Major Depressive Disorder Patients Research Articles

A Retrospective Study of Pattern of Abnormal Findings in Routine Screening Investigations Done of Psychiatry Inpatients in Tertiary Care Teaching Hospital

Abstract Introduction: Physical comorbidity in persons with mental illness is a rule rather than exception. Hence, regular screening investigations in psychiatry inpatients help to detect physical illness at early stage. Aims and Objectives: The aim of this study was to evaluate the results of various screening investigations, referral patterns, and physical comorbidities in psychiatric inpatient records. Methodology: This was a descriptive, analytical, record-based study conducted in the psychiatry department of a tertiary care teaching hospital, wherein case notes of all inpatients admitted to the psychiatry unit during 6 months (January–June 2023) were studied. Inpatient records were evaluated for sociodemographic details, indication for admission, investigations as needed, any physical comorbidity, treatment history, referral patterns, and their outcomes. Records with incomplete details and known physical illnesses mentioned were excluded. Results: Total 100 patients’ records (63 males; mean age = 35 years, 37 females; mean age = 41 years) were included in the study. The common diagnosis was mood disorder 36% (n = 36), followed by substance use disorder 30% (n = 30) and schizophrenia 22% (n = 22). On evaluating investigations, abnormalities found were anemia, diabetes mellitus, hypertension, and thyroid disease (42% [n = 42/100], 24% [n = 24/100], 22% [n = 22/100], and 22% [n = 22/100], respectively). Magnetic resonance imaging (MRI) brain changes were seen in 75% of patients with acute behavior disturbances (n = 4) and MRI spine changes in all major depressive disorder patients presented with backache (n = 4). Ultrasonography abdomen findings were abnormal in 68% (n = 26/38), of which 80% (n = 21/26) had substance use disorder. For the management of abnormal findings, the referral consultation rate was 76% (n = 76/100); 86% (n = 66/76) of patients required more than one consultation with total 154 referrals. Conclusions: The investigations as indicated help in early detection and management of physical illness which mostly remains out of sight due to psychiatry illness.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

A randomized proof-of-mechanism trial of TNF antagonism for motivational deficits and related corticostriatal circuitry in depressed patients with high inflammation.

Chronic, low-grade inflammation has been associated with motivational deficits in patients with major depression (MD). In turn, impaired motivation has been linked to poor quality of life across psychiatric disorders. We thus determined effects of the anti-inflammatory drug infliximab-a potent tumor necrosis factor (TNF) antagonist-on behavioral and neural measures of motivation in 42 medically stable, unmedicated MD patients with a C-reactive protein >3 mg/L. All patients underwent a double-blind, placebo-controlled, single-dose, randomized clinical trial with infliximab (5 mg/kg) versus placebo. Behavioral performance on an effort-based decision-making task, self-report questionnaires, and neural responses during event-related functional magnetic resonance imaging were assessed at baseline and 2 weeks following infusion. We found that relative to placebo, patients receiving infliximab were more willing to expend effort for rewards. Moreover, increase in effortful choices was associated with reduced TNF signaling as indexed by decreased soluble TNF receptor type 2 (sTNFR2). Changes in effort-based decision-making and sTNFR2 were also associated with changes in task-related activity in a network of brain areas, including dorsomedial prefrontal cortex (dmPFC), ventral striatum, and putamen, as well as the functional connectivity between these regions. Changes in sTNFR2 also mediated the relationships between drug condition and behavioral and neuroimaging measures. Finally, changes in self-reported anhedonia symptoms and effort-discounting behavior were associated with greater responses of an independently validated whole-brain predictive model (aka "neural signature") sensitive to monetary rewards. Taken together, these data support the use of anti-inflammatory treatment to improve effort-based decision-making and associated brain circuitry in depressed patients with high inflammation.

Disrupted brain functional asymmetry at rest in patients with major depressive disorder associated with sleep disturbances.

Sleep disturbances (SD) are common in major depressive disorder (MDD) patients. Brain functional asymmetry is crucial for understanding MDD pathophysiology. Previous studies using the parameter of asymmetry (PAS) approach have found brain functional asymmetry disruption in MDD. However, this has not been explored in MDD patients with SD. This study examined 26 MDD patients with SD, 34 MDD patients without SD, and 34 healthy controls using resting-state functional magnetic resonance imaging scans. SD symptoms were quantified using the 17-item Hamilton Rating Scale for Depression. PAS approach was used to evaluate functional asymmetry. MDD patients with SD displayed increased PAS in the left middle frontal gyrus (MFG)/inferior frontal gyrus (IFG) and decreased PAS in the left parahippocampal gyrus (PHG) compared to MDD patients without SD. Increased PAS in the left MFG/IFG was positively correlated with SD severity, and a negative correlation was found between decreased PAS in the left PHG and SD scores in all MDD patients. Receiver operating characteristic analysis indicated that increased PAS in the left MFG/IFG and decreased PAS in the left PHG may serve as potential neuroimaging markers to differentiate MDD patients with SD from those without SD with Area Under Curve values of 0.8157 and 0.8068, respectively. These results highlighted that increased PAS in the left MFG/IFG and decreased PAS in the left PHG may be considered a prominent feature associated with SD symptoms of MDD patients, potentially serving as imaging markers to discriminate between MDD patients with and without SD.

Study of Life Events and Quality of Life in Patients of Major Depressive Disorder

Abstract Background: Depression is one of the most common mental illnesses found across all ages, genders and socioeconomic status. It has significant health, psychosocial, and economic impact. It is worse than many chronic medical diseases and has a negative effect on quality of life which persists even after remission. Objectives: The aim of this research was to study the relationship of major depressive disorder with quality of life and stressful life events in adults. Materials and Methods: This was a cross-sectional study wherein purposive sampling method was employed. Patients from psychiatry outpatient department who fulfilled the criteria for major depressive disorder and belonging to the age range of 18–75 years were shortlisted and requested to participate in the study. After taking informed consent, data on quality of life, enjoyment and satisfaction, stressful life events and severity of depression were gathered using questionnaires and a semi structured interview. Results: Severity of depression was found to be negatively correlated with quality of life, enjoyment and satisfaction P = 0.001 level of significance. There were no significant correlations between severity of depression and stressful life events. Pearson’s Chi-square indicated that the severity of depression has a significant association with highest educational qualification, occupation, marital status, and socioeconomic status. Conclusion: Measuring and keeping track of quality of life through self-report levels is important in improving depressive disorders and should be integral part of management. Other steps comprise of starting early treatment, ensuring treatment adherence, reducing severity, and reducing relapse. Psychosocial interventions should also be taken into consideration.

In vivo evidence of increased vascular endothelial growth factor in patients with major depressive disorder

BackgroundVascular endothelial growth factor (VEGF) is a candidate mediator of blood-brain barrier (BBB) disruption in depression. However, previous studies have mainly focused on peripheral blood VEGF levels, and the results are heterogeneous. Here we use astrocyte-derived extracellular vesicles (ADEVs) isolated from plasma to explore the in vivo changes of VEGF levels in patients with major depressive disorder (MDD). MethodsThirty-five unmedicated patients with MDD and 35 healthy controls (HCs) were enrolled, and plasma ADEVs were isolated from each participant. VEGF levels in ADEVs and glial fibrillary acidic protein (GFAP) in plasma were measured. Additionally, Alix and CD81, two established extracellular vesicle markers, were quantified in ADEVs. ResultsAt baseline, MDD patients exhibited significantly increased levels of VEGF in ADEVs and GFAP in plasma. Following four weeks of selective serotonin reuptake inhibitor treatment, these target protein levels did not significantly change. ROC curve analysis revealed an AUC of 0.711 for VEGF in ADEVs. In exploratory analysis, VEGF levels in ADEVs were positively correlated with Alix and CD81. LimitationsMultiple factors regulate BBB permeability. This study focused solely on VEGF and the sample size for longitudinal analysis was relatively small. ConclusionOur study is the first to confirm increased ADEV-derived VEGF levels in patients with MDD, thereby providing preliminary evidence supporting the hypothesis that the BBB is disrupted in depression.

Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning

Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dynamic and time-varying. The lack of investigation into the dynamic regional homogeneity has hindered the discovery of biomarkers for depression. This study aimed to assess the utility of the dynamic regional homogeneity by a machine learning model (support vector machine). Sixty-five individuals with dynamic regional homogeneity and 57 healthy controls participated in resting-state functional magnetic resonance rescanning and scale estimating. The dynamic regional homogeneity and receiver operating characteristic curve methods were used for analysis of the imaging data. Relative to healthy controls, major depressive disorder patients displayed increased dynamic regional homogeneity values in the left precuneus and right postcentral gyrus. Additionally, receiver operating characteristic curve results of the dynamic regional homogeneity values in the left precuneus and right postcentral gyrus could distinguish major depressive disorder patients from healthy controls; furthermore, changes in the dynamic regional homogeneity were correlated with depression severity.

Probiotic Lactobacillus plantarum 299v supplementation in patients with major depression in a double-blind, randomized, placebo-controlled trial: A metabolomics study

BackgroundUnderstanding the multifactorial nature of major depressive disorder (MDD) is crucial for tailoring treatments. However, the complex interplay of various factors underlying the development and progression of MDD poses significant challenges. Our previous study demonstrated improvements in cognitive functions in MDD patients undergoing treatment with selective serotonin reuptake inhibitors (SSRIs) supplemented with Lactobacillus plantarum 299v (LP299v). MethodsTo elucidate the biochemical mechanisms underlying cognitive functions improvements, we explored underlying metabolic changes. We employed multi-platform metabolomics, including LC-QTOF-MS and CE-TOF-MS profiling, alongside chiral LC-QqQ-MS analysis for amino acids. ResultsSupplementation of SSRI treatment with LP299v intensified the reduction of long-chain acylcarnitines, potentially indicating improved mitochondrial function. LP299v supplementation reduced N-acyl taurines more than four times compared to the placebo, suggesting a substantial impact on restoring biochemical balance. The LP299v-supplemented group showed increased levels of oxidized glycerophosphocholine (oxPC). Additionally, LP299v supplementation led to higher levels of sphingomyelins, L-histidine, D-valine, and p-cresol. LimitationsThis exploratory study suggests potential metabolic pathways influenced by LP299v supplementation. However, the need for further research hinders the ability to draw definitive conclusions. ConclusionsObserved metabolic changes were linked to mitochondrial dysfunction, inflammation, oxidative stress, and gut microbiota disruption. Despite the subtle nature of this alterations, our research successfully detected these differences and connected them to the metabolic disruptions associated with MDD. Our findings emphasise the intricate relationship between metabolism, gut microbiota, and mental health prompting further research into the mechanisms of action of probiotics in MDD treatment.

Shared and distinct patterns of default mode network dysfunction in major depressive disorder and bipolar disorder: A comparative meta-analysis

BackgroundWhile patients with major depressive disorder (MDD) and bipolar disorder (BD) exhibited default mode network (DMN) dysfunction revealed by aberrant resting-state functional connectivity (rsFC) patterns, previous findings have been inconsistent. Little is known about the similarities and differences in DMN rsFC between MDD and BD. MethodsA voxel-wise meta-analysis of seed-based DMN rsFC studies on MDD or BD was performed using the Seed-based d Mapping software with permutation of subject images (SDM-PSI). Aberrant DMN rsFC in both disorders was investigated separately, followed by conjunction and between-disorder comparison analyses. Functional decoding was performed to implicate the psychophysiological underpinnings of derived brain abnormalities. ResultsThirty-four studies comparing 1316 MDD patients with 1327 HC, and 22 studies comparing 1059 BD patients with 1396 HC were included. Compared to HC, MDD patients exhibited DMN hyperconnectivity with frontolimbic systems, and hypoconnectivity with temporal lobe and posterior cingulate cortex. BD patients displayed increased DMN connectivity with bilateral precuneus, and reduced connectivity with prefrontal cortex and middle temporal gyrus. No common patterns of DMN rsFC abnormalities were observed between MDD and BD. Compared to BD, MDD patients showed DMN hyperconnectivity with triangular part of the left inferior frontal gyrus and left fusiform gyrus. Functional decoding found that patterns of DMN rsFC alteration between MDD and BD were primarily related to action and perception domains. ConclusionDistinct DMN dysfunction patterns in MDD and BD enhance current understanding of the neural substrates of mood disorders and may provide a potential biomarker for differentiation.

Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. MethodBaseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. ResultThere exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. ConclusionThis study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.

The effect of repetitive transcranial magnetic stimulation on the Hamilton Depression Rating Scale-17 criterion in patients with major depressive disorder without psychotic features: a systematic review and meta-analysis of intervention studies

AimIn line with the publication of clinical information related to the therapeutic process of repetitive transcranial magnetic stimulation (rTMS) and the updating of relevant treatment guidelines, the present meta-analysis study was designed and conducted to determine the effect of repetitive transcranial magnetic stimulation (rTMS) on the Hamilton Depression Rating Scale-17 (HDRS-17) criterion in patients with major depressive disorder (MDD) without psychotic features.MethodsIn this study, a systematic search was conducted in electronic databases such as PubMed [Medline], Scopus, Web of Science, Embase, Ovid, Cochrane Library, and ClinicalTrials. gov using relevant keywords. The search period in this study was from January 2000 to January 2022, which was updated until May 2023. Randomized controlled trials (RCTs) that determined the effect of repetitive transcranial magnetic stimulation (rTMS) on the Hamilton Depression Rating Scale-17 (HDRS-17) criterion in patients with major depressive disorder (MDD) without psychotic features were included in the analysis. The quality of the included RCTs was assessed using the Cochrane Risk of Bias checklist. Statistical analyses were performed using STATA (Version 16) and RevMan (Version 5).ResultsFollowing the combination of results from 16 clinical trial studies in the present meta-analysis, it was found that the mean Hamilton Depression Rating Scale-17 (HDRS-17) in patients with major depressive disorder (MDD) decreases by an average of 1.46 units (SMD: -1.46; % 95 CI: -1.65, -1.27, I square: 45.74%; P heterogeneity: 0.56). Subgroup analysis results indicated that the standardized mean difference of Hamilton Depression Rating Scale-17 (HDRS-17) varied based on the number of treatment sessions: patients receiving 10 or fewer repetitive transcranial magnetic stimulation (rTMS) sessions showed a mean Hamilton Depression Rating Scale-17 (HDRS-17) reduction of 2.60 units (SMD: -2.60; % 95 CI: -2.86, -2.33, I square: 55.12%; P heterogeneity: 0.55), while those receiving 11 to 20 sessions showed a mean Hamilton Depression Rating Scale-17 (HDRS-17) reduction of 0.28 units (SMD: -0.28; % 95 CI: -0.65, -0.09, I square: 39.91%; P heterogeneity: 0.89).ConclusionIn conclusion, our meta-analysis demonstrates the efficacy of repetitive transcranial magnetic stimulation (rTMS) in reducing depressive symptoms in major depressive disorder (MDD) patients. The complex results of subgroup analysis revealed insight on the possible benefits of a more focused strategy with fewer sessions, as well as the impact of treatment session frequency. These findings add to our understanding of repetitive transcranial magnetic stimulation (rTMS) as a therapeutic intervention for the treatment of major depressive illnesses.

Intranasal Delivery of circATF7IP siRNA via Lipid Nanoparticles Alleviates LPS-induced Depressive-Like Behaviors.

Major depressive disorder (MDD) is a prevalent mental disorder that significantly impacts social and psychological function, but no effective medication is currently available. Circular RNAs (circRNAs) have been reported to participate in the pathogenesis of MDD which are envisioned as promising therapeutic targets. However, nonviral-based delivery strategies targeting circRNA against MDD are not thoroughly investigated. Here, it is identified that circATF7IP is significantly upregulated in plasma samples and positively correlated with 24-Hamilton Depression Scale (HAMD-24) scores of MDD patients. Synergistic amine lipid nanoparticles (SALNPs) are designed to deliver siRNA targeting circATF7IP (si-circATF7IP) into the hippocampus brain region by intranasal administration. Intranasal delivery of SALNP-si-circATF7IP successfully alleviated the depressive-like behaviors in the LPS-induced mouse depression model via decreasing CD11b+CD45dim microglia population and pro-inflammatory cytokine productions (TNF-α and IL-6). These results indicate that the level of circATF7IP positively correlates with MDD pathogenesis, and SALNP delivery of si-circATF7IP via intranasal administration is an effective strategy to ameliorate LPS-induced depressive-like behaviors.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Transdiagnostic depression severity and its relationship to global and prefrontal-amygdala structural properties in people with major depression and post-traumatic stress disorder.

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.

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors.

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants inthe Limited class exhibited an early age of onset and experienced a higher level of emotional abuse.MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BackgroundThe modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated. MethodWe used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands. ResultsThe combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis. ConclusionOur results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

Relationship between social determinants of health and hospitalizations and costs in patients with major depressive disorder.

The relationship of patient characteristics and social determinants of health (SDOH) with hospitalizations and costs in patients with major depressive disorder (MDD) has not been assessed using real-world data. To identify factors associated with higher hospitalizations and costs in patients with MDD. A retrospective observational study identified patients aged 18 years and older newly diagnosed with MDD between July 1, 2016, and December 31, 2018. SDOH were linked to patients at the "near-neighborhood" level. Multivariable models assessed association of patient characteristics with hospitalizations (incidence rate ratios [95% CI]) and costs (cost ratios [95% CI]). Of 1,958,532 patients with MDD, 49.6% had Commercial and 50.4% Medicaid insurance; mean ages were similar (43.9; 43.4) with more female patients (67.6%; 70.5%). MDD patients with Commercial insurance had a mean household income of $75,044; 53.2% were married; 76.5% owned their home; 64.4% completed high school or less; and 2.8% had limited English-language proficiency (LEP). Patients covered by Medicaid had a household income of $46,708; 68.1% lived alone with 41.6% married; 54.6% owned their home; more than 4-in-5 patients (80.8%) completed high school or less, and 6.3% had LEP. Nearly one-third of Medicaid insured patients with MDD had at least 1 hospitalization (29.6%) with a mean length of stay 6.8 days; total health care costs were $21,467 annually. Commercially insured patients with MDD had 14.7% hospitalization rates with a length of stay of 5.9 days; total costs were $14,531. Multivariable models show female patients are less likely (Commercial 0.87; Medicaid 0.80; P < 0.05), and patients with more comorbidities are more likely to be hospitalized (Commercial 1.33; Medicaid 1.27; P < 0.05). All treatment classes relative to antidepressants only increased likelihood of hospitalizations-particularly antipsychotic+antianxiety use (Commercial 2.99; Medicaid 2.29)-and costs (Commercial 2.32; Medicaid 2.00) (all P < 0.05). Household income was inversely associated with hospitalizations for both insured populations. LEP reduced the likelihood of hospitalizations by more than 70% among Medicaid patients (0.27, P < 0.05) and was associated with higher costs for Commercial (2.01) but lower costs for Medicaid (0.37) (P < 0.05). Living in areas with no shortage of mental health practitioners was associated with higher hospitalizations and costs. We identified patient characteristics associated with higher rates of hospitalizations and costs in patients with MDD in 2 insured populations. Female sex, higher comorbidities, and living in areas with no shortage of mental health practitioners were associated with higher hospitalizations and costs, whereas income was inversely associated with hospitalizations. The findings suggest disparities in access to care related to income, LEP, and availability of mental health practitioners that should be addressed to assure equitable care for patients with MDD.

Acute treatment residual depression symptoms and functional impairment among depressive patients of different age groups and education levels in China: A prospective, multicenter, randomized study.

A prospective, multicenter, randomized study evaluated the efficacy of major depressive disorder (MDD) patients after 2-3 months of acute treatment based on the dual factors of education and age. This study classified the included patients into four groups using two classification parameters: age (≤45 years, vs.>45 years) and education years (≤12vs.>12). We analyzed age, gender, marital status, personal income, depression onset history, medication use, and follow-up across various groups. We evaluated residual somatic symptoms and social functioning in depression patients was conducted using the 16-item Quick Inventory of Depressive Symptomatology Self-report (QIDS-SR16), the Patient Health Questionnaire-15 (PHQ15), and the Sheehan Disability Scale (SDS). In China, 16 hospitals, 553 depression patients, and 428 fulfilled the inclusion criteria. Baseline patient data revealed significant differences among the different age groups in gender, marital status, income, first onset age, physical illness, combination of antipsychotics, and benzodiazepines use (all p<.05). Statistically significant differences were observed in overall comparisons among the four groups, encompassing the QIDS-SR16 score, PHQ15 score, and various SDS parameters (all p<.05). However, no statistically significant differences (all p>.05) were found in residual somatic symptoms and social functioning parameters between different education levels (≤12 years vs.>12 years) at baseline, 3 months, and 6 months, based on total scores on the scale. Repeated measures mixed model indicates that the QIDS-SR16 assessment indicates statistical differences among various marital statuses, income levels, medical histories, and antipsychotic medication use (p<.05). Furthermore, PHQ-15 and SDS assessments reveal statistical differences between single and married/cohabiting statuses, physical comorbidities, 3 and 6 months follow-ups compared to baseline (p<.05). This study indicates that compared to depressive patients>45 years old, those ≤45 years old often exhibit more residual depression, somatic symptoms, and severe social functional impairment; patients' education levels less influence this trend.

Herpes simplex reactivation triggers symptom exacerbation in patients with major depressive disorder

BackgroundHerpes Simplex Virus (HSV) infection has been linked to depression, but the underlying mechanism remains largely unknown. We hypothesized that recurring HSV infection could exacerbate depressive symptomatology via activation of the immune response and the kynurenine pathway in patients with Major Depressive Disorder (MDD). Methods32 MDD patients and 17 psychiatrically healthy individuals with a history of HSV recurrence were enrolled into the study. Blood samples were obtained, and psychiatric symptom severity was assessed at three separate visits- asymptomatic, active infection, and follow-up. Plasma levels of tryptophan metabolite levels were measured using ultra-high performance liquid chromatography-mass spectrometry (LC-MS), and inflammation-associated proteins were measured using high sensitivity electrochemiluminescence. ResultsThere was a significant effect of infection recurrence on IFN-γ and sICAM-1 levels, the latter being influenced by MDD diagnosis. This indicates that the effect of infection on sICAM-1 levels differs between control and depressed groups, although post hoc analysis suggests that interaction effect might be subtle. Infection recurrence had a significant effect on the reactivity of psychiatric symptoms. Finally, there was a positive correlation between levels of anti-inflammatory cytokines IL-10 and IL-13 and depression severity in the cohort during infection recurrence. LimitationsRelatively small sample size, limited number of male participants, and the relatively weak inflammatory stimuli studied. ConclusionsMDD patients might react with an exacerbation of symptoms along with elevations of inflammatory markers during HSV recurrence. Further, the association with symptom severity indicates that therapeutic modulation of inflammation may be beneficial in patients with MDD, especially during active infections.

Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla.

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case-control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.