BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern. The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder (MDD) remain poorly understood. Aberrant resting-state functional connectivity (rsFC) in the amygdala, a key region implicated in emotional regulation and threat detection, is strongly implicated in depression and suicidal behavior. AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts. METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts (sMDD) group, 33 adolescents with MDD but without suicide attempts (nsMDD) group, and 34 demographically matched healthy control (HC) group, with the lateral and medial amygdala (MeA) defined as regions of interest. The rsFC patterns of amygdala subregions were compared across the three groups, and associations between aberrant rsFC values and clinical symptom severity scores were examined. RESULTS Compared with the nsMDD group, the sMDD group exhibited reduced rsFC between the right lateral amygdala (LA) and the right inferior occipital gyrus as well as the left middle occipital gyrus. Compared with the HC group, the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus (PHG) and fusiform gyrus. In the sMDD group, right MeA and right temporal pole: Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores (r = -0.409, P = 0.025), while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores (r = 0.372, P = 0.043). CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.

BACKGROUND Major depressive disorder (MDD) and obesity (OB) are bidirectionally comorbid conditions with common neurobiological underpinnings. However, the neurocognitive mechanisms of their comorbidity remain poorly understood. AIM To examine regional abnormalities in spontaneous brain activity among patients with MDD-OB comorbidity. METHODS This study adopted a regional homogeneity (ReHo) analysis of resting-state functional magnetic resonance imaging. The study included 149 hospital patients divided into four groups: Patients experiencing their first episode of drug-naive MDD with OB, patients with MDD without OB, and age- and sex-matched healthy individuals with and without OB. Whole-brain ReHo analysis was conducted using SPM12 software and RESTplus toolkits, with group comparisons via ANOVA and post-hoc tests. Correlations between ReHo values and behavioral measures were examined. RESULTS ANOVA revealed significant whole-brain ReHo differences among the four groups in four key regions: The left middle temporal gyrus (MTG.L), right cuneus, left precuneus, and left thalamus. Post-hoc analyses confirmed pairwise differences between all groups across these regions (P < 0.05). OB was associated with ReHo alterations in the MTG.L, right cuneus, and left thalamus, whereas abnormalities in the precuneus suggested synergistic pathological mechanisms between MDD and OB. Statistically significant correlations were found between the drive and fun-seeking dimensions of the behavioral activation system, as well as behavioral inhibition and the corresponding ReHo values. CONCLUSION Our findings provide novel evidence for the neuroadaptive mechanisms underlying the MDD-OB comorbidity. Further validation could lead to personalized interventions targeting MTG.L hyperactivity and targeting healthy food cues.

Clinically differentiating bipolar II disorder (BD-II) from major depressive disorder (MDD) remains a significant challenge in modern psychiatry. These two conditions share substantial clinical symptomatology, making accurate diagnosis difficult in routine clinical practice. Misdiagnosis may lead to inappropriate treatment strategies, increased psychological and physical burdens, reduced quality of life, and impaired social functioning. Genetic overlap may partially explain the clinical similarities between MDD and BD-II, and biomarkers along with neuroimaging techniques are receiving increasing attention as tools to aid in diagnosis. For example, electroencephalography has been shown to effectively distinguish between unipolar depression and bipolar depression; serum levels of glycogen synthase kinase-3 have also been investigated as a potential tool for differentiating between the two disorders. A comprehensive assessment integrating clinical characteristics, genetic basis research, and multimodal evaluations using neuroimaging and biomarkers through a multidisciplinary approach will help enhance clinicians' ability to distinguish between MDD and BD-II. By improving diagnostic accuracy, more personalized and effective treatment strategies can be developed, ultimately improving patients' health outcomes and quality of life.

AimAvoidance is a key maintaining factor in major depressive disorder (MDD); however, longitudinal evidence remains limited, and its association with personality traits is not fully understood. This study aimed to (1) examine the longitudinal associations among avoidance, depressive symptoms, and rumination and (2) explore the relationship between avoidance and personality traits in outpatients with MDD.MethodsIn this 10‐month prospective study, 53 adult outpatients diagnosed with MDD were assessed while receiving routine outpatient psychiatric care. Avoidance, depressive symptoms, and rumination were measured at baseline and follow‐up using the Cognitive Behavioral Avoidance Scale (CBAS), 17‐item GRID‐Hamilton Depression Rating Scale (GRID‐HAMD‐17), and Ruminative Responses Scale (RRS), respectively. Personality traits were assessed at baseline using the Temperament and Personality Questionnaire (T&P). Patients were dichotomized into an Avoidance Improvement (AI) group (≥9‐point reduction in CBAS scores) or a Non‐Avoidance Improvement (NON‐AI) group based on a median split. Univariate and multivariate regression analyses were conducted to identify factors associated with Avoidance Improvement.ResultsThe AI group demonstrated greater reductions in depressive symptoms (p = 0.001) and rumination (p = 0.031) than the NON‐AI group. In multivariate analysis, greater improvement in depressive symptoms (odds ratio [OR] = 1.130, 95% confidence interval [CI] = 1.016–1.257) and lower baseline Personal Reserve (OR = 0.848, 95% CI = 0.724–0.968) were independently associated with improvement in avoidance.ConclusionReductions in depressive symptoms and lower Personal Reserve may contribute to decreased avoidance in MDD. These findings highlight the importance of targeting depressive symptoms not only for direct relief but also to disrupt maladaptive avoidance cycles.

Psychological resilience varies among major depressive disorder (MDD) patients, with some exhibiting high resilience. This challenges the notion of resilience as purely protective and suggests biological heterogeneity. Both resilience and MDD have been linked to metabolic alterations, but their independent and interactive effects remain unclear. This study aims to investigate how resilience and MDD jointly affect metabolic profiles, with a focus on identifying key metabolic and pathway alterations in high-resilience MDD patients compared to healthy controls, and exploring their potential for diagnostic biomarkers. Targeted serum metabolomics using UPLC-MS/MS was conducted in MDD patients and healthy controls. Resilience was assessed via the Ego Resiliency Scale (ERS). Interaction effect analysis examined the main and interactive influences of resilience and MDD. Key metabolites in high-resilience MDD were identified by OPLS-DA and pathway enrichment. A logistic regression model with cross-validation assessed diagnostic accuracy in training and test sets. A total of 271 participants were enrolled, and about one-third of MDD patients exhibited high resilience. The MDD×resilience interaction was not significant, whereas MDD showed a significant main effect on metabolite levels. Five key metabolites were identified in high-resilience individuals, with arginine, methionine, and kynurenine downregulated and threonic and erythronic acids upregulated in the high-resilience MDD group. The diagnostic model achieved an area under the curve (AUC) of 0.811 in the test set. MDD status-rather than psychologically resilience-was the primary driver of serum metabolic variation. In high-resilience individuals, alterations converged on amino acid pathways (driven by lower arginine, methionine, kynurenine) and pentose-glucuronate axis (with higher threonic and erythronic acids), the latter is potentially linked to redox imbalance. These features may provide novel insights into depression-related metabolic dysregulation. Not applicable.

Premature aging in serious mental illness.

The complex internal organization of subcortical structures forms the foundation of critical neural circuits that support sensorimotor processing, emotion regulation, and memory. However, their complex internal organization poses a significant challenge to reliable, fine-scale parcellation. To overcome the trade-off between anatomical specificity and cross-subject consistency, we propose a novel multiscale subcortical parcellation framework grounded in consensus graph representation learning of diffusion MRI (dMRI) tractography data. We propose a novel fiber-cluster-based connectivity representation to address the limitations of conventional voxel-level tractography features, thereby enhancing anatomical fidelity and reducing tracking noise. Furthermore, our method preserves local structural coherence while significantly mitigating the curse of dimensionality by leveraging 3D-SLIC supervoxel preparcellation. Finally, we integrate consensus graph representation learning with low-rank tensor modeling, enabling population-level regularization that refines individual embeddings and ensures consistent subcortical parcellations across subjects. By utilizing this framework, we create a new, fine-grained subcortical atlas. Evaluations using Ultra-High-Field dMRI from Human Connectome Project demonstrate that our method yields subcortical parcels with enhanced reproducibility and microstructural homogeneity. Across diffusion-derived microstructure indices, our atlas consistently achieves the lowest or second-lowest coefficient of variation, with average reductions of 15-25% compared to existing atlases, thereby supporting robust downstream analyses of structural homology and regional variability. Our pipeline provides a powerful tool for detailed mapping of subcortical organization, offering promising applications in precision neuroimaging and the discovery of clinical biomarkers for neurological and psychiatric disorders that affect these structures (e.g., Parkinson's disease, schizophrenia, and major depressive disorder). Our code is available at https://anonymous.4open.science/r/SubcorticalParcellation-D254/.

Objective Behavioral and neurological studies suggest that major depressive disorder (MDD) is associated with pervasive deficits in executive control of attention. Research using Event-Related Potentials (ERPs) to investigate attentional impairments in depression has provided mixed results. The current study aimed to clarify abnormalities in ERPs associated with depression through use of the Attention Network Test (ANT) which assesses efficiency of three fundamental brain networks: executive control, alerting, and orienting. Methods Participants were 93 volunteers. We compared ERP amplitudes in healthy, subsyndromal depression, and MDD groups (31 participants per group) during performance of an extended-duration version of the ANT. Results Both N100 and P300 ERP amplitudes were generally lower in the MDD group across central-parietal and posterior sites, with medium-to-large effect sizes. There were also significant effects of depression on the ANT indices for executive control and alerting. Further analyses showed that some abnormalities in ERPs were seen in the subsyndromal group and that depression effects were stable across time, despite vigilance decrement. Conclusion Neurocognitive deficits in depression may relate to depletion of a general attentional resource.

To accurately detect individuals' mental health issues using artificial intelligence and self-report scales, it is crucial to recognize how personal characteristics can affect the detection. This study focuses on the role of alexithymia-a condition where individuals struggle to recognize and articulate emotions and symptoms-in the detection of depression. We aimed to determine whether deep learning models could enhance the accuracy of depression detection in people with alexithymia compared to self-report scales. We analyzed data from 194 patients with major depressive disorder and 105 community controls, employing eight large language models (LLMs) trained on transcript text from clinician-administered structured interviews using the Hamilton Depression Rating Scale (HAMD). Here we show that generalized logistic regression analysis indicates a positive relationship between alexithymia and depression. Using the HAMD as the gold standard, individuals with alexithymia show poorer performance on the self-reported Hospital Anxiety and Depression Scale-Depression Subscale (HADS-D) in identifying depression (b = -0.37, p = .002). Four of the eight LLMs (AUCs=0.87-0.89) significantly outperform the HADS-D (AUC = 0.79) in depression detection (p <0.05). Subgroup analysis demonstrates that while LLMs achieve AUCs ranging from 0.79 to 0.96, the HADS-D only reaches an AUC of 0.35 for individuals with alexithymia. Our findings reveal that LLMs can potentially outperform self-report scales in detecting depression, particularly in those with alexithymia. These results highlight the importance of considering patient characteristics, such as alexithymia, when detecting depression. Deep learning analyses can enhance the accuracy of clinical assessments for depression and potentially for other mental health disorders.

The Food and Drug Administration (FDA) on Jan. 12 announced its approval of the first prescription, physician‐directed, at‐home brain neuromodulation therapy as an adjunctive treatment for adults with major depressive disorder (MDD) who failed to achieve satisfactory improvement from at least one previous antidepressant.

Introduction Cognitive behavioral therapy (CBT) is effective for major depressive disorder (MDD), yet individual responses vary. Personality may relate to outcomes, but its role in brooding rumination during CBT remains unclear. This study tested whether baseline personality traits are associated with brooding change in patients with MDD receiving CBT. Methods In this prospective observational cohort, 75 outpatients were allocated to CBT + treatment-as-usual (TAU) (n=33 baseline; n=30 longitudinal) or TAU alone (n=42; n=38) based on clinical course and preference. The Ruminative Responses Scale (RRS) brooding subscale and the 17-item Grid-Based Hamilton Depression Rating Scale (GRID-HAMD 17 ) were assessed at baseline and 16 weeks; the Temperament and Personality Questionnaire (T&amp;amp;P) was assessed at baseline. Multiple linear regressions within each group examined associations between baseline traits and brooding change, adjusting for sex, baseline brooding, and baseline GRID-HAMD 17 . A trait-wise single-predictor sensitivity analysis used the same covariates. Results Both groups demonstrated significant within-group improvements. Hedges’ g (95% CI): CBT—brooding 0.48 (0.11–0.85), GRID-HAMD 17 1.07 (0.63–1.50); TAU—brooding 0.84 (0.47–1.21), GRID-HAMD 17 1.46 (1.01–1.91). In CBT, higher anxious worrying was associated with greater brooding reduction (eight-predictor model: B = 0.71, p = 0.026) and remained significant in the single-predictor analysis (B = 0.36, p = 0.014). Self-criticism showed a negative association in the eight-predictor model (B=−0.60, p = 0.043) but did not persist in the single-predictor analysis. In TAU, no personality trait was associated with brooding change. Adjusted between-group differences in change (brooding, GRID-HAMD 17 , total DDD) were not significant. Limitations Nonrandomized allocation, modest sample sizes, and two assessment time points limit precision and power; the eight-predictor model in CBT may be prone to overfitting, so findings are exploratory. Conclusion In this observational cohort, personality traits, with anxious worrying appearing relevant, were associated with brooding change within CBT, whereas between-group differences were not significant. These hypothesis-generating results require validation in adequately powered randomized trials to inform stratified depression care.

Neuropsychiatric symptoms such as depression and apathy are frequently reported in all subgroups of frontotemporal dementia (FTD) or frontotemporal lobe degeneration (FTLD) and represent a substantial burden for both patients and caregivers. Both symptoms are most common in its behavioral variant (bvFTD) with a prevalence of up to 90%. Their severity is particularly high in bvFTD and the semantic variant of primary progressive aphasia (svPPA). Due to its clinical heterogeneity, bvFTD can mimic depression, especially in its initial stages, making their differentiation a major clinical challenge. The relationship between depression and the even more often presenting apathy is unclear, although they frequently co-occur and overlap. Depression in FTD is associated with atrophy in the left prefrontal/orbitofrontal cortex, supramarginal and postcentral gyrus, connected with dysfunction of neuronal networks involved in the pathogenesis of major depressive disorder and frontotemporal hypometabolism. Apathy, a core behavioral symptom in bvFTD, is associated with similar cortical atrophies on the right side and disordered networks between prefronto-striatal and other circuits. Since currently no curative treatment for FTD is available, the management of the basic disease and associated depression and apathy is mostly symptomatic. Most pharmacological interventions are without convincing effect; for depression, electroconvulsive therapy is a beneficial option, as is transcranial magnetic stimulation for apathy, but there is an impetus to develop general guidelines for the diagnosis and effective treatment of FTD and associated depression and apathy.

Revealing and validating the biomarkers associated with demethylation in major depressive disorder: comprehensive insights based on bulk RNA sequencing data, single-nucleus RNA sequencing data, and clinical experiments.

Shared and distinct neural signatures in major depressive disorder and comorbid post-traumatic stress disorder: Insights from structural and functional imaging.

Predictive and mechanistic biomarkers of treatment response to Transcranial Magnetic Stimulation (TMS) in Psychiatric and Neurocognitive Disorders, identified via TMS-Electroencephalography (EEG) and Resting-State EEG: A systematic review.

Gamma-band qEEG biomarkers as trait indicators in depression.

Efficacy and safety of lower dose electroconvulsive therapy for major depression in adolescents with suicide ideation: A non-inferiority randomized controlled trial in China.

Hippocampal volume and treatment outcome in postpartum depression: Naturalistic data from a psychiatric parent-baby unit.

Background Major depressive disorder (MDD) represents a grave worldwide concern, particularly afflicting the adolescent population. Electroconvulsive therapy (ECT) is widely regarded as a gold-standard intervention for severe forms of MDD, although treatment response varies considerably among individuals. Growing evidence suggests that hematological parameters may influence therapeutic outcomes. This study sought to examine the link between admission anemia and response to ECT treatment. Methods We analyzed 381 adolescent MDD patients who underwent ECT, comparing demographic and hematological indicators between responders and non-responders. Subgroup analyses were conducted based on gender and depressive subtypes. Results Among the 381 patients treated with ECT, 272 (71.4%) were classified as responders. Non-responders showed significantly lower baseline hemoglobin levels compared to responders (mean ± SD: 119.0 ± 9.7 vs. 128.7 ± 13.1, p &amp;lt; 0.001). Analysis identified a significant link between hemoglobin levels at admission and the percentage improvement on the HAMD-17 (r = 0.231, p &amp;lt; 0.001). Following confounder adjustment in a binary logistic regression model, anemia at admission was correlated with a lower probability of ECT response [OR (95% CI): 4.051 (2.399-6.840), p &amp;lt; 0.001]. Females and patients with psychotic depression were particularly more susceptible to the impact of admission anemia. Conclusion Admission anemia is associated with poorer ECT efficacy in adolescent MDD patients. Assessing baseline hemoglobin levels may help optimize ECT treatment planning, especially in female patients and those with psychotic depression.

Background: Pediatric Major Depressive Disorder (pMDD) is one of the leading causes of disability in adolescents. There is currently no single explanation that fully accounts for the cause of the disorder, but various factors, including dysregulation of the immune and stress responses, have been linked to its onset. Oxylipins and endocannabinoids, derived from metabolization of n-3 and n-6 polyunsaturated fatty acids (PUFAs), regulate inflammation and have been suggested to attenuate inflammation associated with depression. This study aims to understand whether adolescents with pMDD have altered baseline levels of oxylipins and endocannabinoids compared to healthy adolescents. Methods: In this case–control study, we measured 60 oxylipins and endocannabinoids in plasma from 82 adolescents with pMDD and their matching healthy controls. Results: A Principal Component Analysis revealed substantial variability within each group and only a moderate degree of separation between them. In a paired analysis, the lipid mediators of controls exhibited higher concentrations of n-6 PUFA-derived prostaglandins and thromboxanes (PGE2, PGD2, PGF2a and TXB2), n-3 PUFA-derived TxB3, and the endocannabinoids AEA, EPEA, and DHEA. In contrast, cases had higher concentrations of the n-6 PUFA-derived 6-keto-PGF1a and the n-3 PUFA-derived PGD3. In addition, we observed a higher percentage of oxylipins and endocannabinoids derived from DHA (5.65 ± 5.46% vs. 4.72 ± 4.94%) and AA (16.31 ± 11.10% vs. 12.76 ± 13.46%) in plasma from controls, in line with the higher DHA and AA levels observed in erythrocytes from controls compared to cases. Conclusions: Overall, our results show lower plasma levels of endocannabinoids and lower DHA- and AA-derived oxylipins in adolescents with pMDD, supporting their role in the pathophysiology of pMDD. To infer a causative role of the n-3 and n-6 PUFA-derived oxylipins and endocannabinoids in pMDD, an intervention study with n-3 PUFA supplementation and monitoring of oxylipins and endocannabinoids would be necessary.