicide (1 patient) and seizure (1 patient). With individual stimulation analysis, we identify optimal stimulation sites, fiber tracts, and functional networks (false discovery rate [FDR] p < 0.05). This study demonstrates the efficacy and safety of BNST-NAc DBS for TRD and reveals optimal brain targets for stimulation. This study was registered at ClinicalTrials.gov (NCT04530942).

Mood disorders, including major depressive disorder and bipolar disorder, are frequently comorbid with metabolic syndrome, contributing to greater illness severity and poorer outcomes. Sodium-glucose cotransporter-2 inhibitors (SGLT-2i), a novel class of anti-diabetic agents, not only improve glycemic control but have also shown potential neuroprotective and cognitive benefits. This narrative review summarizes preclinical and clinical evidence on the application of SGLT-2i in mood disorders, with emphasis on their multifaceted mechanisms, including metabolic modulation, anti-inflammatory and antioxidant effects, mitochondrial protection, autophagy regulation, and enhancement of neurotrophic signaling. But it is also notable that current evidence remains limited and heterogeneous. Most clinical data derive from observational studies in diabetic populations and evidence supporting therapeutic effects in patients with established mood disorders is still preliminary. Other important limitations include the scarcity of disorder-specific randomized controlled trials, uncertainty regarding long-term safety in psychiatric populations, and potential interactions with psychotropic medications. Collectively, SGLT-2i may represent a mechanistically relevant and clinically exploratory approach at the interface of metabolic and psychiatric disorders, though further high-quality studies are required to establish their efficacy and safety in mood disorder populations.

BACKGROUND:The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD). METHODS:Cross-sectional analysis of 3247 MDD patients from the National Survey on Symptomatology of Depression (NSSD) was conducted, with data split 7:3 for training/validation. Boruta's Algorithm and Lasso screened predictors, while logistic regression and nomogram were used to identify independent risk factors of SA and visualize the overall impact of these factors on the SA risk of each patient. RESULTS:392 (12.1 %) patients were found to have a history of SA. Boruta's Algorithm and Lasso analysis identified 20 variables as significant risk factors of SA, especially self-harm and a sense of decreased ability. Logistic regression analysis found that No. of hospitalization (OR = 1.14, 95 %CI:1.06, 1.23), SSRIs use(OR = 1.57,95 %CI:1.03,2.38), antipsychotics use (OR = 1.82,95 %CI:1.08,3.04), mood congruent psychosis (OR = 1.32,95 %CI:1.03,1.70), self-harm (OR = 70.90,95 %CI:45.70, 113.00), gastrointestinal system complaints (OR = 1.42,95 %CI:1.13, 1.78), weight gain (OR = 2.11,95 %CI:1.04, 4.29),the feelings of helplessness(OR = 1.47,95 %CI:1.11,1.95), unhappiness (OR = 1.52,95 %CI:1.14, 2.00) and derealization (OR = 1.33,95 %CI:1.02, 1.72) were independently and significantly associated with increased risk of SA in MDD patients. The prediction model showed robust performance, with an area under the curves (AUC) of the receiver-operator characteristics of 0.935 and 0.937 in the training set and validation set, respectively. CONCLUSION:The findings may help develop assessment tools for suicide risk in MDD patients and provide clues for further mechanistic studies. LIMITATIONS:Because of the study's cross-sectional design, causality could not be established between the predictors and SA, and the retrospective data collection approach may introduce recall bias.

Major depressive disorder (MDD) is a significant neurological disorder that imposes a substantial burden on society, characterized by its high recurrence rate and associated suicide risk. Clinical diagnosis, which relies on interviews with psychiatrists and questionnaires used as auxiliary diagnostic tools, lacks precision and objectivity in diagnosing MDD. To address these challenges, this study proposes an assessment method based on EEG. It involves calculating the phase lag index (PLI) in alpha and gamma bands to construct functional brain connectivity. This method aims to find biomarkers to assess the severity of MDD and suicidal ideation. The convolutional inception with shuffled attention network (CISANET) was introduced for this purpose. The study included 61 patients with MDD, who were classified into mild, moderate, and severe levels based on depression scales, and the presence of suicidal ideation was evaluated. Two paradigms were designed for the study, with EEG analysis focusing on 32 selected electrodes to extract alpha and gamma bands. In the gamma band, the classification accuracy reached 77.37% in the visual paradigm and 80.12% in the auditory paradigm. The average accuracy in classifying suicidal ideation was 93.60%. The findings suggest that gamma bands can be used as potential biomarkers differentiating illness severity and identifying suicidal ideation of MDD, and that objective assessment methods can effectively assess MDD The objective assessment method can effectively assess the severity of MDD and identify suicidal ideation of MDD patients, which provides a valuable theoretical basis for understanding the biological characteristics of MDD.

Background: Depressive symptoms (DS) and body pain are prevalent conditions that significantly impact the quality of life of older adults, often co-occurring with chronic diseases. This study aimed to explore the patterns of body pain characteristics and their association with DS among middle-aged and older Chinese adults. Methods: This cross-sectional study analysed data from 16,039 participants aged ≥45 years in the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS). DS were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CESD-10). Body pain characteristics included pain severity and location. Multiple linear regression and mediation analyses were conducted to examine the relationships between chronic diseases, body pain, and DS. Results: Among participants, 5442 (33.9%) reported DS (CESD-10 score ≥12). The DS group showed significantly higher body pain severity (2.66 ± 1.42 vs. 1.84 ± 1.11, p < 0.001) and more painful body parts (5.06 ± 3.87 vs. 3.68 ± 3.03, p < 0.001) compared to the non-depressive group. Both pain severity and number of pain sites were independently associated with higher CESD-10 scores. Mediation analysis revealed that body pain severity mediated 29.0% of the total effect between chronic diseases and DS. Conclusion: Body pain plays a significant mediating role in the relationship between chronic diseases and DS among middle-aged and older Chinese adults. These findings emphasize the importance of implementing comprehensive healthcare approaches that integrate pain management with mental health support in primary care settings.

BackgroundDifferentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other digital devices to assess mental health, has emerged as a promising tool for distinguishing between these two disorders. ObjectiveThis systematic review aimed to achieve two goals: (1) to summarize the existing literature on the use of digital phenotyping to directly distinguish between UD and BD and (2) to review studies that use digital phenotyping to classify UD, BD, and healthy control (HC) individuals. Furthermore, the review sought to identify gaps in the current research and propose directions for future studies. MethodsWe systematically searched the Scopus, IEEE Xplore, PubMed, Embase, Web of Science, and PsycINFO databases up to March 20, 2025. Studies were included if they used portable or wearable digital tools to directly distinguish between UD and BD, or to classify UD, BD, and HC. Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. Articles were excluded if the diagnosis was not made through a professional medical evaluation or if they relied on electronic health records or clinical data. For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. ResultsWe included 21 studies, of which 11 (52%) focused on directly distinguishing between UD and BD, while 10 (48%) classified UD, BD, and HC. The studies were categorized into 4 groups based on the type of digital tool used: 6 (29%) used smartphone apps, 3 (14%) used wearable devices, 11 (52%) analyzed audiovisual recordings, and 1 (5%) used multimodal technologies. Features such as activity levels from smartphone apps or wearable devices emerged as potential markers for directly distinguishing UD and BD. Patients with BD generally exhibited lower activity levels than those with UD. They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. Moreover, speech modalities or the integration of multiple modalities achieved better classification performance across UD, BD, and HC groups, although the specific contributing features remained unclear. ConclusionsDigital phenotyping shows potential in distinguishing BD from UD, but challenges like data privacy, security concerns, and equitable access must be addressed. Further research should focus on overcoming these challenges and refining digital phenotyping methodologies to ensure broader applicability in clinical settings. Trial RegistrationPROSPERO CRD42024624202; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202

BACKGROUND:Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy. METHODS:A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated. RESULTS:ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD. CONCLUSION:This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

There is a high prevalence of suicidal ideation (SI), suicide attempts (SA), and non-suicidal self-injury (NSSI) in bipolar disorder (BD). Understanding the nature of suicidality and NSSI in BD is an important way to inform optimal intervention for reducing suicide risk. We aimed to investigate the prevalence and correlates of SI, SA, and NSSI in patients with BD using data from a national survey. We used network analysis to explore the associations among suicidality, NSSI, addictive features of NSSI, and symptoms of BD. Participants with BD were recruited from 20 research centers in China. Suicidality, NSSI, addictive features, and symptoms of BD were measured via a standardized electronic case report form. We used logistic regression and network analysis for data analysis. Of the 1,055 participants recruited, over 50

Observational studies have found isolation or loneliness to be associated with depression. However, the causal relationship between isolation or loneliness and depression, as well as the mediating factors involved, remains unclear. This study aims to establish the causal effects of social isolation and loneliness on depression and to identify key mediators using a two-sample Mendelian randomization (MR) approach. Using genetic variants from the UK Biobank (N = 455,364 for social isolation/loneliness; N = 462,933 for depression), we applied univariate and multivariate MR (UVMR and MVMR) to assess causal relationships and mediation effects. Genetically predicted social isolation and loneliness (β = 0.188, 95

Major depressive disorder (MDD) often leads to cognitive impairments such as impaired inhibitory control, involving complex circuits in both cortical and subcortical regions. We applied non-invasive temporal interference (TI) stimulation to specific neural circuits to explore its modulatory effects on the abnormal neural networks in MDD. In a randomized, crossover study, 15 patients received TI stimulation targeting the left DLPFC, left sgACC, right amygdala, left VS and sham stimulation delivered in a random order. Functional connectivity and behavioral changes from before and after stimulation were assessed using rs-fMRI and an emotional Stroop task. TI stimulation of the sgACC reduced the FC between the sgACC and the frontal cortex, while stimulation of the right amygdala enhanced the FC between the amygdala and bilateral hippocampi. These FC changes were accompanied by an improvement in Stroop task reaction time, along with significant clinical symptom improvement. This study is one of the first to demonstrate that TI can effectively modulate MDD-related network activity, providing a new approach to understanding and regulating cognitive-emotional functions at the neural circuit level.