Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagnostic classification of psychiatric diseases can be achieved based on brain images. However, due to divergences in scanning machines or parameters, the generalization capability of diagnostic classification models has always been an issue. We propose Meta-learning with Meta batch normalization and Distance Constraint (M2DC) for training diagnostic classification models. The framework can simulate the train-test domain shift situation and promote intra-class cohesion, as well as inter-class separation, which can lead to clearer classification margins and more generalizable models. To better encode dynamic brain graphs, we propose a concatenated spatiotemporal attention graph isomorphism network (CSTAGIN) as the backbone. The network is trained for the diagnostic classification of major depressive disorder (MDD) based on multi-site brain graphs. Extensive experiments on brain images from over 3261 subjects show that models trained by M2DC achieve the best performance on cross-site diagnostic classification tasks compared to various contemporary domain generalization methods and SOTA studies. The proposed M2DC is by far the first framework for multi-source closed-set domain generalizable training of diagnostic classification models for MDD and the trained models can be applied to reliable auxiliary diagnosis on novel data.

INTRODUCTION:Inflammatory and metabolic processes are linked to depression, but only 25-30% of depressed patients show low-grade inflammation and metabolic dysregulation associated with atypical, energy-related symptoms (AES). Interventions targeting immuno-metabolic dysregulation could benefit depressed patients, but currently no consensus exists how to best select patients with immuno-metabolic dysregulations. Therefore, we investigated which combinations of circulating C-reactive protein (CRP) and AES could identify those depressed individuals with significant immuno-metabolic dysregulation. METHODS:Data are from 1,077 persons with a current Major Depressive Disorder (MDD) of the Netherlands Study of Depression and Anxiety. Immuno-metabolic markers were Interleukin-6 (IL-6), Tumor Necrosis Factor alpha (TNF-α), glycoprotein acetyls, body mass index (BMI), waist circumference, triglycerides, high-density-lipoprotein cholesterol (HDL cholesterol), glucose and leptin. Strata for CRP (≤ 1, < 1 CRP ≤ 3, > 3 mg/L) and AES (score of ≤ 3, 4-5, ≥ 6) were compared on immuno-metabolic markers using analyses of covariance. RESULTS:Across strata of CRP and AES, there was a dose-response pattern with all higher immuno-metabolic marker levels across higher strata of CRP and AES, with the exception for an association between AES and TNF-α. Persons with both elevated CRP (> 1 mg/L) and high AES (≥ 6) showed a more dysregulated inflammatory and metabolic profile compared to persons with lower CRP and/or AES (p < 0.001). CONCLUSION:Our results show a dose-response relationship between both CRP levels and AES with immuno-metabolic risk biomarkers, indicating that CRP and AES combined can capture immuno-metabolic features of MDD. Combining these available and scalable indexes may be an effective strategy to select a patient sample with immuno-metabolic dysregulation who may benefit from treatments targeting inflammatory or metabolic pathways.

The assessment of major depressive disorder (MDD) treatment efficacy typically relies on clinician-rated scales of depressive symptoms with a notable absence of predictive biomarkers. In this study, a concentration ratio of serum brain-derived neurotrophic factor (BDNF) to interleukin-6 (IL-6) was identified as a promising predictive biomarker. To overcome the significant disparity in blood levels between BDNF (ng·mL range) and IL-6 (pg·mL range), a dual-pathway signal amplification method involving small carbon dots (Fe-CDs and Pb-CDs) was employed. Fe-CDs exhibited a nanozyme-like behavior, effectively enhancing the signal through solution reactions, rendering it ideal for IL-6 detection at low concentrations. Meanwhile, Pb-CDs, laden with numerous signal molecules, amplified signals via the surface pathway and were suitable for BDNF detection. This dual signal output approach met the distinct sensitivity needs for quantifying IL-6 and BDNF, achieving detection limits of 0.1 pg·mL and 0.02 ng·mL, respectively. Analysis of clinical samples showed that the concentration ratio of BDNF to IL-6 not only effectively differentiates MDD patients from healthy controls but also strongly correlates with individual treatment responses, affirming its value as a biomarker for MDD diagnosis and treatment efficacy evaluation.

Probiotics may improve mood, but their role as adjunctive therapy for major depressive disorder (MDD) is not well understood. This study examines the effects of probiotics on depression severity, brain-derived neurotrophic factor (BDNF), adrenocorticotropic hormone (ACTH), and cortisol levels in MDD patients. Fifty medication-free MDD patients were randomized to receive probiotics with fluoxetine ( = 25) or placebo with fluoxetine ( = 25) for 8 weeks. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS-24), and fasting blood samples were collected at baseline and study conclusion. Forty-four patients completed the trial. The probiotic group showed a significant reduction in depression severity compared with the placebo group ( = 0.001). No significant differences were observed in serum cortisol ( = 0.46) and ACTH levels ( = 0.44). Plasma BDNF levels increased slightly in the probiotic group but were not statistically significant. Probiotic supplementation with fluoxetine significantly reduces depression severity in MDD patients.

BACKGROUND:Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks. METHODS:To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes. RESULTS:Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations. CONCLUSIONS:Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.

OBJECTIVE:This umbrella review aims to consolidate the evidence concerning the influence of exercise on depressive symptoms and cognitive performance in adults diagnosed with depression. METHODS:An umbrella review of systematic reviews with meta-analyses investigating the effect of exercise on depression and cognition was performed. Databases (PubMed, Cochrane Library, Web of Science, Embase) were searched from inception until February 2024 for reviews of randomized controlled trials. Overlap within included reviews was assessed using the corrected covered area method (CCA). A random effects model was used to calculate overall pooled effect size with sub-analyses. RESULTS:This study included 27 reviews, encompassing 190 experiments. In patients with depression, compared to the control group, exercise demonstrated a moderate effect size in reducing depressive symptoms (SMD = -0.67; SE = 0.04; p < 0.0001), with a Number Needed to Treat (NNT) of 2.78. When analyses were limited to data from studies with a low risk of bias, the effect on depressive symptoms was smaller (SMD = -0.38; SE = 0.07; p < 0.0001; NNT = 4.72). Exercise had a small effect on cognitive functions (SMD = 0.12; SE = 0.03; p < 0.0001), with an NNT for cognitive function treatment of 14.79. CONCLUSIONS:All forms of exercise benefit patients with depression. However, for optimal intervention outcomes, it is essential to customize the intensity and type of exercise according to the specific characteristics of the patient's depressive symptoms and cognitive functions. It is recommended to implement a phased treatment approach for patients based on the intensity of exercise.

Depression is a multifactorial clinical syndrome with a low pharmacological treatment response rate. Therefore, identifying predictors of treatment response capable of providing the basis for future developments of individualized therapies is crucial. Here, we applied model-free and model-based measures of whole-brain turbulent dynamics in resting-state functional magnetic resonance imaging (fMRI) in healthy controls and unmedicated depressed patients. After eight weeks of treatment with selective serotonin reuptake inhibitors (SSRIs), patients were classified as responders and non-responders according to the Hamilton Depression Rating Scale 6 (HAMD6). Using the model-free approach, we found that compared to healthy controls and responder patients, non-responder patients presented disruption of the information transmission across spacetime scales. Furthermore, our results revealed that baseline turbulence level is positively correlated with beneficial pharmacological treatment outcomes. Importantly, our model-free approach enabled prediction of which patients would turn out to be non-responders. Finally, our model-based approach provides mechanistic evidence that non-responder patients are less sensitive to stimulation and, consequently, less prone to respond to treatment. Overall, we demonstrated that different levels of turbulent dynamics are suitable for predicting response to SSRIs treatment in depression.

OBJECTIVE:Depression is a global mental health issue, particularly affecting adolescents and young adults. While the role of gut microbiota in depression has been extensively studied, the influence of the oral microbiome remains underexplored. Recent studies suggest that the oral microbiome may affect systemic and brain health through the oral-brain axis. This study aimed to investigate the relationship between oral microbiome diversity and depression using data from 6212 participants in the National Health and Nutrition Examination Survey (NHANES) 2009-2012. METHODS:Oral microbiome diversity was assessed through oral rinse samples using 16S rRNA sequencing, focusing on α-diversity metrics (observed ASVs and Faith's phylogenetic diversity) and β-diversity measures. Depressive symptoms were evaluated with the Patient Health Questionnaire (PHQ-9). Weighted logistic regression models were employed to assess associations between α-diversity and depression, while linear regression was used to examine the relationship between α-diversity and PHQ-9 scores. β-diversity differences were analyzed via permutational analysis of variance (PERMANOVA). RESULTS:10.03% of the participants were diagnosed with depression. Higher α-diversity in the oral microbiome was negatively correlated with depression: observed ASVs (OR: 0.713 [CI: 0.508-0.999], P = 0.050) and Faith's phylogenetic diversity (OR: 0.584 [CI: 0.367-0.931], P = 0.025). Linear regression indicated that greater α-diversity was associated with lower PHQ-9 scores, reflecting fewer depressive symptoms. Furthermore, β-diversity analysis revealed significant differences in the microbiome composition between depressed and non-depressed individuals. CONCLUSION:Reduced oral microbiome diversity is associated with an increased risk and severity of depression. The study underscores the importance of exploring the oral-brain axis and highlights the need for further research into the mechanisms and therapeutic strategies targeting this relationship.

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Major depressive disorder (MDD) is a widespread and severe mental health condition characterized by persistent low mood and loss of interest. Emerging evidence suggests that ferroptosis, an iron-dependent form of cell death, and epigenetic dysregulation contribute to the pathogenesis of MDD. This study investigates the role of RNA demethylase FTO and autophagy regulator BECN1 in ferroptosis and their regulation by the active compound ginsenoside Rb1 (GRb1) as a potential antidepressant strategy. Hippocampal tissues from postmortem MDD patient brains and mice with chronic restraint stress (CRS)-induced depression were analyzed. Ferroptosis was evaluated by analyzing the levels of markers such as glutathione (GSH) and malondialdehyde (MDA). GRb1 was administered to CRS model mice by gavage to explore its effects on ferroptosis-related pathways. The results showed that FTO and BECN1 expression was reduced in the hippocampal tissues of MDD patients and CRS model mice, promoting ferroptosis via disruption of the antioxidant system. Moreover, GRb1 treatment increased FTO and BECN1 expression, modulated m6A methylation, restored the antioxidant balance, and inhibited ferroptosis in CRS model mice. These findings reveal a novel epigenetic mechanism of ferroptosis in MDD and highlight GRb1 as a promising agent for treating depression through the targeting of ferroptosis pathways.

Major depressive disorder (MDD) is a common mental disorder with chronic tendencies that seriously affect regular work, life, and study. However, its exact pathogenesis remains unclear. Patients with MDD experience systemic and localized impairments in glucose metabolism throughout the disease course, disrupting various processes such as glucose uptake, glycoprotein transport, glycolysis, the tricarboxylic acid cycle (TCA), and oxidative phosphorylation (OXPHOS). These impairments may result from mechanisms including insulin resistance, hyperglycemia-induced damage, oxidative stress, astrocyte abnormalities, and mitochondrial dysfunction, leading to insufficient energy supply, altered synaptic plasticity, neuronal cell death, and functional and structural damage to reward networks. These mechanical changes contribute to the pathogenesis of MDD and severely interfere with the prognosis. Herein, we summarized the impairment of glucose metabolism and its pathophysiological mechanisms in patients with MDD. In addition, we briefly discussed potential pharmacological interventions for glucose metabolism to alleviate MDD, including glucagon-like peptide-1 receptor agonists, metformin, topical insulin, liraglutide, and pioglitazone, to encourage the development of new therapeutics.

N6-methyladenosine (m6A) is one of the most common post-transcriptional RNA modifications, which plays a critical role in various bioprocesses such as immunological processes, stress response, cell self-renewal, and proliferation. The abnormal expression of m6A-related proteins may occur in the central nervous system, affecting neurogenesis, synapse formation, brain development, learning and memory, etc. Accumulating evidence is emerging that dysregulation of m6A contributes to the initiation and progression of psychiatric disorders including depression. Until now, the specific pathogenesis of depression has not been comprehensively clarified, and further investigations are warranted. Stress, inflammation, neurogenesis, and synaptic plasticity have been implicated as possible pathophysiological mechanisms underlying depression, in which m6A is extensively involved. Considering the extensive connections between depression and neurofunction and the critical role of m6A in regulating neurological function, it has been increasingly proposed that m6A may have an important role in the pathogenesis of depression; however, the results and the specific molecular mechanisms of how m6A methylation is involved in major depressive disorder (MDD) were varied and not fully understood. In this review, we describe the underlying molecular mechanisms between m6A and depression from several aspects including inflammation, stress, neuroplasticity including neurogenesis, and brain structure, which contain the interactions of m6A with cytokines, the HPA axis, BDNF, and other biological molecules or mechanisms in detail. Finally, we summarized the perspectives for the improved understanding of the pathogenesis of depression and the development of more effective treatment approaches for this disorder.

Major depressive disorder and treatment-resistant depression are significant worldwide health problems that need new therapies. The success of the anesthetic ketamine as an antidepressant is well known. It is less widely known that several other anesthetic agents have also shown antidepressant effects. These include nitrous oxide, propofol, isoflurane, sevoflurane, dexmedetomidine, and xenon. We review clinical and basic science investigations that have studied the therapeutic value of these anesthetics for treating depression. We propose potential neurophysiological mechanisms underlying the antidepressant effects of anesthetics by combining our understanding of how anesthetics modulate brain dynamics to alter arousal states, current theories of depression pathophysiology, and findings from other depression treatment modalities.

Depression is a common affective disorder characterized by persistent low mood, diminished interest or pleasure in normally enjoyable activities, disturbances in sleep patterns, and suicidal ideation. Conventional treatments often yield unsatisfactory results and are associated with several adverse effects. However, emerging literature has highlighted the potential of stem cell (SC) transplantation as a promising avenue for treating depression owing to its favorable anti-inflammatory and neurotrophic properties. This review summarizes the therapeutic effects and underlying mechanisms associated with SC transplantation in depression, offering a conceptual framework for the future application of SCs in the clinical treatment of depression.

Major depressive disorder is a prevalent mental disorder, yet its pathogenesis remains poorly understood. Accumulating evidence implicates dysregulated immune mechanisms as key contributors to depressive disorders. This review elucidates the complex interplay between peripheral and central immune components underlying depressive disorder pathology. Peripherally, systemic inflammation, gut immune dysregulation, and immune dysfunction in organs including gut, liver, spleen and adipose tissue influence brain function through neural and molecular pathways. Within the central nervous system, aberrant microglial and astrocytes activation, cytokine imbalances, and compromised blood-brain barrier integrity propagate neuroinflammation, disrupting neurotransmission, impairing neuroplasticity, and promoting neuronal injury. The crosstalk between peripheral and central immunity creates a vicious cycle exacerbating depressive neuropathology. Unraveling these multifaceted immune-mediated mechanisms provides insights into major depressive disorder's pathogenic basis and potential biomarkers and targets. Modulating both peripheral and central immune responses represent a promising multidimensional therapeutic strategy.

Major depressive disorder (MDD) is a prevalent psychiatric disorder that has damage to people's quality of life. Tryptophan is the precursor to serotonin, a critical neurotransmitter in mood modulation. In mammals, most free tryptophan is degraded by the kynurenine pathway (KP), resulting in a range of metabolites involved in inflammation, immune response, and neurotransmission. The imbalance between quinolinic acid (QA), a toxic metabolite, and kynurenic acid (KynA), a protective metabolite, is a relevant phenomenon involved in the pathophysiology of MDD. Proinflammatory cytokines increase the activity of the enzyme indoleamine 2,3-dioxygenase (IDO), leading to the degradation of tryptophan in the KP and an increase in the release of QA. IDO activates proinflammatory genes, potentiating neuroinflammation and deregulating other physiological mechanisms related to chronic stress and MDD. This review highlights the physiological mechanisms involved with stress and MDD, which are underlying an imbalance of the KP and discuss potential therapeutic targets.

Major depressive disorder (MDD) is a severe mental illness with a complex etiology. Currently, many medications employed in clinical treatment exhibit limitations such as delayed onset of action and a high incidence of adverse reactions. Therefore, there is a pressing need to develop antidepressants that exhibit enhanced efficacy and safety. The N-methyl-D-aspartate receptor (NMDAR), a distinctive glutamate-gated ion channel receptor, has been implicated in the onset and progression of depressive disorder, as evidenced by both preclinical and clinical research. The NMDAR antagonist, ketamine, exhibits rapid and sustained antidepressant effects, holding promise as a novel therapeutic approach for depressive disorder. However, its psychotomimetic impact and potential for addiction have restricted its widespread clinical application. Notably, over the past decade, studies have suggested that enhancing NMDAR functionality can produce antidepressant effects with improved safety, especially with the emergence of NMDAR-positive allosteric modulators (PAMs). We view this as a potential novel strategy for treating depression, forming the basis for the narrative review that follows.

While some studies have used a transdiagnostic approach to relate depression to metabolic or functional brain alterations, the structural substrate of depression across clinical diagnostic categories is underexplored. In a cross-sectional study of 52 patients with major depressive disorder and 51 with post-traumatic stress disorder, drug-naïve, and spanning mild to severe depression severity, we examined transdiagnostic depressive correlates with regional gray matter volume and the topological properties of gray matter-based networks. Locally, transdiagnostic depression severity correlated positively with gray matter volume in the right middle frontal gyrus and negatively with nodal topological properties of gray matter-based networks in the right amygdala. Globally, transdiagnostic depression severity correlated positively with normalized characteristic path length, a measure implying brain integration ability. Compared with 62 healthy control participants, both major depressive disorder and post-traumatic stress disorder patients showed altered nodal properties in regions of the fronto-limbic-striatal circuit, and global topological organization in major depressive disorder in particular was characterized by decreased integration and segregation. These findings provide evidence for a gray matter-based structural substrate underpinning depression, with the prefrontal-amygdala circuit a potential predictive marker for depressive symptoms across clinical diagnostic categories.

Depressive disorder is a chronic, recurring, and potentially life-endangering neuropsychiatric disease. According to a report by the World Health Organization, the global population suffering from depression is experiencing a significant annual increase. Despite its prevalence and considerable impact on people, little is known about its pathogenesis. One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression. Furthermore, the neural circuit mechanism of depression induced by various factors is particularly complex. Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression, a comparison between the neural circuits of depression induced by various factors is essential for its treatment. In this review, we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression, aiming to provide a theoretical basis for depression prevention.

BACKROUND:Depression is a significant concern in clinical and preclinical psychoneurobiological sciences due to its high prevalence and its individual and collective consequences. Identifying efficient biomarkers for accurate diagnosis is crucial, with ideal biomarkers having detectable serum levels and conformational and thermal stability. This study aims to identify stable plasma biomarkers for the diagnosis and prognosis of major depressive disorder, as the pathogenesis of the disorder remains incompletely understood, affecting diagnosis accuracy. METHODS:Thus, this study included ten MDD patients and eight healthy controls. The present work analyzed miRNAs in patients with major depressive disorder compared to healthy controls. RESULTS:Eleven specific miRNAs, particularly hsa-miR-874-3p; hsa-let-7d-5p; and hsa-miR-93-3p showed upregulation-type plasma variations in the group of patients with major depressive disorder. miRNA functionality is linked to depressive pathophysiology. CONCLUSIONS:This study identifies a "bouquet" of miRNAs with significant upregulation variations in patients with major depressive disorder, suggesting further research to determine their suitability for personalization and evaluation, ultimately becoming integral components of major depression serological evaluations.

Major depressive disorder is a complex and multifactorial condition, increasingly linked to neuroinflammation and astrocytic dysfunction. Astrocytes, along with other glial cells, beyond their classic functions in maintaining brain homeostasis, play a crucial role in regulating neuroinflammation and neuroplasticity, key processes in the pathophysiology of depression. This mini-review explores the involvement of astrocytes in depression emphasizing their mediation in neuroinflammation processes, the impact of astrocytic dysfunction on neuroplasticity, and the effect of some antidepressants on astrocyte reactivity. Recent evidence suggests that targeting astrocyte-related signaling pathways, particularly the balance between different astrocytic phenotypes, could offer promising evidence for therapeutic strategies for affective disorders. Therefore, a deeper understanding of astrocyte biology may open the way to innovative treatments aimed at mitigating depressive symptoms by impacting both neuroinflammation and imbalances in neuroplasticity.

BACKGROUND:C-reactive protein (CRP) levels have been implicated in the severity and symptomatology of major depressive disorder (MDD). The aim of this study was to explore the structure of depressive symptoms in patients with MDD according to different groups of CRP levels using network analysis. METHODS:The study included 864 individuals (mean age = 54.05, 67.48 % male) diagnosed with MDD from the 2015-2020 National Health and Nutrition Examination Survey (NHANES). Analyses examined how depressive symptoms and CRP level were related to each other, and how the network structure of depressive symptoms differed across groups with different CRP levels. RESULTS:A direct positive correlation was observed between CRP levels and specific depressive symptoms (e.g., appetite change, energy loss, and feelings of worthlessness). Moreover, there was a stronger correlation between depressive symptoms in the medium CRP and high CRP groups compared to the low CRP group. Furthermore, it was observed that there were notable structural differences between the high-CRP and moderate-CRP groups. LIMITATIONS:The study is based on cross-sectional data, which precludes the drawing of causal conclusions. Furthermore, it does not take into account confounding factors such as body mass index (BMI) and lifestyle. CONCLUSIONS:The findings underscore the pivotal role of CRP as a marker of the severity of depressive symptoms. Routine CRP level testing and anti-inflammatory therapies may be beneficial for depressed patients with elevated CRP levels in clinical practice.

Introduction:Ketamine demonstrates robust and rapidly occurring antidepressant effects in patients with difficult-to-treat major depressive disorder. Ketamine's antidepressant effects and its impact on functional networks in non-resistant forms of major depressive disorder are expected to provide valuable insight into ketamine's mechanism of action related to depression. Methods:This study employs an existing network model of major depressive disorder to investigate the effects of ketamine on resting state connectivity in a therapy-non-resistant major depressive disorder population. In a randomized, double-blind, placebo-controlled, cross-over study, 0.5 mg/kg racemic ketamine or 0.9%NaCl was administered intravenously in 16 MDD patients. We applied resting-state functional magnetic resonance imaging (rs-fMRI) to explore changes in functional brain connectivity directly at 50, 80 and 165 min (acute) and 24 h (delayed) following ketamine administration. A clinician-rated 10-item scale (MADRS) was administered at 165 min and 24 h after ketamine administration. Connections-of-interest (COIs) were based on the previously published corticolimbic-insular-striatalpallidal-thalamic (CLIPST) circuitry model of major depressive disorder. Results:Compared with placebo, ketamine significantly ( < 0.0014) reduced the mean (SD) MADRS total score from 21.2 (5.9) pre-dose to 10.3 (4.6) 24 h post-dose. At both acute ( < 0.0172) and delayed ( < 0.0488) time points, significant rs-fMRI connectivity changes occurred only in MDD-related COIs as proposed by the CLIPST model. No changes in functional connectivity were found in non-CLIPST connections. Discussion:This study demonstrates that ketamine specifically affects depression-related circuitry. Analyzing functional connectivity based on a neurocircuitry model of a specific CNS disease and drug action may be an effective approach that could result in a more targeted analysis in future pharmaco-fMRI studies in CNS drug development.

BACKGROUND:Current evidence is insufficient to support specific tools for screening Major Depressive Disorder (MDD). Early detection of subthreshold depression (SD) is crucial in preventing its progression to MDD. This study aims to develop nomograms that visualize the weights of predictors to improve the performance of screening tools. METHODS:Participants were recruited from Peking University Sixth Hospital and Beijing Physical Examination Center between October 2022 and April 2024. The Mini-International Neuropsychiatric Interview (MINI) 5.0.0 was employed as the diagnostic gold standard, and Generalized Anxiety Disorder questionnaire-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess anxiety, depression, and sleep state. The nomograms were constructed by incorporating optimal predictors, selected through the Least Absolute Shrinkage and Selection Operator (LASSO), into a multivariate logistic regression model to estimate the probability of MDD and SD. RESULTS:After matching age and education, 164 participants were included in each group for analysis. Both nomograms demonstrated superior discrimination, calibration, and clinical applicability compared to PHQ-9. Anxiety emerged as a most significant predictor for SD, while sleep problems exhibited high rankings for both SD and MDD. The two predictors subsequently affect concentration and daytime functioning. LIMITATIONS:With a lack of external validation data, the performance of nomograms may be overestimated. CONCLUSIONS:This study is the first attempt to develop a nomogram for predicting SD, while also providing a nomogram for MDD. The crucial predictors offer valuable insights into potential variables for clinical intervention.

Major depressive disorder (MDD), also known as depression, is a prevalent mental disorder that leads to severe disease burden worldwide. Over the past two decades, significant progress has been made in understanding the pathogenesis and developing novel treatments for MDD. Among the complicated etiologies of MDD, chronic stress is a major risk factor. Exploring the underlying brain circuit mechanisms of chronic stress regulation has been an area of active research for recent years. A growing body of preclinical and clinical research has revealed that abnormalities in the brain circuits are closely associated with failures in coping with stress in depressed individuals. Nevertheless, neural circuit mechanisms underlying chronic stress processing and the onset of depression remain a major puzzle. Here, we review recent literature focusing on circuit- and cell-type-specific dissection of depression-like behaviors in chronic stress-related animal models of MDD and outline the key questions.

Major depressive disorder (MDD) is a prevalent psychiatric illness and a significant contributor to the global burden of disease. However, the molecular mechanisms underlying depression are complex and have yet to be fully elucidated. Previous studies demonstrated that collapsin response mediator protein 2 (CRMP2) involved in the onset of depression, but its role is unclear yet. To explore the mechanism of CRMP2 in depression and whether it ameliorates depressive-like behaviours by modulating synaptic functions, we manipulate the expression of CRMP2 by adeno-associated virus (AAV) injected into the hippocampal CA1 region and then induced depressive-like behaviour by subjecting the mice to chronic unpredictable mild stress (CUMS). Sucrose preference test (SPT), open field test (OFT), elevated plus maze test (EPM), forced swimming test (FST), and tail suspension test (TST) are utilized to detect behavioral changes. Golgi-Cox staining and electron microscopy were applied to examine alterations in the structure and morphology of neural synapses. Synaptophysin (SYP), synaptophysin 1 (SYN1), growth-associated protein 43 (GAP43), glutamate receptor 2 (GLUR2) and postsynaptic density protein 95 (PSD95) is tested for synaptic function. The proteins interacting with CRMP2 were comprehensively investigated utilizing Immunoprecipitation-Mass Spectrometry (IP-MS) analysis and the direct binding between CRMP2 and PSD95 was validated. In our study, we observed CRMP2 in the hippocampal CA1 region was downregulated following CUMS. Knockdown of CRMP2 resulted in impaired synaptic structure and decreased expression of synapse-associated proteins, accompanied by increased depressive-like behaviour, like anhedonia and hopelessness. Conversely, overexpression of CRMP2 significantly ameliorated behavioural deficits associated with depression and restore the compromised synaptic structure and function. Our findings suggest that CRMP2 exerts a crucial function in modulating depressive-like behaviours by influencing the synaptic structure and function, and it can directly interact with PSD95.