Recurrent Major Depressive Disorder Research Articles

Balancing the beautiful and the good in pursuit of biomarkers for depression.

Balancing the beautiful and the good in pursuit of biomarkers for depression.

Decreased cortical gyrification in major depressive disorder.

Early neurodevelopmental deviations, such as abnormal cortical folding patterns, are candidate biomarkers of major depressive disorder (MDD). We aimed to investigate the association of MDD with the local gyrification index (LGI) in each cortical region at the whole-brain level, and the association of the LGI with clinical characteristics of MDD. We obtained T1-weighted images from 234 patients with MDD and 215 healthy controls (HCs). The LGI values from 66 cortical regions in the bilateral hemispheres were automatically calculated according to the Desikan-Killiany atlas. We compared the LGI values between the MDD and HC groups using analysis of covariance, including age, sex, and years of education as covariates. The association between the clinical characteristics and LGI values was investigated in the MDD group. Compared with HCs, patients with MDD showed significantly decreased LGI values in the cortical regions, including the bilateral ventrolateral and dorsolateral prefrontal cortices, medial and lateral orbitofrontal cortices, insula, right rostral anterior cingulate cortex, and several temporal and parietal regions, with the largest effect size in the left pars triangularis (Cohen's f2 = 0.361; p = 1.78 × 10-13). Regarding the association of clinical characteristics with LGIs within the MDD group, recurrence and longer illness duration were associated with increased gyrification in several occipital and temporal regions, which showed no significant difference in LGIs between the MDD and HC groups. These findings suggest that the LGI may be a relatively stable neuroimaging marker associated with MDD predisposition.

Research and Diagnostic Algorithmic Rules (RADAR) and RADAR Plots for the First Episode of Major Depressive Disorder: Effects of Childhood and Recent Adverse Experiences on Suicidal Behaviors, Neurocognition and Phenome Features.

Recent studies have proposed valid precision models and valid Research and Diagnostic Algorithmic Rules (RADAR) for recurrent major depressive disorder (MDD). The aim of the current study was to construct precision models and RADAR scores in patients experiencing first-episode MDD and to examine whether adverse childhood experiences (ACE) and negative life events (NLE) are associated with suicidal behaviors (SB), cognitive impairment, and phenome RADAR scores. This study recruited 90 patients with major depressive disorder (MDD) in an acute phase, of whom 71 showed a first-episode MDD (FEM), and 40 controls. We constructed RADAR scores for ACE; NLE encountered in the last year; SB; and severity of depression, anxiety, chronic fatigue, and physiosomatic symptoms using the Hamilton Depression and Anxiety Rating Scales and the FibroFatigue scale. The partial least squares analysis showed that in FEM, one latent vector (labeled the phenome of FEM) could be extracted from depressive, anxiety, fatigue, physiosomatic, melancholia, and insomnia symptoms, SB, and cognitive impairments. The latter were conceptualized as a latent vector extracted from the Verbal Fluency Test, the Mini-Mental State Examination, and ratings of memory and judgement, indicating a generalized cognitive decline (G-CoDe). We found that 60.8% of the variance in the FEM phenome was explained by the cumulative effects of NLE and ACE, in particular emotional neglect and, to a lesser extent, physical abuse. In conclusion, the RADAR scores and plots constructed here should be used in research and clinical settings, rather than the binary diagnosis of MDD based on the DSM-5 or ICD.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Electrophysiological evidence of mal-adaptation to error in remitted depression

Identifying risk markers for major depressive disorder (MDD) that persist into remission is key to address MDD’s high rate of recurrence. Central to MDD recurrence are the disorder’s negative information processing biases, such as heightened responses to errors, which may subsequently impair abilities to monitor performance and adjust behaviors based on environmental demands. However, little is known regarding the neurophysiological correlates of post-error adaptation in depression. The current study investigated event-related potentials (ERPs) and behavioral performance following errors from a flanker task in 58 participants with remitted MDD (rMDD) and 118 healthy controls (HC). Specifically, using trial-level data, we tested: (a) the impact of errors on response-locked ERPs of the current and post-error trials (error-related negativity [ERN] and correct response negativity [CRN]) and (b) longer-term adaptation to errors (ERN/CRN) over the course of the task. Compared to HC, rMDD participants showed a larger ERN to the current trial and smaller habituation in ERN over time. On trials immediately following errors, rMDD participants showed slower reaction times that were predicted by the previous-trial ERN amplitude but comparable accuracy to HC, suggesting a deficient ability to disengage from errors and/or a compensatory effort to mitigate accuracy decrements. Critically, this pattern of responding: (a) was concurrently associated with greater levels of anhedonia symptoms, more severe MDD history, and interpersonal impairment (but lower impairment in life activities) and (b) predicted more anhedonia symptoms at one-year follow-up. Collectively, a hyperactive performance monitoring system may be a useful risk marker for future MDD recurrence.

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

Abstract P3-03-08: Depression and breast cancer recurrence among female veterans in the United States: a retrospective cohort study

Abstract Background: The importance of mental health to improve the quality of cancer survivorship and outcomes is gaining increased attention. Major depressive disorder (MDD) has been associated with higher breast cancer (BC) incidence, BC-specific and all-cause mortality. The effect of MDD on BC recurrence remains under-studied. The Veterans Health Administration (VHA) provides a unique opportunity to examine this relationship as it mandates annual MDD screening in the primary care setting and reports national screening rates of 96%. Objective: To examine the relationship between pre-existing MDD and BC recurrence among a diverse cohort of women in the United States. Methods: We established a retrospective cohort of women (age ≥ 18 years) diagnosed with early-stage incident BC from 2010 through 2019 using the VHA’s electronic medical record database. We used a two-year window to identify women with clinically diagnosed MDD prior to their BC diagnosis. Our outcome of interest was BC recurrence (local recurrence/second primary and distant recurrence). We used multivariable proportional hazards regression to examine the association between MDD and BC recurrence, accounting for competing risk of death. Our analyses were adjusted for age, race, ethnicity, marital status, priority group rating (proxy for socioeconomic status), rurality of primary residence, geographical region, cancer stage, year of BC diagnosis, and comorbidity burden. Priority group rating is a metric that incorporates patients’ income, receipt of VHA assistance benefits, capacity for gainful employment, and severity of service-connected conditions. We conducted subgroup analyses by tumor subtype. RESULTS: Our cohort consists of 6,045 women with BC, of whom 1,750 (29.0%) had a pre-existing MDD diagnosis. The median length of follow-up from BC diagnosis was 4.5 years (IQR=4.8 years). The average age at BC diagnosis was 57 years (SD=11 years) overall and 56 years (SD=10 years) among women with MDD. Sixty-one percent of women were white, 29% were black, 27% were married, and 28% were rural dwelling. Forty-four percent of invasive carcinomas were localized and 21% were regional. Thirty-five percent of patients had ductal carcinoma in situ. Women with MDD had a higher average Charlson Comorbidity Index score (mean=0.75, SD=1.13 vs. mean=0.58, SD=1.03) and proportion with a priority group rating between 1-4 (40% vs. 32%). The distribution of other described patient characteristics among women with MDD were similar to the overall cohort. On multivariable analysis, women with MDD had a 31% (95% CI: 1.11, 1.56) higher risk of BC recurrence compared to women without MDD. MDD was associated with a 29% (95% CI: 1.01, 1.66) and 33% (95% CI: 1.05, 1.67) higher risk of local recurrence/second primary and distant recurrence, respectively. The relationship between MDD and BC recurrence remained significant after adjusting for tumor subtype (HR=1.40, 95% CI: 1.07, 1.83). In subgroup analyses stratified by tumor subtype, MDD was associated with a higher risk of BC recurrence among women with estrogen receptor positive versus negative tumors (HR=1.65, 95% CI: 1.27, 2.14 vs. HR=0.71, 95% CI: 0.34, 1.46) and HER2/neu negative versus positive tumors (HR=1.53, 95% CI: 1.16, 2.00 vs. HR=1.24, 95% CI: 0.51, 2.96). The interaction between estrogen receptor status and MDD was statistically significant (p-interaction=0.02). CONCLUSION: Women with incident BC should be screened for MDD early in the cancer care continuum given the prevalence of MDD and association with increased risk of local recurrence/second primary and distant BC recurrence. Further research is needed to understand the interplay between these two diseases and their treatments. Citation Format: Maya Aboumrad, Brian Shiner, Avonne Connor, Bradley V. Watts, Kala Visvanathan. Depression and breast cancer recurrence among female veterans in the United States: a retrospective cohort study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-08.

Regionally decreased cortical gyrification in patients with major depressive disorder

IntroductionEarly neurodevelopmental deviations, such as abnormal cortical folding patterns, are a candidate biomarker for major depressive disorder (MDD). Previous studies on patterns of abnormal cortical gyrification in MDD have provided valuable insights; however, the findings on cortical folding are controversial.ObjectivesWe aimed to investigate the association of MDD with the local gyrification index (LGI) in each cortical region at the whole-brain level and the association of the LGI with clinical characteristics of MDD, including recurrence, remission status, illness duration, severity of depression, and medication status of patients with MDD.MethodsWe obtained T1-weighted images of 234 patients with MDD and 215 healthy controls (HCs). LGI values were automatically calculated using the FreeSurfer software according to the Desikan–Killiany atlas. LGI values from 66 cortical regions in the bilateral hemispheres were analyzed. We compared the LGI values between the MDD and HC groups using the analysis of covariance, including patients’ age, sex, and years of education as covariates. The association between clinical characteristics and LGI values was investigated in the MDD group.ResultsCompared with HCs, patients with MDD showed significantly decreased LGI values in the cortical regions, including the bilateral ventrolateral and dorsolateral prefrontal cortices, medial and lateral orbitofrontal cortices, insula, right rostral anterior cingulate cortex, and several temporal and parietal regions, with the highest effect size in the left pars triangularis (Cohen’s f = 0.361; P = 1.78 × 10-13). As for the association of clinical characteristics with LGIs within the MDD group, recurrence and longer illness duration of MDD were associated with increased gyrification in several occipital and temporal regions, which showed no significant difference in LGIs between MDD and HC groups.ConclusionsConsidering that the aforementioned cortical regions are involved in emotion regulation, abnormal cortical folding patterns in such regions may be associated with the dysfunction of emotion regulation-related neural circuits, which may lead to MDD. These findings suggest that LGI may be a relatively stable neuroimaging marker associated with the trait of MDD predisposition.Disclosure of InterestNone Declared

Intranasal Esketamine administration in catatonia: a case report.

IntroductionCatatonia is a complex psychomotor syndrome that often goes unrecognized and, consequently, untreated. Prompt and correct identification of catatonia allows for highly effective treatment and prevention of possible complications. Benzodiazepines and electroconvulsive therapy (ECT) are the most widely studied treatment methods. However, no uniform treatment method has yet been brought forward and no previous attempts to treat catatonia on a patient suffering concomitant major depressive disorder (MDD) with NMDA receptor antagonists have been documented so far.ObjectivesTo describe the unknown and novel management of catatonia and MDD with intranasal esketamine, a NMDA receptor antagonist.MethodsA 55-year-old woman with a diagnosis of long-standing recurrent major depressive disorder who was admitted to the psychiatric inpatient unit of UniversityHospital Marqués de Valdecilla (Santander, Spain) suffering a complex catatonic, mutative state framed on a severe MDD. Different ineffective therapeutic interventions were deployed during the course of her illness. After failing to improve under conventional pharmacological treatment and ECT, and given the complexity of peripheral venous access on this patient (which disabled the option for iv ketamine use), we decided to initiate compassionate treatment with intranasal esketamine.ResultsIntranasal esketamine was effective in the resolution of patient’s complex catatonic state. Clinical response from catatonia was observed after 6 intranasal esketamine administrations (2-week follow-up), reaching full catatonia and MDD remission after 12 sessions in absence of significant adverse eventsConclusionsEsketamine showed promising effectiveness for the treatment of catatonia in the context of MDD, although further research on this topic is needed.Disclosure of InterestNone Declared

Special Report: Bipolar Disorder II—Frequently Neglected, Misdiagnosed

Special Report: Bipolar Disorder II—Frequently Neglected, Misdiagnosed

Eight-week antidepressant treatment changes intrinsic functional brain topology in first-episode drug-naïve patients with major depressive disorder

BackgroundA recent study revealed disrupted topological organization of whole-brain networks in patients with major depressive disorder (MDD); however, these results were mostly driven by recurrent MDD patients, rather than first-episode drug-naïve (FEDN) patients. Furthermore, few longitudinal studies have explored the effects of antidepressant therapy on the topological organization of whole-brain networks. MethodsWe collected clinical and neuroimaging data from 159 FEDN MDD patients and 152 normal controls (NCs). A total of 115 MDD patients completed an eight-week antidepressant treatment procedure. Topological features of brain networks were calculated using graph theory-based methods and compared between FEDN MDD patients and NCs, as well as before and after treatment. ResultsDecreased global efficiency, local efficiency, small-worldness, and modularity were found in pretreatment FEDN MDD patients compared with NCs. Nodal degrees, betweenness, and efficiency decreased in several networks compared with NCs. After antidepressant treatment, the global efficiency increased, while the local efficiency, the clustering coefficient of the network, the path length, and the normalized characteristic path length decreased. Moreover, the reduction rate of the normalized characteristic path length was positively correlated with the reduction rate of retardation factor scores. LimitationsThe interaction effects of groups and time on the topological features were not explored because of absence of the eighth-week data of NC group. ConclusionsThe topological architecture of functional brain networks is disrupted in FEDN MDD patients. After antidepressant therapy, the global efficiency shifted toward recovery, but the local efficiency deteriorated, suggesting a correlation between recovery of retardation symptoms and global efficiency.

Association between decreased interhemispheric functional connectivity of the insula and duration of illness in recurrent depression

ObjectiveTo investigate the altered interhemispheric functional connectivity in the resting state in patients with recurrent major depressive disorder (MDD). MethodsVoxel-mirrored homotopic connectivity (VMHC), a measure of the functional connectivity between any pair of symmetrical interhemispheric voxels, and pattern classification were examined in 41 recurrent MDD patients (22 during the depressive state and 19 during the remitted state) and 60 age, sex, and education level-matched healthy controls (HC) using resting-state functional magnetic resonance imaging (fMRI). ResultsCompared with HC, the recurrent MDD patients exhibited decreased VMHC values in the bilateral fusiform, inferior occipital gyrus, posterior insula, precentral gyrus, precuneus, superior temporal gyrus, and thalamus. A significant negative correlation between the VMHC value of the bilateral posterior insula and illness duration in recurrent MDD was identified. Support vector machine (SVM) analysis showed that VMHC in the fusiform and posterior insula could be used to distinguish recurrent MDD patients from HC with a sensitivity and accuracy >0.6. ConclusionOur findings revealed a reduction in the resting-state brain activity across several neural networks in patients with recurrent MDD, including within the posterior insula. Lower VMHC values in the posterior insula were associated with longer illness duration, suggesting that impairment in interhemispheric synchronization within the salience network may be due to the accumulated pathology of depression and may contribute to future depression relapse. VMHC changes in the posterior insula may serve as a potential imaging marker to discriminate recurrent MDD patients from HC.

Amygdala substructure volumes and serotonin transporter in first-episode, drug- naïve major depressive disorder: A pilot study

IntroductionAmygdala and serotonergic system abnormalities have been documented in major depressive disorder (MDD). However, most studies have been conducted on recurrent MDD, and only a few have assessed their interaction. This study aimed to concurrently examine both the amygdala and serotonergic systems and their clinical relevance in first-episode, drug-naïve MDD. MethodsThis study included 27 patients with first-episode, drug-naïve MDD and 27 age- and gender-matched healthy controls (HCs). The amygdala substructure volumes were performed with Freesurfer from a 1.5 T magnetic resonance image. Serotonin transporter (SERT) availability was detected by single-photon emission computed tomography with 123I-ADAM. The Benjamini-Hochberg method was applied to adjust for multiple comparisons. ResultsNo significant difference was found in the amygdala substructure volume and SERT availability between the two groups, respectively. Within MDD patients, the right medial, cortical nucleus, and centromedial volumes were positively associated with caudate SERT availability, respectively. Moreover, the right lateral nucleus volume in the amygdala was positively correlated with depression severity. However, these significances did not survive correction for multiple testing. ConclusionsThere were no significant abnormalities in the amygdala substructure volumes and SERT availability in patients with first-episode, drug-naïve MDD. We did not observe an association between amygdala substructure volume and serotonergic dysregulation and their correlations with depression severity in patients with MDD. A larger sample size is warranted to elucidate the actual correlation.

Neuroimaging features of depression-frailty phenotype in older adults: a pilot study.

Frailty and late-life depression (LLD) often coexist and share several structural brain changes. We aimed to study the joint effect LLD and frailty have on brain structure. Cross-sectional study. Academic Health Center. Thirty-one participants (14 LLD+Frail and 17 Never-depressed+Robust). LLD was diagnosed by a geriatric psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition for single episode or recurrent major depressive disorder without psychotic features. Frailty was assessed using the FRAIL scale (0-5), classifying subjects as robust (0), prefrail (1-2), and frail (3-5). Participants underwent T1-weighted magnetic resonance imaging in which covariance analysis of subcortical volumes and vertex-wise analysis of cortical thickness values were performed to access changes in grey matter. Participants also underwent diffusion tensor imaging in which tract-based spatial statistics was used with voxel-wise statistical analysis on fractional anisotropy and mean diffusion values to assess changes in white matter (WM). We found a significant difference in mean diffusion values (48,225 voxels; peak voxel: pFWER=0.005, MINI coord. (X,Y,Z) = -26,-11,27) between the LLD-Frail group and comparison group. The corresponding effect size (f=0.808) was large. We showed the LLD+Frailty group is associated with significant microstructural changes within WM tracts compared to Never-depressed+Robust individuals. Our findings indicate the possibility of a heightened neuroinflammatory burden as a potential mechanism underlying the co-occurrence of both conditions and the possibility of a depression-frailty phenotype in older adults.

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.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level.International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

The association between psychiatric comorbidities and adherence to antidepressants and recurrence of major depressive disorder in the real-world

The association between psychiatric comorbidities and adherence to antidepressants and recurrence of major depressive disorder in the real-world

Effects of social isolation on neurotransmitter levels in the pallidum in recurrent major depressive disorder

Effects of social isolation on neurotransmitter levels in the pallidum in recurrent major depressive disorder

Add-On Intermittent Theta Burst Stimulation Improves the Efficacy of First-Episode and Recurrent Major Depressive Disorder: Real-World Clinical Practice.

Repetitive transcranial magnetic stimulation (rTMS) is an effective and evidence-based treatment for major depressive disorder (MDD). This retrospective study aimed to explore the efficacy of add-on iTBS treatment in MDD in real-world clinical practice. One hundred and fifty-nine inpatients with MDD in a general hospital were included in this study. These patients were treated with at least 8 sessions of iTBS, in addition to antidepressants and supportive psychotherapy. Symptoms of depression and anxiety were assessed with the Hamilton Depression Rating Scale (HDRS) and the Hamilton Rating Scale for Anxiety (HAMA) at baseline and after 2-4 weeks of treatment. The improvement degree of depressive and anxious symptoms was compared between the first-episode MDD (n=107) and recurrent MDD (n=52) groups. Depressive and anxious symptoms were reduced significantly after the add-on iTBS treatment; the response and remission rates in the first-episode MDD group were 55.14% and 28.97%, which were 63.46% and 28.85% for the recurrent MDD group, respectively (P>0.05). The response rate and remission rate of anxiety in the first-episode MDD group was 64.13% and 57.45% for HAMA, and 66.67% and 62.50% for the recurrent MDD group (P>0.05). Our findings indicated that antidepressant and anti-anxiety efficacy of add-on iTBS treatment remains equivocal in real-world clinical practice, regardless of a first-episode depression diagnosis or recurrent depression.

Functional MRI correlates of emotion regulation in major depressive disorder related to depressive disease load measured over nine years

Major Depressive Disorder (MDD) often is a recurrent and chronic disorder. We investigated the neurocognitive underpinnings of the incremental risk for poor disease course by exploring relations between enduring depression and brain functioning during regulation of negative and positive emotions using cognitive reappraisal.We used fMRI-data from the longitudinal Netherlands Study of Depression and Anxiety acquired during an emotion regulation task in 77 individuals with MDD. Task-related brain activity was related to disease load, calculated from presence and severity of depression in the preceding nine years. Additionally, we explored task related brain-connectivity. Brain functioning in individuals with MDD was further compared to 35 controls to explore overlap between load-effects and general effects related to MDD history/presence.Disease load was not associated with changes in affect or with brain activity, but with connectivity between areas essential for processing, integrating and regulating emotional information during downregulation of negative emotions. Results did not overlap with general MDD-effects. Instead, MDD was generally associated with lower parietal activity during downregulation of negative emotions. During upregulation of positive emotions, disease load was related to connectivity between limbic regions (although driven by symptomatic state), and connectivity between frontal, insular and thalamic regions was lower in MDD (vs controls).Results suggest that previous depressive load relates to brain connectivity in relevant networks during downregulation of negative emotions. These abnormalities do not overlap with disease-general abnormalities and could foster an incremental vulnerability to recurrence or chronicity of MDD. Therefore, optimizing emotion regulation is a promising therapeutic target for improving long-term MDD course.