Abnormal energy metabolism, oxidative stress, and polyunsaturated fatty acid metabolism in depressed adolescents associated with childhood maltreatment: A targeted metabolite analysis

The purpose of this study was to explore the metabolomic differences between Major depressive disorder (MDD) and healthy individuals among adolescents and the association between childhood maltreatment (CM) and differentially abundant metabolites. The exploratory study included 40 first-episode drug-naïve adolescents with MDD and 20 healthy volunteers. We used the Beck Depression Inventory (BDI-13) to assess the severity of depression and the Childhood Trauma Questionnaire (CTQ) to assess the presence of childhood maltreatment. The plasma samples from all participants were collected for targeted metabolomics analysis using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC‒MS/MS) methods. Spearman correlation was applied to analyse the correlations between clinical variables and metabolites. We found 11 increased metabolites and 37 decreased metabolites that differed between adolescents with MDD and healthy individuals. Pathway enrichment analysis of differentially abundant metabolites showed abnormalities in energy metabolism and oxidative stress in MDD. Importantly, we found that creatine, valine, isoleucine, glutamic acid and pyroglutamic acid were negatively correlated with the BDI-13, while isocitric acid, fatty acid and acylcarnitine were negatively associated with CTQ, and 4-hydroxyproline was positively related to CTQ in adolescents with MDD. These studies provide new ideas for the pathogenesis and potential treatment of adolescents with MDD.

Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis

Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.

Association Between Taurine Level in the Hippocampus and Major Depressive Disorder in Young Women: A Proton Magnetic Resonance Spectroscopy Study at 7T

BACKGROUND

Major depressive disorder (MDD) is characterized by depressed mood or loss of interest or pleasure. Generally, women are twice as likely as men to have depression. Taurine, a type of amino acid, plays critical roles in neuronal generation, differentiation, arborization, and formation of synaptic connections. Importantly, it enhances proliferation and synaptogenesis in the hippocampus. When injected into animals, taurine has an antidepressant effect. However, there is no in vivo evidence to show an association between taurine concentration in the human brain and the development of MDD.

METHODS

Forty-one unmedicated young women with MDD (ages 18-29) and 43 healthy control participants matched for gender and age were recruited in South Korea. Taurine concentration was measured in the hippocampus, anterior cingulate cortex, and occipital cortex of the MDD and healthy control groups using proton magnetic resonance spectroscopy at 7T. Analysis of covariance was used to examine differences in taurine concentration, adjusting for age as a covariate.

RESULTS

Taurine concentration in the hippocampus was lower (F1,75 = 5.729, p = .019, Δη2 = 0.073) for the MDD group (mean [SEM] = 0.91 [0.06] mM) than for the healthy control group (1.13 [0.06] mM). There was no significant difference in taurine concentration in the anterior cingulate cortex or occipital cortex between the two groups.

CONCLUSIONS

This study demonstrates that a lower level of taurine concentration in the hippocampus may be a novel characteristic of MDD.

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.

Bilateral rTMS Shows No Advantage in Depression nor in Comorbid Depression and Anxiety: A Naturalistic Study

The objective was to determine if adding low-frequency right-sided rTMS treatment to the standard high-frequency left-sided treatment (LUL), referred to as sequential bilateral treatment (SBT), confers additional benefit for depression or anxiety outcomes. A retrospective chart review from January 2015 through December 2018 yielded 275 patients, all of whom were treated with a figure-8 coil for a major depressive episode. Their protocol was either LUL or SBL. Outcome measures were the Generalized Anxiety Disorder 7-item scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). There was no significant difference in GAD-7 change scores between patients who had LUL or SBL (4.2 vs 4.8). This was also true when the sample was restricted to only patients who started with high GAD-7 scores. There was likewise no significant difference in PHQ-9 change scores between patients who had LUL or SBL (6.8 vs 5.1). Patients switching from LUL to SBL mid-course had poorer overall outcomes as compared to patients who stayed with the same protocol throughout treatment. This large naturalistic study shows no advantage for SBL treatment any group or condition examined. The results of this study have clinical applicability and sound a cautionary note regarding the use of combination rTMS protocols.

Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study

As human beings, we have always sought to expand on our abilities, including our cognitive and motor skills. One of the still-underrated tools employed to this end is repetitive transcranial magnetic stimulation (rTMS). Until recently, rTMS was almost exclusively used in studies with rehabilitation purposes. Only a small strand of literature has focused on the application of rTMS on healthy people with the aim of enhancing cognitive abilities such as decision-making, working memory, attention, source memory, cognitive control, learning, computational speed, risk-taking, and impulsive behaviors. It, therefore, seems that the findings in this particular field are the indirect results of rehabilitation research. In this review paper, we have set to investigate such studies and evaluate the rTMS effectuality in terms of how it improves the cognitive skills in healthy subjects. Furthermore, since the most common brain site used for rTMS protocols is the dorsolateral prefrontal cortex (DLPFC), we have added theta burst stimulation (TBS) wave patterns that are similar to brain patterns to increase the effectiveness of this method. The results of this study can help people who have high-risk jobs including firefighters, surgeons, and military officers with their job performance.

Recent Advances in the Treatment of Treatment-Resistant Depression: A Narrative Review of Literature Published from 2018 to 2023

PURPOSE OF REVIEW

We review recent advances in the treatment of treatment-resistant depression (TRD), a disorder with very limited treatment options until recently. We examine advances in psychotherapeutic, psychopharmacologic, and interventional psychiatry approaches to treatment of TRD. We also highlight various definitions of TRD in recent scientific literature.

RECENT FINDINGS

Recent evidence suggests some forms of psychotherapy can be effective as adjunctive treatments for TRD, but not as monotherapies alone. Little recent evidence supports the use of adjunctive non-antidepressant pharmacotherapies such as buprenorphine and antipsychotics for the treatment of TRD; side effects and increased medication discontinuation rates may outweigh the benefits of these adjunctive pharmacotherapies. Finally, a wealth of recent evidence supports the use of interventional approaches such as electroconvulsive therapy, ketamine/esketamine, and transcranial magnetic stimulation for TRD. Recent advances in our understanding of how to treat TRD have largely expanded our knowledge of best practices in, and efficacy of, interventional psychiatric approaches. Recent research has used a variety of TRD definitions for study inclusion criteria; research on TRD should adhere to inclusion criteria based on internationally defined guidelines for more meaningfully generalizable results.

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

Structural and biochemical alterations in dendritic spines as key mechanisms for severe mental illnesses

Severe mental illnesses (SMI) collectively affect approximately 20% of the global population, as estimated by the World Health Organization (WHO). Despite having diverse etiologies, clinical symptoms, and pharmacotherapies, these diseases share a common pathophysiological characteristic: the misconnection of brain areas involved in reality perception, executive control, and cognition, including the corticolimbic system. Dendritic spines play a crucial role in excitatory neurotransmission within the central nervous system. These small structures exhibit remarkable plasticity, regulated by factors such as neurotransmitter tone, neurotrophic factors, and innate immunity-related molecules, and other mechanisms - all of which are associated with the pathophysiology of SMI. However, studying dendritic spine mechanisms in both healthy and pathological conditions in patients is fraught with technical limitations. This is where animal models related to these diseases become indispensable. They have played a pivotal role in elucidating the significance of dendritic spines in SMI. In this review, the information regarding the potential role of dendritic spines in SMI was summarized, drawing from clinical and animal model reports. Also, the implications of targeting dendritic spine-related molecules for SMI treatment were explored. Specifically, our focus is on major depressive disorder and the neurodevelopmental disorders schizophrenia and autism spectrum disorder. Abundant clinical and basic research has studied the functional and structural plasticity of dendritic spines in these diseases, along with potential pharmacological targets that modulate the dynamics of these structures. These targets may be associated with the clinical efficacy of the pharmacotherapy.

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND

Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate to visit psychiatrists especially during early phase. We hypothesize that application of humanoid robots would be a novel solution. Depressed patients may feel more comfortable talking with such robots than humans.

METHODS

We recruited 13 patients with major depressive disorder (MDD) and 27 healthy volunteers as controls. Participants took both tele-operated humanoid robot and human interviews to evaluate severity of depression using the Hamilton Depression Rating Scale (HDRS). In addition, participants completed a self-administered questionnaire asking about their impressions of the robot interview.

RESULTS

Confidence interval and t-test analysis have revealed that the HDRS scores are equally reliable between robot and human interviews. No significant differences were observed between the two interviews regarding "nervousness about the interview" and "hesitancy to talk about depressed moods and suicidal ideation." Compared to human interviews, robot interviews yielded significantly lower scores on shame-related factors especially among patients with MDD.

LIMITATION

Small sample size, and the evaluator is male only.

CONCLUSIONS

This is the first report to show the reliability of tele-operated humanoid robot interviews for assessment of depression. Robot interviews are potentially equally reliable as human interviews. Robot interviews are suggested to be more appropriate in assessing shame-related suppressed emotions and hidden thoughts of depressed patients in clinical practice, which may reduce the stigma associated with depression.

Converged avenues: depression and Alzheimer's disease- shared pathophysiology and novel therapeutics

Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Prevalence and risk factors for subclinical hypothyroidism in older patients with major depressive disorder

BACKGROUND

Subclinical hypothyroidism (SCH) is highly correlated with major depressive disorder (MDD). However, the prevalence and risk factors for SCH in older patients with MDD have rarely been reported in China.

METHODS

This cross-sectional study included 266 older MDD patients with SCH was performed. Clinical and anthropometric, biochemical, and thyroid function data were collected. Depression, anxiety, and psychotic symptoms were assessed using the Hamilton Depression Scale, the Hamilton Anxiety Scale, and the Positive and Negative Syndrome Scale positive subscale, respectively.

RESULTS

Among older patients with MDD, the prevalence of SCH was 64.7% (172/266). Compared to patients without SCH, older MDD patients with SCH had a longer disease course and higher TSH, A-TG, A-TPO, HDL-C, LDL-C, TC, FPG, and systolic pressure levels (all P ≤ 0.002). Furthermore, disease progression (OR 1.082, 95% CI 1.020-1.147, P = 0.009), A-TG (OR 1.005, 95% CI 1.001-1.009, P = 0.017), TC (OR 2.024, 95% CI 1.213-3.377, P = 0.007), FPG (OR 2.916, 95% CI 1.637-5.194, P < 0.001), systolic pressure (OR 1.053, 95% CI 1.008-1.100, P = 0.022) were independently associated with SCH, in older patients with MDD.

CONCLUSIONS

Our findings suggest a high prevalence of SCH in older patients with MDD. Several demographic and clinical variables were independently associated with SCH in older patients with MDD.

Adjunctive duration-doubled transcranial direct current stimulation for the treatment of depressive patients with suicidal ideation: study protocol for a double-blind, randomized, sham-controlled trial

BACKGROUND

The problem of suicide has become increasingly common in individuals with major depressive disorder (MDD). Transcranial direct current stimulation (tDCS) is an effective treatment for MDD with 2 milliamperes (mA) for at least 30 min per day for 2 weeks. This study aims to investigate the efficacy of daily duration-doubled tDCS as an adjunctive intervention for rapidly reducing suicidal ideation and improving depression in MDD patients.

METHODS

In this double-blind, randomized, sham-controlled study, 76 MDD patients with suicidal ideation are randomly assigned to either active (n=38) or sham (n=38) tDCS group. The anode and cathode are placed over the scalp areas corresponding to left and right dorsolateral prefrontal cortex (DLPFC), respectively, and each stimulation lasts for 60 min. The primary outcome is defined as change of Beck Scale for Suicide Ideation (BSI) after 5 and 10 sessions. The change of other clinical assessments, blood biomarkers related to suicidal ideation and depressive sumptoms are defined as secondary outcomes. Blood biomarkers related to suicidal ideation are collected at baseline and after 10 sessions.

DISCUSSION

This study suggests the adjunctive duration-doubled tDCS might be a novel method to rapidly reduce suicidal ideation and improve depressive symptom. The variation of biomarkers could be potential predictive models of suicide risk.

TRIAL REGISTRATION

The trial protocol is registered with ClinicalTrials.gov under protocol registration number NCT05555927. Registered on September 25, 2022.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

Whole blood mitochondrial copy number in clinical populations with mood disorders: A meta-analysis: Blood mitochondrial copy number and mood disorders

Major depressive disorder (MDD) and bipolar disorder (BD), are globally prevalent, contributing to significant disease burden and adverse health outcomes. These mood disorders are associated with changes in many aspects of brain reward pathways, yet cellular and molecular changes in the brain are not readily available in clinical populations. Therefore, the use of biomarkers as proxies for changes in the brain are necessary. The proliferation of mitochondria in blood has emerged as a potentially useful biomarker, yet a clear consensus on how these mood disorders impact mitochondrial DNA copy number (mtDNAcn) has not been reached. To determine the current available consensus on the relationship of mood disorder diagnosis and blood mtDNcn, we performed a meta-analysis of available literature measuring this biomarker. Following PRISMA guidelines for a systematic search, 22 papers met inclusion criteria for meta-analysis (10 MDD, 10 BD, 2 both MDD and BD). We extracted demographic, disorder, and methodological information with mtDNAcn. Using the metafor package for R, calculated effect sizes were used in random effects or meta regression models for MDD and BD. Overall, our data suggest blood mtDNAcn may be a useful biomarker for mood disorders, with MDD and BD Type II associated with higher mtDNAcn, and BD Type I associated with lower mtDNAcn. Initially, we observed a trending increase in mtDNAcn in patients with MDD, which reached significance when one study with outlying demographic characteristics was excluded. Subgroup and meta-regression analysis indicated the relationship between mtDNAcn and diagnosis in patients with BD is dependent on BD type, while no relationship is detectable when BD types are mixed. Further study of blood mtDNAcn could predict downstream health outcomes or treatment responsivity in individuals with mood disorders.

Atrophy, hypometabolism and implication regarding pathology in late-life major depression with suspected non-alzheimer pathophysiology (SNAP)

BACKGROUND

A substantial proportion of individuals with late-life major depression could be classified as having a suspected non-Alzheimer disease pathophysiology (SNAP), as indicated by a negative test for the biomarker β-amyloid (Aβ-) but a positive test for neurodegeneration (ND+). This study investigated the clinical features, characteristic patterns of brain atrophy and hypometabolism, and implications regarding pathology in this population.

METHODS

Forty-six amyloid-negative patients with late-life major depressive disorder (MDD) patients, including 23 SNAP (Aβ-/ND+) and 23 Aβ-/ND- MDD subjects, and 22 Aβ-/ND-healthy control subjects were included in this study. Voxel-wise group comparisons between the SNAP MDD, Aβ-/ND- MDD and control subjects were performed, adjusting for age, gender and level of education. For exploratory comparisons, 8 Aβ+/ND- and 4 Aβ+/ND + MDD patients were included in the Supplementary Material.

RESULTS

The SNAP MDD patients had atrophy extending to regions outside the hippocampus, predominately in the medial temporal, dorsomedial and ventromedial prefrontal cortex; hypometabolism involving a large portion of the lateral and medial prefrontal cortex in addition to the bilateral temporal, parietal and precuneus cortex within typical Alzheimer disease regions were observed. Metabolism ratios of the inferior to the medial temporal lobe were significantly elevated in the SNAP MDD patients. We further discussed the implications with regards to underlying pathologies.

CONCLUSION

The present study demonstrated characteristic patterns of atrophy and hypometabolism in patients with late-life major depression with SNAP. Identifying individuals with SNAP MDD may provide insights into currently unspecified neurodegenerative processes. Future refinement of neurodegeneration biomarkers is essential in order to identify potential pathological correlates while in vivo reliable pathological biomarkers are not forthcoming.

Association between rare earth elements and depression: Evidence from pilot mice model of chronic unpredictable mild stress-induced depression and human studies of major depressive disorder

The etiology of Major Depressive Disorder (MDD) has been associated with levels of trace elements in the human body. The source of trace elements in the human body may be rare earth elements (REEs). Our study aimed to identify the potential relationship between t REEs in blood and brain samples and depression from two paths: animal experiments and population studies. In the animal experiments, 35 adult Sprague-Dawley rats were randomly allocated to the control group (n = 14) and treatment group (n = 21), which received the chronic unpredictable mild stress (CUMS) procedure for four weeks and further categorized into the sensitive group (n = 9) and resilient group (n = 12) by sucrose water preference test. Then, all rats were executed to obtain serum and brain tissue samples. We also recruited 197 participants and divided them into the major depressive disorder (MDD) group (n = 100) and the control group (n = 97) then serum samples were collected for REEs detection. Our finding reported that significant differences were found in the levels of La and Ce in blood samples from different groups in the CUMS rat model (sensitive group < resilient group < control group) (all p < 0.05), with similar patterns for other elements (Pr, Nd, and Y) (but p > 0.5). No significant inter-group difference was reported in rat brain tissue samples. After adjusting for demographic variables, we found that the concentrations of all five REEs (La, Ce, Pr, Nd, Y) were lower in depression group than in control group (all p < 0.01). The current conjoint animal and human data supported appropriate levels of REEs have a certain protective effect on body health. These results may be attributed to Hormesis effects. Whether the possible favorable effects of REEs on improving symptoms of depression or can be applied to drug development remains to be further investigated.

Comparison of 60-minute vs 30-minute transcranial direct current stimulation (tDCS) in major depressive disorder: Effects on depression suicidal ideation and anxiety

We investigated whether changes through doubling the duration of each tDCS session would increase efficacy of tDCS for depression. tDCS was applied for 10 sessions, followed by two additional weekly sessions. 63 patients with MDD underwent randomization, with 22 being assigned to 60-min/d group, 25 to 30 min/d group, and 16 to sham group. HAMD-17 reductive ratios at week 2 and 4 were of no significant differences among treatment groups. 60 min group had a greater decrease in anxiety compared to 30 min group and sham group based on HAMA at 4 weeks but only in the completer analysis, not in ITT analysis.

Daily Affective Dynamics in Major Depressive Disorder: The Role of Daily Stressors and Positive Events

This study examined daily affective dynamic indices among individuals with a major depressive disorder (MDD) diagnosis in the past one year at the time of the interview, focusing on affective variability and change in affect in response to daily events (affective reactivity). Data were from the main survey and daily diary project of the Midlife in the United States (MIDUS) study. Participants (N = 1,970; nMDD = 202; nnon-MDD = 1,768) completed structured clinical interviews on mental health and telephone interviews about their daily experiences spanning eight consecutive days. Multilevel models revealed that the MDD group experienced greater positive (PA) and negative affect (NA) variability than the non-MDD group. On days that at least one stressful event was reported, the MDD group experienced a greater decrease in PA and a greater increase in NA. On days that at least one positive event was reported, the MDD group experienced a greater increase in PA and a greater decrease in NA. Changes in affect to daily events, particularly the mood brightening effect, may be indicators of depression and potential targets for intervention. Limitations of the study include a community sample, reliance on self-reported measures of daily stressors and positive events, inclusion of remitted and current MDD participants, and the DSM-III-R based criteria for MDD diagnosis.

Escitalopram versus other antidepressive agents for major depressive disorder: a systematic review and meta-analysis

BACKGROUND

Escitalopram is selective serotonin reuptake inhibitors (SSRIs) and one of the most commonly prescribed newer antidepressants (ADs) worldwide. We aimed to explore the efficacy, acceptability and tolerability of escitalopram in comparison with other ADs in the acute-phase treatment of major depressive disorder (MDD).

METHODS

Medline/PubMed, EMBASE, the Cochrane Library, CINAHL, and Clinical Trials.gov were searched from inception to July 10, 2023. Trial databases of drug-approving agencies were hand-searched for published, unpublished and ongoing controlled trials. All randomized controlled trials comparing escitalopram against any other antidepressant for patients with MDD. Responders and remitters to treatment were calculated on an intention-to-treat basis. For dichotomous data, risk ratios (RRs) were calculated with 95% confidence intervals (CI). Continuous data were analyzed using standardized mean differences (with 95% CI) using the random effects model.

RESULTS

A total of 30 studies were included in this meta‑analysis, among which sixteen trials compared escitalopram with another SSRI and 14 compared escitalopram with a newer AD. Escitalopram was shown to be significantly more effective than citalopram in achieving acute response (RR 0.67, 95% CI 0.50-0.87). Escitalopram was also more effective than citalopram in terms of remission (RR 0.53, 95% CI 0.30-0.93).

CONCLUSIONS

Escitalopram was superior to other ADs for the acute phase treatment of MDD in terms of efficacy, acceptability and tolerability. However, no significant difference was found between escitalopram and other ADs in early response or follow-up response to treatment of MDD.

A marginal structural model analysis for the effect modification by education on the association between cancer diagnosis history and major depressive symptoms: Findings from Midlife Development in the U.S. (MIDUS)

BACKGROUND

Limited research has employed a longitudinal approach to investigate the role of education level as an effect modifier on the relationship between cancer diagnosis history and the experience of major depressive disorder (MDD) with a nationally representative sample.

METHODS

We harnessed data from three installments of the MIDUS Longitudinal study (n = 7108). A Marginal Structural Model facilitated the investigation of associations between a history of cancer diagnosis, MDD, and potential modifying effects of education level. Inverse probability weighting helped manage confounding factors.

RESULTS

Findings indicated that a cancer diagnosis made one year prior was linked with 3.741 times greater odds of experiencing MDD (95 % CI: 1.411-9.918, p < 0.01). This connection was absent for diagnoses made two years earlier. Among individuals with education up to high school, a recent cancer diagnosis significantly increased the likelihood of MDD in the subsequent wave by 3.45 times (95 % CI: 1.31-9.08, p < 0.05). This pattern was not apparent among better-educated individuals.

LIMITATIONS

As the exposure variable was dependent on self-reported questionnaires, recall bias could be a potential limitation. Moreover, unaccounted variables like genetic factors could introduce confounding.

CONCLUSIONS

A recent cancer diagnosis, particularly among less educated individuals, correlated with an increased probability of MDD, while the impact was not observed for older diagnoses. These findings emphasize that the timing of a cancer diagnosis and education level need consideration in the mental health assessment of cancer survivors.

An association study of clock genes with major depressive disorder

OBJECTIVE

To study the relationship between clock genes and Major Depressive Disorder (MDD).

METHODS

GEO database was used to obtain the chip data and clinical information of datasets GSE98793, GSE39653 and GSE52790. The differentially expressed clock genes were found through the analysis of the differentially expressed genes between MDD and healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) enrichment analysis were performed on the differential expressed clock genes. Lasso Regression and Support Vector Machine (SVM) method were used for screening the differential expressed clock genes. Logistic regression was used to establish a diagnostic model for depression with the screened genes. Receiver Operating Characteristic (ROC) Curve was used to verify the model. Gene differential expression analysis was performed for MDD with high scores and MDD with low scores in the diagnostic model. Gene Set Enrichment Analysis (GSEA) enrichment analysis was performed for differentially expressed genes. Single-gene GSEA was used to analyze each gene in the model separately. Cibersort method was used to analyze the immune infiltration of MDD and healthy controls, and the correlation between immune cells and clock genes was analyzed. Cytoscape was used to analyze the clock gene interaction network. The DGIdb website was used to predict potentially effective therapeutic drugs for clock genes closely related to MDD.

RESULTS

Six genes were identified by differential expression analysis of clock genes between MDD and healthy controls. GO and KEGG enrichment analysis of 6 genes showed that their pathways were concentrated such as circadian rhythm, rhythmic process, TGF - beta signaling pathway, longevity regulating pathway-multiple species, adipocytokine signaling pathway and so on. Lasso regression and SVM were used to screen out 5 clock genes (HDAC1, ID3, NFIL3, PRKAA1, TNF) for MDD. The diagnostic model of depression was established according to the 5 clock genes. The area under the curve (AUC) of the established depression diagnostic model was 0.686. Gene difference analysis was performed between MDD patients with high score of clock gene diagnostic model and MDD patients with low score. GSEA was performed for the differential genes showed that the most enriched pathways were:adipocytokine signaling pathway, TGF beta signaling pathway, oxidative phosphorylation, primary immunodeficiency, and so on. The single gene GSEA showed that the most enriched pathways were Toll like receptor signaling pathway, glucolipid metabolism, amino acid metabolism, neuroactive ligand receptor interaction, and so on. The results of immune infiltration analysis showed that NK cells resting and Macrophages M2 were different between MDD and control groups. In MDD, the gene closely related to NK cells resting was HDAC1, and the genes closely related to Macrophages M2 were HDAC1 and NFIL3. The RNA interactions network of clock genes shows that the regulation process is complex, which can provide a reference for subsequent related research. Potential therapeutic drugs predict display, among the 5 clock genes, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs.

CONCLUSION

Among all CLOCK genes, HDAC1, ID3, NFIL3, PRKAA1, TNF are closely related to MDD. Among them, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs.

A multicenter, randomized controlled study on the efficacy of agomelatine in ameliorating anhedonia, reduced motivation, and circadian rhythm disruptions in patients with major depressive disorder (MDD)

OBJECTIVE

To evaluate the clinical efficacy and safety of Agomelatine in improving symptoms in patients with major depressive disorder (MDD), providing more scientific evidence for the treatment of depression, and offering more effective therapeutic options for patients.

METHODS

A total of 180 MDD patients in acute phase from 10 psychiatric hospitals of Grade three in Zhejiang Province were enrolled in this 12-week study with the competitive and consecutive pattern, and they were randomized into two different groups treated with flexible-dosage antidepressants of selective serotonin reuptake inhibitors (SSRI) or agomelatine, respectively. The subjects were evaluated with psychological scales of HAMD-17, HAMA, SHAPS for anhedonia, MFI-20 for fatigue, PQSI for sleep quality and MEQ for disturbances in chronobiologic rhythms at baseline, 2, 4, 8 and 12-weekend points, and TESS was used for side-effect. The results were analyzed with repeated measurement analysis of variance.

RESULTS

The two groups each had 90 participants, and there were no significant differences at baseline. The scores of various assessment scales showed statistically significant time main effects during the visits (P < 0.01). The Agomelatine group demonstrated faster efficacy within 2 weeks, with better improvement in SHAPS, MEQ, and PSQI compared to the SSRIs group. However, the remission rate at 12 weeks was lower in the Agomelatine group than in the SSRIs group (63.3% and 72.2%), but the difference between the groups was not statistically significant. The Agomelatine group had fewer adverse reactions (14.4% and 16.7%), but there was a slightly higher incidence of liver function impairment (6.7% and 4.4%), with no statistically significant difference between the groups.

CONCLUSION

Agomelatine, as a novel antidepressant, shows certain advantages in improving depression and anxiety symptoms and is comparable to SSRIs in terms of safety. However, its long-term efficacy and safety on MDD or other depressive subtypes still require further observation and research.

Deciphering the neural mechanisms of miR-134 in major depressive disorder with population-based and person-specific imaging transcriptomic techniques

MiR-134 has emerged as a potential molecular biomarker for the detection and management of major depressive disorder (MDD). Nevertheless, the specific effects of miR-134 as a regulatory element on brain function and its implications for the clinical presentation of MDD are not yet fully understood. In order to investigate the potential neural mechanisms that contribute to the relationship between miR-134 and MDD, we employed a parallel two-stage cross-scale multi-omics approach. This involved utilizing the anterior cingulate cortex (ACC) functional connectivity as a means to connect microscopic molecular structures with macroscopic brain function in two separate cohorts: the MDD-I dataset (56 MDD patients and 51 healthy controls) and the MDD-II dataset (57 MDD patients and 52 healthy controls). We found a stable ACC functional dysconnectivity pattern of MDD and established the hierarchical cross-scale association from molecular organizations of miR-134 target genes to macroscopic brain functional dysconnectivity and associated behavior, as revealed by population-based analysis. Additionally, our person-specific imaging transcriptomic study revealed that individual exosomal miR-134 expression levels impact on individual clinical symptoms of MDD by modulating ACC-related functional dysconnectivity. Together, our findings provide compelling evidence of the correlation between miR-134 and depression across multi scales within the gene-brain-behavior context.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

BACKGROUND

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS

Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS

After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS

Relatively small sample size and not a double-blind design.

CONCLUSIONS

The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Lateralized grey matter volume changes in adolescents versus adults with major depression: SDM-PSI meta-analysis

The current study is the first meta-analysis to examine grey matter volume (GMV) changes in adolescents and across the lifespan in major depressive disorder (MDD). Seed-based d mapping-with permutation of subject images (SDM-PSI) has advantages over previous coordinate-based meta-analytical methods (CBMA), such as reducing bias (via the MetaNSUE algorithm) and including non-statistically significant unreported effects. SDM-PSI was used to analyze 105 whole-brain GMV voxel-based morphometry (VBM) studies comparing 6,530 individuals with MDD versus 6,821 age-matched healthy controls (HC). A laterality effect was observed in which adults with MDD showed lower GMV than adult HC in left fronto-temporo-parietal structures (superior temporal gyrus, insula, Rolandic operculum, and inferior frontal gyrus). However, these abnormalities were not statistically significant for adolescent MDD versus adolescent HC. Instead, adolescent MDD showed lower GMV than adult MDD in right temporo-parietal structures (angular gyrus and middle temporal gyrus). These regional differences may be used as potential biomarkers to predict and monitor treatment outcomes as well as to choose the most effective treatments in adolescents versus adults. Finally, due to the paucity of youth, older adult, and longitudinal studies, future studies should attempt to replicate these GMV findings and examine whether they correlate with treatment response and illness severity.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis

BACKGROUND

Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM.

METHODS

313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM.

RESULTS

We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety.

CONCLUSION

Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

Study protocol: effects of treatment expectation toward repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder-a randomized controlled clinical trial

BACKGROUND

Patients' expectations toward any given treatment are highly important for the effectiveness of such treatment, as has been demonstrated for several disorders. In particular, in major depressive disorder (MDD), one of the most frequent and most serious mental disorders with severe consequences for the affected, the augmentation of available treatment options could mean a ground-breaking success. Repetitive transcranial magnetic stimulation (rTMS), a new, non-invasive, and well-tolerated intervention with proven effects in the treatment of MDD, appears particularly suitable in this context as it is assumed to exert its effect via structures implicated in networks relevant for both expectation and depression.

METHODS

All patients will receive rTMS according to its approval. Half of the patients will be randomized to a psychological intervention, which is a comprehensive medical consultation aiming to improve positive treatment expectations; the control group will receive a conventional informed consent discussion (in the sense of a treatment-as-usual condition). As outcome parameters, instruments for both self-assessment and external assessment of depression symptoms will be applied. Furthermore, psycho-immunological parameters such as inflammation markers and the cortisol awakening response in saliva will be investigated. Resting-state functional magnetic resonance imaging (rs fMRI) will be performed to analyze functional connectivity, including the cerebellum, and to identify neuronal predictors of expectation effects. In addition, possible cerebellar involvement will be assessed based on a cerebellar-dependent motor learning paradigm (i.e., eyeblink conditioning).

DISCUSSION

In this study, the effects of treatment expectations towards rTMS are investigated in patients with MDD. The aim of this study is to identify the mechanisms underlying the expectation effects and, beyond that, to expand the potential of non-invasive and well-tolerated treatments of MDD.

TRIAL REGISTRATION

German Registry of Clinical Studies (DRKS DRKS00028017. Registered on 2022/03/07. URL: https://www.drks.de/drks_web/ .

Efficacy of repetitive transcranial magnetic stimulation (rTMS) adjunctive therapy for major depressive disorder (MDD) after two antidepressant treatment failures: meta-analysis of randomized sham-controlled trials

BACKGROUND

Several meta-analyses demonstrated the efficacy of unilateral High-Frequency Left-sided (HFL) repetitive Transcranial Magnetic Stimulation (rTMS) for individuals with Major Depressive Disorder (MDD); however, results are contradictory due to heterogeneity of the included studies.

METHODS

A systematic literature review (SLR) of English language articles published since 2000 was performed in March 2022 on PubMed and Scopus databases. Empirical evidence on the relative efficacy of rTMS treatment compared with standard pharmacotherapy in Treatment-Resistant Depression (TRD) were extracted. Random effects models were used to assess the effects of rTMS on response and remission rates.

RESULTS

19 randomized double-blinded sham-controlled studies were included for quantitative analysis for response (n = 854 patients) and 9 studies for remission (n = 551 patients). The risk ratio (RR) for response and remission are 2.25 and 2.78, respectively for patients after two treatment failures using rTMS as add-on treatment compared to standard pharmacotherapy. Cochrane's Q test showed no significant heterogeneity. No publication bias was detected.

CONCLUSIONS

rTMS is significantly more effective than sham rTMS in TRD in response and remission outcomes and may be beneficial as an adjunctive treatment in patients with MDD after two treatment failures. This finding is consistent with previous meta-analyses; however, the effect size was smaller than in the formerly published literature.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Vortioxetine in patients with major depressive disorder and high levels of anxiety symptoms: An updated analysis of efficacy and tolerability

BACKGROUND

Patients with major depressive disorder (MDD) often experience comorbid anxiety symptoms. Vortioxetine has demonstrated efficacy in treating anxiety symptoms in patients with MDD; however, efficacy and tolerability have not been assessed across the entire approved dosage range.

METHODS

The efficacy and tolerability of vortioxetine 5-20 mg/day were assessed in patients with MDD and high levels of anxiety symptoms (Hamilton Anxiety Rating Scale [HAM-A] total score ≥ 20) using pooled data from four randomized, fixed-dose, placebo-controlled studies (n = 842). Data from a randomized, double-blind study of vortioxetine 10-20 mg/day versus agomelatine 25-50 mg/day in patients with an inadequate response to prior therapy (n = 299) were analyzed separately. Mean changes from baseline in Montgomery-Åsberg Depression Rating Scale (MADRS), HAM-A, and Sheehan Disability Scale (SDS) total scores were analyzed by vortioxetine dosage.

RESULTS

The pooled analysis of fixed-dose studies demonstrated a clear dose-response relationship for vortioxetine 5-20 mg/day for improvements in MADRS, HAM-A, and SDS total scores. Vortioxetine 20 mg/day demonstrated significant effects versus placebo from week 4 onwards. In the post-hoc analysis of the active-controlled study in patients with an inadequate response to prior therapy, vortioxetine 10-20 mg/day was superior to agomelatine across all outcome measures from week 4 onwards. Up-titration of vortioxetine to 20 mg/day was not associated with an increase in adverse events.

LIMITATIONS

Short-term trials.

CONCLUSIONS

Vortioxetine is efficacious and well tolerated in patients with MDD and high levels of anxiety symptoms, including those with an inadequate response to prior therapy. The greatest therapeutic benefits were observed with vortioxetine 20 mg/day.

TRIAL REGISTRATION

NCT01140906, NCT01153009, NCT01163266, NCT01255787, NCT01488071.

Bidirectional motivated bimodal isothermal strand displacement amplifier with a table tennis-like movement for the ultrasensitive fluorescent and colorimetric detection of depression-related microRNA

An increasing number of studies have highlighted the potential of microRNAs (miRNAs) as physiological indicators of major depressive disorder (MDD). Herein, we developed a bidirectional-motivated bimodal isothermal strand displacement amplifier (BB-ISDA) for the ultrasensitive fluorescent and colorimetric detection of MDD-related miRNA-132. In the BB-ISDA system, a pair of functionalized hairpin probes (HP1 and HP2) with nicking recognition sites are designed to recognize target miRNA. The recognition of target miRNA by HP1 (or HP2) generates copious numbers of nicked triggers by HP1 (or HP2)-based ISDA to recognize HP2 (or HP1) by autonomous strand polymerization, cleavage, and displacement, which in turn induces the subsequent generation of copious numbers of nicked G-quadruplex triggers by HP2 (or HP1)-based ISDA to recognize HP1 (or HP2) along a same line. After many cycles, this bidirectional motivated table-tennis-like movement amplifies the fluorescent signal from HP1 and the colorimetric signal from HP2, simultaneously. The dual-signal output pattern was cross-validated for sensing miRNA-132. Each of the detection modal shows the capability for qualitative and quantitative detection of miRNA-132 with high sensitivity and specificity. The adaptability of the bimodal mechanism was verified via the detection of target miRNA-132 from clinical human blood samples. We envision that this BB-ISDA with dual-signal output for accurate and reliable analysis of miRNA is promising for the molecular diagnosis of human mental diseases.

Combinations of dextromethorphan for the treatment of mood disorders - a review of the evidence

INTRODUCTION

Major depressive disorder (MDD) is one of the leading causes of disability worldwide. However, many patients do not achieve an adequate clinical improvement with pharmacotherapies targeting monoamine receptors, and the onset of therapeutic benefit typically lags by 4-6 weeks. There is a significant need for mechanistically novel treatments with more rapid efficacy. Combinations of dextromethorphan, an oral N-methyl-D-aspartate (NMDA) receptor antagonist, can potentially fill this gap.

AREAS COVERED

US Clinical Trials registration was systematically searched for studies examining the effects of dextromethorphan in mood disorders. Results were gathered via a PubMed search, adding also press releases, and poster presentations. Two case reports and eight clinical trials were identified for the treatment of MDD or treatment resistant depression (TRD); we also reviewed additional studies in bipolar disorder.

EXPERT OPINION

Clinical studies show that the combinations of dextromethorphan with quinidine or bupropion have been effective in decreasing depressive symptomatology in MDD. However, dextromethorphan studies in adults with TRD or with bipolar depression have shown mixed results. The combination of dextromethorphan and bupropion is a well-tolerated, safe, and efficacious treatment option for adults with MDD. Additional studies analyzing the effects of dextromethorphan and bupropion for TRD and bipolar depression are needed.

Gray matter alterations in adolescent major depressive disorder and adolescent bipolar disorder

BACKGROUND

Gray matter volume (GMV) alterations in several emotion-related brain areas are implicated in mood disorders, but findings have been inconsistent in adolescents with major depressive disorder (MDD) or bipolar disorder (BD).

METHODS

We conducted a comprehensive meta-analysis of 35 region-of-interest (ROI) and 18 whole-brain voxel-based morphometry (VBM) MRI studies in adolescent MDD and adolescent BD, and indirectly compared the results in the two groups. The effects of age, sex, and other demographic and clinical scale scores were explored using meta-regression analysis.

RESULTS

In the ROI meta-analysis, right putamen volume was decreased in adolescents with MDD, while bilateral amygdala volume was decreased in adolescents with BD compared to healthy controls (HC). In the whole-brain VBM meta-analysis, GMV was increased in right middle frontal gyrus and decreased in left caudate in adolescents with MDD compared to HC, while in adolescents with BD, GMV was increased in left superior frontal gyrus and decreased in limbic regions compared with HC. MDD vs BD comparison revealed volume alteration in the prefrontal-limbic system.

LIMITATION

Different clinical features limit the comparability of the samples, and small sample size and insufficient clinical details precluded subgroup analysis or meta-regression analyses of these variables.

CONCLUSIONS

Distinct patterns of GMV alterations in adolescent MDD and adolescent BD could help to differentiate these two populations and provide potential diagnostic biomarkers.

Associations between the kynurenine pathway and the brain in patients with major depressive disorder-A systematic review of neuroimaging studies

Previous studies have indicated that an imbalance in the kynurenine (KYN) pathway is an important pathophysiological mechanism of depression. Several studies have reported that an imbalance in the KYN pathway and its metabolites is associated with abnormalities in cerebral structure and function in depression, but the available evidence has been inconsistent. In this review, we systematically reviewed and integrated the findings concerning the associations between the KYN pathway and the brain in patients with major depressive disorder (MDD). A total of 22 neuroimaging studies were ultimately included in the present study. The neuroimaging modalities used in the studies included structural magnetic resonance imaging (MRI), diffusion tensor imaging, functional MRI, magnetic resonance spectroscopy, arterial spin labelling and positron emission tomography. The results revealed that an imbalance in the KYN pathway was associated with structural and functional abnormalities in several brain regions in patients with MDD. The brain regions most frequently associated with an imbalance in the KYN pathway were cortical regions (i.e., anterior cingulate cortex and orbitofrontal cortex), subcortical regions (i.e., striatum, thalamus and amygdala) and white matter fibres (i.e., inner capsule and left superior longitudinal tract). Our study provides robust evidence that cerebral abnormalities associated with the KYN pathway may be the underlying pathophysiological mechanisms of MDD. Future prospective studies are needed to further elucidate the causal relationships between the imbalanced KYN pathway and cerebral abnormalities in patients with MDD.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

RAGE signaling pathway is involved in CUS-induced depression-like behaviors by regulating the expression of NR2A and NR2B in rat hippocampus DG

NMDA receptors play pivotal roles in the neurobiology of chronic stress-induced mood disorders. But the mechanism for chronic stress to disturb the expression of NMDA receptor subunits is still unclear. Recent researches indicated the involvement RAGE signaling pathway in regulation of glutamate system functions. In this study, we hypothesized RAGE signaling pathway mediated chronic stress-induced alteration in the expression of NMDA receptor subunits, leading to depressive-like behaviors. CUS decreased the expression of RAGE, NR2A, and NR2B, inhibited the phosphorylation of transcript factor ERK and CREB in rat hippocampus DG. RAGE knockdown in hippocampus DG by RAGE shRNA lentiviral particles induced depressive-like behaviors, reduced the mRNA and protein expression of NR2A and NR2B, and inhibited the phosphorylation of ERK and CREB. RAGE over-expression in hippocampus DG by RAGE adenovirus particles reversed the effects of CUS on depressive-like behaviors, ERK and CREB phosphorylation, and NR2A and NR2B expression. Our findings suggests that RAGE signaling pathway at least partially participates in the regulation of NR2A and NR2B expression, which mediates the effects of chronic stress on the depressive-like behaviors. These data provide evidence for RAGE signaling as a possible new pathway through which chronic stress results in the maladaptation of NMDA receptors.

The mediating effect of distress tolerance on the relationship between stressful life events and suicide risk in patients with major depressive disorder

BACKGROUND

Despite widespread acknowledgment of the impact of stressful life events on suicide risk, the understanding of the psychological mechanisms underlying the relationship between stressful life events and suicide risk in major depressive disorder (MDD) remain unclear. This study aim to examine whether the distress tolerance mediates the relationship between the stressful life events and suicide risk in patients with MDD.

METHODS

A cross-sectional study was carried out among 125 Chinese patients with MDD, mean age was 27.05 (SD=0.68) and 68.8% were females. The 17-item Hamilton Depression Rating scale (HAMD-17), the validated Chinese version of the Mini International Neuropsychiatric Interview (MINI) suicide module, Life Events Scale (LES) and Distress Tolerance Scale (DTS) were utilized to evaluate depressive symptoms, stressful life events, levels of distress tolerance, and suicide risk, respectively. Mediation analyses was used to test the mediation effect of distress tolerance on the relationship between stressful life events and suicide risk.

RESULTS

The ratio of suicide risk in patients with MDD was 75.2%. Pearson correlation analysis showed that stressful life events were positively correlated with suicide risk(r=0.182, p<0.05). Stressful life events(r=-0.323, p<0.01) and suicide risk(r=-0.354, p<0.01) were negatively correlated with distress tolerance. Mediation analyses showed that the direct path from stressful life events to suicide risk was not significant (B= 0.012, 95% confidence interval (CI) [-0.017, 0.042]). Stressful life events affected suicide risk indirectly through distress tolerance (B= 0.018, 95% CI [0.007, 0.031]), and the mediating effect accounted for 60.0% of the total effect.

CONCLUSION

Distress tolerance completely played a mediating role between stressful life events and suicide risk. Further suicide prevention and intervention strategies should focus on increasing levels of distress tolerance in patients with MDD.

Explaining the Female Preponderance in Adolescent Depression-A Four-Wave Cohort Study

In the transition from childhood into adolescence, a female preponderance in depression emerges. Despite substantial empirical research to test theoretical propositions as to why this happens, our understanding is still limited. One explanation claims that girls become exposed to more stress (stress exposure model) whereas another proposes that girls become more vulnerable to the impact of stress (stress reactivity model) than boys when entering adolescence. Stressful life events (SLEs) and bullying victimization are established risk factors for adolescent depression. However, whether these factors contribute to the gender difference in depression is undetermined and thus investigated herein. Children (49.9% boys; n = 748) and parents from two birth cohorts in Trondheim, Norway, were followed biennially from ages 8 to 14 with clinical interviews about symptoms of depressive disorders and self-reports on SLEs. Teachers reported on bullying victimization. Prospective associations were investigated using an autoregressive latent trajectory model with structured residuals, examining within-person longitudinal associations while accounting for all time-invariant confounding effects. The number of depressive symptoms increased from ages 12 to 14 among girls. In the period before (ages 10 to 12), girls and boys were equally exposed to SLEs and bullying victimization. Increased stress (both SLEs and bullying victimization) at age 12 predicted increased depression at age 14 more strongly among girls than boys. Hence, increased impact-but not exposure-of SLEs and bullying victimization in girls may partly explain the emerging female preponderance in depression, in line with a stress reactivity model.

Repetitive transcranial magnetic stimulation treatment of major depressive disorder and comorbid chronic pain: response rates and neurophysiologic biomarkers

BACKGROUND

Major depressive disorder (MDD) and chronic pain are highly comorbid, and pain symptoms are associated with a poorer response to antidepressant medication treatment. It is unclear whether comorbid pain also is associated with a poorer response to treatment with repetitive transcranial magnetic stimulation (rTMS).

METHODS

162 MDD subjects received 30 sessions of 10 Hz rTMS treatment administered to the left dorsolateral prefrontal cortex (DLPFC) with depression and pain symptoms measured before and after treatment. For a subset of 96 patients, a resting-state electroencephalogram (EEG) was recorded at baseline. Clinical outcome was compared between subjects with and without comorbid pain, and the relationships among outcome, pain severity, individual peak alpha frequency (PAF), and PAF phase-coherence in the EEG were examined.

RESULTS

64.8% of all subjects reported pain, and both depressive and pain symptoms were significantly reduced after rTMS treatment, irrespective of age or gender. Patients with severe pain were 27% less likely to respond to MDD treatment than pain-free individuals. PAF was positively associated with pain severity. PAF phase-coherence in the somatosensory and default mode networks was significantly lower for MDD subjects with pain who failed to respond to MDD treatment.

CONCLUSIONS

Pain symptoms improved after rTMS to left DLPFC in MDD irrespective of age or gender, although the presence of chronic pain symptoms reduced the likelihood of treatment response. Individual PAF and baseline phase-coherence in the sensorimotor and midline regions may represent predictors of rTMS treatment outcome in comorbid pain and MDD.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Response Inhibition and Predicting Response to Pharmacological and Cognitive Behavioral Therapy Treatments for Major Depressive Disorder: A Canadian Biomarker Integration Network for Depression Study

BACKGROUND

Major depressive disorder (MDD) is associated with various cognitive impairments, including response inhibition. Deficits in response inhibition may also underlie poor antidepressant treatment response. Recent studies revealed that the neurobiological correlates of response inhibition can predict response to pharmacological treatments. However, the generalizability of this finding to first-line nonpharmacological treatments, particularly cognitive behavioral therapy, remains to be investigated.

METHODS

Data from two independent treatment protocols were combined, one in which 65 patients with MDD underwent treatment with escitalopram, and the other in which 41 patients with MDD underwent a course of cognitive behavioral therapy. A total of 25 healthy control subjects were also recruited. Neural correlates of response inhibition were captured by participants completing a Go/NoGo task during electroencephalography recording. Response inhibition-related measures of interest included the amplitudes of the N2 and P3 event-related potentials.

RESULTS

Pretreatment P3 amplitude, which has been linked to both the motor and cognitive aspects of response inhibition, was a significant predictor of change in depressive symptoms following escitalopram and cognitive behavioral therapy treatment. A greater pretreatment P3 amplitude was associated with a greater reduction in depressive severity. In addition, the pretreatment P3 amplitude was found to be significantly greater at baseline in remitters than in nonremitters and healthy control subjects.

CONCLUSIONS

The integrity of response inhibition may be critical for a successful course of pharmacological or psychological treatment for MDD. Electrophysiological correlates of response inhibition may have utility as a general prognostic marker of treatment response in MDD. Future studies may investigate the benefit of preceding first-line treatments with interventions that improve response inhibition in MDD.

Abnormal amygdala functional connectivity in MDD patients with insomnia complaints

BACKGROUND

Insomnia is one of the major symptom relevant factors in major depressive disorder (MDD), but the neurological mechanisms underlying the multiple effect between insomnia and depression have not been well interpreted. This study aimed at exploring the potential mechanisms between insomnia and depression based on amygdala-based resting-state functional connectivity (RSFC).

METHODS

In total 56 MDD patients with low insomnia (MDD-LI) patients, 46 MDD patients with high insomnia (MDD-HI) patients, and 57 healthy controls (HCs) were employed and underwent a resting-state functional magnetic resonance imaging (fMRI) scan. ANOVA test was performed on RSFC value for three groups. Correlation analysis was conducted to evaluate the relationship between abnormal RSFC values and clinical features.

RESULTS

We found that MDD-HI mainly showed increased RSFC in (bilateral superior temporal gyrus (STG), and decreased RSFC in left supplementary motor area (SMA) and bilateral postcentral gyrus (PoCG) compared with MDD-LI. Correlation analysis indicated that RSFC of the bilateral amygdala with STG were positively associated with the sleep disturbance score and adjust HAMD score.

CONCLUSION

Our findings suggest that RSFC in temporal lobe and other specifically activated regions may be associated with neural circuits involved with insomnia in MDD. These provide new evidence for understanding the potential mechanisms of major depression and insomnia from the perspective of functional connectivity.

Putamen Structure and Function in Familial Risk for Depression: A Multimodal Imaging Study

BACKGROUND

The putamen has been implicated in depressive disorders, but how its structure and function increase depression risk is not clearly understood. Here, we examined how putamen volume, neuronal density, and mood-modulated functional activity relate to family history and prospective course of depression.

METHODS

The study includes 115 second- and third-generation offspring at high or low risk for depression based on the presence or absence of major depressive disorder in the first generation. Offspring were followed longitudinally using semistructured clinical interviews blinded to their familial risk; putamen structure, neuronal integrity, and functional activation were indexed by structural magnetic resonance imaging (MRI), proton magnetic resonance spectroscopy (N-acetylaspartate/creatine ratio), and functional MRI activity modulated by valence and arousal components of a mood induction task, respectively.

RESULTS

After adjusting for covariates, the high-risk individuals had lower putamen volume (standardized betas, β-left = -0.17, β-right = -0.15, ps = .002), N-acetylaspartate/creatine ratio (β-left= -0.40, β-right= -0.37, ps < .0001), and activation modulated by valence (β-left = -0.22, β-right = -0.27, ps < .05) than low-risk individuals. Volume differences were greater at younger ages, and N-acetylaspartate/creatine ratio differences were greater at older ages. Lower putamen volume also predicted major depressive disorder episodes up to 8 years after the scan (β-left = -0.72, p = .013; β-right = -0.83, p = .037). Magnetic resonance spectroscopy and task functional MRI measures were modestly correlated (0.27 ≤ r ≤ 0.33).

CONCLUSIONS

Findings demonstrate abnormalities in putamen structure and function in individuals at high risk for major depressive disorder. Future studies should focus on this region as a potential biomarker for depressive illness, noting meanwhile that differences attributable to family history may peak at different ages based on which MRI modality is being used to assay them.

Disorder-specific impaired neurocognitive function in major depression and generalized anxiety disorder

BACKGROUND

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are both highly prevalent and comorbid psychiatric disorders. Neurocognitive dysfunction has been commonly found in MDD, but the findings in GAD are inconsistent. Few studies have directly compared cognitive performance between GAD and MDD. Therefore, the present study aimed to reveal the similar and distinct cognitive impairments between both disorders.

METHODS

Three non-overlapping and non-comorbid groups were enrolled in the current study including patients with GAD (n = 37), MDD (n = 107) and healthy controls (n = 74). Levels of anxiety and depression were assessed using the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD) respectively. The Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to compare the cognitive performance, including sustained attention, visual memory, executive functions and learning.

RESULTS

Both MDD and GAD groups demonstrated common significant deficits in sustained attention, visual memory, working memory and learning when compared to healthy controls. Despite the similarities, the MDD group had significantly greater impairment in learning, particularly generalization, while the GAD group demonstrated more pronounced deficits in visual memory.

LIMITATIONS

Patients involved were medicated and the sample size for GAD was relatively small.

CONCLUSIONS

The significant differences in visual memory and learning between MDD and GAD groups might be indicators to distinguishing both disorders. These results confirm that cognitive function is of great importance as a future target for treatment in order to improve wellbeing, quality of life and functionality in both GAD and MDD.

Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates

Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.

The effects of walking in nature on negative and positive affect in adult psychiatric outpatients with major depressive disorder: A randomized-controlled study

BACKGROUND

While walking in nature has been shown to improve affect in adults from the community to a greater extent than walking in urban settings, it is unknown whether such benefits apply to individuals suffering from depression. Using a parallel group design, this randomized controlled trial examined the effects of a single walk in nature versus urban settings on negative and positive affect in adult psychiatric outpatients diagnosed with major depressive disorder (MDD).

METHOD

Participants recruited from a psychiatric outpatient clinic for adults with MDD were randomly assigned to a nature or urban walk condition. Thirty-seven adults (mean age = 49 years) completed a single 60-minute walk. Negative and positive affect were assessed using The Positive and Negative Affect Schedule or PANAS at 6 time points: before the walk, halfway during the walk, immediately post-walk, at home before bedtime, 24 h post-walk, and 48 h post-walk.

RESULTS

Controlling for baseline levels of affect before the walk, individuals who walked in nature experienced overall lower levels of negative affect, F(1, 35.039) = 4.239, p = .047, compared to those who walked in urban settings. Positive affect did not differ across walk conditions.

LIMITATIONS

The generalizability of results are limited by the small sample size and the presence of more female than male participants.

CONCLUSIONS

Walking in nature might be a useful strategy to improve negative affect in adults with MDD. Future research should investigate different ways to integrate the beneficial effects of nature exposure into existing treatment plans for psychiatric outpatients with MDD.

Overlapping and segregated changes of functional hubs in melancholic depression and non-melancholic depression

BACKGROUND

Previous research found associations between neuropsychiatric disorders and patterns of highly connected "hub" nodes, which are crucial in coordinating brain functions. Melancholic depression is considered a relatively distinct and homogenous subtype of major depressive disorder (MDD), which responds better to pharmacological treatments than placebos or psychotherapies. Accordingly, melancholic depression probably has distinct neuropathological underpinnings. This study aims to examine the overlapping and segregated changes of functional hubs in melancholic and non-melancholic MDD.

METHODS

Thirty-one melancholic patients, 28 non-melancholic patients, and 32 healthy controls were included. Resting-state functional imaging data were analyzed using global functional connectivity.

RESULTS

Both melancholic and non-melancholic patients had increased GFC in the bilateral insula and decreased GFC in the PCC/precuneus compared to HCs. The distinction was that melancholic patients showed increased GFC in the bilateral thalamus, right inferior parietal lobule (IPL), and left cerebellum Crus I and decreased GFC in the left temporal lobe, whereas non-melancholic patients showed increased GFC in the left superior parietal lobe. Additionally, compared with non-melancholic patients, melancholic individuals displayed significant increases of GFC in the left IPL and cerebellum.

CONCLUSION

Increased GFC of the insula and decreased GFC of the PCC and precuneus are the common abnormalities of melancholic and non-melancholic MDD. Hyperconnectivity of the IPL and cerebellum might be distinctive neuropathological features of melancholic MDD.