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Treatment-resistant depression (TRD) is a subtype of major depressive disorder (MDD) that fails to respond to first-line pharmacotherapy. This cross-sectional study compared blood concentrations of folic acid, vitamin B12, and homocysteine between female depressed patients with or without TRD, and examined the association of these parameters with the severity of depression. It included 116 female patients treated for MDD, of whom 59 (51%) developed TRD. The diagnosis of MDD was established via a structured clinical interview, while the severity of depression was measured with the Montgomery–Asberg Depression Rating Scale. Blood samples were taken at the initial psychiatric examination to determine the serum levels of folic acid and vitamin B12 and plasma levels of homocysteine. Folic acid levels were significantly lower in the female TRD group (p < 0.001), whereas homocysteine levels were significantly higher in the female TRD group (p < 0.001), compared to the female depressed group without TRD. In the regression analyses, higher levels of homocysteine (p < 0.001) were associated with TRD, while lower levels of folic acid (p = 0.036) were related to higher severity of depression, independently of sociodemographic and clinical parameters. Our findings showed that folate correlated with symptom severity, while homocysteine correlated with the TRD status in female MDD patients. Full article

Emerging evidence highlights a strong association between cerebrovascular accident (CVA) and major depressive disorder (MDD), mediated by immune dysregulation. Elevated levels of proinflammatory cytokines, reduced adaptive immune responses, altered immune cell composition, and increased microglial activation characterize this bidirectional relationship. Microglial activation appears to be a central molecular mechanism linking CVA and MDD, underscoring the immune system’s crucial role in disease pathogenesis. This interplay suggests that immune-driven processes not only exacerbate neurological damage but also contribute to psychiatric manifestations. Based on current literature, the role of proinflammatory processes, particularly microglial activation, in the relationship between CVA and MDD warrants special attention. In this context, the participation of myeloid differentiation factor 88 (MyD88), a cytosolic adaptor protein, appears to play a key role in proinflammatory signaling pathways driving microglial activation. Thus, focusing on MyD88 emerges as a promising complementary strategy for future research and for advancing our understanding of the mechanisms underlying microglial homeostasis dysregulation and its link to the pathophysiology of MDD and CVA. Full article

Major depressive disorder (MDD), identified by the World Health Organization as the leading cause of disability worldwide, remains underdiagnosed due to the lack of objective diagnostic tools. Electroencephalogram (EEG) signals offer potential biomarkers, yet conventional analyses often overlook the brain’s nonlinear dynamics. In this study, we analyzed resting-stage EEG data to identify four microstate types in MDD patients. Symmetrical microstate–brain networks were then constructed for each microstate by using time series of four types of microstates as dynamic windows. Then, we compared microstate features (duration, occurrence, coverage, transition probability) and brain network parameters (clustering coefficient, characteristic path length, local and global efficiency) between MDD patients and healthy controls to analyze the characteristics of the changes in the brain activities of the patients with MDD and the topological patterns of the functional connectivity. The comparative analysis showed that MDD patients showed more frequent microstate transitions and reduced network efficiency, suggesting elevated energy consumption and impaired neural integration, which may imply a cognitive shift in MDD patients toward internal focus and psychological withdrawal from external stimuli. By integrating microstate and brain network features, we captured the temporal and spatial characteristics of MDD-related brain activity and validated their diagnostic utility using our previously proposed multiscale spatiotemporal convolutional attention network (MSCAN). Our MSCAN achieved an accuracy of 98.64% for MDD detection, outperforming existing approaches. Our study can offer promising implications for the intelligent diagnosis of MDD and a deeper understanding of its neurophysiological underpinnings. Full article

Depression is a common, debilitating, and potentially life-threatening mental disorder affecting individuals across all age groups and populations. It represents one of the major challenges of contemporary medicine. It is estimated that more than 300 million people worldwide are affected, and patients with major depressive disorder (MDD) exhibit a significantly increased risk of suicide, underscoring the urgent need for effective and long-lasting therapeutic strategies. Growing evidence indicates that the pathophysiology of depression involves a complex interplay of genetic vulnerability, chronic stress, dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, neuroinflammation, oxidative stress, mitochondrial dysfunction, and impaired synaptic plasticity, collectively contributing to symptom heterogeneity and treatment resistance. In this review, we synthesize data derived from PubMed, Google Scholar, and ClinicalTrials.gov databases concerning pharmacological and non-pharmacological treatment strategies, with particular emphasis on their cellular and molecular mechanisms of action. We present currently used classes of antidepressant drugs, including selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), and monoamine oxidase inhibitors (MAOIs), discussing their limitations in the context of contemporary pathophysiological models of depression. We then focus on emerging therapies targeting the glutamatergic, GABAergic, and dopaminergic systems, including ketamine, esketamine, (R)-ketamine, the dextromethorphan–bupropion combination (DMX–BUP), neurosteroids (zuranolone, brexanolone), as well as selective serotonin receptor modulators (gepirone ER) and dopaminergic modulators (cariprazine). The review is complemented by a discussion of non-pharmacological neuromodulatory approaches, such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and photobiomodulation. Rather than providing another summary of clinical response indicators, this article integrates the molecular underpinnings of novel antidepressant agents and neuromodulation techniques with current concepts of depression pathophysiology, highlighting their relevance for the development of precise, mechanistically targeted, and multimodal treatment strategies. Full article

Major depressive disorder (MDD) is a prevalent mental health condition that requires accurate and objective diagnostic tools. Electroencephalogram (EEG) signals provide valuable insights into brain activity and have been widely studied for mental disorder classification. In this study, we propose a novel DML + ANFIS framework that integrates deep metric learning (DML) with an adaptive neuro-fuzzy inference system (ANFIS) for the automated diagnosis of major depressive disorder (MDD) using EEG time series signals. Time–frequency features are first extracted from raw EEG signals using the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT). These features are then embedded into a low-dimensional space using a DML approach, which enhances the inter-class separability between MDD and healthy control (HC) groups in the feature space. The resulting time–frequency feature embeddings are finally classified using an ANFIS, which integrates fuzzy logic-based nonlinear inference with deep metric learning. The proposed DML + ANFIS framework was evaluated on a publicly available EEG dataset comprising MDD patients and healthy control (HC) subjects. Under subject-dependent evaluation, the STFT-based DML + ANFIS and CWT-based models achieved an accuracy of 92.07% and 98.41% and an AUC of 97.28% and 99.50%, respectively. Additional experiments using subject-independent cross-validation demonstrated reduced but consistent performance trends, thus indicating the framework’s ability to generalize to unseen subjects. Comparative experiments showed that the proposed approach generally outperformed conventional deep learning models, including Bi-LSTM, 2D CNN, and DML + NN, under identical experimental conditions. Notably, the DML module compressed 1280-dimensional EEG features into a 10-dimensional embedding, thus achieving substantial dimensionality reduction while preserving discriminative information. These results suggest that the proposed DML + ANFIS framework provides an effective balance between classification performance, generalization capability, and computational efficiency for EEG-based MDD diagnosis. Full article

Background: Diffusion-prepared pseudo-continuous arterial spin labelling (DP-pCASL) can quantify the cerebral blood flow (CBF) and the water exchange rate (kw) across the blood–brain barrier (BBB). Little is known about the BBB water exchange in major depressive disorder (MDD). Objective: We aimed to explore the associations between kw, CBF, peripheral inflammation, and MDD. Methods: Using DP-pCASL, we measured the global and selected regional kw and CBF together with blood plasma levels of lipopolysaccharide (LPS) and inflammatory cytokines in 85 patients with MDD and 51 controls. Results: The global CBF was significantly lower in MDD patients compared with controls (means of 51 and 57 mL/100 g/min, respectively; p = 0.006), with similar reductions found in the dorsolateral and ventromedial prefrontal, anterior, and posterior cingulate regions, while no differences were found in the amygdala and the isthmic cingulate. There were no differences in the kw between groups globally (means of 128 min⁻¹; p = 0.958) and in the studied regions. Among MDD patients, the kw was weakly correlated with the MADRS scores (r = 0.231, p = 0.034). There were no associations between kw, CBF, and inflammatory markers (LPS, IL-6, IL-10, TNF-α, IFN-γ). Logistic regression showed that a combination of the regional CBF < 59.22 mL/100 g/min together with LPS > 143.58 pg/mL and/or IL-10 > 0 pg/mL distinguished MDD patients from controls with a moderate accuracy of 83.1% (sensitivity = 94.1%, specificity = 64.7%, AUC = 0.876). Conclusions: DP-pCASL imaging confirmed previous findings of reduced CBF in MDD, which together with LPS and IL-10 concentrations were independent significant predictors of MDD. However, no changes in the BBB water exchange were found, suggesting that it may not be as significant as CBF in MDD pathophysiology. Full article

Major depressive disorder (MDD) is a highly prevalent psychiatric condition characterized by complex neurobiological mechanisms, including oxidative stress and neuroinflammation, with microglial activation playing a key role in its pathophysiology. Conventional antidepressants, though widely used, often fail to achieve remission due to limited efficacy, adverse effects, and poor patient adherence. In this context, nanotechnology-based drug delivery systems have emerged as promising strategies to overcome pharmacological limitations, enhance blood–brain barrier (BBB) penetration, and target neuroinflammatory pathways. This narrative review explores the role of microglia as both mediators of neuroinflammation and potential therapeutic targets in MDD. We examine different nanocarriers and their ability to modulate microglial activation, promote a shift from a pro-inflammatory (M1) to an anti-inflammatory (M2) phenotype, and enhance antidepressant efficacy. Preclinical studies have demonstrated that nanoparticle-based systems not only improve drug bioavailability and brain targeting but also potentiate neuroprotective effects by reducing oxidative stress, promoting neurogenesis, and restoring synaptic plasticity. These findings highlight the potential of nanotechnology as a novel approach to precision neuropsychopharmacology. This review aims to provide an integrative perspective on how nanocarrier-based strategies targeting microglia could redefine future therapeutic paradigms for MDD. Full article

Major depressive disorder (MDD) is increasingly recognized as a disorder of impaired neuroplasticity and large-scale network dysfunction rather than a simple monoaminergic deficit. Converging evidence indicates that chronic stress and depression erode synaptic connectivity, reduce glial support, and destabilize functional interactions among the default mode, salience, and executive networks. Conventional antidepressants indirectly restore circuit function over weeks, but the advent of rapid-acting glutamatergic agents has opened a new path for targeting these abnormalities directly. In this narrative review, we synthesize molecular, cellular, and connectomic findings to outline a conceptual Connectomic Glutamate Framework of Depression. We first examine how NMDAR blockade and subsequent AMPAR facilitation activate mTORC1 and BDNF signaling, driving synaptogenesis and dendritic spine formation. We then highlight the role of astrocytes and microglia in shaping the “quad-partite synapse” and sustaining network integrity. Neuroimaging studies demonstrate that glutamatergic modulators remodel dysfunctional networks: dampening DMN hyperconnectivity, enhancing fronto-limbic coupling, and normalizing salience-driven switching. Integrating these domains, we propose a hypothesis-generating, two-phase model in which glutamatergic agents destabilize maladaptive attractor states and then reintegrate circuits through structural remodeling. This framework bridges molecules, cells, and networks, offering mechanistic insight into the rapid efficacy of glutamatergic antidepressants and highlighting priorities for clinical translation. Full article

Major depressive disorder (MDD) was long framed as a single clinical entity arising from a linear stress–monoamine–hypothalamic–pituitary–adrenal (HPA) axis cascade. This view was shaped by forced swim and learned helplessness tests in animals and by short-term symptom-based trials using scales such as the Hamilton Depression Rating Scale (HAM-D) and the Montgomery–Åsberg Depression Rating Scale (MADRS). This “unitary cascade” view has been dismantled by advances in neuroimaging, immune–metabolic profiling, sleep phenotyping, and plasticity markers, which reveal divergent circuit-level, inflammatory, and chronobiological patterns across anxiety-linked, pain-burdened, and cognitively weighted depressive presentations, all characterized by high rates of non-response and relapse. Translationally, face-valid rodent assays that equated immobility with despair have yielded limited bedside benefit, whereas cross-species bridges—electroencephalography (EEG) motifs, rapid eye movement (REM) architecture, effort-based reward tasks, and inflammatory/metabolic panels—are beginning to provide mechanistically grounded, clinically actionable readouts. In current practice, depression care is shifting toward systems psychiatry: inflammation-high and metabolic-high archetypes, anhedonia- and circadian-dominant subgroups, formal treatment-resistant depression (TRD) staging, connectivity-guided neuromodulation, esketamine, selected pharmacogenomic panels, and early digital phenotyping, as endpoints broaden to functioning and durability. A central gap is that heterogeneity is acknowledged but rarely built into trial design or implementation. This perspective advances a plasticity-centered systems psychiatry in which a testable prediction is that manipulating defined prefrontal–striatal and prefrontal–limbic circuits in sex-balanced, chronic-stress models will reproduce human network-defined biotypes and treatment response, and proposes hybrid effectiveness–implementation platforms that embed immune–metabolic and sleep panels, circuit-sensitive tasks, and digital monitoring under a shared, preregistered data standard. Full article

Background/Objectives: Research confirms that diet can influence the onset or course of depression. Polyphenols are bioactive plant compounds with proven beneficial effects on health. The aim of this study was to assess the relationship between polyphenol intake and the health status of individuals with major depressive disorder (MDD). Methods: The study included 44 participants. Health status was assessed using questionnaires adapted into Polish, body composition analysis, and laboratory blood tests. Polyphenol intake was estimated using the Phenol-Explorer program. Results: Among men, polyphenol intake was positively associated with glycated hemoglobin levels (R = 0.70; p = 0.038). Lower polyphenol intake in women was associated with poorer physical health (p = 0.014) and overall quality of life (p = 0.013). Polyphenol intake enhanced the effects of visceral fat content, muscle mass, severity of depressive symptoms (positive), and severity of stress symptoms (negative) on triglyceride levels. Polyphenol intake was positively associated with LDL cholesterol levels, and this relationship was attenuated by body water and fat content. Polyphenol intake weakened the relationship between fat content (negative) and quality of life (positive) with cortisol levels (R² = 0.61; p < 0.001). Conclusions: Polyphenols act both directly and mediate the effects of other factors on the health status of individuals with MDD. Despite their proven beneficial effects, further research is needed to explore their potential impact and mechanisms of action in patients with MDD. Full article

Background: Major Depressive Disorder (MDD) is more common in patients with Inflammatory Bowel Disease (IBD) than in the general population, suggesting a shared but unclear pathogenesis. Autophagy, a conserved intracellular cleaning process, maintains cellular health by removing debris and recycling nutrients. Given the limited research on autophagy in this comorbidity, this study investigated the role of autophagy-related genes in both disorders. Aim: This study aimed to identify shared autophagy-related mechanisms between IBD and MDD and to explore potential therapeutic strategies. Methods: We identified differentially expressed autophagy-related genes (DE-ARGs) in diseased versus normal tissues. Shared DE-ARGs between IBD and MDD were designated Co-DEGs. We analyzed correlations among Co-DEGs and their association with immune cell infiltration. Four machine-learning algorithms were used to pinpoint key biomarkers. Potential therapeutic agents were predicted and validated via molecular docking. Results: We identified 47 shared Co-DEGs. Among these, CASP1 emerged as a cross-disease shared susceptibility-associated gene (SSAG), consistently selected by all machine-learning models. Drug-gene interaction analysis and molecular docking identified compounds that could regulate CASP1. Single-cell analysis suggested CASP1 helps reshape the immune microenvironment in Crohn’s disease. Furthermore, Mendelian randomization identified WDR6 as a shared genetic risk factor for both conditions. Conclusions: Our findings illuminate autophagy-mediated mechanisms linking gut and brain disorders. The identification of CASP1 as a SSAG, along with candidate therapeutics, provides a foundation for future research and targeted treatments for IBD and MDD comorbidity. Full article

Background and Objectives: Anxiety is prevalent in patients with major depressive disorder (MDD) and bipolar disorder (BD). Understanding its interaction with mood disorders may provide deeper insight into symptom clustering, severity, and interventions. We compared the networks of MDD and BD using the Beck Anxiety Inventory (BAI) to identify central symptoms and interconnections. Materials and Methods: This cross-sectional study involved 815 individuals with MDD (n = 332) and BD (n = 483) who had clinically significant anxiety symptoms (BAI score > 8). Network analysis identified anxiety symptom clusters. Network centrality, stability, and comparison tests assessed the structural differences and global strength variations between the groups. Results: Both the MDD and BD networks showed strong interconnections among several BAI items and demonstrated stable centrality measures. Core symptoms with high centrality included “Losing control”, “Choking”, “Breathing”, “Unsteady”, and “Shaky” in both MDD and BD. Although no significant differences were found in the overall network structures between MDD and BD, the global strength of the network differed significantly, with MDD exhibiting modestly higher overall anxiety network connectivity than BD. Conclusions: Network clusters revealed aspects of both cognitive and somatic symptoms of anxiety. Although overall structures were similar between the groups, the MDD group showed stronger interconnections for central anxiety symptoms. Targeting central anxiety symptoms can enhance prevention and intervention strategies for mood disorders and improve clinical outcomes. Full article

Major Depressive Disorder (MDD) poses a significant global health burden, characterized by a complex and heterogeneous pathophysiology insufficiently targeted by conventional single-treatment approaches. This review presents an integrative framework incorporating network pharmacology, artificial intelligence (AI), and multi-omics technologies to advance a systems-level understanding and management of MDD. Its central contribution lies in moving beyond reductionist methods by embracing a holistic perspective that accounts for dynamic interactions within biological networks. The primary objective is to demonstrate how AI-powered integration of multi-omics data—spanning genomics, proteomics, and metabolomics—can enable the construction of predictive network models. These models are designed to uncover fundamental disease mechanisms, identify clinically relevant biotypes, and reveal novel therapeutic targets tailored to specific pathological contexts. Methodologically, the review examines the microbiota–gut–brain (MGB) axis as an illustrative case study, detailing its pathogenic roles through neuroimmune alterations, metabolic dysfunction, and disrupted neuro-plasticity. Furthermore, we propose a translational roadmap that includes AI-assisted biomarker discovery, computational drug repurposing, and patient-specific “digital twin” models to advance precision psychiatry. Our analysis confirms that this integrated framework offers a coherent route toward mechanism-based personalized therapies and helps bridge the gap between computational biology and clinical practice. Nevertheless, important challenges remain, particularly pertaining to data heterogeneity, model interpretability, and clinical implementation. In conclusion, we stress that future success will require integrating prospective longitudinal multi-omics cohorts, high-resolution digital phenotyping, and ethically aligned, explainable AI (XAI) systems. These concerted efforts are essential to realize the full potential of precision psychiatry for MDD. Full article

Background: Loneliness (LON) is a heritable psychosocial trait that frequently co-occurs with major depressive disorder (MDD) and sleep traits. Despite known genetic contributions, the shared genetic architecture and molecular mechanisms underlying their co-occurrence remain largely unknown. This study aimed to uncover novel genetic risk loci and cross-trait gene expression effects. Methods: Large-scale genome-wide association study (GWAS) datasets were analyzed using the causal mixture model (MiXeR) to estimate polygenicity and shared genetic architecture. Genetic correlation analyses were performed using linkage disequilibrium score regression (LDSC) and local analysis of [co]variant annotation (LAVA). Conditional and conjunctional FDR methods further identified single nucleotide polymorphisms (SNPs). FUMA was used for gene mapping and annotation, and transcriptome-wide association studies (TWAS) assessed cross-trait gene expression effects. Results: Analyses revealed extensive polygenic overlap between LON, MDD, and sleep-related traits, with concordant and discordant effects. Several novel loci were identified, and cross-trait gene expression effects were observed in multiple brain-expressed genes, including WNT3, ARHGAP27, PLEKHM1, and FOXP2. These findings provide insight into the shared genetic architecture and relevance of these traits. Conclusions: This study demonstrates a significant shared polygenic architecture among LON, MDD, and sleep traits, providing new biological insights. It advances our understanding of cross-trait genetic mechanisms and identifies potential targets for future research, offering broader implications for trait co-occurrence. Full article

Background: Major Depressive Disorder (MDD) is a leading global health concern, yet its pharmacological management is hampered by a «trial-and-error» approach, with a significant proportion of patients failing to achieve remission with initial therapy. This challenge stems from the disorder’s marked biological heterogeneity, which is poorly captured by current broad diagnostic categories. This literature review synthesizes the latest evidence across three complementary fields poised to revolutionize MDD treatment: pharmacogenetics testing (PGT), therapeutic drug monitoring (TDM), and artificial intelligence (AI). We hypothesize that integrating all three facilitates the transition from empirical prescribing to model-informed precision dosing (MIPD), enabling prediction of optimal antidepressant selection and dosage before the first dose is administered. The convergence of these technologies, supported by an interdisciplinary framework, has the potential to enhance current treatment strategies and contribute to more individualized psychiatric care. Conclusions: Antidepressant therapy for MDD may be further optimized through the combined use of TDM, PGT, and digital tools. However, the development of this field requires ongoing research and interdisciplinary work. Full article