Models Of Major Depressive Disorder Research Articles

Simulated synapse loss induces depression-like behaviors in deep reinforcement learning

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network—simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines—though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

Phosphatidic acid is involved in regulation of autophagy in neurons in vitro and in vivo.

Major depressive disorder (MDD) is a common and severe psychiatric disease, which does not only lead to variety of neuropsychiatric symptoms, but unfortunately in a relatively large proportion of cases also to suicide. The pathogenesis of MDD still requires definition. We have previously shown that ceramide is increased in the blood plasma of patients with MDD. In mouse models of MDD, which are induced by treatment with corticosterone or application of chronic unpredictable stress, increased blood plasma ceramide also increased and caused an inhibition of phospholipase D in endothelial cells of the hippocampus and reduced phosphatidic acid levels in the hippocampus. Here, we demonstrated that corticosterone treatment of PC12 cells resulted in reduced cellular autophagy, which is corrected by treatment with phosphatidic acid. In vivo, treatment of mice with corticosterone or chronic unpredictable stress also reduced autophagy in hippocampus neurons. Autophagy was normalized upon i.v. injection of phosphatidic acid in these mouse models of MDD. In an attempt to identify targets of phosphatidic acid in neurons, we demonstrated that corticosterone reduced levels of the ganglioside GM1 in PC-12 cells and the hippocampus of mice, which were normalized by treatment of cells or i.v. injection of mice with phosphatidic acid. GM1 application also normalized autophagy in cultured neurons. Phosphatidic acid and GM1 corrected stress-induced alterations in behavior, i.e., mainly anxiety and anhedonia, in experimental MDD in mice. Our data suggest that phosphatidic acid may regulate via GM1 autophagy in neurons.

The Alpha-7 Nicotinic Receptor Positive Allosteric Modulator PNU120596 Attenuates Lipopolysaccharide-Induced Depressive-Like Behaviors and Cognitive Impairment by Regulating the PPAR-α Signaling Pathway in Mice.

The brain α7 nicotinic acetylcholine receptor (α7 nAChR) has a critical role in the pathophysiology of Major Depressive Disorder (MDD) involving neuroinflammation. The α7 nAChR stimulation has been shown to modulate the anti-inflammatory effects of nuclear peroxisome proliferator-activated receptor-α (PPAR-α) via its endogenous ligands in the brain. The present study determined the effects of α7 nAChR modulator PNU120596 on PPAR-α, an inhibitor of κB (IκB) and nuclear factor-κB (NF-κB) expression and interleukin-1β (IL-1β) level in the hippocampus and prefrontal cortex (PFC) in an inflammatory mouse model of MDD induced by lipopolysaccharide (LPS). We also evaluated the combined effects of PNU120596 and GW6471, a PPAR-α antagonist, on depressive-like and cognitive deficit-like behaviors in mice. Male C57BL/6J mice were treated with PNU120596, followed by systemic LPS (1 mg/kg, i.p.) administration. The effects of PNU120596 on the mRNA expression of PPAR-α and IκB were assessed in the hippocampus and PFC using qRT-PCR following LPS administration. Similarly, the effects of PNU120596 on the immunoreactivity of PPAR-α and NF-κB were measured in the hippocampus and PFC using an immunofluorescence assay. Furthermore, the effects of PNU120596 on pro-inflammatory cytokine IL-1β levels were measured in the hippocampus and PFC using ELISA. The combined effects of PNU120596 and GW6471 were also assessed against LPS-induced depressive-like and cognitive deficit-like behaviors using the Tail Suspension Test (TST), Forced Swim Test (FST), and Y-maze test. PNU120596 (4 mg/kg) significantly prevented LPS-induced dysregulation of PPAR-α, IκB, p-NF-κB p65, and IL-1β in the hippocampus and PFC. Pretreatment with PNU120596 showed significant antidepressant-like effects by reducing immobility time in the TST and FST. Similarly, pretreatment with PNU120596 significantly reduced cognitive deficit-like behavior in the Y-maze test. The antidepressant and pro-cognitive-like effects of PNU120596 were reversed by PPAR-α antagonist GW6471 (2 mg/kg). These results suggest that PNU120596 prevented LPS-induced MDD and cognitivelike behavior by regulating α7 nAChR/PPAR-α signaling pathway in the hippocampus and PFC.

Atlas-based structural analysis of prefrontal cortex atrophy in major depressive disorder: Correlations with severity and episode frequency

BackgroundCurrent models of major depressive disorder (MDD) primarily focus on the structural and functional changes in key prefrontal areas responsible for emotional regulation. Among these regions some sections such as the dorsal prefrontal area, has received limited attention regarding its structural abnormalities in MDD. This study aims to evaluate volumetric abnormalities in brain regions associated with markers of depression severity and episode frequency. MethodsThe study included 33 MDD patients and 33 healthy subjects. Using an atlas-based method, we measured the volumes of several key brain regions based on MRI data. The regions of interest included prefrontal and posterior sections of the middle frontal gyrus (MFG) and superior frontal gyrus (SFG). Additionally, we evaluated the volumes of the dorsal anterior cingulate cortex (dACC), perigenual (rostral) anterior cingulate cortex (pgACC), subgenual cingulate cortex (sgACC), posterior cingulate cortex (PCC), hippocampus (HPC), and parahippocampus (paraHPC). Hamilton Depression Scale (HAM-D) scores and count of the depressive episodes of patients were also obtained. A regression analysis with sex as the confounding factor has been made. ResultsAnalysis of covariances, controlling for sex, showed significant atrophy in the sgACC in the depression group: F(1, 63) = 4.013, p = 0.049 (left) and F(1, 63) = 8.786, p < 0.004 (right). Poisson regression, also controlling for sex, found that each additional depressive episode was associated with a significant reduction in left posterior MFG volume (0.952 times, 95 % CI, 0.906 to 1.000; p = 0.049). ConclusionsFindings in this study highlight the structural abnormalities in MDD patients in correlation to either current depression severity or chronicity of the disease.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via “trial and error”. Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets can be used as input for feature selection. Using Bayesian optimization, we identified an optimal multi-layer dense neural network that used data from 21 clinical and sociodemographic features as input in order to perform differential treatment benefit prediction. With this combined dataset of 5032 individuals and 6 drugs, we created a differential treatment benefit prediction model. Our model generalized well to the held-out test set and produced similar accuracy metrics in the test and validation set with an AUC of 0.7 when predicting binary remission. To address the potential for bias propagation, we used a bias testing performance metric to evaluate the model for harmful biases related to ethnicity, age, or sex. We present a full pipeline from data preprocessing to model validation that was employed to create the first differential treatment benefit prediction model for MDD containing 6 treatment options.

Activation of pyramidal neurons in the infralimbic cortex alleviates LPS-induced depressive-like behavior in mice

The infralimbic (IL) cortex dysfunction has been implicated in major depressive disorder (MDD), yet the precise cellular and molecular mechanisms remain poorly understood. In this study, we investigated the role of layer V pyramidal neurons in a mouse model of MDD induced by repeated lipopolysaccharide (LPS) administration. Our results demonstrate that three days of systemic LPS administration induced depressive-like behavior and upregulated mRNA levels of interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-β (TGF-β) in the IL cortex. Electrophysiological recordings revealed a significant decrease in the intrinsic excitability of layer V pyramidal neurons in the IL following systemic LPS exposure. Importantly, chemogenetic activation of IL pyramidal neurons ameliorated LPS-induced depressive-like behavior. Additionally, LPS administration significantly increased microglial activity in the IL, as evidenced by a greater number of Ionized calcium binding adaptor molecule-1 (IBA-1)-positive cells. Morphometric analysis further unveiled enlarged soma, decreased branch numbers, and shorter branch lengths of microglial cells in the IL cortex following LPS exposure. Moreover, the activation of pyramidal neurons by clozapine-N-oxide increased the microglia branch length but did not change branch number or cytosolic area. These results collectively suggest that targeted activation of pyramidal neurons in the IL cortex mitigates microglial response and ameliorates depressive-like behaviors induced by systemic LPS administration. Therefore, our findings offer potential therapeutic targets for the development of interventions aimed at alleviating depressive symptoms by modulating IL cortical circuitry and microglial activity.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

BackgroundMajor depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. MethodsTranscriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the “Enrichr” platform was utilized to identify potential drugs for MDD. ResultsTotally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. ConclusionOur work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

A Machine Learning Model for Predicting Major Depressive Disorder Using Diffusion-Tensor Imaging Data

IntroductionMajor Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses.ObjectivesThis study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data.MethodsThe DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination.Results Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified.ConclusionsDespite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder.Disclosure of InterestNone Declared

A Canadian Simulation Model for Major Depressive Disorder: Study Protocol.

Major depressive disorder (MDD) is a common, often recurrent condition and a significant driver of healthcare costs. People with MDD often receive pharmacological therapy as the first-line treatment, but the majority of people require more than one medication trial to find one that relieves symptoms without causing intolerable side effects. There is an acute need for more effective interventions to improve patients' remission and quality of life and reduce the condition's economic burden on the healthcare system. Pharmacogenomic (PGx) testing could deliver these objectives, using genomic information to guide prescribing decisions. With an already complex and multifaceted care pathway for MDD, future evaluations of new treatment options require a flexible analytic infrastructure encompassing the entire care pathway. Individual-level simulation models are ideally suited for this purpose. We sought to develop an economic simulation model to assess the effectiveness and cost effectiveness of PGx testing for individuals with major depression. Additionally, the model serves as an analytic infrastructure, simulating the entire patient pathway for those with MDD. Key stakeholders, including patient partners, clinical experts, researchers, and modelers, designed and developed a discrete-time microsimulation model of the clinical pathways of adults with MDD in British Columbia (BC), including all publicly-funded treatment options and multiple treatment steps. The Simulation Model of Major Depression (SiMMDep) was coded with a modular approach to enhance flexibility. The model was populated using multiple original data analyses conducted with BC administrative data, a systematic review, and an expert panel. The model accommodates newly diagnosed and prevalent adult patients with MDD in BC, with and without PGx-guided treatment. SiMMDep comprises over 1500 parameters in eight modules: entry cohort, demographics, disease progression, treatment, adverse events, hospitalization, costs and quality-adjusted life-years (payoff), and mortality. The model predicts health outcomes and estimates costs from a health system perspective. In addition, the model can incorporate interactive decision nodes to address different implementation strategies for PGx testing (or other interventions) along the clinical pathway. We conducted various forms of model validation (face, internal, and cross-validity) to ensure the correct functioning and expected results of SiMMDep. SiMMDep is Canada's first medication-specific, discrete-time microsimulation model for the treatment of MDD. With patientpartner collaboration guiding its development, it incorporates realistic care journeys. SiMMDep synthesizes existing information and incorporates provincially-specific data to predict the benefits and costs associated with PGx testing. These predictions estimate the effectiveness, cost-effectiveness, resource utilization, and health gains of PGx testing compared with the current standard of care. However, the flexible analytic infrastructure can be adapted to support other policy questions and facilitate the rapid synthesis of new data for a broader search for efficiency improvements in the clinical field of depression.

Divergent effects of sex on hippocampal subfield alterations in drug-naive patients with major depressive disorder

BackgroundThe hippocampus is a crucial brain structure in etiological models of major depressive disorder (MDD). It remains unclear whether sex differences in the incidence and symptoms of MDD are related to differential illness-associated brain alterations, including alterations in the hippocampus. This study investigated divergent the effects of sex on hippocampal subfield alterations in drug-naive patients with MDD. MethodsHigh-resolution structural MR images were obtained from 144 drug-naive individuals with MDD early in their illness course and 135 age- and sex-matched healthy controls (HCs). Hippocampal subfields were segmented using FreeSurfer software and analyzed in terms of both histological subfields (CA1–4, dentate gyrus, etc.) and more integrative larger functional subregions (head, body and tail). ResultsWe observed a significant overall reduction in hippocampal volume in MDD patients, with deficits more prominent deficits in the posterior hippocampus. Differences in anatomic alterations between male and female patients were observed in the CA1-head, presubiculum-body and fimbria in the left hemisphere. Exploratory analyses revealed different patterns of clinical and memory function correlations with histological subfields and functional subregions between male and female patients primarily in the hippocampal head and body. LimitationsThis cross-sectional study cannot clarify the causality of hippocampal alterations or their association with illness risk or onset. ConclusionsThese findings represent the first reported sex-specific alterations in hippocampal histological subfields in patients with MDD early in the illness course prior to treatment. Sex-specific hippocampal alterations may contribute to diverse sex differences in the clinical presentation of MDD.

Risk Factor of Asian Americans’ Mental Health: A Psychological Review

This article is an examination of the risk factors of Asian Americans’ Mental Health and whether they are more prone to depression. The term “Asian American” and “Asian or Pacific Islander American” include over 25 groups in the US and make up a major proportion of the immigrants in the 21st century. Researchers and scholars have put in an arduous effort to analyze their mental states as it is essential to the American population. This article is going to discuss perspectives such as cultural differences, social support and discrimination, and family origin and education. Due to cultural differences, some Asian Americans might receive indigenous treatments such as herbal medicines in Chinese Medicine, and their diagnosis could also be different compared to the Western model of major depressive disorder in DSM-V. They also tend to have less social support, more stigma in their family, and face discrimination in educational or work settings. To conclude, due to the hardship of collecting data from the survey and experiments, future scholars or social workers should be more considerate of patients’ ethnic backgrounds and attain more information on the treatments they have received.

Comprehensive bioinformatics analysis of co-expressed genes of post-traumatic stress disorder and major depressive disorder

BackgroundPost-traumatic stress disorder (PTSD) is one of the most serious sequelae of trauma with serious impact worldwide. Studies have suggested an association between PTSD and major depressive disorder (MDD), but the underlying common mechanisms remain unclear. This study aimed to further explore the molecular mechanism between PTSD and MDD via comprehensive bioinformatics analysis. MethodsThe microarray data of PTSD and MDD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify the co-expressed genes associated with PTSD and MDD. Gene Set Enrichment Analysis (GSEA), enrichment analyses based on Disease Ontology (DO), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using R software. Then, R software was used for single-sample gene set enrichment analysis (ssGSEA) and immune infiltration analysis on the co-expressed genes in the two datasets., Therefore, a logistic regression model was constructed to predict PTSD and MDD using the R language. Ultimately, this study employed PTSD and MDD models to assess alterations in the expression of target genes within the mouse hippocampus. ResultsFour core genes (GNAQ, DPEP3, ICAM2, PACSIN2) were obtained through different analyses, and these genes had predictive validity for PTSD and MDD, playing an important role in the common mechanism of PTSD and MDD. The study findings reveal decreased expression levels of DPEP3, GNAQ, and PACDIN2 in PTSD samples, accompanied by an increased expression of ICAM2. In MDD samples, the expression of DPEP3 and ICAM2 is reduced, whereas GNAQ and PACDIN2 show an increase in expression. ConclusionsThis study provides a new perspective on the common molecular mechanisms of PTSD and MDD. These common pathways and core genes may provide promising clues for further experimental studies.

Improvement of astrocytic gap junction involves the anti-depressive effect of celecoxib through inhibition of NF-κB

ContextCelecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, has been shown to exhibit anti-depressive effects in clinical trials. However, the direct mechanism underlying its effect on neuroinflammation remains unclear. Neuroinflammatory reaction from astrocytes leads to depression, and our previous study found that gap junction disorder between astrocytes aggravated neuroinflammatory reaction in depressed mice. ObjectiveTo investigate the potential mechanism of celecoxib's effects on astrocytic gap junctions during the central nervous inflammation-induced depression. Materials & methodsStereotaxic injection of lipopolysaccharide (LPS) into the prefrontal cortex (PFC) to establish a model of major depressive disorder (MDD). Celecoxib was administrated into PFC 15 min after LPS injection. The depressive performance was tested by tail suspension test and forced swimming test, and the levels of proinflammation cytokines were determined at mRNA and protein levels. Resting-state functional connection (rsFC) was employed to assess changes in the default mode network (DMN). Additionally, astrocytic gap junctions were also determined by lucifer yellow (LY) diffusion and transmission electron microscope (TEM), and the expression of connexin 43 (Cx43) was measured by western blotting, quantitative polymerase chain reaction, and immunofluorescence. ResultsLPS injection induced significant depressive performance, which was ameliorated by celecoxib treatment. Celecoxib also improved rsFC in the DMN. Furthermore, celecoxib improved astrocytic gap junctions as evidenced by increased LY diffusion, shortened gap junction width, and normalized levels of phosphorylated Cx43. Celecoxib also blocked the phosphorylation of p65, and inhibition of p65 abolished the improvement of Cx43. Discussion & conclusionAnti-depressive effects of celecoxib are mediated, at least in part, by the inhibition of nuclear factor- kappa B (NF-κB) and the subsequent improvement of astrocytic gap junction function

Dysfunction of neurotransmitter metabolism is associated with the severity of depression in first-diagnosed, drug-naïve depressed patients

Background & aimsBiochemical changes of neurotransmitters underlying major depressive disorder (MDD) are unknown. This study preliminarily explored the association between neurotransmitters with MDD and the possibility of objective laboratory prediction of neurotransmitter involvement in MDD. MethodsA total of 87 first-diagnosed, drug-naïve patients with depression and 50 healthy controls (HCs) were included in the cross-sectional study. The levels and turnovers of neurotransmitters (glutamine (GLN), glutamic acid (GLU), γ-2Aminobutiric acid (GABA), kainate (KA), vanillylmandelic acid (VMA), 3-methoxy 4-hydroxyphenyl ethylene glycol (MHPG), noradrenaline (NE), homovanillic acid (HVA), dihydroxy-phenyl acetic acid (DOPAC), dopamine (DA), tryptophane (TRP), kynurenine (KYN), serotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA)) were determined and the confounding factors were adjusted. Then a correlation and a predictive analysis towards neurotransmitters for MDD were performed. ResultsAfter adjusting confounding factors, GLU (OR = 1.159), (GLU+ GABA)/GLN (OR = 1.217), DOPAC (OR = 1.106), DOPAC/DA (OR = 1.089) and (DOPAC+ HVA)/DA (OR = 1.026) enacted as risk factors of MDD, while KYN (OR = 0.992) was a protective factor. GABAergic and TRPergic pathways were associated with severity of depressive and anxiety symptoms in patients with depression. The predictive model for MDD (AUC = 0.775, 95%CI 0.683–0.860) consisted of KYN (OR = 0.990) and (GLU + GABA)/GLN (OR = 4.101). ConclusionsFirst-diagnosed, drug-naïve depression patients showed abnormal neurotransmitter composition. GLU, (GLU + GABA)/GLN, DOPAC, DOPAC/DA and (DOPAC + HVA)/DA were risk factors of MDD, while KYN was a protective factor. GABAergic and TRPergic pathways were correlated with MDD clinical characteristics. KYN and (GLU + GABA)/GLN may have a predictive value for MDD.

Identification of the antidepressant effect of electroconvulsive stimulation-related genes in hippocampal astrocyte

Major depressive disorder (MDD) remains a significant global health concern, with limited and slow efficacy of existing antidepressants. Electroconvulsive therapy (ECT) has superior and immediate efficacy for MDD, but its action mechanism remains elusive. Therefore, the elucidation of the action mechanism of ECT is expected to lead to the development of novel antidepressants with superior and immediate efficacy. Recent studies suggest a potential role of hippocampal astrocyte in MDD and ECT. Hence, we investigated antidepressant effect of electroconvulsive stimulation (ECS), an animal model of ECT, -related genes in hippocampal astrocyte with a mouse model of MDD, in which corticosterone (CORT)-induced depression-like behaviors were recovered by ECS. In this model, both of CORT-induced depression-like behaviors and the reduction of hippocampal astrocyte were recovered by ECS. Following it, astrocytes were isolated from the hippocampus of this model and RNA-seq was performed with these isolated astrocytes. Interestingly, gene expression patterns altered by CORT were reversed by ECS. Additionally, cell proliferation-related signaling pathways were inhibited by CORT and recovered by ECS. Finally, serum and glucocorticoid kinase-1 (SGK1), a multi-functional protein kinase, was identified as a candidate gene reciprocally regulated by CORT and ECS in hippocampal astrocyte. Our findings suggest a potential role of SGK1 in the antidepressant effect of ECS via the regulation of the proliferation of astrocyte and provide new insights into the involvement of hippocampal astrocyte in MDD and ECT. Targeting SGK1 may offer a novel approach to the development of new antidepressants which can replicate superior and immediate efficacy of ECT.

Major depressive disorder and bistability in an HPA-CNS toggle switch.

Major depressive disorder (MDD) is the most common psychiatric disorder. It has a complex and heterogeneous etiology. Most treatments take weeks to show effects and work well only for a fraction of the patients. Thus, new concepts are needed to understand MDD and its dynamics. One of the strong correlates of MDD is increased activity and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis which produces the stress hormone cortisol. Existing mathematical models of the HPA axis describe its operation on the scale of hours, and thus are unable to explore the dynamic on the scale of weeks that characterizes many aspects of MDD. Here, we propose a mathematical model of MDD on the scale of weeks, a timescale provided by the growth of the HPA hormone glands under control of HPA hormones. We add to this the mutual inhibition of the HPA axis and the hippocampus and other regions of the central nervous system (CNS) that forms a toggle switch. The model shows bistability between euthymic and depressed states, with a slow timescale of weeks in its dynamics. It explains why prolonged but not acute stress can trigger a self-sustaining depressive episode that persists even after the stress is removed. The model explains the weeks timescale for drugs to take effect, as well as the dysregulation of the HPA axis in MDD, based on gland mass changes. This understanding of MDD dynamics may help to guide strategies for treatment.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H–H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Impaired myelin ultrastructure is reversed by citalopram treatment in a mouse model for major depressive disorder

Major depressive disorder (MDD) is the most common and widespread mental disorder. Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for MDD. The relation between the inhibition of serotonin reuptake in the central nervous system and remission from MDD remains controversial, as reuptake inhibition occurs rapidly, but remission from MDD takes weeks to months. Myelination-related deficits and white matter abnormalities were shown to be involved in psychiatric disorders such as MDD. This may explain the delay in remission following SSRI administration. The raphe nuclei (RN), located in the brain stem, consist of clusters of serotonergic (5-HT) neurons that project to almost all regions of the brain. Thus, the RN are an intriguing area for research of the potential effect of SSRI on myelination, and their involvement in MDD. MicroRNAs (miRNAs) regulate many biological features that might be altered by antidepressants. Two cohorts of chronic unpredictable stress (CUS) mouse model for depression underwent behavioral tests for evaluating stress, anxiety, and depression levels. Following application of the CUS protocol and treatment with the SSRI, citalopram, 48 mice of the second cohort were tested via magnetic resonance imaging and diffusion tensor imaging for differences in brain white matter tracts. RN and superior colliculus were excised from both cohorts and measured for changes in miRNAs, mRNA, and protein levels of candidate genes. Using MRI-DTI scans we found lower fractional anisotropy and axial diffusivity in brains of stressed mice. Moreover, both miR-30b-5p and miR-101a-3p were found to be downregulated in the RN following CUS, and upregulated following CUS and citalopram treatment. The direct binding of these miRNAs to Qki, and the subsequent effects on mRNA and protein levels of myelin basic protein (Mbp), indicated involvement of these miRNAs in myelination ultrastructure processes in the RN, in response to CUS followed by SSRI treatment. We suggest that SSRIs are implicated in repairing myelin deficits resulting from chronic stress that leads to depression.