Depression Subtypes in Predicting Antidepressant Response: A Report From the iSPOT-D Trial

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials

BACKGROUND

Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

AIMS

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

METHOD

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

RESULTS

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

CONCLUSIONS

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach.

METHODS

We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes.

RESULTS

A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms.

CONCLUSION

It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

Melancholic features and treatment outcome to selective serotonin reuptake inhibitors in major depressive disorder: A re-analysis of the STAR*D trial

BACKGROUND

Melancholia has been positioned as a qualitatively different form of Major Depressive Disorder (MDD). Some studies have suggested that melancholic MDD patients may show lower remission when receiving treatment with Selective Serotonin Reuptake Inhibitors, but this has not yet been explored in large, representative samples of MDD.

METHODS

We used data from the STAR*D, a multisite randomized controlled trial (n = 4041). We defined melancholia status through the BA Melancholia Empirical Index, constructed using items from the Inventory of Depressive Symptomatology (IDSC). The main outcome of interest was symptomatic remission defined as a Quick Inventory of Depressive Symptoms (Clinician version) (QIDS-C) below or equal to 5. Inverse probability weighting was used to control for confounding.

RESULTS

3827 patients were eligible for this study. Melancholic patients were more likely to be unemployed, never married, to self-report an African American race, and to have a higher depressive severity. The adjusted 4-month probability of remission was 26.9 % (22.0, 45.5) for melancholic and 53.8 % (53.2, 58.5), for nonmelancholic patients. Compared with nonmelancholic, the difference in 4-month probability of remission was -26.9 % (-37.0, -15.6). Results were consistent across sensitivity analyses.

LIMITATIONS

Items from IDSC were used as a surrogate measure of the BA Melancholia Index, and extrapolation of the results to agents other than citalopram and to psychotic MDD patients requires caution.

CONCLUSIONS

Melancholic MDD patients showed lower probabilities of remission at 4-months receiving treatment with citalopram. The results of this study show how validly subtyping episodes could contribute to the personalized treatment of depression.

Putative role of glial cells in treatment resistance depression: An updated critical literation review and evaluation of single-nuclei transcriptomics data

AIMS

Major depressive disorder (MDD) is a prevalent global mental illness with diverse underlying causes. Despite the availability of first-line antidepressants, approximately 10-30 % of MDD patients do not respond to these medications, falling into the category of treatment-resistant depression (TRD). Our study aimed to elucidate the precise molecular mechanisms through which glial cells contribute to depression-like episodes in TRD.

MATERIALS AND METHODS

We conducted a comprehensive literature search using the PubMed and Scopus electronic databases with search terms carefully selected to be specific to our topic. We strictly followed inclusion and exclusion criteria during the article selection process, adhering to PRISMA guidelines. Additionally, we carried out an in-depth analysis of postmortem brain tissue obtained from patients with TRD using single-nucleus transcriptomics (sn-RNAseq).

KEY FINDINGS

Our data confirmed the involvement of multiple glia-specific markers (25 genes) associated with TRD. These differentially expressed genes (DEGs) primarily regulate cytokine signaling, and they are enriched in important pathways such as NFκB and TNF-α. Notably, DEGs showed significant interactions with the transcription factor CREB1. sn-RNAseq analysis confirmed dysregulation of nearly all designated DEGs; however, only Cx30/43, AQP4, S100β, and TNF-αR1 were significantly downregulated in oligodendrocytes (OLGs) of TRD patients. With further exploration, we identified the GLT-1 in OLGs as a hub gene involved in TRD.

SIGNIFICANCE

Our findings suggest that glial dysregulation may hinder the effectiveness of existing therapies for TRD. By targeting specific glial-based genes, we could develop novel interventions with minimal adverse side effects, providing new hope for TRD patients who currently experience limited benefits from invasive treatments.

Music prevents stress-induced depression and anxiety-like behavior in mice

Depression is the most prevalent psychiatric disorder worldwide and remains incurable; however, there is little research on its prevention. The leading cause of depression is stress, and music has been hypothesized to alleviate stress. To examine the potential beneficial effects of music on stress and depression, we subjected mice to chronic unpredictable mild stress (CUMS) during the day and music at night. Strikingly, our results indicated that music completely prevented CUMS-induced depression and anxiety-like behaviors in mice, as assessed by the open field, tail suspension, sucrose preference, novelty suppressed feeding, and elevated plus maze tests. We found that listening to music restored serum corticosterone levels in CUMS mice, which may contribute to the beneficial effects of music on the mouse brain, including the restoration of BDNF and Bcl-2 levels. Furthermore, listening to music prevented CUMS-induced oxidative stress in the serum, prefrontal cortex, and hippocampus of mice. Moreover, the CUMS-induced inflammatory responses in the prefrontal cortex and hippocampus of mice were prevented by listening to music. Taken together, we have demonstrated for the first time in mice experiments that listening to music prevents stress-induced depression and anxiety-like behaviors in mice. Music may restore hypothalamus-pituitary-adrenal axis homeostasis, preventing oxidative stress, inflammation, and neurotrophic factor deficits, which had led to the observed phenotypes in CUMS mice.

Symptom clustering of major depression in a national telehealth sample

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous disorder whose possible symptom combinations have not been well delineated. The aim of this study was to explore the heterogeneity of symptoms experienced by those with MDD to characterize phenotypic presentations.

METHODS

Cross-sectional data (N = 10,158) from a large telemental health platform were used to identify subtypes of MDD. Symptom data, gathered from both clinically-validated surveys and intake questions, were analyzed via polychoric correlations, principal component analysis, and cluster analysis.

RESULTS

Principal components analysis (PCA) of baseline symptom data revealed 5 components, including anxious distress, core emotional, agitation/irritability, insomnia, and anergic/apathy components. PCA-based cluster analysis resulted in four MDD phenotypes, the largest of which was characterized by a prominent elevation on the anergic/apathy component, but also core emotional. The four clusters differed on demographic and clinical characteristics.

LIMITATIONS

The primary limitation of this study is that the phenotypes uncovered are limited by the questions asked. These phenotypes will need to be cross validated with other samples, potentially expanded to include biological/genetic variables, and followed longitudinally.

CONCLUSIONS

The heterogeneity in MDD, as illustrated by the phenotypes in this sample, may explain the heterogeneity of treatment response in large-scale treatment trials. These phenotypes can be used to study varying rates of recovery following treatment and to develop clinical decision support tools and artificial intelligence algorithms. Strengths of this study include its size, breadth of included symptoms, and novel use of a telehealth platform.

Depression Subtypes in Systolic Heart Failure: A Secondary Analysis From a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is associated with an elevated risk of morbidity, mortality, hospitalization, and impaired quality of life. One potential contributor to these poor outcomes is depression. Yet the effectiveness of treatments for depression in patients with HF is mixed, perhaps due to the heterogeneity of depression.

METHODS

This secondary analysis applied latent class analysis (LCA) to data from a clinical trial to classify patients with systolic HF and comorbid depression into LCA subtypes based on depression symptom severity, and then examined whether these subtypes predicted treatment response and mental and physical health outcomes at 12 months follow-up.

RESULTS

In LCA of 629 participants (mean age 63.6 ± 12.9; 43% females), we identified 4 depression subtypes: mild (prevalence 53%), moderate (30%), moderately severe (12%), and severe (5%). The mild subtype was characterized primarily by somatic symptoms of depression (e.g., energy loss, sleep disturbance, poor appetite), while the remaining LCA subtypes additionally included nonsomatic symptoms of depression (e.g., depressed mood, anhedonia, worthlessness). At 12 months, LCA subtypes with more severe depressive symptoms reported significantly greater improvements in mental quality of life and depressive symptoms compared to the LCA mild subtype, but the incidence of cardiovascular- and noncardiovascular-related readmissions, and mortality was similar among all subtypes.

CONCLUSIONS

In patients with depression and systolic heart failure those with the LCA mild depression subtype may not meet full criteria for major depressive disorder, given the overlap between HF and somatic symptoms of depression. We recommend requiring depressed mood or anhedonia as a necessary symptom for major depressive disorder in patients with HF.

Difficult decision-making in major depressive disorder: Practical guidance based on clinical research and experience

OBJECTIVES

To extend current published guidance regarding the management of major depression in clinical practice, by examining complex cases that reflect real-world patients, and to integrate evidence and experience into recommendations.

METHODS

The authors who contributed to recently published clinical practice guidelines were invited to identify important gaps in extant guidance. Drawing on clinical experience and shared knowledge, they then generated four fictional case studies to illustrate the real-world complexities of managing mood disorders. The cases focussed specifically on issues that are not usually addressed in clinical practice guidelines.

RESULTS

The four cases are discussed in detail and each case is summarised using a life chart and accompanying information. The four cases reflect important real-world challenges that clinicians face when managing mood disorders in day-to-day clinical practice. To partly standardise the presentation of each case and for ease of reference we provide a Time Line, History Box and Management Chart, along with a synopsis where relevant. Discussion and formulation of the cases illustrate how to manage the complexities of each case and provide one possible pathway to achieving functional recovery.

CONCLUSION

These cases draw on the combined clinical experience of the authors and illustrate how to approach diagnostic decision-making when treating major depressive disorder and having to contend with complex presentations. The cases are designed to stimulate discussion and provide a real-world context for the formulation of mood disorders.

Intraindividual phenotyping of depression in high-risk youth: An application of a multilevel hidden Markov model

BACKGROUND

Traditionally, depression phenotypes have been defined based on interindividual differences that distinguish between subgroups of individuals expressing distinct depressive symptoms often from cross-sectional data. Alternatively, depression phenotypes can be defined based on intraindividual differences, differentiating between transitory states of distinct symptoms profiles that a person transitions into or out of over time. Such within-person phenotypic states are less examined, despite their potential significance for understanding and treating depression.

METHODS

The current study used intensive longitudinal data of youths (N = 120) at risk for depression. Clinical interviews (at baseline, 4, 10, 16, and 22 months) yielded 90 weekly assessments. We applied a multilevel hidden Markov model to identify intraindividual phenotypes of weekly depressive symptoms for at-risk youth.

RESULTS

Three intraindividual phenotypes emerged: a low-depression state, an elevated-depression state, and a cognitive-physical-symptom state. Youth had a high probability of remaining in the same state over time. Furthermore, probabilities of transitioning from one state to another did not differ by age or ethnoracial minority status; girls were more likely than boys to transition from a low-depression state to either the elevated-depression state or the cognitive-physical symptom state. Finally, these intraindividual phenotypes and their dynamics were associated with comorbid externalizing symptoms.

CONCLUSION

Identifying these states as well as the transitions between them characterizes how symptoms of depression change over time and provide potential directions for intervention efforts.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Characteristics and symptomatology of major depressive disorder with atypical features from symptom to syndromal level

OBJECTIVE

To explore clinical characteristics and symptomatology of major depressive disorder (MDD) with atypical features based on DSM criteria or only reversed vegetative symptoms.

METHOD

A total of 3187 patients who met DSM-IV TR criteria for MDD were enrolled. Demographics and symptomatology covering multiple symptom domains were assessed and compared between three groups of cases: those who met DSM criteria for atypical specifier (the DAD group), those who had at least one reversed vegetative symptoms (hypersomnia or hyperphagia) (the SAD group) without meeting DSM atypical specifier criteria, and those without any reversed vegetative symptoms (the NAD group).

RESULTS

The DAD and SAD group accounted for 4.4 % and 14.4 % of the participants, respectively. The DAD cases were characterized by a highest proportion of hospitalizations, longest duration of current episode and worst quality of life. The DAD and SAD cases were more likely to adopt unhealthy behaviors (smoking and alcohol drinking). Most depressive symptoms related to higher illness severity and treatment resistance were more frequent in the DAD cases, followed by the SAD cases, and least frequent in the NAD cases.

LIMITATIONS

A cross-sectional design and a non-validated questionnaire were used.

CONCLUSIONS

The findings support the role of DSM defined atypical depression as a valid MDD subtype and provide evidence for clinical utility of the simplified approach of defining atypical features based on only reversed vegetative symptoms. This has implications for illness screening, public health, suicide prevention and better treatment planning for depressed individuals with atypical features even below syndromal level.

Potential of Circulating miRNAs as Molecular Markers in Mood Disorders and Associated Suicidal Behavior

Mood disorders are the most prevalent psychiatric disorders associated with significant disability, morbidity, and mortality. The risk of suicide is associated with severe or mixed depressive episodes in patients with mood disorders. However, the risk of suicide increases with the severity of depressive episodes and is often presented with higher incidences in bipolar disorder (BD) patients than in patients with major depression (MDD). Biomarker study in neuropsychiatric disorders is critical for developing better treatment plans by facilitating more accurate diagnosis. At the same time, biomarker discovery also provides more objectivity to develop state-of-the-art personalized medicine with increased accuracy through clinical interventions. Recently, colinear changes in miRNA expression between brain and systemic circulation have added great interest in examining their potential as molecular markers in mental disorders, including MDD, BD, and suicidality. A present understanding of circulating miRNAs in body fluids implicates their role in managing neuropsychiatric conditions. Most notably, their use as prognostic and diagnostic markers and their potential role in treatment response have significantly advanced our knowledge base. The present review discusses circulatory miRNAs and their underlying possibilities to be used as a screening tool for assessing major psychiatric conditions, including MDD, BD, and suicidal behavior.

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

BACKGROUND

To address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes.

METHODS

Pretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group.

RESULTS

Three subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline.

LIMITATIONS

Due to the exploratory nature of these analyses, there were no adjustments for multiple comparisons.

CONCLUSIONS

Baseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

Apathy in depression: An arterial spin labeling perfusion MRI study

INTRODUCTION

Apathy, as defined as a deficit in goal-directed behaviors, is a critical clinical dimension in depression associated with chronic impairment. Little is known about its cerebral perfusion specificities in depression. To explore neurovascular mechanisms underpinning apathy in depression by pseudo-continuous arterial spin labeling (pCASL) magnetic resonance imaging (MRI).

METHODS

Perfusion imaging analysis was performed on 90 depressed patients included in a prospective study between November 2014 and February 2017. Imaging data included anatomical 3D T1-weighted and perfusion pCASL sequences. A multiple regression analysis relating the quantified cerebral blood flow (CBF) in different regions of interest defined from the FreeSurfer atlas, to the Apathy Evaluation Scale (AES) total score was conducted.

RESULTS

After confound adjustment (demographics, disease and clinical characteristics) and correction for multiple comparisons, we observed a strong negative relationship between the CBF in the left anterior cingulate cortex (ACC) and the AES score (standardized beta = -0.74, corrected p value = 0.0008).

CONCLUSION

Our results emphasized the left ACC as a key region involved in apathy severity in a population of depressed participants. Perfusion correlates of apathy in depression evidenced in this study may contribute to characterize different phenotypes of depression.

Dopamine, Immunity, and Disease

The neurotransmitter dopamine is a key factor in central nervous system (CNS) function, regulating many processes including reward, movement, and cognition. Dopamine also regulates critical functions in peripheral organs, such as blood pressure, renal activity, and intestinal motility. Beyond these functions, a growing body of evidence indicates that dopamine is an important immunoregulatory factor. Most types of immune cells express dopamine receptors and other dopaminergic proteins, and many immune cells take up, produce, store, and/or release dopamine, suggesting that dopaminergic immunomodulation is important for immune function. Targeting these pathways could be a promising avenue for the treatment of inflammation and disease, but despite increasing research in this area, data on the specific effects of dopamine on many immune cells and disease processes remain inconsistent and poorly understood. Therefore, this review integrates the current knowledge of the role of dopamine in immune cell function and inflammatory signaling across systems. We also discuss the current understanding of dopaminergic regulation of immune signaling in the CNS and peripheral tissues, highlighting the role of dopaminergic immunomodulation in diseases such as Parkinson's disease, several neuropsychiatric conditions, neurologic human immunodeficiency virus, inflammatory bowel disease, rheumatoid arthritis, and others. Careful consideration is given to the influence of experimental design on results, and we note a number of areas in need of further research. Overall, this review integrates our knowledge of dopaminergic immunology at the cellular, tissue, and disease level and prompts the development of therapeutics and strategies targeted toward ameliorating disease through dopaminergic regulation of immunity. SIGNIFICANCE STATEMENT: Canonically, dopamine is recognized as a neurotransmitter involved in the regulation of movement, cognition, and reward. However, dopamine also acts as an immune modulator in the central nervous system and periphery. This review comprehensively assesses the current knowledge of dopaminergic immunomodulation and the role of dopamine in disease pathogenesis at the cellular and tissue level. This will provide broad access to this information across fields, identify areas in need of further investigation, and drive the development of dopaminergic therapeutic strategies.

Perspectives on the success rate of current antidepressant pharmacotherapy

INTRODUCTION

There has been growing debate about the effectiveness of traditional antidepressants for the treatment of depression, and whether the clinical trials literature overstates the value of existing agents. Antidepressant efficacy is limited by suboptimal remission rates, lack of robust efficacy across diverse depressed subgroups, slow onset, and challenges managing tolerability. Clinicians can better navigate uncertainties in this area by recognizing patient-specific clinical and prognostic factors that influence the likelihood of antidepressant drug response.

AREAS COVERED

The author summarizes pertinent literature regarding drug-placebo differences in antidepressant outcome as well as patient-specific factors that influence antidepressant drug responsivity across subtypes of depressive disorders.

EXPERT OPINION

Standardized effect sizes for most monoaminergic antidepressants are relatively modest. At least one-third of treatment response derives from nonspecific (yet substantial) placebo effects, limiting the ability to compare antidepressant medication effects to that of "no treatment." Patients with high baseline depressive symptom severity are less likely to respond to placebo but may be more responsive to antidepressant pharmacotherapy than is the case in mild forms of depression. Patient satisfaction with antidepressant response must take into consideration not only efficacy for reducing symptoms but also drug tolerability/acceptability and tangible improvement in functional outcome and quality of life.

TNF-α, IL-6 and hsCRP in patients with melancholic, atypical and anxious depression: an antibody array analysis related to somatic symptoms

Background

The association between inflammation and major depressive disorder (MDD) remains poorly understood, given the heterogeneity of patients with MDD.

Aims

We investigated inflammatory markers, such as interleukin (IL)-6, high-sensitivity C reactive protein (hsCRP) and tumour necrosis factor-α (TNF-α) in melancholic, atypical and anxious depression and explored whether baseline inflammatory protein levels could indicate prognosis.

Methods

The sample consisted of participants (aged 18-55 years) from a previously reported multicentre randomised controlled trial with a parallel-group design registered with ClinicalTrials.gov, including melancholic (n=44), atypical (n=37) and anxious (n=44) patients with depression and healthy controls (HCs) (n=33). Subtypes of MDD were classified according to the 30-item Inventory of Depressive Symptomatology, Self-Rated Version and the 17-item Hamilton Depression Rating Scale. Blood levels of TNF-α, IL-6 and hsCRP were assessed using antibody array analysis.

Results

Patients with MDD, classified according to melancholic, atypical and anxious depression subtypes, and HCs did not differ significantly in baseline TNF-α, IL-6 and hsCRP levels after adjustment. In patients with anxious depression, hsCRP levels increased significantly if they experienced no pain (adjusted (adj.) p=0.010) or mild to moderate pain (adj. p=0.038) compared with those with severe pain. However, the patients with anxious depression and severe pain showed a lower trend in hsCRP levels than patients with atypical depression who experienced severe pain (p=0.022; adj. p=0.155). Baseline TNF-α (adj. p=0.038) and IL-6 (adj. p=0.006) levels in patients in remission were significantly lower than those in patients with no remission among the participants with the atypical depression subtype at the eighth-week follow-up.

Conclusions

This study provides evidence of differences in inflammatory proteins in patients with varied symptoms among melancholic, atypical and anxious depression subtypes. Further studies on the immunoinflammatory mechanism underlying different subtypes of depression are expected for improved individualised therapy.

Trial registration number

NCT03219008.

Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the early medication change trial in major depressive disorder

OBJECTIVES

Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcomes.

METHODS

826 MDD patients who had participated in the randomised-controlled Early Medication Change (EMC) trial were analysed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined.

RESULTS

Patients with blood pressure <120mmHg showed higher depression severity (p = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (p = 0.004). Na⁺/K⁺ ratio was positively and K⁺-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day 14, and 56 respectively (p < 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcomes.

CONCLUSIONS

We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K⁺ or high Na⁺/K⁺ ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment. Trial Registration: clinicaltrials.gov Identifier: NCT00974155; https://www.clinicaltrials.gov/ct2/results?term=NCT00974155.

Blood-based biomarkers of antidepressant response to ketamine and esketamine: A systematic review and meta-analysis

(R,S)-ketamine (ketamine) and its enantiomer (S)-ketamine (esketamine) can produce rapid and substantial antidepressant effects. However, individual response to ketamine/esketamine is variable, and there are no well-accepted methods to differentiate persons who are more likely to benefit. Numerous potential peripheral biomarkers have been reported, but their current utility is unclear. We conducted a systematic review/meta-analysis examining the association between baseline levels and longitudinal changes in blood-based biomarkers, and response to ketamine/esketamine. Of the 5611 citations identified, 56 manuscripts were included (N = 2801 participants), and 26 were compatible with meta-analytical calculations. Random-effect models were used, and effect sizes were reported as standardized mean differences (SMD). Our assessments revealed that more than 460 individual biomarkers were examined. Frequently studied groups included neurotrophic factors (n = 15), levels of ketamine and ketamine metabolites (n = 13), and inflammatory markers (n = 12). There were no consistent associations between baseline levels of blood-based biomarkers, and response to ketamine. However, in a longitudinal analysis, ketamine responders had statistically significant increases in brain-derived neurotrophic factor (BDNF) when compared to pre-treatment levels (SMD [95% CI] = 0.26 [0.03, 0.48], p = 0.02), whereas non-responders showed no significant changes in BDNF levels (SMD [95% CI] = 0.05 [-0.19, 0.28], p = 0.70). There was no consistent evidence to support any additional longitudinal biomarkers. Findings were inconclusive for esketamine due to the small number of studies (n = 2). Despite a diverse and substantial literature, there is limited evidence that blood-based biomarkers are associated with response to ketamine, and no current evidence of clinical utility.

Revisiting the theoretical and methodological foundations of depression measurement

Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes and treatments for these disorders. In this Perspective, we suggest that such progress crucially depends on our ability to measure depression. We review the many problems with depression measurement, including limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as diagnosis or tracking treatment progress. We argue that shortcomings arise because depression measurement rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field's tradition that has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for depression measurement to progress.

Mindfulness-Enhanced Computerized Cognitive Training for Depression: An Integrative Review and Proposed Model Targeting the Cognitive Control and Default-Mode Networks

Depression is often associated with co-occurring neurocognitive deficits in executive function (EF), processing speed (PS) and emotion regulation (ER), which impact treatment response. Cognitive training targeting these capacities results in improved cognitive function and mood, demonstrating the relationship between cognition and affect, and shedding light on novel targets for cognitive-focused interventions. Computerized cognitive training (CCT) is one such new intervention, with evidence suggesting it may be effective as an adjunct treatment for depression. Parallel research suggests that mindfulness training improves depression via enhanced ER and augmentation of self-referential processes. CCT and mindfulness training both act on anti-correlated neural networks involved in EF and ER that are often dysregulated in depression—the cognitive control network (CCN) and default-mode network (DMN). After practicing CCT or mindfulness, downregulation of DMN activity and upregulation of CCN activity have been observed, associated with improvements in depression and cognition. As CCT is posited to improve depression via enhanced cognitive function and mindfulness via enhanced ER ability, the combination of both forms of training into mindfulness-enhanced CCT (MCCT) may act to improve depression more rapidly. MCCT is a biologically plausible adjunct intervention and theoretical model with the potential to further elucidate and target the causal mechanisms implicated in depressive symptomatology. As the combination of CCT and mindfulness has not yet been fully explored, this is an intriguing new frontier. The aims of this integrative review article are four-fold: (1) to briefly review the current evidence supporting the efficacy of CCT and mindfulness in improving depression; (2) to discuss the interrelated neural networks involved in depression, CCT and mindfulness; (3) to present a theoretical model demonstrating how MCCT may act to target these neural mechanisms; (4) to propose and discuss future directions for MCCT research for depression.

Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial

BACKGROUND

The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict.

METHODS

A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors.

RESULTS

During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor.

CONCLUSIONS

Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.

Associations between anterior cingulate thickness, cingulum bundle microstructure, melancholia and depression severity in unipolar depression

BACKGROUND

Structural and functional alterations of the anterior cingulate cortex (ACC) have been related to emotional, cognitive and behavioral domains of major depressive disorder. In this study, we investigate cortical thickness of rostral and caudal ACC. In addition, we explore white matter microstructure of the cingulum bundle (CB), a white matter pathway connecting multiple segments of the ACC. We hypothesized reduced cortical thickness and reduced white matter microstructure of the CB in MDD, in particular in the melancholic subtype. In addition, we expect an association between depression severity and CB microstructure.

METHODS

Fifty-four patients with a current depressive episode and 22 healthy controls matched for age, gender and handedness underwent structural and diffusion-weighted MRI-scans. Cortical thickness of rostral and caudal ACC were computed. The CB was reconstructed bilaterally using manual tractography. Cortical thickness and fractional anisotropy (FA) of bilateral CB were compared first between all patients and healthy controls and second between healthy controls, melancholic and non-melancholic patients. Correlations between FA and depression severity were calculated.

RESULTS

We found no group differences in rostral and caudal ACC cortical thickness or in FA of the CB comparing all patients with healthy controls. Melancholic patients had reduced cortical thickness of bilateral caudal ACC compared to non-melancholic patients and compared to healthy controls. Across all patients, depression severity was associated with reduced FA in bilateral CB.

LIMITATIONS

Impact of medication CONCLUSIONS: Cortical thickness of the caudal ACC is associated with the melancholic syndrome. CB microstructure may represent a marker of depression severity.

The self on its axis: a framework for understanding depression

The self is experienced differently in depression. It is infused with pervasive low mood, and structured by negative self-related thoughts. The concept of the self has been difficult to define-one of the reasons it is now infrequently an object of enquiry for psychiatry-but findings from functional brain imaging and other neuroscience studies have provided new insights. They have elucidated how the self is supported by complex, hierarchical brain processes. Bodily sensations rise through the spinal cord, brainstem, and subcortical regions through to cortical networks, with the default mode network sitting at the apex, integrating interoceptive signals with information about the extended social environment. We discuss how this forms a "self axis", and demonstrate how this axis is set awry by depression. Our self-axis model of depression establishes a new perspective on the disorder. It emphasises the multi-level nature of depression, and how impacts made at different explanatory levels influence others along the axis. It suggests that diverse treatments might be effective for depression, from lifestyle interventions to psychotherapies to medications: they target different aspects of the self, but changes at one level of the self axis can affect others along it. Our framework for depression establishes a central role for the self, which might again become a useful focus of investigation.

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

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Baseline measures alone not able to predict escitalopram response above default.

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This poor baseline performance contradicts results from smaller studies.

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Accuracy improved using change in functional connectivity from baseline to week 2.

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Measures of early change following treatment may be crucial for accurate prediction.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

Transtorno Depressivo Maior: Um Estudo Comparativo sobre Cognição Socioemocional e Funções Executivas

Resumo O objetivo deste estudo foi avaliar a cognição socioemocional e as funções executivas em pacientes com Transtorno Depressivo Maior unipolar. A amostra incluiu 22 pacientes entre 36 e 93 anos de idade (M = 59,32; DP = 12,89) e 23 indivíduos controles entre 30 e 81 anos de idade (M = 63,00; DP = 13,56). Além de dados demográficos, foram avaliados sintomas de ansiedade e de depressão, empatia, teoria da mente, reconhecimento de emoções, controle inibitório, flexibilidade cognitiva e fluência verbal. Não houve diferença estatística significativa entre os grupos quanto à idade e à escolaridade. Os pacientes apresentaram significativamente mais ansiedade, depressão e angústia pessoal do que os controles. Indivíduos com sintomas depressivos mais graves apresentaram menor velocidade de processamento.

Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling

BACKGROUND

It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).

METHODS

Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session. Latent Markov modelling was used on these data to define depressive states and explore transition probabilities between states. Clinical outcomes and patient demographics were compared between patients starting at different depressive states.

RESULTS

A model with seven depressive states emerged as the best compromise between optimal fit and interpretability. States loading preferentially on cognitive/affective v. somatic symptoms of depression were identified. Analysis of transition probabilities revealed that patients in cognitive/affective states do not typically transition towards somatic states and vice-versa. Post-hoc analyses also showed that patients who start in a somatic depressive state are less likely to engage with or improve with therapy. These patients are also more likely to be female, suffer from a comorbid long-term physical condition and be taking psychotropic medication.

CONCLUSIONS

This study presents a novel approach for depression sub-typing, defining fluid depressive states and exploring transitions between states in response to CBT. Understanding how different symptom profiles respond to therapy will inform the development and delivery of stratified treatment protocols, improving clinical outcomes and cost-effectiveness of psychological therapies for patients with depression.

Extracellular vesicle-mediated delivery of circDYM alleviates CUS-induced depressive-like behaviours

Major depressive disorder (MDD) is the most prevalent psychiatric disorder worldwide and severely limits psychosocial function and quality of life, but no effective medication is currently available. Circular RNAs (circRNAs) have been revealed to participate in the MDD pathological process. Targeted delivery of circRNAs without blood-brain barrier (BBB) restriction for remission of MDD represents a promising approach for antidepressant therapy. In this study, RVG-circDYM-extracellular vesicles (RVG-circDYM-EVs) were engineered to target and preferentially transfer circDYM to the brain, and the effect on the pathological process in a chronic unpredictable stress (CUS) mouse model of depression was investigated. The results showed that RVG-circDYM-EVs were successfully purified by ultracentrifugation from overexpressed circDYM HEK 293T cells, and the characterization of RVG-circDYM-EVs was successfully demonstrated in terms of size, morphology and specific markers. Beyond demonstrating proof-of-concept for an RNA drug delivery technology, we observed that systemic administration of RVG-circDYM-EVs efficiently delivered circDYM to the brain, and alleviated CUS-induced depressive-like behaviours, and we discovered that RVG-circDYM-EVs notably inhibited microglial activation, BBB leakiness and peripheral immune cells infiltration, and attenuated astrocyte disfunction induced by CUS. CircDYM can bind mechanistically to the transcription factor TAF1 (TATA-box binding protein associated factor 1), resulting in the decreased expression of its downstream target genes with consequently suppressed neuroinflammation. Taken together, our findings suggest that extracellular vesicle-mediated delivery of circDYM is effective for MDD treatment and promising for clinical applications.

Mood Disorders

PURPOSE OF REVIEW

This comprehensive review of mood disorders brings together the past and current literature on the diagnosis, evaluation, and treatment of the depressive and bipolar disorders. It highlights the primary mood disorders and secondary neurologic causes of mood disorders that are commonly encountered in a clinical setting. As the literature and our understanding evolve, recent additions to the current literature are important to bring forth to the readers.

RECENT FINDINGS

Advancements in clinical medicine have strengthened our understanding of the associations of neurologic and psychiatric diseases. This article highlights the medications frequently used with newly identified mood disorders and the common side effects of these medications. A paradigm shift has moved toward newer treatment modalities, such as the use of ketamine, repetitive transcranial magnetic stimulation, and complementary and alternative medicine. The risks and benefits of such therapies, along with medications, are reviewed in this article.

SUMMARY

Mood disorders are extraordinarily complex disorders with significant association with many neurologic disorders. Early identification of these mood disorders can prevent significant morbidity and mortality associated with them. With further expansion of pharmacologic options, more targeted therapy is possible in improving quality of life for patients.

Treatment selection using prototyping in latent-space with application to depression treatment

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

Heterogeneity in major depression and its melancholic and atypical specifiers: a secondary analysis of STAR*D

OBJECTIVES

The melancholic and atypical specifiers for a major depressive episode (MDE) are supposed to reduce heterogeneity in symptom presentation by requiring additional, specific features. Fried et al. (2020) recently showed that the melancholic specifier may increase the potential heterogeneity in presenting symptoms. In a large sample of outpatients with depression, our objective was to explore whether the melancholic and atypical specifiers reduced observed heterogeneity in symptoms.

METHODS

We used baseline data from the Inventory of Depression Symptoms (IDS), which was available for 3,717 patients, from the Sequenced Alternatives to Relieve Depression (STAR*D) trial. A subsample met criteria for MDE on the IDS ("IDS-MDE"; N =2,496). For patients with IDS-MDE, we differentiated between those with melancholic, non-melancholic, non-melancholic, atypical, and non-atypical depression. We quantified the observed heterogeneity between groups by counting the number of unique symptom combinations pertaining to their given diagnostic group (e.g., counting the melancholic symptoms for melancholic and non-melancholic groups), as well as the profiles of DSM-MDE symptoms (i.e., ignoring the specifier symptoms).

RESULTS

When considering the specifier and depressive symptoms, there was more observed heterogeneity within the melancholic and atypical subgroups than in the IDS-MDE sample (i.e., ignoring the specifier subgroups). The differences in number of profiles between the melancholic and non-melancholic groups were not statistically significant, irrespective of whether focusing on the specifier symptoms or only the DSM-MDE symptoms. The differences between the atypical and non-atypical subgroups were smaller than what would be expected by chance. We found no evidence that the specifier groups reduce heterogeneity, as can be quantified by unique symptom profiles. Most symptom profiles, even in the specifier subgroups, had five or fewer individuals.

CONCLUSION

We found no evidence that the atypical and melancholic specifiers create more symptomatically homogeneous groups. Indeed, the melancholic and atypical specifiers introduce heterogeneity by adding symptoms to the DSM diagnosis of MDE.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Depression subtypes and their response to cognitive behavioral therapy: A latent transition analysis

BACKGROUND

Depression is a heterogeneous condition, with multiple possible symptom-profiles leading to the same diagnosis. Descriptive depression subtypes based on observation and theory have so far proven to have limited clinical utility.

AIM

To identify depression subtypes and to examine their time-course and prognosis using data-driven methods.

METHODS

Latent transition analysis was applied to a large (N = 8380) multi-service sample of depressed patients treated with cognitive behavioral therapy (CBT) in outpatient clinics. Patients were classed into initial latent states based on their responses to the Patient Health Questionnaire-9 of depression symptoms, and transition probabilities to other states during treatment were quantified. Qualitatively similar states were clustered into overarching depression subtypes and we statistically compared indices of treatment engagement and outcomes between subtypes using post hoc analyses.

RESULTS

Fourteen latent states were clustered into five depression subtypes: mild (2.7%), severe (9.8%), cognitive-affective (23.7%), somatic (21.4%), and typical (42.4%). These subtypes had high temporal stability, and the most common transitions during treatment were from severe toward milder states within the same subtype. Differential response to treatment was evident, with the highest improvement rate (63.6%) observed in the cognitive-affective subtype.

CONCLUSION

Replicated evidence indicates that depression subtypes are temporally stable and associated with differential response to CBT.

Melancholic features in major depression - a European multicenter study

There is still a debate, if melancholic symptoms can be seen rather as a more severe subtype of major depressive disorder (MDD) or as a separate diagnostic entity. The present European multicenter study comprising altogether 1410 MDD in- and outpatients sought to investigate the influence of the presence of melancholic features in MDD patients. Analyses of covariance, chi-squared tests, and binary logistic regression analyses were accomplished to determine differences in socio-demographic and clinical variables between MDD patients with and without melancholia. We found a prevalence rate of 60.71% for melancholic features in MDD. Compared to non-melancholic MDD patients, they were characterized by a significantly higher likelihood for higher weight, unemployment, psychotic features, suicide risk, inpatient treatment, severe depressive symptoms, receiving add-on medication strategies in general, and adjunctive treatment with antidepressants, antipsychotics, benzodiazepine (BZD)/BZD-like drugs, low-potency antipsychotics, and pregabalin in particular. With regard to the antidepressant pharmacotherapy, we found a less frequent prescription of selective serotonin reuptake inhibitors (SSRIs) in melancholic MDD. No significant between-group differences were found for treatment response, non-response, and resistance. In summary, we explored primarily variables to be associated with melancholia which can be regarded as parameters for the presence of severe/difficult-to treat MDD conditions. Even if there is no evidence to realize any specific treatment strategy in melancholic MDD patients, their prescribed medication strategies were different from those for patients without melancholia.

Circadian depression: A mood disorder phenotype

Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.

Differential response to SSRI versus Placebo and distinct neural signatures among data-driven subgroups of patients with major depressive disorder

OBJECTIVE

To identify data-driven subgroups in Major Depressive Disorder (MDD) in order to elucidate underlying neural correlates and determine if these subgroups have utility in predicting response to antidepressant versus placebo.

METHODS

Using 27 clinical measures at baseline of Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study, participants with MDD (n=244) were sub grouped using principal component (PC) analysis. Baseline-to-week-8 changes in depression severity with sertraline versus placebo were compared in these subgroups. Resting-state functional connectivity of these subgroups were compared to those of healthy controls (n=38).

RESULTS

Eight subgroups were identified from four PCs: (PC1) severity of depression-associated symptoms, (PC2) sub-threshold mania and anhedonia, (PC3) childhood trauma, medical comorbidities, and sexual dysfunction, and (PC4) personality traits of openness and agreeableness. Participants with high childhood trauma experienced greater improvement with sertraline (Cohen's d=0.87), whereas those with either higher levels of subthreshold hypomanic symptoms (Cohen's d=0.67) or with lower levels of agreeableness and openness experienced greater improvement with placebo (Cohen's d=0.71). Participants with high childhood trauma had greater connectivity between salience and dorsal attention networks, whereas those with higher levels of subthreshold hypomanic symptoms and lower levels of agreeableness and openness had greater connectivity within limbic network and that of visual network with hippocampus and dorsal attention network.

CONCLUSION

Assessing history of childhood trauma, presence of subthreshold hypomanic symptoms and personality traits may help to identify subgroups of patients with MDD who respond differentially to sertraline or placebo and have distinct neural signatures.

Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

Combining machine learning algorithms for prediction of antidepressant treatment response

OBJECTIVES

Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes.

EXPERIMENTAL PROCEDURES

Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross-center validation design. In total, 88 predictors were implemented.

RESULTS

Clustering revealed four distinct HAM-D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability.

CONCLUSION

Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment- and symptom-specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.

Does One Treatment Fit All? Effectiveness of a Multicomponent Cognitive Behavioral Therapy Program in Data-Driven Subtypes of Perinatal Depression

Background: Current diagnostic systems of mental disorders are criticized for their poor validity and reliability, owing to the within disorder heterogeneity and between disorder homogeneity. The issue is important if treatments for mental disorders are to be tailored to individual needs. There is little information in this area on perinatal depression (PND), a highly prevalent condition globally. Aims: i) Quantify heterogeneity attributable to the polythetic diagnostic framework for PND and, ii) present evidence for the effectiveness of a multicomponent and low-intensity cognitive behavioral Thinking Healthy Programme (THP) across the heterogeneous presentations of PND. Methods: This investigation presents secondary analyses of a cluster randomized controlled trial, conducted in Kallar Syedan, Pakistan. A total of 903 pregnant women were randomized to an intervention group receiving the THP intervention or control group receiving enhanced usual care. Principal component analyses and clustering algorithm were utilized to identify heterogenous subtypes of PND. Linear mixed effects models were used to assess effectiveness of the intervention across the identified subtypes of PND. Results: Four different clusters of PND were identified: mixed anxiety-depression, somatic depression, mild depression, and atypical depression. All clinical phenotypes responded well to the THP intervention. Compared to their counterparts in the control group, mothers with mild depression in the treatment group yielded lowest risk ratios 0.24 (95% CI: 0.15 to 0.37), followed by mothers with anxiety-depression 0.50 (95% CI: 0.37 to 0.68), atypical depression 0.51 (95% CI: 0.27 to 0.99) and somatic depression 0.59 (95% CI: 0.42 to 0.83). Conclusion: The Thinking Healthy Programme was found to be effective in reducing severity of depressive symptoms and disability across the four subtypes of PND.

Genetic stratification of depression by neuroticism: revisiting a diagnostic tradition

BACKGROUND

Major depressive disorder and neuroticism (Neu) share a large genetic basis. We sought to determine whether this shared basis could be decomposed to identify genetic factors that are specific to depression.

METHODS

We analysed summary statistics from genome-wide association studies (GWAS) of depression (from the Psychiatric Genomics Consortium, 23andMe and UK Biobank) and compared them with GWAS of Neu (from UK Biobank). First, we used a pairwise GWAS analysis to classify variants as associated with only depression, with only Neu or with both. Second, we estimated partial genetic correlations to test whether the depression's genetic link with other phenotypes was explained by shared overlap with Neu.

RESULTS

We found evidence that most genomic regions (25/37) associated with depression are likely to be shared with Neu. The overlapping common genetic variance of depression and Neu was genetically correlated primarily with psychiatric disorders. We found that the genetic contributions to depression, that were not shared with Neu, were positively correlated with metabolic phenotypes and cardiovascular disease, and negatively correlated with the personality trait conscientiousness. After removing shared genetic overlap with Neu, depression still had a specific association with schizophrenia, bipolar disorder, coronary artery disease and age of first birth. Independent of depression, Neu had specific genetic correlates in ulcerative colitis, pubertal growth, anorexia and education.

CONCLUSION

Our findings demonstrate that, while genetic risk factors for depression are largely shared with Neu, there are also non-Neu-related features of depression that may be useful for further patient or phenotypic stratification.

Predictors of response to antidepressants in women with postpartum depression: a systematic review

Antidepressants are the mainstay of drug treatment for moderate or severe postpartum depression. Knowledge of predictors of response could help optimize treatment and reduce the adverse consequences of postpartum depression. The purpose of this systematic review was to ascertain predictors of response or remission to antidepressant treatment in women with postpartum depression. The electronic databases of MEDLINE/PubMed, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews, and Evidence-based Medicine Reviews were searched through December 2019. The search was limited to studies published in the English language. Reference lists of articles that met the inclusion criteria were also searched. We identified some predictors of response and remission that could potentially assist in the optimization of drug treatment of postpartum depression; however, caution is needed to apply these findings in clinical practice due to the heterogeneous nature of postpartum depression. The results of our review highlight the urgent need to identify predictors of response, non-response, or remission to antidepressants in women with postpartum depression.

Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression

Epidemiological evidence indicates the presence of dysregulated homeostatic biological pathways in depressed patients, such as increased inflammation and disrupted energy-regulating neuroendocrine signaling (e.g., leptin, insulin). Alterations in these biological pathways may explain the considerable comorbidity between depression and cardiometabolic conditions (e.g., obesity, metabolic syndrome, diabetes) and represent a promising target for intervention. This review describes how immunometabolic dysregulations vary as a function of depression heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioral symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel (e.g., anti-inflammatory) therapeutic approaches. These lines of evidence are integrated in a conceptual model of immunometabolic depression emerging from the clustering of immunometabolic biological dysregulations and specific behavioral symptoms. The review finally elicits questions to be answered by future research and describes how the immunometabolic depression dimension could be used to dissect the heterogeneity of depression and potentially to match subgroups of patients to specific treatments with higher likelihood of clinical success.

Depression profilers and immuno-metabolic dysregulation: Longitudinal results from the NESDA study

BACKGROUND

Major depressive disorder (MDD) is linked to higher cardio-metabolic comorbidity that may in part be due to the low-grade inflammation and poorer metabolic health observed in MDD. Heterogeneity of MDD is however large, and immune-inflammatory and metabolic dysregulation is present in only part of the MDD cases. We examined the associations of four depression dimensional profilers (atypical energy-related symptom dimension, melancholic symptom dimension, childhood trauma severity, and anxious distress symptom dimension) with immuno-metabolic outcomes, both cross-sectionally and longitudinally.

METHODS

Three waves covering a 6-year follow-up (>7000 observations) of the Netherlands Study of Depression and Anxiety (NESDA) were used. Depression profilers were based on the Inventory of Depressive Symptomatology, the Beck Anxiety Inventory, and the Childhood Trauma index. An inflammatory index (based on IL-6 and CRP), a metabolic syndrome index (based on the five metabolic syndrome components), and a combination of these two indices were constructed. Mixed models were used for cross-sectional and longitudinal models, controlling for covariates.

RESULTS

Of the four depression profilers, only the atypical, energy-related symptom dimension showed robust associations with higher scores on the inflammatory, metabolic syndrome and combined inflammatory-metabolic indexes cross-sectionally, as well as at follow-up. The melancholic symptom dimension was associated with lower scores on the metabolic syndrome index both cross-sectionally and longitudinally.

CONCLUSION

The atypical energy-related symptom dimension was linked to poorer immune-inflammatory and metabolic health, while the melancholic symptom dimension was linked to relatively better metabolic health. Persons with high atypical energy-related symptom burden, representing an immuno-metabolic depression, may be the most important group to target in prevention programs for cardiometabolic disease, and may benefit most from treatments targeting immuno-metabolic pathways.

Positive and negative valence systems in major depression have distinct clinical features, response to antidepressants, and relationships with immunomarkers

BACKGROUND

Heterogeneity in major depressive disorder (MDD) is well recognized but not well understood. Core depressive features are reward and emotional symptoms, which reflect dysfunctions in the positive valence (PV) and negative valence (NV) systems, respectively. This study assessed whether PV and NV systems (based on selected symptoms) were associated with different clinical features, antidepressant response, and levels of immunomarkers in adults with MDD.

METHODS

These analyses used data from combining medications to enhance depression outcomes study (N = 665; n = 166 for immunomarkers). PV and NV symptom scores were extracted from the clinician-rated 30-item Inventory of Depressive Symptomatology. Correlational analyses were conducted.

RESULTS

PV and NV symptom scores were substantially associated with different clinical features. PV symptoms (impaired motivation, impaired energy, and anhedonia) were independently associated with female gender (p < .001), older age (p = .012), and higher cognitive and physical impairment (p < .001) according to the 7-item Cognitive and Physical Functioning Questionnaire. Conversely, NV symptoms (anxiety and interpersonal sensitivity) were independently associated with younger age (p = .013), more anxious comorbidities (p = .001 for generalized anxiety disorder and p = .002 for social phobia) and other commonly associated noncriterion symptoms (p < .001). Overall, PV symptoms were more responsive to antidepressants than NV symptoms (p < .0001; Cohen's d = .455). A PV symptom score was positively correlated with the concentration of three proinflammatory and one anti-inflammatory factor. In contrast, an NV symptom score was negatively associated with only one proinflammatory immunomarker.

CONCLUSIONS

PV and NV system functions appear to be reflected in selected clinical symptoms that differentially relate to other clinical features, treatment outcomes, and immunological function.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

Symptom networks in acute depression across bipolar and major depressive disorders: A network analysis on a large, international, observational study

Major Depressive Episode (MDE) is a transdiagnostic nosographic construct straddling Major Depressive (MDD) and Bipolar Disorder (BD). Prognostic and treatment implications warrant a differentiation between these two disorders. Network analysis is a novel approach that outlines symptoms interactions in psychopathological networks. We investigated the interplay among depressive and mixed symptoms in acutely depressed MDD/BD patients, using a data-driven approach. We analyzed 7 DSM-IV-TR criteria for MDE and 14 researched-based criteria for mixed features (RBDC) in 2758 acutely depressed MDD/BD patients from the BRIDGE-II-Mix study. The global network was described in terms of symptom thresholds and symptom centrality. Symptom endorsement rates were compared across diagnostic subgroups. Subsequently, MDD/BD differences in symptom-network structure were examined using permutation-based network comparison test. Mixed symptoms were the most central and highly interconnected nodes in the network, particularly agitation followed by irritability. Despite mixed symptoms, appetite gain and hypersomnia were significantly more endorsed in BD patients, associations between symptoms were highly correlated across MDD/BD (Spearman's r = 0.96, p<0.001). Network comparison tests showed no significant differences among MDD/BD in network strength, structure, or specific edges, with strong edges correlations (0.66-0.78). Upstream differences in MDD/BD may produce similar symptoms networks downstream during acute depression. Yet, mixed symptoms, appetite gain and hypersomnia are associated to BD rather than MDD. Symptoms during mixed-MDE might aggregate according to 2 different clusters, suggesting a possible stratification within mixed states. Future symptom-based studies should implement clinical, longitudinal, and biological factors, in order to establish tailored therapeutic strategies for acute depression.