Models Of Major Depressive Disorder Research Articles

The core inflammatory factors in patients with major depressive disorder: a network analysis.

The symptoms of major depressive disorder (MDD) vary widely. Psycho-neuro-inflammation has shown that MDD's inflammatory factors can accelerate or slow disease progression. This network analysis study examined the complex interactions between depressed symptoms and inflammatory factors in MDD prevention and treatment. We gathered participants' inflammatory factor levels, used the Hamilton Depression Scale (HAMD-17), and network analysis was used to analyzed the data. Network analysis revealed the core inflammatory (nodes) and their interactions (edges). Stability and accuracy tests assessed these centrality measures' network robustness. Cluster analysis was used to group persons with similar dimension depressive symptoms and examine their networks. Interleukin-1β (IL-1β) is the core inflammatory factor in the overall sample, and IL-1β-interleukin-4 (IL-4) is the strongest correlation. Network precision and stability passed. Network analysis showed significant differences between Cluster 1 (with more severe anxiety/somatization and sleep disruption) and Cluster 3 (with more severe retardation and cognitive disorders), as well as between Cluster 2 (with more severe anxiety/somatization, sleep disruption and body weight) and Cluster 3. IL-1β is the core inflammatory factor in Cluster 1 and Cluster 2, while tumor necrosis factor alpha (TNF-α) in Cluster 3. IL-1β is the central inflammatory factor in the network, and there is heterogeneity in the core inflammatory factor of MDD with specific depressive dimension symptoms as the main manifestation. In conclusion, inflammatory factors and their links should be prioritized in future theoretical models of MDD and may provide new research targets for MDD intervention and treatment.

GLP-1 plays a protective role in hippocampal neuronal cells by activating cAMP-CREB-BDNF signaling pathway against CORT+HG-induced toxicity

Major depressive disorder (MDD) with diabetes mellitus (DM) significantly reduces the quality of the patient's life, and currently, there is no effective treatment. This study explored the feasibility of Glucagon-like peptide-1 (GLP-1) in treating MDD combined with DM. The protective effects of GLP-1 on mouse hippocampal neuronal cell line HT22 cultured with corticosterone (CORT) and high glucose (HG) were assessed. HT22 cells were cultured with CORT + HG to construct a cell model of MDD combined with DM. Cell viability and cell apoptosis/necrocytosis were detected by CCK-8 assay and flow cytometry/confocal laser scanning microscopy, respectively, after treatment with GLP-1. In addition, BDNF and neurotransmitter levels, lactic dehydrogenase (LDH) and glucose levels, and proteins of cAMP-CREB-BDNF signal pathway in the culture supernatants were measured through an enzyme-linked immunosorbent assay and colorimetric assays and Western blot, respectively. The ideal intervention combination to construct a cell model of MDD combined with DM was CORT 200 μM and HG 50 mM for 48 h. After treatment of 50 nM GLP-1 for 48 h, the model+50 nM GLP-1 group's apoptosis and necrocytosis rates and LDH and glucose concentrations in the culture supernatants decreased significantly compared with the model group. However, the BDNF, 5-hydroxytryptamine (5-HT), dopamine (DA), norepinephrine (NE), PKA, p-CREB, and p-Trkb concentrations in the culture supernatants increased significantly. GLP-1 functioned against CORT + HG-induced toxicity by activating the cAMP-CREB-BDNF signaling pathway in hippocampal neuronal cells.

P2X7 receptor-mediated depression-like reactions arising in the mouse medial prefrontal cortex.

Major depressive disorder is a frequent and debilitating psychiatric disease. We have shown in some of the acute animal models of major depressive disorder (tail suspension test and forced swim test) that depression-like behavior can be aggravated in mice by the microinjection into the medial prefrontal cortex of the P2X7R agonistic adenosine 5'-triphosphate or its structural analog dibenzoyl-ATP, and these effects can be reversed by the P2X7R antagonistic JNJ-47965567. When measuring tail suspension test, the prolongation of immobility time by the P2YR agonist adenosine 5'-[β-thio]diphosphate and the reduction of the adenosine 5'-(γ-thio)triphosphate effect by P2Y1R (MRS 2179) or P2Y12R (PSB 0739) antagonists, but not by JNJ-47965567, all suggest the involvement of P2YRs. In order to elucidate the localization of the modulatory P2X7Rs in the brain, we recorded current responses to dibenzoyl-ATP in layer V astrocytes and pyramidal neurons of medial prefrontal cortex brain slices by the whole-cell patch-clamp procedure; the current amplitudes were not altered in preparations taken from tail suspension test or foot shock-treated mice. The release of adenosine 5'-triphosphate was decreased by foot shock, although not by tail suspension test both in the hippocampus and PFC. In conclusion, we suggest, that in the medial prefrontal cortex, acute stressful stimuli cause supersensitivity of P2X7Rs facilitating the learned helplessness reaction.

Exploration of the Gut Microbiome in Thai Patients with Major Depressive Disorder Shows a Specific Bacterial Profile with Depletion of the Ruminococcus Genus as a Putative Biomarker.

Maes et al. (2008) published the first paper demonstrating that major depressive disorder (MDD) is accompanied by abnormalities in the microbiota-gut-brain axis, as evidenced by elevated serum IgM/IgA to lipopolysaccharides (LPS) of Gram-negative bacteria, such as Morganella morganii and Klebsiella Pneumoniae. The latter aberrations, which point to increased gut permeability (leaky gut), are linked to activated neuro-immune and oxidative pathways in MDD. To delineate the profile and composition of the gut microbiome in Thai patients with MDD, we examined fecal samples of 32 MDD patients and 37 controls using 16S rDNA sequencing, analyzed α- (Chao1 and Shannon indices) and β-diversity (Bray-Curtis dissimilarity), and conducted linear discriminant analysis (LDA) effect size (LEfSe) analysis. Neither α- nor β-diversity differed significantly between MDD and controls. Rhodospirillaceae, Hungatella, Clostridium bolteae, Hungatella hathewayi, and Clostridium propionicum were significantly enriched in MDD, while Gracillibacteraceae family, Lutispora, and Ruminococcus genus, Ruminococcus callidus, Desulfovibrio piger, Coprococcus comes, and Gemmiger were enriched in controls. Contradictory results have been reported for all these taxa, with the exception of Ruminococcus, which is depleted in six different MDD studies (one study showed increased abundance), many medical disorders that show comorbidities with MDD, and animal MDD models. Our results may suggest a specific profile of compositional gut dysbiosis in Thai MDD patients, with increases in some pathobionts and depletion of some beneficial microbiota. The results suggest that depletion of Ruminococcus may be a more universal biomarker of MDD that may contribute to increased enteral LPS load, LPS translocation, and gut-brain axis abnormalities.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption. Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD. We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS. Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Applying Functional Imaging to Clinical Practice: Are We Making Progress Toward Its Promise?

Applying Functional Imaging to Clinical Practice: Are We Making Progress Toward Its Promise?

Developing a Genetic Biomarker-based Diagnostic Model for Major Depressive Disorder using Random Forests and Artificial Neural Networks.

The clinical diagnosis of major depressive disorder (MDD) mainly relies on subjective assessment of depression-like behaviors and clinical examination. In the present study, we aimed to develop a novel diagnostic model for specially predicting MDD. The human brain GSE102556 DataSet and the blood GSE98793 and GSE76826 Data Sets were downloaded from the Gene Expression Omnibus (GEO) database. We used a novel algorithm, random forest (RF) plus artificial neural network (ANN), to examine gene biomarkers and establish a diagnostic model of MDD. Through the "limma" package in the R language, 2653 differentially expressed genes (DEGs) were identified in the GSE102556 DataSet, and 1786 DEGs were identified in the GSE98793 DataSet, and a total of 100 shared DEGs. We applied GSE98793 TrainData 1 to an RF algorithm and thereby successfully selected 28 genes as biomarkers. Furthermore, 28 biomarkers were verified by GSE98793 TestData 1, and the performance of these biomarkers was found to be perfect. In addition, we further used an ANN algorithm to optimize the weight of each gene and employed GSE98793 TrainData 2 to build an ANN model through the neural net package by R language. Based on this algorithm, GSE98793 TestData 2 and independent blood GSE76826 were verified to correlate with MDD, with AUCs of 0.903 and 0.917, respectively. To the best of our knowledge, this is the first time that the classifier constructed via DEG biomarkers has been used as an endophenotype for MDD clinical diagnosis. Our results may provide a new entry point for the diagnosis, treatment, outcome prediction, prognosis and recurrence of MDD.

Blocking Two-Pore Domain Potassium Channel TREK-1 Inhibits the Activation of A1-Like Reactive Astrocyte Through the NF-κB Signaling Pathway in a Rat Model of Major Depressive Disorder

Major depressive disorder (MDD) refers to a widespread psychiatric disorder. Astrocytes play a pivotal role in regulating inflammation which is a well-acknowledged key component in depression pathogenesis. However, the effects of the neuroinflammation-inducing A1-like astrocytes on MDD are still unknown. TWIK-related K+ channel 1 (TREK-1) has been demonstrated to regulate the action of antidepressants. Nevertheless, its mechanisms and effects on A1-like astrocyte stimulation in MDD are not clear. Therefore, we conducted in vivo and in vitro experiments using TREK-1 specific inhibitor spadin. In vivo, rats were subjected to a 6-week chronic unpredictable mild stress (CUMS) followed by spadin treatment. Behavioral tests were employed to surveil depressive-like behaviors. Hippocampal proteomic analysis was carried out with the purpose of identifying differentially expressed proteins after CUMS and spadin treatments. In vitro, astrocyte-conditioned medium and spadin were used to treat rat astrocyte cell line. The activated microglia, inflammatory factors, A1 astrocyte markers, and activated nuclear factor kappa B (NF-κB) pathway were later analyzed using immunofluorescence, western blot, and RT-qPCR. Our findings indicated that blockage of TREK-1 reduced CUMS-induced depressive-like behavior in rats, inhibited the microglial stimulation, reduced inflammatory factor levels, and suppressed the activation of A1-like reactive astrocytes in the hippocampus. We also verified that the suppression of A1-like astrocytes by spadin necessitated the NF-κB pathway. According to the findings, blocking TREK-1 inhibited the activation of A1-like reactive astrocytes via the NF-κB signaling pathway in MDD. Our study preliminarily identifies a novel antidepressant mechanism of TREK-1 action and provides a therapeutic path for MDD.

Minocycline treatment improves cognitive and functional plasticity in a preclinical mouse model of major depressive disorder

Major depressive disorder (MDD) is a chronic, recurring, and potentially life-threatening illness, which affects over 300 million people worldwide. MDD affects not only the emotional and social domains but also cognition. However, the currently available treatments targeting cognitive deficits in MDD are limited. Minocycline, an antibiotic with anti-inflammatory properties recently identified as a potential antidepressant, has been shown to attenuate learning and memory deficits in animal models of cognitive impairment. Here, we explored whether minocycline recovers the deficits in cognition in a mouse model of depression. C57BL6/J adult male mice were exposed to two weeks of chronic unpredictable mild stress to induce a depressive-like phenotype. Immediately afterward, mice received either vehicle or minocycline for three weeks in standard housing conditions. We measured anhedonia as a depressive-like response, and place learning to assess cognitive abilities. We also recorded long-term potentiation (LTP) as an index of hippocampal functional plasticity and ran immunohistochemical assays to assess microglial proportion and morphology. After one week of treatment, cognitive performance in the place learning test was significantly improved by minocycline, as treated mice displayed a higher number of correct responses when learning novel spatial configurations. Accordingly, minocycline-treated mice displayed higher LTP compared to controls. However, after three weeks of treatment, no difference between treated and control animals was found for behavior, neural plasticity, and microglial properties, suggesting that minocycline has a fast but short effect on cognition, without lasting effects on microglia. These findings together support the usefulness of minocycline as a potential treatment for cognitive impairment associated with MDD.

Understanding the Role of Oxidative Stress, Neuroinflammation and Abnormal Myelination in Excessive Aggression Associated with Depression: Recent Input from Mechanistic Studies.

Aggression and deficient cognitive control problems are widespread in psychiatric disorders, including major depressive disorder (MDD). These abnormalities are known to contribute significantly to the accompanying functional impairment and the global burden of disease. Progress in the development of targeted treatments of excessive aggression and accompanying symptoms has been limited, and there exists a major unmet need to develop more efficacious treatments for depressed patients. Due to the complex nature and the clinical heterogeneity of MDD and the lack of precise knowledge regarding its pathophysiology, effective management is challenging. Nonetheless, the aetiology and pathophysiology of MDD has been the subject of extensive research and there is a vast body of the latest literature that points to new mechanisms for this disorder. Here, we overview the key mechanisms, which include neuroinflammation, oxidative stress, insulin receptor signalling and abnormal myelination. We discuss the hypotheses that have been proposed to unify these processes, as many of these pathways are integrated for the neurobiology of MDD. We also describe the current translational approaches in modelling depression, including the recent advances in stress models of MDD, and emerging novel therapies, including novel approaches to management of excessive aggression, such as anti-diabetic drugs, antioxidant treatment and herbal compositions.

Minocycline treatment improves cognitive and functional plasticity in a preclinical mouse model of major depressive disorder

Minocycline treatment improves cognitive and functional plasticity in a preclinical mouse model of major depressive disorder

Development and validation of a machine learning-based vocal predictive model for major depressive disorder

BackgroundVariations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic.The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. MethodsMachine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. ResultsA vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). LimitationsThe main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. ConclusionsThe computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Current Evidence and Theories in Understanding the Relationship between Cognition and Depression in Childhood and Adolescence: A Narrative Review.

The present narrative review has covered the current evidence regarding the role of cognitive impairments during the early phase of major depressive disorder (MDD), attempting to describe the cognitive features in childhood, adolescence and in at-risk individuals. These issues were analyzed considering the trait, scar and state hypotheses of MDD by examining the cold and hot dimensions, the latter explained in relation to the current psychological theoretical models of MDD. This search was performed on several electronic databases up to August 2022. Although the present review is the first to have analyzed both cold and hot cognitive impairments considering the trait, scar and state hypotheses, we found that current evidence did not allow to exclusively confirm the validity of one specific hypothesis since several equivocal and discordant results have been proposed in childhood and adolescence samples. Further studies are needed to better characterize possible cognitive dysfunctions assessing more systematically the impairments of cold, hot and social cognition domains and their possible interaction in a developmental perspective. An increased knowledge on these topics will improve the definition of clinical endophenotypes of enhanced risk to progression to MDD and, to hypothesize preventive and therapeutic strategies to reduce negative influences on psychosocial functioning and well-being.

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

Electrical stimulus combined with venlafaxine and mirtazapine improves brain Ca2+ activity, pre-pulse inhibition, and immobility time in a model of major depressive disorder in schizophrenia

BackgroundThe prevalence of major depressive disorder in patients with schizophrenia (SZ-MDD) has been reported to be about 32.6 %, but it varies considerably depending on the stage (early or chronic) and state (acute or post-psychotic) of schizophrenia. The exploration of ideal strategies for the treatment of major depressive disorder in the context of schizophrenia is urgently needed. Thus, the present study was conducted to investigate the treatment effects of clozapine, electrical stimulation (ECS; the mouse model equivalent of electroconvulsive therapy for humans), venlafaxine, and mirtazapine for SZ-MDD. MethodsA mouse model of SZ-MDD was established with MK801 administration and chronic unpredictable mild stress exposure. Clozapine and ECS, alone and with mirtazapine and/or venlafaxine, were used as treatment strategies. In-vivo two-photon imaging was performed to visualize Ca2+ neural activity in the prefrontal cortex (PFC). Mouse performance on behavioral assays was taken to reflect acute treatment effects. ResultsECS + venlafaxine + mirtazapine performed significantly better than other treatments in alleviating major depressive disorder, as reflected by PFC Ca2+ activity and behavioral assay performance. Clozapine + venlafaxine + mirtazapine did not have an ideal treatment effect. Brain Ca2+ activity alterations did not correlate with behavioral expression in any treatment group. ConclusionsIn this mouse model of SZ-MDD, ECS + venlafaxine + mirtazapine improved brain Ca2+ activity, pre-pulse inhibition, and immobility time. These findings provide useful information for the further exploration of treatment methods for patients with SZ-MDD, although the mechanisms underlying this comorbidity needed to be investigated further.

Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis

BackgroundRadiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis.MethodsA consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features.ResultsThe results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively.ConclusionThese findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions. Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow-up (a median of 2-3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model-predicted risk score thresholds were associated with the greatest clinical benefit. The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development (C-statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples (C-statistic = .722, IQR = -.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C-slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance (C-statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01-0.05). The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low-intensity, selective preventive intervention.

A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data.

BackgroundIdentifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods.MethodsThe GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model.ResultsA total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort.ConclusionWe analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.

Response to unexpected social inclusion: A study using the cyberball paradigm.

BackgroundDysfunctional expectations are considered core characteristics of Major Depressive Disorder (MDD) and should be focused in psychotherapy. Dysfunctional expectations are especially pronounced in the interpersonal area (social expectations). In the present study, we examine the effect of unexpected social inclusion (expectation violation) on the change of generalized and specific depression-typical social expectations.MethodWe conducted an online study to investigate the impact of social inclusion after a period of social exclusion (unexpected social inclusion) on social expectation change (sample size 144) in a non-clinical sample. Depressive symptoms were assessed via self-reporting. Participants took part in two rounds of the online ball-game Cyberball. In the first round, all participants were socially excluded by their two co-players (acquisition of negative social expectations). In the second round, participants were either once more excluded (expectation confirmation) or included equally (expectation violation) by the same co-players. Specific and generalized social expectations were assessed after each round.ResultsSpecific and generalized social expectations increased following expectation violation. Even though depressive symptoms were related to lower levels of social expectations, we found that depressive symptoms did not moderate expectation change after positive expectation violations.ConclusionsIn the present experimental setup including the use of the online ball-game Cyberball, the establishment and change of social expectations can be experimentally manipulated. Under the given circumstances and in a non-clinical sample, negative expectations can be updated after unexpected positive experiences regardless of the number of depressive symptoms. The results are discussed in the context of current models of Major Depressive Disorder (MDD), expectation change, and cognitive behavioral therapy.

Driving brain state transitions in major depressive disorder through external stimulation.

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.