Major Depressive Disorder Sample Research Articles

Computational Analysis of Cell Type-Specific Transcriptional Dynamics in Major Depressive Disorder

Depression, also known as major depressive disorder (MDD), is a complex mental health condition that affects millions of people worldwide. Since disease treatments often depend on levels of understanding, this project strives to learn more about the transcriptional components of MDD to delineate the transcriptomic dynamics in MDD. The sample includes the single-cell transcriptomic profiles in the dorsolateral prefrontal cortex (dlPFC) of 34 postmortem patients. This data was acquired from publicly available datasets and run through R-Studio code. Cell clusters were classified into cell types using cell-type-specific signature markers in the original resource datasets. After analyzing immediate early genes (IEGs), MDD-associated genes, and differentially expressed genes, the trend showed broadly lowered expression levels and frequency in MDD patients than in healthy control with some exceptions. After studying 10 different IEGs, the trend showed a lowered gene expression frequency in MDD patients than in control. For MDD-associated genes, a similar trend showed lower frequency and expression levels in the MDD sample, apart from GAD1 and RELN, which had a higher expression level in the MDD sample. Then, by analyzing the top differentially expressed genes in each cell type, most cells showed a lowered frequency in MDD patients besides macrophage/microglial, which had higher expression frequency in IGHG1 and RP11-315A16.1 for the control sample. With each analytical finding, the underlying elements of MDD may be better understood, leading to more precise wet lab experiments and the eventual treatment of MDD.

Motor versus Psychomotor? Deciphering the Neural Source of Psychomotor Retardation in Depression.

Major depressive disorder (MDD) is characterized by psychomotor retardation whose underlying neural source remains unclear. Psychomotor retardation may either be related to a motor source like the motor cortex or, alternatively, to a psychomotor source with neural changes outside motor regions, like input regions such as visual cortex. These two alternative hypotheses in main (n = 41) and replication (n = 18) MDD samples using 7 Tesla MRI are investigated. Analyzing both global and local connectivity in primary motor cortex (BA4), motor network and middle temporal visual cortex complex (MT+), the main findings in MDD are: 1) Reduced local and global synchronization and increased local-to-global output in motor regions, which do not correlate with psychomotor retardation, though. 2) Reduced local-to-local BA4 - MT+ functional connectivity (FC) which correlates with psychomotor retardation. 3) Reduced global synchronization and increased local-to-global output in MT+ which relate to psychomotor retardation. 4) Reduced variability in the psychophysical measures of MT+ based motion perception which relates to psychomotor retardation. Together, it is shown that visual cortex MT+ and its relation to motor cortex play a key role in mediating psychomotor retardation. This supports psychomotor over motor hypothesis about the neural source of psychomotor retardation in MDD.

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center

BackgroundDepression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressive disorder (MDD) and matched controls. We also analyzed subgroups within our MDD sample, including late-life depression and recurrent depression. We further investigated whether machine learning (ML) algorithms could distinguish MDD and each subgroup from controls based on their CoD.MethodsWe conducted a comprehensive analysis of CoD in individuals who died from nontraumatic causes. The diagnosis of lifetime MDD was ascertained based on the DSM-5 criteria using information from a structured interview with a knowledgeable informant. Eleven established ML algorithms were used to differentiate MDD individuals from controls by simultaneously analyzing different disease category groups to account for multiple tests. The McNemar test was further used to compare paired nominal data.ResultsThe initial dataset included records of 1,102 individuals, among whom 232 (21.1%) had a lifetime diagnosis of MDD. Each MDD individual was strictly paired with a control non-psychiatric counterpart. In the MDD group, the most common CoD were circulatory (67.2%), respiratory (13.4%), digestive (6.0%), and cancer (5.6%). Despite employing a range of ML models, we could not find distinctive CoD patterns that could reliably distinguish individuals with MDD from individuals in the control group (average accuracy: 50.6%; accuracy range: 39-59%). These findings were consistent even when considering factors within the MDD group, such as late-life or recurrent MDD. When comparing groups with paired nominal tests, no differences were found for circulatory (p=0.450), respiratory (p=0.790), digestive (p=1.000), or cancer (p=0.855) CoD.ConclusionsOur analysis revealed that autopsy-confirmed CoD exhibited remarkable similarity between individuals with depression and their matched controls, underscoring the existing heterogeneity in the literature. Future research should prioritize more severe manifestations of depression and larger sample sizes, particularly in the context of CoD related to cancer.

Exploration-Exploitation and Suicidal Behavior in Borderline Personality Disorder and Depression

Clinical theory and behavioral studies suggest that people experiencing suicidal crisis are often unable to find constructive solutions or incorporate useful information into their decisions, resulting in premature convergence on suicide and neglect of better alternatives. However, prior studies of suicidal behavior have not formally examined how individuals resolve the tradeoffs between exploiting familiar options and exploring potentially superior alternatives. To investigate exploration and exploitation in suicidal behavior from the formal perspective of reinforcement learning. Two case-control behavioral studies of exploration-exploitation of a large 1-dimensional continuous space and a 21-day prospective ambulatory study of suicidal ideation were conducted between April 2016 and March 2022. Participants were recruited from inpatient psychiatric units, outpatient clinics, and the community in Pittsburgh, Pennsylvania, and underwent laboratory and ambulatory assessments. Adults diagnosed with borderline personality disorder (BPD) and midlife and late-life major depressive disorder (MDD) were included, with each sample including demographically equated groups with a history of high-lethality suicide attempts, low-lethality suicide attempts, individuals with BPD or MDD but no suicide attempts, and control individuals without psychiatric disorders. The MDD sample also included a subgroup with serious suicidal ideation. Behavioral (model-free and model-derived) indices of exploration and exploitation, suicide attempt lethality (Beck Lethality Scale), and prospectively assessed suicidal ideation. The BPD group included 171 adults (mean [SD] age, 30.55 [9.13] years; 135 [79%] female). The MDD group included 143 adults (mean [SD] age, 62.03 [6.82] years; 81 [57%] female). Across the BPD (χ23 = 50.68; P < .001) and MDD (χ24 = 36.34; P < .001) samples, individuals with high-lethality suicide attempts discovered fewer options than other groups as they were unable to shift away from unrewarded options. In contrast, those with low-lethality attempts were prone to excessive behavioral shifts after rewarded and unrewarded actions. No differences were seen in strategic early exploration or in exploitation. Among 84 participants with BPD in the ambulatory study, 56 reported suicidal ideation. Underexploration also predicted incident suicidal ideation (χ21 = 30.16; P < .001), validating the case-control results prospectively. The findings were robust to confounds, including medication exposure, affective state, and behavioral heterogeneity. The findings suggest that narrow exploration and inability to abandon inferior options are associated with serious suicidal behavior and chronic suicidal thoughts. By contrast, individuals in this study who engaged in low-lethality suicidal behavior displayed a low threshold for taking potentially disadvantageous actions.

Functional connectivity of the default mode network in first-episode drug-naïve patients with major depressive disorder

BackgroundAlterations in the default mode network (DMN) have been reported in major depressive disorder (MDD), well-replicated robust alterations of functional connectivity (FC) of DMN remain to be established. Investigating the functional connections of DMN at the overall and subsystem level in early MDD patients has the potential to advance our understanding of the physiopathology of this disorder. MethodsWe recruited 115 first-episode drug-naïve patients with MDD and 137 demographic-matched healthy controls (HCs). We first compared FC within the DMN, within/between the DMN subsystems, and from DMN subsystems to the whole brain between groups. Subsequently, we explored correlations between clinical features and identified alterations in FC. ResultsFirst-episode drug-naïve patients with MDD showed significantly increased FC within the DMN, dorsal DMN and medial DMN. Each subsystem showed a distinct FC pattern with other brain networks. Increased FC between the subsystems (core DMN, dorsal DMN) and other networks was associated with more severe depressive symptoms, while medial DMN-related connectivity correlated with memory performance. LimitationsThe relatively large “pure” MDD sample could only be generalized to a limited population. And, atypical asymmetric FCs in the DMN related to MDD might be missed for only left-lateralized ROIs were used to avoid strong correlations between mirrored (right/left) seed regions. ConclusionThese findings suggest patients with early MDD showed distinct patterns of FC alterations throughout DMN and its subsystems, which were related to illness severity and illness-associated cognitive impairment, highlighting their clinical significance.

Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning.

This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learning approaches. Gene expression data of 128 MDD patients and 64 healthy controls from blood samples were obtained. Differentially expressed were identified and weighted gene co-expression network analysis (WGCNA) was first performed to screen MDD-related key genes. These genes were then intersected with 1475 known circadian rhythm genes to identify circadian rhythm genes associated with MDD. Finally, multiple machine learning algorithms were applied for further selection, to determine the most critical 4 circadian rhythm biomarkers. Four key circadian rhythm genes (ABCC2, APP, HK2 and RORA) were identified that could effectively distinguish MDD samples from controls. These genes were significantly enriched in circadian pathways and showed strong correlations with immune cell infiltration. Drug target prediction suggested that small molecules like melatonin and escitalopram may target these circadian rhythm proteins. This study revealed discovered 4 key circadian rhythm genes closely associated with MDD, which may serve as diagnostic biomarkers and therapeutic targets. The findings highlight the important roles of circadian disruptions in the pathogenesis of MDD, providing new insights for precision diagnosis and targeted treatment of MDD.

Neural correlates of stress-reactive rumination in depression – The role of childhood trauma and social anxiety

Recent work showed an association of prefrontal dysfunctions in patients with Major Depressive Disorder (MDD) and social stress induced rumination. However, up to date it is unclear which etiological features of MDD might cause prefrontal dysfunctions. In the study at hand, we aimed to replicate recent findings, that showed prefrontal activation alterations during the Trier Social Stress Test (TSST) and subsequently increased stress-reactive rumination in MDD compared to healthy controls. Moreover, we aimed to explore the role of adverse childhood experiences and other clinical variables in this relationship. N = 55 patients currently suffering from MDD and n = 42 healthy controls (HC) underwent the TSST, while cortical activity in areas of the Cognitive Control Network (CCN) was measured via functional near-infrared spectroscopy (fNIRS). The TSST successfully induced a stress reaction (physiologically, as well as indicated by subjective stress ratings) and state rumination in all subjects with moderate to large effect sizes. In comparison to HC, MDD patients showed elevated levels of state rumination with large effect sizes, as well as a typical pattern of reduced cortical oxygenation during stress in the CCN with moderate effect sizes. Self-reported emotional abuse and social anxiety were moderately positively associated with increased stress-reactive rumination. Within the MDD sample, emotional abuse was negatively and social anxiety positively associated with cortical oxygenation within the CCN with moderate to large effect sizes. In conclusion, our results replicate previous findings on MDD-associated prefrontal hypoactivity during stress and extends the research toward specific subtypes of depression.

An association study of m6A methylation with major depressive disorder

ObjectiveTo find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).MethodsDifferential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.ResultsDifferential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.ConclusionAmong all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

TiRNA-Gly-GCC-001 in major depressive disorder: Promising diagnostic and therapeutic biomarker.

In major depressive disorder (MDD), exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment and predicting the treatment response. Currently, tRNA-derived small ribonucleic acids (tsRNAs) have been established as promising non-invasive biomarker candidates that may enable a more reliable diagnosis or monitoring of various diseases. Herein, we aimed to explore tsRNA expression together with functional activities in MDD development. Serum samples were obtained from patients with MDD and healthy controls, and small RNA sequencing (RNA-Seq) was used to profile tsRNA expression. Dysregulated tsRNAs in MDD were validated by quantitative real-time polymerase chain reaction (qRT-PCR). The diagnostic utility of specific tsRNAs and the expression of these tsRNAs after antidepressant treatment were analysed. In total, 38 tsRNAs were significantly differentially expressed in MDD samples relative to healthy individuals (34 up-regulated and 4 down-regulated). qRT-PCR was used to validate the expression of six tsRNAs that were up-regulated in MDD (tiRNA-1:20-chrM.Ser-GCT, tiRNA-1:33-Gly-GCC-1, tRF-1:22-chrM.Ser-GCT, tRF-1:31-Ala-AGC-4-M6, tRF-1:31-Pro-TGG-2 and tRF-1:32-chrM.Gln-TTG). Interestingly, serum tiRNA-Gly-GCC-001 levels exhibited an area under the ROC curve of 0.844. Moreover, tiRNA-Gly-GCC-001 is predicted to suppress brain-derived neurotrophic factor (BDNF) expression. Furthermore, significant tiRNA-Gly-GCC-001 down-regulation was evident following an 8-week treatment course and served as a promising baseline predictor of patient response to antidepressant therapy. Our current work reports for the first time that tiRNA-Gly-GCC-001 is a promising MDD biomarker candidate that can predict patient responses to antidepressant therapy.

The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder–Fine-Grained Meta-Analyses

Background. While the brain correlates of major depressive disorder (MDD) have been extensively studied, there is no consensus conclusion so far. Various meta-analyses tried to determine the most consistent findings, but the results are often discordant for grey matter volume (GMV) atrophy and hypertrophy. Applying rigorous and stringent inclusion criteria and controlling for confounding factors, such as the presence of anxiety comorbidity, we carried out two novel meta-analyses on the existing literature to unveil MDD signatures. Methods. A systematic literature search was performed up to January 2023. Seventy-three studies on MDD patients reporting GMV abnormalities were included in the first meta-analysis, for a total of 6167 patients and 6237 healthy controls (HC). To test the effects of anxiety comorbidity, we conducted a second meta-analysis, by adding to the original pure MDD sample a new cohort of MDD patients with comorbid anxiety disorders (308 patients and 342 HC). An activation likelihood estimation (ALE) analysis and a coordinate-based mapping approach separate for atrophy and hypertrophy were used to identify common brain structural alterations among patients. Results. The pure MDD sample exhibited atrophy in the left insula, as well as hypertrophy in the bilateral amygdala and parahippocampal gyri. When we added patients with comorbid anxiety to the original sample, bilateral insula atrophy emerged, whereas the hypertrophy results were not replicated. Conclusions. Our findings revealed important structural alterations in pure MDD patients, particularly in the insula and amygdala, which play key roles in sensory input integration and in emotional processing, respectively. Additionally, the amygdala and parahippocampal gyrus hypertrophy may be related to MDD functional overactivation to emotional stimuli, rumination, and overactive self-referential thinking. Conversely, the presence of anxiety comorbidity revealed separate effects which were not seen in the pure MDD sample, underscoring the importance of strict inclusion criteria for investigations of disorder-specific effects.

Latent Profile Analysis of the Five Facet Mindfulness Questionnaire (FFMQ) Subscales in a Naturalistic Sample of Patients with Past or Present Major Depression: A Replication and Extension Study

ObjectivesMindfulness is a multifaceted concept which might be best captured by person-centered profiles rather than by variable-centered assessment of its subcomponents. Patients with different mindfulness profiles may vary in mental health status and might experience differential clinical outcomes of Mindfulness-Based Cognitive Therapy (MBCT). We therefore aimed to assess the relationship between mindfulness profiles and mental health in patients with major depressive disorder (MDD) and to examine whether mindfulness profiles are predictive for MBCT-induced changes in mental health.MethodLatent profile analysis (LPA), a method used to identify hidden subgroups of individuals within a population, was performed on pre-MBCT subscale scores of the Five Facet Mindfulness Questionnaire in patients with current or remitted MDD (n=754). Equality of means of pre-MBCT, post-MBCT, and residualized change scores of several mental health measures across the latent profiles was tested.ResultsLPA identified four distinct mindfulness profiles. Three profiles were similar to previously identified profiles in another MDD sample (“very low mindfulness,” “high mindfulness,” “non-judgmentally aware”) and one profile was similar to that identified in non-clinical populations (“judgmentally observing”). The “high mindfulness” subgroup scored best, the “very low mindfulness” worst, and the other subgroups intermediate on mental health in terms of depressive symptoms, worry, overall functional impairment, and self-compassion. Mindfulness profiles were not predictive of MBCT-induced changes in mental health.ConclusionsMindfulness profiles were differentially related to mental health, but were not predictive of treatment outcome. Future research would benefit from longitudinal assessment of latent mindfulness profiles to examine whether a patient’s profile changes after MBCT and whether hypothesized change in profile would be related to treatment outcome.PreregistrationThis study was not preregistered.

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.

Genetic Contribution to the Heterogeneity of Major Depressive Disorder: Evidence From a Sibling-Based Design Using Swedish National Registers.

Major depressive disorder (MDD) is highly heterogeneous. Standard typology partly captures the disorder's symptomatic heterogeneity, although whether it adequately captures etiological heterogeneity remains elusive. The aim of this study was to investigate the genetic characterization of MDD heterogeneity. Using Swedish patient register data on 1.5 million individuals, the authors identified 46,255 individuals with specialist-diagnosed MDD. Eighteen subgroups were identified based on nine comparison groups defined by clinical and psychosocial features, including severity, recurrence, comorbidities, suicidality, impairment, disability, care unit, and age at diagnosis. A sibling-based design and classic quantitative genetic models were applied to estimate heritability of MDD subgroups and genetic correlations between subgroups. Estimates of heritability ranged from 30.5% to 58.3% across subgroups. The disabled and youth-onset subgroups showed significantly higher heritability (55.1%-58.3%) than the overall MDD sample (45.3%, 95% CI=43.0-47.5), and the subgroups with single-episode MDD and without psychiatric comorbidity showed significantly lower estimates (30.5%-34.4%). Estimates of genetic correlations between the subgroups within comparison groups ranged from 0.33 to 0.90. Seven of nine genetic correlations were significantly smaller than 1, suggesting differences in underlying genetic architecture. These results were largely consistent with previous work using genomic data. The findings of differential heritability and partially distinct genetic components in subgroups provide important insights into the genetic heterogeneity of MDD and a deeper etiological understanding of MDD clinical subgroups.

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes.

Major depressive disorder (MDD) negatively affects patients' behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD. Gene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes. A diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes. Our current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD.

Single-Nucleus Transcriptome Profiling of Dorsolateral Prefrontal Cortex: Mechanistic Roles for Neuronal Gene Expression, Including the 17q21.31 Locus, in PTSD Stress Response.

Multidisciplinary studies of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) implicate the dorsolateral prefrontal cortex (DLPFC) in disease risk and pathophysiology. Postmortem brain studies have relied on bulk-tissue RNA sequencing (RNA-seq), but single-cell RNA-seq is needed to dissect cell-type-specific mechanisms. The authors conducted the first single-nucleus RNA-seq postmortem brain study in PTSD to elucidate disease transcriptomic pathology with cell-type-specific resolution. Profiling of 32 DLPFC samples from 11 individuals with PTSD, 10 with MDD, and 11 control subjects was conducted (∼415K nuclei; >13K cells per sample). A replication sample included 15 DLPFC samples (∼160K nuclei; >11K cells per sample). Differential gene expression analyses identified significant single-nucleus RNA-seq differentially expressed genes (snDEGs) in excitatory (EX) and inhibitory (IN) neurons and astrocytes, but not in other cell types or bulk tissue. MDD samples had more false discovery rate-corrected significant snDEGs, and PTSD samples had a greater replication rate. In EX and IN neurons, biological pathways that were differentially enriched in PTSD compared with MDD included glucocorticoid signaling. Furthermore, glucocorticoid signaling in induced pluripotent stem cell (iPSC)-derived cortical neurons demonstrated greater relevance in PTSD and opposite direction of regulation compared with MDD, especially in EX neurons. Many snDEGs were from the 17q21.31 locus and are particularly interesting given causal roles in disease pathogenesis and DLPFC-based neuroimaging (PTSD: ARL17B, LINC02210-CRHR1, and LRRC37A2; MDD: LRRC37A and LRP4), while others were regulated by glucocorticoids in iPSC-derived neurons (PTSD: SLC16A6, TAF1C; MDD: CDH3). The study findings point to cell-type-specific mechanisms of brain stress response in PTSD and MDD, highlighting the importance of examining cell-type-specific gene expression and indicating promising novel biomarkers and therapeutic targets.

Repetitive Negative Thinking–Specific and –Nonspecific White Matter Tracts Engaged by Historical Psychosurgical Targets for Depression

BackgroundRepetitive negative thinking (RNT) is a frequent symptom of major depressive disorder (MDD) that is associated with poor outcomes and treatment resistance. While most studies on RNT have focused on structural and functional characteristics of gray matter, this study aimed to examine the association between white matter (WM) tracts and interindividual variability in RNT. MethodsA probabilistic tractography approach was used to characterize differences in the size and anatomical trajectory of WM fibers traversing psychosurgery targets historically useful in the treatment of MDD (anterior capsulotomy, anterior cingulotomy, and subcaudate tractotomy) in patients with MDD and low (n = 53) or high (n = 52) RNT, and healthy control subjects (n = 54). MDD samples were propensity matched on depression and anxiety severity and demographics. ResultsWM tracts traversing left hemisphere targets and reaching the ventral anterior body of the corpus callosum (thus extending to contralateral regions) were larger in the high-RNT MDD group compared with low-RNT (effect size D = 0.27, p = .042) and healthy control (D = 0.23, p = .02) groups. MDD was associated with greater size of tracts that converge onto the right medial orbitofrontal cortex regardless of RNT intensity. Other RNT-nonspecific findings in MDD involved tracts reaching the left primary motor and right primary somatosensory cortices. ConclusionsThis study provides the first evidence to our knowledge that WM connectivity patterns, which could become targets of intervention, differ between high- and low-RNT participants with MDD. These WM differences extend to circuits that are not specific to RNT, possibly subserving reward mechanisms and psychomotor activity.

Alcohol use as a predictor of the course of major depressive disorder: a prospective population-based study.

There are indications that problematic alcohol use may negatively impact the course of major depressive disorder (MDD). However, most studies on alcohol use and adverse MDD outcomes are conducted amongst MDD populations with (severe) alcohol use disorder in psychiatric treatment settings. Therefore, it remains unclear whether these results can be generalised to the general population. In light of this, we examined the longitudinal relationship between alcohol use and MDD persistence after a 3-year follow-up amongst people with MDD from the general population. Data were derived from the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2), a psychiatric epidemiological prospective study comprising four waves amongst the adult Dutch general population (n = 6.646). The study sample (n = 642) consisted of those with 12-month MDD who participated at the follow-up wave. The outcome was 12-month MDD persistence after the 3-year follow-up, which was assessed via the Composite International Diagnostic Interview version 3.0. Weekly alcohol consumption was operationalised as non-drinking (0 drinks), low-risk drinking (⩽7 drinks; reference), at-risk drinking (women 8-13 drinks, men 8-20 drinks) and high-risk drinking (women ⩾14, men ⩾21 drinks). We performed univariate and multiple logistic regression analyses, which were adjusted for various socio-demographic and health-related factors. The majority (67.4%) of the MDD sample were female, while the mean age was 47.1 years. Amongst these, 23.8% were non-drinkers, 52.0% were low-risk drinkers and 14.3% and 9.4% were at-risk and high-risk drinkers, respectively. Around one-quarter of the sample (23.6%) met the criteria for a persistent MDD after 3-year follow-up. No statistically significant association was found between alcohol use and MDD persistence, either for the crude model or the adjusted models. In comparison to low-risk drinking, the full adjusted model showed no statistically significant associations between MDD persistence and non-drinking (odds ratio (OR) = 1.15, p = 0.620), at-risk drinking (OR = 1.25, p = 0.423), or high-risk drinking (OR = 0.74, p = 0.501). Contrary to our expectations, our findings showed that alcohol use was not a predictor of MDD persistence after 3-year follow-up amongst people with MDD from the general population.

Physical fitness is associated with neural activity during working memory performance in major depressive disorder

BackgroundDeficits in cognition like working memory (WM) are highly prevalent symptoms related to major depressive disorder (MDD). Neuroimaging studies have described frontoparietal abnormalities in patients with MDD as a basis for these deficits. Based on research in healthy adults, it is hypothesized that increased physical fitness might be a protective factor for these deficits in MDD. However, the relationship between physical fitness and WM-related neural activity and performance has not been tested in MDD, to date. Understanding these associations could inform the development of physical exercise interventions in MDD. MethodsWithin a larger project, 111 (53female) MDD outpatients and 56 (34female) healthy controls performed an n-back task (0-, 1-, 2-, 3-back) during functional Magnetic Resonance Imaging. Physical fitness from a graded exercise test on a cycle ergometer was performed by 106 MDD patients. ResultsPatients showed reduced performance particularly at high loads of the n-back WM task and prolonged reaction times at all n-back loads. A whole-brain interaction analysis of group by WM load revealed reduced neural activity in six frontoparietal clusters at medium and high WM loads in MDD patients compared to healthy controls. Analysis of covariance within the MDD sample showed that physical fitness was associated with neural activity in right and left superior parietal lobules. Externally defined Regions of Interest confirmed this analysis. ConclusionsResults indicate frontoparietal hypoactivity in MDD at high demands, arguing for decreased WM capacity. We demonstrate a parietal fitness correlate which could be used to guide future research on effects of exercise on cognitive functioning in MDD.

Disrupted effective connectivity of the default, salience and dorsal attention networks in major depressive disorder: a study using spectral dynamic causal modelling of resting-state fMRI

<h3>Background:</h3> Understanding the neural basis for major depressive disorder (MDD) is essential for its diagnosis and treatment. Aberrant activation and functional connectivity of the default mode network (DMN), salience network (SN) and dorsal attention network (DAN) have been found consistently in patients with MDD. However, whether effective connectivity within and between these networks is altered in MDD remains unknown. The primary objective of this study was to investigate the effective connectivity of the 3 networks in patients with MDD at rest. <h3>Methods:</h3> We included 63 patients with MDD (35 first-episode and 28 recurrent) and 74 healthy controls, and collected resting-state functional MRI data for all participants. We defined 15 regions of interest from the 3 functional brain networks of interest using group independent component analysis. We estimated the coupling parameters that reflected the causal interactions among these regions using spectral dynamic causal modelling. We used parametric empirical Bayes to determine commonalities across groups, differences between patients with MDD and healthy controls, and differences between patients with recurrent and first-episode MDD. <h3>Results:</h3> We found positive (excitatory) connections within each network, negative (inhibitory) connections from the SN and DAN to the DMN, and positive connections from the DAN to the SN across groups. Compared to healthy controls, patients with MDD showed increased positive connections within the DMN, a decreased absolute value of negative connectivity from the SN to the DMN, and increased positive connections from the SN to the DAN. We also found that patients with recurrent MDD showed remarkably different effective connections compared to patients with first-episode MDD, especially related to the DAN. <h3>Limitations:</h3> Because of the relatively small sample size and the unclear medication history of the MDD sample, the present findings are in need of replication. <h3>Conclusion:</h3> These findings suggest that effective connectivity among high-order brain functional networks during rest was disrupted in patients with MDD. Moreover, patients with recurrent MDD exhibited different effective connections compared to patients with first-episode MDD. These differences in effective connectivity might provide new insights into the neural substrates of MDD.

Comprehensive analysis of endoplasmic reticulum stress and immune infiltration in major depressive disorder.

Major depressive disorder (MDD) is a life-threatening, debilitating mental health condition. An important factor in the development of depression is endoplasmic reticulum stress (ERS). However, their roles in MDD have not yet been established. The goal of this study was to examine ERS and its underlying molecular mechanisms in MDD. We used data from two microarray datasets (GSE98793 and GSE39653) and the GeneCards database to examine the reticulum stress-related differentially expressed genes (ERSR-DEGs) associated with MDD. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were used to further investigate the function and mechanism of ERS in MDD. Moreover, we constructed protein-protein interaction (PPI) networks to identify hub genes as well as the regulatory network of microRNAs (miRNAs), transcription factors (TFs), and potential drugs related to ERSR-DEGs. CIBERSORT was then used to evaluate the immune activity of MDD samples and conduct a correlation analysis between the hub genes and immune cells. In total, 37 ERSR-DEGs and five hub genes were identified (NCF1, MAPK14, CASP1, CYBA, and TNF). Functional enrichment analysis revealed that ERSR-DEGs were predominantly enriched in inflammation-and immunity-related pathways, such as tumor necrosis factor signaling, NF-κB signaling, and Toll-like receptor signaling pathways. Additionally, 179 miRNAs, 25 TFs, and 15 potential drugs were tested for their interactions with the ERSR-DEGs. CIBERSORT found high proportions of Tregs, monocytes, and macrophages M0 in the MDD samples. Among these, hub genes showed a significant correlation with immune cell infiltration in patients with MDD. NCF1, MAPK14, CASP1, CYBA, and TNF are potential ERS-related biomarkers for the diagnosis of MDD. Our research has revealed a significant correlation between immune cells and ERS-related genes with MDD. Not only did our study contribute to a better understanding of the regulatory mechanisms of ERS in underlying MDD pathology, but it also established a paradigm for future studies on ERS.