Major Depressive Disorder Individuals Research Articles

Associations between major depressive disorder and performance-based and self-reported music cognition

BackgroundThere is evidence that major depressive disorder (MDD) comes with multiple cognitive impairments including deficits in perception and memory. Music cognition is one of the least explored cognitive functions in relation to MDD, with some studies pointing to mild amusic deficits. These findings, however, are derived only from performance-based tests. Our objectives were to explore differences in music perception and memory between individuals with MDD and a control group, in both performance test and self-report of amusic dysfunction, and to assess the correlation between these measures.MethodWe recruited 62 participants, including MDD individuals (n = 34) diagnosed with the Mini International Neuropsychiatric Interview and controls (n = 18). All the participants were evaluated with the Montreal Battery for Evaluation of Amusia (MBEA) and the Amusic Dysfunction Inventory (ADI).ResultsNone of the assessed dimensions from the MBEA or the ADI showed statistical differences between groups. Some significant associations were found between ADI's Vocal Production and the MBEA's three tests of the melodic dimension (Scale, Contour and Interval) and between MBEA's Scale and Memory, Meter and ADI's Melodic Perception, and tests of Memory from each respective instrument.ConclusionResults suggest that perception and memory of basic music stimuli are not among the cognitive deficits within MDD, however, they may be indirectly affected by other cognitive phenomena common to this psychopathology, such as poor sustained concentration due to mental fatigue.

Thalamic Volumes and Functional Networks Linked With Self-Regulation Dysfunction in Major Depressive Disorder.

Self-regulation (SR) dysfunction is a crucial risk factor for major depressive disorder (MDD). However, neural substrates of SR linking MDD remain unclear. Sixty-eight healthy controls and 75 MDD patients were recruited to complete regulatory orientation assessments with the Regulatory Focus Questionnaire (RFQ) and Regulatory Mode Questionnaire (RMQ). Nodal intra and inter-network functional connectivity (FC) was defined as FC sum within networks of 46 thalamic subnuclei (TS) or 88 AAL brain regions, and between the two networks separately. Group-level volumetric and functional difference were compared by two sample t-tests. Pearson's correlation analysis and mediation analysis were utilized to investigate the relationship among imaging parameters and the two behaviors. Canonical correlation analysis (CCA) was conducted to explore the inter-network FC mode of TS related to behavioral subscales. Network-based Statistics with machine learning combining powerful brain imaging features was applied to predict individual behavioral subscales. MDD patients showed no group-level volumetric difference in 46 TS but represented significant correlation of TS volume and nodal FC with behavioral subscales. Specially, inter-network FC of the orbital part of the right superior frontal gyrus and the left supplementary motor area mediated the correlation between RFQ/RMQ subscales and depressive severity. Furthermore, CCA identified how the two behaviors are linked via the inter-network FC mode of TS. More crucially, thalamic functional subnetworks could predict RFQ/RMQ subscales and psychomotor retardation for MDD individuals. These findings provided neurological evidence for SR affecting depressive severity in the MDD patients and proposed potential biomarkers to identify the SR-based risk phenotype of MDD individuals.

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

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

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders. Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD). From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored. The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01). ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Combining bioinformatics, network pharmacology and artificial intelligence to predict the target genes of S-ketamine for treating major depressive disorder.

Ketamine has received attention owing to its rapid and long-lasting antidepressant effects; however, its clinical application is restricted by its addictiveness and adverse effects. S-ketamine, which is the S-enantiomer of ketamine, is considered safer and better tolerated by patients than ketamine. This study aimed to identify the key gene targets and potential signalling pathways associated with the mechanism of S-ketamine in major depressive disorder (MDD) treatment. The GSE98793 dataset was extracted from the Gene Expression Omnibus database, and differentially expressed genes were identified in blood samples from patients with MDD and healthy individuals. The hub genes among the differentially expressed genes were identified and enrichment analysis was performed. The therapeutic targets and related signalling pathways of S-ketamine in MDD treatment were analysed. The 3D structures of the target proteins were predicted using AlphaFold2, and molecular docking was performed to verify whether S-ketamine could be successfully docked to the predicted targets. A quantitative polymerase chain reaction was performed to determine the effect of ketamine on the screened targets. Among 228 target genes annotated using pharmacophore target gene analysis, 3 genes were identified and 2 therapeutic signalling pathways were discovered. S-ketamine exerts downregulatory effects on TGM2 and HSP90AB1 expression but exerts an up-regulatory effect on ADORA3 expression. The protein structures of the therapeutic targets were successfully predicted using AlphaFold2. S-ketamine may alleviate depression by targeting specific genes, including TGM2, HSP90AB1 and ADORA3, as well as signalling pathways, including the gonadotropin-releasing hormone and relaxin signalling pathways.

The Reward Positivity Mediates the Association Between Adverse Childhood Experiences and Anhedonia in Young Adults With Drug-Naïve Major Depressive Disorder

BackgroundCurrent clinical studies have indicated that major depressive disorder (MDD) concurrent with adverse childhood experiences (ACEs) is associated with greater anhedonia. However, little is known about whether the change in reward sensitivity among young individuals with MDD and ACEs is related to anhedonia. MethodsWe evaluated anhedonia and ACEs in 86 patients with MDD (31 with no or 1 ACE and 55 with 2 or more ACEs) and 44 healthy control participants. Then, participants completed the Iowa Gambling Task during electroencephalography to measure the reward positivity (RewP) and its difference (ΔRewP; gains minus losses). Furthermore, we constructed a mediation model to assess whether aberrant ΔRewP mediated the relationship between ACEs and anhedonia. ResultsCompared with healthy control participants and MDD patients with no or 1 ACE, MDD patients with 2 or more ACEs had the most severe symptoms of anhedonia and impaired decision making and showed significantly reduced reward sensitivity (most blunted ΔRewP). More importantly, ΔRewP mediated the relationship between ACEs and anhedonia in MDD. ConclusionsWe found that the ΔRewP partially mediated the association between ACEs and anhedonia in patients with MDD, which provides evidence for the neurobiological basis of abnormal changes in the reward system in MDD individuals with early adverse experiences.

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.

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study.

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

Insulin resistance, clinical presentation and resistance to selective serotonin and noradrenaline reuptake inhibitors in major depressive disorder

BackgroundThe understanding of mechanisms underlying non-response to antidepressants is limited. The latest data highlights the role of insulin resistance (IR) in major depressive disorder (MDD) pathophysiology, presentation, and treatment efficacy. This work aimed to assess IR in MDD and explore the relationships between IR, MDD presentation and non-response to selective serotonin and noradrenaline reuptake inhibitors (SNRI).Methods67 MDD individuals: 36 responsive (MDD T[+]), 31 non-responsive (MDD T[−]) to SNRI and 30 healthy controls were recruited. The treatment response criteria were: Clinical Global Impression Scale-Improvement score of 1 or 2 after ≥ 8 weeks of treatment. Participants were assessed by physician and self-report tools measuring depression, anhedonia, anxiety, bipolarity, sleep quality. Blood samples were collected to assess fasting glucose and insulin levels and calculate HOMA-IR (homeostasis model assessment of insulin resistance).ResultsMDD T[-] vs. MDD T[+] had significantly higher body mass index, insulin levels, and HOMA-IR. MDD T[-] presented higher levels of depressed mood, appetite/weight changes, loss of interest, energy, overall depressive symptoms, and sleep impairment; some evaluations suggested higher anhedonia and anxiety in MDD T[-] vs. MDD T[+]. Insulin and IR were weakly but significantly correlated with the severity of psychomotor symptoms, energy level, thoughts of death/suicide, self-criticism, appetite/weight, depressed mood symptoms, sleep problems. IR was weakly but significantly correlated with anhedonia.ConclusionIR appears to be linked to depressive symptoms characteristic of the “metabolic” MDD subtype, such as psychomotor changes, energy level, anhedonia, sleep problems, appetite/weight changes, state and trait anxiety, sleep quality, and non-response to SNRI.

Abnormality in Peripheral and Brain Iron Contents and the Relationship with Grey Matter Volumes in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18-35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of "MCCA+jICA" showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the "MCCA+jICA" analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from "MCCA+jICA" analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV.

Low-grade inflammation and serotonin 4 receptor binding in the healthy and the depressed brain

Inflammatory status affects healthy brain function and also low-grade inflammation putatively contributes to the pathophysiology of Major Depressive Disorder (MDD). This psychoneuro-immunological interplay is complex, bidirectional, and not fully understood. Also, it is not clear to what extent depressive states induce inflammation and/or if increased inflammation leads to depressive symptoms by affecting brain biology including serotonin signaling. The serotonin 4 receptor (5-HT4R) is an interesting new antidepressant target; direct stimulation has antidepressant-like, and pro-cognitive effects, and may also index serotonin tone at least in the adult healthy brain.Here, we investigate whether a peripheral marker of low-grade inflammation (hsCRP) is associated with 5-HT4R brain binding in both a healthy and in an unmedicated MDD group. 5-HT4R PET imaging data and hsCRP measures from 112 healthy and 79 unmedicated MDD individuals were available from the Cimbi database. We evaluated the associations between hsCRP level and 5-HT4R binding in three regions of interest (neocortex, hippocampus, and neostriatum) using multiple linear regression models adjusted for relevant covariates.We did not observe a statistically significant association between hsCRP and 5-HT4R binding. This applied to both the healthy and the MDD group.Our findings do not support a coupling between low-grade inflammation and brain 5-HT4R availability, which suggests that the serotonergic system is not sensitive to low-grade inflammation captured by hsCRP, neither in healthy nor in depressed states. Future studies are needed to test if the brain serotonin system is coupled to other inflammatory markers, for instance in conditions with high-grade and/or prolonged immunoactivation.

The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records

BackgroundThis study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses.MethodsIn UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered.ResultsT2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis (β=0.51\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta =0.51$$\\end{document}, 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13–1.19) times higher in post-T2D MDD individuals.ConclusionsThe timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

Rumination and Overrecruitment of Cognitive Control Circuits in Depression

BackgroundRumination is associated with greater cognitive dysfunction and treatment resistance in major depressive disorder (MDD), but its underlying neural mechanisms are not well understood. Because rumination is characterized by difficulty in controlling negative thoughts, the current study investigated whether rumination was associated with aberrant cognitive control in the absence of negative emotional information. MethodsIndividuals with MDD (n = 176) and healthy control individuals (n = 52) completed the stop signal task with varied stop signal difficulty during functional magnetic resonance imaging. In the task, a longer stop signal asynchrony made stopping difficult (hard stop), whereas a shorter stop signal asynchrony allowed more time for stopping (easy stop). ResultsIn participants with MDD, higher rumination intensity was associated with greater neural activity in response to difficult inhibitory control in the frontoparietal regions. Greater activation for difficult inhibitory control associated with rumination was also positively related to state fear. The imaging results provide compelling evidence for the neural basis of inhibitory control difficulties in individuals with MDD with high rumination. ConclusionsThe association between higher rumination intensity and greater neural activity in regions involved in difficult inhibitory control tasks may provide treatment targets for interventions aimed at improving inhibitory control and reducing rumination in this population.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Understanding mechanisms of depression prevention: study protocol of a randomized cross-over trial to investigate mechanisms of mindfulness and positive fantasizing as intervention techniques for reducing perseverative cognition in remitted depressed individuals

BackgroundMajor Depressive Disorder (MDD) is one of the most prevalent psychiatric disorders, and involves high relapse rates in which persistent negative thinking and rumination (i.e., perseverative cognition [PC]) play an important role. Positive fantasizing and mindfulness are common evidence-based psychological interventions that have been shown to effectively reduce PC and subsequent depressive relapse. How the interventions cause changes in PC over time, is unknown, but likely differ between the two. Whereas fantasizing may change the valence of thought content, mindfulness may operate through disengaging from automatic thought patterns. Comparing mechanisms of both interventions in a clinical sample and a non-clinical sample can give insight into the effectivity of interventions for different individuals. The current study aims to 1) test whether momentary psychological and psychophysiological indices of PC are differentially affected by positive fantasizing versus mindfulness-based interventions, 2) test whether the mechanisms of change by which fantasizing and mindfulness affect PC differ between remitted MDD versus never-depressed (ND) individuals, and 3) explore potential moderators of the main effects of the two interventions (i.e., what works for whom).MethodsIn this cross-over trial of fantasizing versus mindfulness interventions, we will include 50 remitted MDD and 50 ND individuals. Before the start of the measurements, participants complete several individual characteristics. Daily-life diary measures of thoughts and feelings (using an experience sampling method), behavioural measures of spontaneous thoughts (using the Sustained Attention to Response Task), actigraphy, physiological measures (impedance cardiography, electrocardiography, and electroencephalogram), and measures of depressive mood (self-report questionnaires) are performed during the week before (pre-) the interventions and the week during (peri-) the interventions. After a wash-out of at least one month, pre- and peri-intervention measures for the second intervention are repeated.DiscussionThis is the first study integrating self-reports, behavioural-, and physiological measures capturing dynamics at multiple time scales to examine the differential mechanisms of change in PC by psychological interventions in individuals remitted from multiple MDD episodes and ND individuals. Unravelling how therapeutic techniques affect PC in remitted individuals might generate insights that allows development of personalised targeted relapse prevention interventions.Trial registrationClinicalTrials.gov: NCT06145984, November 16, 2023.

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

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

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = –18.3, 95% CI (–34.3 to –2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

No association between serotonin 4 receptor brain binding and personality trait Neuroticism - A positron emission tomography study in depressed patients and healthy individuals

Serotonergic neurotransmission has been linked to normal personality functioning, including the five-factor personality trait Neuroticism, which is a robust risk factor for Major Depressive Disorder (MDD). The serotonin 4 receptor (5-HT4R) is involved in regulating mood and has been shown to be altered in patients with MDD and map onto anxiety levels and memory performance. We here evaluate the link between 5-HT4R levels and Neuroticism in patients with MDD and replicate a previous study in healthy individuals. Neuroticism scores and brain 5-HT4R binding potential assessed with [11C]SB207145 positron emission tomography (PET) imaging were available from the CIMBI database for 63 unmedicated patients with MDD (48 W) and 120 healthy individuals (67 W). We investigated the association between Neuroticism and 5-HT4R binding in six regions of interest: frontal cortex, anterior cingulate cortex, amygdala, hippocampus, neostriatum, and thalamus for each group. There was no association between regional 5-HT4R binding and total Neuroticism score in patients or healthy individuals (all padj > 0.99), although post hoc analyses indicated that in patients, Neuroticism facets of Depression (padj = 0.046) and Impulsivity (padj = 0.009) were positively associated with thalamic and frontal 5-HT4R binding respectively. While trait Neuroticism does not appear to be associated with brain 5-HT4R in either patients with MDD or healthy individuals, we found evidence that the facets Depression and Impulsiveness may be linked to regional 5-HT4R levels in patients.

PREVIDA- IMPACT OF VORTIOXETINE ON MAJOR DEPRESSIVE DISORDER AND PERCEIVED COGNITIVE DYSFUNCTION: A MULTICENTER STUDY

Objective: To find out how common cognitive impairment is in people with major depressive disorder (MDD) and assess how well vortioxetine works for treating both cognitive dysfunction and depressive symptoms. Study Design: Multi-centred, Cross-sectional, prospective follow up design Place &amp; Duration of Study: The research took place over the course of 12 weeks at 16 different psychiatric outpatient clinics throughout Pakistan, from September 2020 to November 2020. Method: The study included 498 individuals with serious depressive disorder diagnoses. The degree of depression symptoms and cognitive impairment was assessed. Psychiatrists gave trial participants vortioxetine after completing the Clinical Global Impression-Severity scale (CGI-S), the Patient Health Questionnaire-9 (PHQ-9), and the Perceived Deficits Questionnaire (PDQ). The variables were reassessed one week (+/- 3 days), one month (+/- 7 days), and three months (+/- 14 days) after the treatment began. Results: After completing a 12-week course of therapy with vortioxetine, mean PHQ 9 and PDQ scores of MDD individuals showed significant improvements. This indicates that vortioxetine is effective in reducing depression symptoms as well as cognitive impairments in MDD patients. A strong connection was seen between PHQ-9 and PDQ scores, indicating a direct relationship between cognitive impairment and depressed symptoms. Conclusions: The results of the research demonstrate how well vortioxetine works to treat cognitive impairments in MDD patients while also reducing depressed symptoms. These findings demonstrate the possibility of vortioxetine as a beneficial therapeutic option for those with MDD diagnoses who also have cognitive impairment. Additional investigation is required to validate these findings and assess their suitability for other demographics.

Major Depression and the Perception of Affective Instrumental and Expressive Gestures: An fMRI Investigation

Major depressive disorder (MDD) is associated with biased perception of human movement. Gesture is important for communication and in this study we investigated neural correlates of gesture perception in MDD. We hypothesised different neural activity between individuals with MDD and typical individuals when viewing instrumental and expressive gestures that were negatively or positively valenced. Differences were expected in brain areas associated with gesture perception, including superior temporal, frontal, and emotion processing regions. We recruited 12 individuals with MDD and 12 typical controls matched on age, gender, and handedness. They viewed gestures displayed by stick figures while functional magnetic resonance imaging (fMRI) was performed. Results of a random effects three-way mixed ANOVA indicated that individuals with MDD had greater activity in the right claustrum compared to controls, regardless of gesture type or valence. Additionally, we observed main effects of gesture type and valence, regardless of group. Perceiving instrumental compared to expressive gestures was associated with greater activity in the left cuneus and left superior temporal gyrus, while perceiving negative compared to positive gestures was associated with greater activity in the right precuneus and right lingual gyrus. We also observed a two-way interaction between gesture type and valence in various brain regions.