Measuring depression: comparison and integration of three scales in the GENDEP study

Acute effects of aerobic exercise on corticomotor plasticity in individuals with and without depression

BACKGROUND

Although complex in nature, the pathophysiology of depression involves reduced or impaired neuroplastic capabilities. Restoring or enhancing neuroplasticity may serve as a treatment target for developing therapies for depression. Aerobic exercise (AEx) has antidepressant benefits and may enhance neuroplasticity in depression although the latter has yet to be substantiated. Therefore, we sought to examine the acute effect of AEx on neuroplasticity in depression.

METHODS

Sixteen individuals with (DEP; 13 female; age = 28.5 ± 7.3; Montgomery-Äsberg Depression Rating Scale [MADRS] = 21.3 ± 5.2) and without depression (HC; 13 female; age 27.2 ± 7.5; MADRS = 0.8 ± 1.2) completed three experimental visits consisting of 15 min of low intensity AEx (LO) at 35% heart rate reserve (HRR), high intensity AEx (HI) at 70% HRR, or sitting (CON). Following AEx, excitatory paired associative stimulation (PAS25ms) was employed to probe neuroplasticity. Motor evoked potentials (MEP) were assessed via transcranial magnetic stimulation before and after PAS25ms to indicate acute changes in neuroplasticity.

RESULTS

PAS25ms primed with HI AEx led to significant increases in MEP amplitude compared to LO and CON. HI AEx elicited enhanced PAS25ms-induced neuroplasticity for up to 1-h post-PAS. There were no significant between-group differences.

CONCLUSION

HI AEx enhances PAS measured neuroplasticity in individuals with and without depression. HI AEx may have a potent influence on the brain and serve as an effective primer, or adjunct, to therapies that seek to harness neuroplasticity.

Childcare sharing and family happiness: analyzing parental and child well-being in the actor-partner interdependence model

Introduction

The exploration of the relationship between parental and child happiness, particularly in the context of shared childcare responsibilities, has not been examined in Korean families.

Methods

Using a two-wave longitudinal design and data from 1,403 families from the Panel Study on Korean Children, this study employed the actor-partner interdependence model to examine the dynamics of childcare sharing between mothers and fathers in South Korea.

Results

Mothers' childcare sharing was found to have no significant impact on their own and their partner's happiness, reflecting traditional gender norms that undervalue mothers' contributions. In contrast, fathers' childcare sharing had a positive impact on their own and their spouse's happiness, suggesting a growing recognition of fathers' involvement. Furthermore, fathers' active participation in childcare was found to promote their children's happiness through their own happiness.

Discussion

This study reflects the complexity of evolving family roles and the covert persistence of traditional gender roles in modern Korean parenting. It suggests the importance of work and family policies that support changes in family dynamics by providing a more nuanced understanding of how changing family roles and responsibilities can enhance overall family well-being.

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.

Motor performance and functional connectivity between the posterior cingulate cortex and supplementary motor cortex in bipolar and unipolar depression

Although implicated in unsuccessful treatment, psychomotor deficits and their neurobiological underpinnings in bipolar (BD) and unipolar (UD) depression remain poorly investigated. Here, we hypothesized that motor performance deficits in depressed patients would relate to basal functional coupling of the hand primary motor cortex (M1) and the posterior cingulate cortex (PCC) with the supplementary motor area (SMA). We performed a longitudinal, naturalistic study in BD, UD and matched healthy controls comprising of two resting-state functional MRI measurements five weeks apart and accompanying assessments of motor performance using a finger tapping task (FTT). A subject-specific seed-based analysis describing functional connectivity between PCC-SMA as well as M1-SMA was conducted. The basal relationships with motor performance were investigated using linear regression models and all measures were compared across groups. Performance in FTT was impaired in BD in comparison to HC in both sessions. Behavioral performance across groups correlated significantly with resting state functional coupling of PCC-SMA, but not of M1-SMA regions. This relationship was partially reflected in a reduced PCC-SMA connectivity in BD vs HC in the second session. Exploratory evaluation of large-scale networks coupling (SMN-DMN) exhibited no correlation to motor performance. Our results shed new light on the association between the degree of disruption in the SMA-PCC anticorrelation and the level of motor impairment in BD.

Using the Beck Depression Inventory to Assess Anhedonia: A Scale Validation Study

Anhedonia is central to several psychological disorders and a frequent target of psychosocial and pharmacological treatments. We evaluated the psychometric properties of two widely used anhedonia measures derived from the Beck Depression Inventory: a 3-item (BDI-Anh3) and a 4-item version (BDI-Anh4). We evaluated these measures in a large undergraduate sample, a community sample, and a clinical sample. Both the BDI-Anh3 and the BDI-Anh4 showed adequate internal consistency, with BDI-Anh4 performing somewhat better, across the three samples. Both measures showed good convergent and discriminant validity, even after controlling for shared variance with other items on the BDI. These findings indicate that both measures have sufficient reliability and validity to support their use by researchers and clinicians.

Establishing the measurement precision of the patient health questionnaire in an adolescent sample

BACKGROUND

Technically sound measures are necessary for accurately identifying youth at risk for depression, but many studies rely on classical test theory metrics or adult samples to evaluate measures. This study examined the use of the PHQ-8, a common and freely available pediatric depression screener, in an adolescent sample using item response theory (IRT).

METHODS

Secondary analyses were conducted on data from a study conducted in Midwestern middle schools in which 1224 youth completed the PHQ-8 as part of a battery of surveys. Polytomous IRT analyses (a Graded Response Model) were used to evaluate the PHQ-8. Items were examined for their ability to distinguish between respondents of different latent depression severity and for differential item functioning (DIF) across demographic categories.

RESULTS

All PHQ-8 items had adequate discriminative abilities. Items measuring anhedonia and psychomotor disturbances performed relatively poorly, and items measuring somatic symptoms (appetite and sleep) were most informative when respondents endorsed extreme response options ("not at all" or "nearly every day"). No DIF was found across grade level or race, but several items were flagged for DIF by gender and student income level.

LIMITATIONS

These results might not be generalizable to a broader youth population due to administration setting and the unique demographic characteristics of this sample (76.0 % African American).

CONCLUSIONS

Tools such as the PHQ-8 are appropriate to quickly screen for depression in adolescents, but further scrutiny of adolescent response patterns is warranted. Future research should examine items measuring anhedonia and psychomotor and somatic disturbances in adolescents.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

SuPAR in major depression: Association with 26 weeks antidepressant response and 10-year depression outcomes

Introduction

Inflammation has been associated with depression and differential antidepressant (AD) treatment response. Soluble urokinase plasminogen activator receptor (suPAR) is a novel measure of chronic inflammation. We investigated whether suPAR is associated with depression severity and AD response.

Methods

We included 90 patients with major depressive disorder (MDD) who participated in a part-randomized clinical trial of 26 weeks of treatment with escitalopram or nortriptyline. suPAR levels were measured in serum samples collected at baseline and after 8, 12 and 26 weeks. Mixed effects models for the association between suPAR levels and AD response were performed. By merging with Danish nationwide registers, we included information on psychiatric hospital contacts during ten years after the GENDEP trial. Cox regression analyses calculated the hazard rate ratios between suPAR levels and subsequent hospitalizations.

Results

At baseline, higher suPAR levels were not associated with overall depression severity but with greater severity of neurovegetative depressive symptoms, specifically appetite and weight changes. 57 (63.3%) patients responded positively to treatment. Among 57 (63.3%) patients who achieved response, those who responded had significantly higher baseline suPAR levels levels, and response was associated with a significant decrease in suPAR during AD treatment. Remitters decreased from 3.1 ng/ml at baseline to 2.8 ng/ml after 26 weeks (p = 0.003) and responders from 3.0 to 2.8 ng/ml (p = 0.02), whereas non-remitters and non-responders showed unchanged suPAR levels. We found no correlation between a change in suPAR and a change in MADRS, but a lowering of suPAR correlated with a decrease in neurovegetative symptoms. We found no association between suPAR levels and 10-year risk for hospitalizations.

Discussion

The present study suggests that an elevated level of chronic inflammation, measured as the suPAR level, is associated with better response to AD treatment.

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

Objective

Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.

Methods

English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.

Results

Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.

Conclusion

ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Importance

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.

Objective

To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.

Design, Setting, and Participants

This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.

Exposures

In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.

Main Outcomes and Measures

The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.

Results

The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response.

Conclusions and Relevance

In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Depressive symptom dimensions predict the treatment effect of repetitive transcranial magnetic stimulation for post-stroke depression

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) has attracted considerable attention because of its non-invasiveness, minimal side effects, and treatment efficacy. Despite an adequate duration of rTMS treatment, some patients with post-stroke depression (PSD) do not achieve full symptom response or remission.

METHODS

This was a prospective randomized controlled trial. Participants receiving rTMS were randomly assigned to the ventromedial prefrontal cortex (VMPFC), left dorsolateral prefrontal cortex (DLPFC), or contralateral motor area (M1) groups in a ratio of 1:1:1. Enrollment assessments and data collection were performed in weeks 0, 2, 4, and 8. The impact of depressive symptom dimensions on treatment outcomes were tested using a linear mixed-effects model fitted with maximum likelihood. Univariate analysis of variance (ANOVA) and back-testing were used to analyze the differences between the groups.

RESULTS

In total, 276 patients were included in the analysis. Comparisons across groups showed that 17-item Hamilton Rating Scale for Depression (HAMD-17) scores of the DLPFC group significantly differed from those of the VMPFC and M1 groups at 2, 4, and 8 weeks after treatment (p < 0.05). A higher observed mood score (β = -0.44, 95% confidence interval [CI]: -0.85-0.04, p = 0.030) could predict a greater improvement in depressive symptoms in the DLPFC group. Higher neurovegetative scores (β = 0.60, 95% CI: 0.25-0.96, p = 0.001) could predict less improvement of depressive symptoms in the DLPFC group.

CONCLUSION

Stimulation of the left DLPFC by high-frequency rTMS (HF-rTMS) could significantly improve depressive symptoms in the subacute period of subcortical ischemic stroke, and the dimension of depressive symptoms at admission might predict the treatment effect.

Dimensions of temperament and character as predictors of antidepressant discontinuation, response and adverse reactions during treatment with nortriptyline and escitalopram

BACKGROUND

Personality traits may predict antidepressant discontinuation and response. However, previous studies were rather small, only explored a few personality traits and did not include adverse drug effects nor the interdependency between antidepressant discontinuation patterns and response.

METHODS

GENDEP included 589 patients with unipolar moderate-severe depression treated with escitalopram or nortriptyline for 12 weeks. Seven personality dimensions were measured using the self-reported 240-item Temperament and Character Inventory-Revised (TCI-R). We applied Cox proportional models to study discontinuation patterns, logistic and linear regression to investigate response and remission after 8 and 12 weeks, and mixed-effects linear models regarding time-varying treatment response and adverse drug reactions.

RESULTS

Low harm avoidance, low cooperativeness, high self-transcendence and high novelty seeking were associated with higher risks for antidepressant discontinuation, independent of depressed mood, adverse drug reactions, drug, sex and age. Regression analyses showed that higher novelty seeking and cooperativeness scores were associated with a greater likelihood of response and remission after 8 and 12 weeks, respectively, but we found no correlations with response in the mixed-effects models. Only high harm avoidance was associated with more self-reported adverse effects.

CONCLUSIONS

This study, representing the largest investigation between several personality traits and response to two different antidepressants, suggests that correlations between personality traits and antidepressant treatment response may be confounded by differential rates of discontinuation. Future trials on personality in the treatment of depression need to consider this interdependency and study whether interventions aiming at improving compliance for some personality types may improve response to antidepressants.

Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS

BACKGROUNDMajor depressive disorder (MDD) can benefit from novel interventions and personalization. Deep transcranial magnetic stimulation (Deep TMS) targeting the lateral prefrontal cortex (LPFC) using the H1 coil was FDA cleared for treatment of MDD. However, recent preliminary data indicate that targeting the medial prefrontal cortex (MPFC) using the H7 coil might induce outcomes that are as good or even better. Here, we explored whether Deep TMS targeting the MPFC is noninferior to targeting the LPFC and whether electrophysiological or clinical markers for patient selection can be identified.METHODSThe present prospective, multicenter, randomized study enrolled 169 patients with MDD for whom antidepressants failed in the current episode. Patients were randomized to receive 24 Deep TMS sessions over 6 weeks, using either the H1 coil or the H7 coil. The primary efficacy endpoint was the change from baseline to week 6 in Hamilton Depression Rating Scale scores.RESULTSClinical efficacy and safety profiles were similar and not significantly different between groups, with response rates of 60.9% for the H1 coil and 64.2% for the H7 coil. Moreover, brain activity measured by EEG during the first treatment session correlated with clinical outcomes in a coil-specific manner, and a cluster of baseline clinical symptoms was found to potentially distinguish between patients who can benefit from each Deep TMS target.CONCLUSIONThis study provides a treatment option for MDD, using the H7 coil, and initial guidance to differentiate between patients likely to respond to LPFC versus MPFC stimulation targets, which require further validation studies.TRIAL REGISTRATIONClinicalTrials.gov NCT03012724.FUNDINGBrainsWay Ltd.

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

BACKGROUND

Efforts 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.

METHODS

We 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.

RESULTS

We 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.

CONCLUSION

We 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.

Regression analysis of logistic model with latent variables

We propose a joint modeling approach to investigating the effects of social-psychological factors on the onset of depression. The proposed model comprises two components. The first one is a confirmatory factor analysis model that summarizes latent factors through multiple correlated observed variables. The second one is a logistic regression model that investigates the effects of observed and latent influence factors on the occurrence of depression. We develop a hybrid procedure based on the borrow-strength estimation procedure and the weighted score function to estimate the model parameters. The asymptotic properties of the proposed estimators are established. Simulation studies demonstrate that the method we proposed performs well. An application to a study concerning the social-psychological factors of depression is provided.

Comparison on the clinical features in patients with or without treatment-resistant depression: A National Survey on Symptomatology of Depression report

Patients with treatment-resistant depression (TRD) have fewer treatment options and worse prognoses than those without TRD. Although the etiology or pathophysiology of TRD remains unclear, certain clinical variables have been found to be related to its severity and prognosis. Therefore, 1151 patients with recurrent depression were recruited from the National Survey on Symptomatology of Depression (NSSD) and their depressive symptoms were assessed by using the doctor-rating assessment questionnaire. Then, the differences between patients with or without TRD were compared by parametric or nonparametric tests and the risk factors for TRD were explored by logistic regression. The results showed there were differences in clinical variables between patients with and without TRD. Additionally, we found depression with more somatic symptoms had a higher risk for TRD. Further analysis by stepwise logistic regression showed that age, gender, religious belief, drinking habit, the total course of depression, the number of hospitalizations, characteristics of seasonal episode remission, depressed mood, hypersexuality, emotionally incoherent psychotic symptoms, psychomotor agitation, respiratory system symptoms and history of suicide attempts were strongly associated with TRD. So, it is crucial for clinicians to identify these clinical features and adjust treatments timely.

Optimizing the predictive power of depression screenings using machine learning

Objective

Mental health self-report and clinician-rating scales with diagnoses defined by sum-score cut-offs are often used for depression screening. This study investigates whether machine learning (ML) can detect major depressive episodes (MDE) based on screening scales with higher accuracy than best-practice clinical sum-score approaches.

Methods

Primary data was obtained from two RCTs on the treatment of depression. Ground truth were DSM 5 MDE diagnoses based on structured clinical interviews (SCID) and PHQ-9 self-report, clinician-rated QIDS-16, and HAM-D-17 were predictors. ML models were trained using 10-fold cross-validation. Performance was compared against best-practice sum-score cut-offs. Primary outcome was the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve. DeLong's test with bootstrapping was used to test for differences in AUC. Secondary outcomes were balanced accuracy, precision, recall, F1-score, and number needed to diagnose (NND).

Results

A total of k = 1030 diagnoses (no diagnosis: k = 775; MDE: k = 255) were included. ML models achieved an AUCQIDS-16 = 0.94, AUCHAM-D-17 = 0.88, and AUCPHQ-9 = 0.83 in the testing set. ML AUC was significantly higher than sum-score cut-offs for QIDS-16 and PHQ-9 (ps ≤ 0.01; HAM_D-17: p = 0.847). Applying optimal prediction thresholds, QIDS-16 classifier achieved clinically relevant improvements (Δbalanced accuracy = 8%, ΔF1-score = 14%, ΔNND = 21%). Differences for PHQ_9 and HAM-D-17 were marginal.

Conclusions

ML augmented depression screenings could potentially make a major contribution to improving MDE diagnosis depending on questionnaire (e.g., QIDS-16). Confirmatory studies are needed before ML enhanced screening can be implemented into routine care practice.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Montgomery-Åsberg Depression Rating Scale factors in treatment-resistant depression at onset of treatment: Derivation, replication, and change over time during treatment with esketamine

OBJECTIVE

Derive and confirm factor structure of the Montgomery-Åsberg Depression Rating Scale (MADRS) in patients with treatment-resistant depression (TRD) and evaluate how the factors evident at baseline change over 4 weeks of esketamine treatment.

METHODS

Two similarly-designed, short-term TRANSFORM trials randomized adults to esketamine or matching placebo nasal spray, each with a newly-initiated oral antidepressant, for 4 weeks (TRANSFORM-1: N = 342 patients; TRANSFORM-2: N = 223 patients). The factor structure of MADRS item scores at baseline was determined by exploratory factor analysis in TRANSFORM-2 and corroborated by confirmatory factor analysis in TRANSFORM-1. Change in MADRS factor scores from baseline (day 1) to the end of the 28-day double-blind treatment phase of TRANSFORM-2 was analyzed using a mixed-effects model for repeated measures (MMRM).

RESULTS

Three factors were identified based on analysis of MADRS items: Factor 1 labeled affective and anhedonic symptoms (apparent sadness, reported sadness, lassitude, inability to feel), Factor 2 labeled anxiety and vegetative symptoms (inner tension, reduced sleep, reduced appetite, concentration difficulties), and Factor 3 labeled hopelessness (pessimistic thoughts, suicidal thoughts). The three-factor structure observed in TRANSFORM-2 was verified in TRANSFORM-1. Treatment benefit at 24 h with esketamine versus placebo was observed on all 3 factors and continued throughout the 4-week double-blind treatment period.

CONCLUSIONS

A three-factor structure for MADRS appears to generalize to TRD. All three factors improved over 4 weeks of treatment with esketamine nasal spray.

Suicidality and relief of depressive symptoms with intermittent theta burst stimulation in a sham-controlled randomized clinical trial

OBJECTIVES

Suicidality is a serious public health problem and is closely associated with the severity of depression. In this work, we examined the effects of accelerated intermittent theta burst stimulation (iTBS) on suicidal status, risk factors for suicide, and severity of depressive symptoms in subjects with major depressive disorder (MDD).

METHODS

We present data from a quadruple-blind (patient, care provider, investigator, rater) sham-controlled crossover randomized clinical trial. During a 6-week observation period, each participant underwent 2 weeks of stimulation - each week with 20 sessions of active or sham iTBS. A suicide score was created using a composite of individual items from Montgomery-Åsberg Depression Scale (MADRS), Hamilton Depression Scale, and Beck Depression Inventory. The severity of depression was determined by MADRS total scores. In addition, we used demographic and Columbia Suicidality Rating Scale information to assess suicide risk.

RESULTS

Among 81 participants, we observed a significant reduction in suicidality and this change was positively correlated with a change in depressive symptoms. A significant difference between active and sham iTBS provided evidence for antidepressant effects. Higher changes in levels of anxiety and impulsiviness also correlated with larger changes in suicidality.

CONCLUSIONS

As neither suicide nor other serious adverse events were evidenced, this intervention was a safe and viable procedure to reduce suicidality and severity of depressive symptoms. Moreover, we identified more pronounced anti-suicidal effects in those with higher risk profiles. Unlike MADRS, composite suicidal scores did not provide evidence of an effect between stimulation conditions in this crossover design study. Even so, based on our promising results, parallel and larger studies could contribute to a better characterization of the anti-suicidal placebo effect and the benefit of using iTBS against suicidal symptoms.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

BACKGROUND

The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo.

METHODS

The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment.

RESULTS

Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants.

CONCLUSIONS

PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

A comprehensive approach to predicting weight gain and therapy response in psychopharmacologically treated major depressed patients: A cohort study protocol

Background

A subgroup of patients with Major Depressive Disorder shows signs of low-grade inflammation and metabolic abberances, while antidepressants can induce weight gain and subsequent metabolic disorders, and lacking antidepressant response is associated with inflammation.

Objectives

A comprehensive investigation of patient phenotypes and their predictive capability for weight gain and treatment response after psychotropic treatment will be performed. The following factors will be analyzed: inflammatory and metabolic markers, gut microbiome composition, lifestyle indicators (eating behavior, physical activity, chronotype, patient characteristics (childhood adversity among others), and polygenic risk scores.

Methods

Psychiatric inpatients with at least moderate Major Depressive Disorder will be enrolled in a prospective, observational, naturalistic, monocentric study using stratified sampling. Ethical approval was obtained. Primary outcomes at 4 weeks will be percent weight change and symptom score change on the Montgomery Asberg Depression Rating Scale. Both outcomes will also be binarized into clinically relevant outcomes at 5% weight gain and 50% symptom score reduction. Predictors for weight gain and treatment response will be tested using multiple hierachical regression for continuous outcomes, and multiple binary logistic regression for binarized outcomes. Psychotropic premedication, current medication, eating behavior, baseline BMI, age, and sex will be included as covariates. Further, a comprehensive analysis will be carried out using machine learning. Polygenic risk scores will be added in a second step to estimate the additional variance explained by genetic markers. Sample size calculation yielded a total amount of N = 171 subjects.

Discussion

Patient and physician expectancies regarding the primary outcomes and non-random sampling may affect internal validity and external validity, respectively. Through the prospective and naturalistic design, results will gain relevance to clinical practice. Examining the predictive value of patient profiles for weight gain and treatment response during pharmacotherapy will allow for targeted adjustments before and concomitantly to the start of treatment.

Association of cognitive impairment and reduced cortical thickness in prefrontal cortex and anterior cingulate cortex with treatment-resistant depression

Accumulating evidence suggests the critical role of cortical thinning in the pathophysiology of major depressive disorder. However, the association of cortical thickness and cognitive impairment with treatment-resistant depression (TRD) has rarely been investigated. In total, 48 adult patients with TRD and 48 healthy controls were recruited and administered a series of neurocognitive and neuroimaging examinations, including 1-back and 2-back working memory tasks and brain magnetic resonance imaging (MRI). Whole-brain cortical thickness analysis was performed to investigate the differences in the cortical thickness between patients with TRD and controls. The patients had reduced cortical thickness in the frontal cortex, particularly at the left frontal pole, left inferior frontal cortex, and left anterior cingulate cortex, and left middle temporal cortex compared with the healthy controls. Moreover, in the 2-back working memory task, the cortical thickness in the left frontal pole and left anterior cingulate cortex was positively associated with mean error in the patients, but not in the controls. Reduced cortical thickness in the frontal pole and anterior cingulate cortex is associated with TRD and related cognitive impairment. Our study indicated the crucial effects of the frontal and temporal cortical thickness on the pathophysiology of TRD and cognitive impairment in patients with TRD.

A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

Chronic vs non-chronic depression in psychiatric inpatient care - Data from a large naturalistic multicenter trial

BACKGROUND

Around 20% - 30% of depressed individuals experience a chronic form of depression lasting two or more years. This naturalistic study investigates the characteristics and the course of chronic depressed patients (CD) during standard antidepressant treatment in comparison to not chronically depressed (NCD) patients.

METHODS

Data of 954 patients were drawn from the prospective naturalistic, multicenter study of the German research network on depression, CD was met as classifier by 113 patients (11.8%), whereas 841 patients (88.2%) had non-chronic courses (NCD).

RESULTS

CD was significantly associated with a low age at onset, use of benzodiazepines, psychotherapy at baseline, substance abuse, a depressive personality disorder and a low degree of extraversion. CD patients showed a longer hospital stay, lower remission rates, increased rates of suicidal ideation as well as higher depression scores at discharge. In addition, individuals with chronic depression continued to obtain higher neuroticism scores and lower extraversion scores at discharge.

LIMITATION

Results were assessed by a post-hoc analysis, based on prospectively collected data.

CONCLUSION

CD patients have an inferior outcome in clinical measures as well as personality dimensions (i.e. low extraversion) compared to non-CD patients. These findings support the notion that CD patients entering a setting of standard psychiatric inpatient care will show less benefit compared to non-CD patients, and that this difference as such may be used as a stratifying marker for providing specialized psychiatric treatment with optimized pharmacological and psychotherapeutic protocols.

Biological Diagnosis of Depression: A Biomarker Panel from Several Nonspecial Indicators Instead of the Specific Biomarker(s)

It is a consensus that the diagnosis efficiency of depression is rather low in clinic. The traditional way of diagnosing depression by symptomatology is flawed. Recent years, a growing body of evidence has underlined the importance of physiological indicators in the diagnosis of depression. However, the diagnosis of depression is difficult to be like some common clinical diseases, which have clear physiological indicators. A single physiological index provides limited information to clinicians and is of little help in the diagnosis of depression. Thus, it is more rational and practical to diagnose depression with a biomarker panel, which covers a few non-specific indicators, such as hormones, cytokines, and neurotrophins. This open review suggested that biomarker panel had a bright future in creating a new model of depression diagnosis or at least providing a reference to the existing depression criteria. The viewpoint is also the future of other psychiatric diagnosis.

Depression and inflammation: Correlation between changes in inflammatory markers with antidepressant response and long-term prognosis

Inflammation may correlate with a specific subgroup of depression and differential antidepressant response, but no trial has studied changes of many inflammatory markers over several time points and evaluated symptom-specific antidepressant response and long-term prognosis. We performed secondary analyses among 90 outpatients with moderate-severe depression (71% female, mean age 38 years) treated for 26 weeks with escitalopram or nortriptyline. We measured 27 pro- and anti-inflammatory markers at week 0, 8, 12, and 26 and calculated composite inflammation scores. Three depression rating scales were applied and symptom dimensions of depression calculated. Via Danish nationwide registers, 10 years follow-up were included on psychiatric hospital contacts, indicating relapse. Pearson correlation analyses were performed between baseline inflammatory markers and depressive symptom severity, mixed effects models during the 26-week trial, and Cox regression analyses for the register-based outcomes, adjusted for age, sex, BMI, and smoking. Baseline inflammatory markers correlated with differential severity on specific symptom dimensions but not with overall depression severity. A total of 17 of 27 inflammatory markers decreased significantly during treatment. We found no correlation between baseline nor change in inflammatory markers nor composite inflammation scores with differential treatment response on the MADRS, but small correlations between changes in inflammatory markers and differential response on neurovegetative symptoms. Findings were similar among 59 treatment-naïve patients. Inflammatory markers were not associated with differential risks for 10-year relapse. These findings support the importance of studying specific depressive symptoms to further explore the correlation between inflammation with differential antidepressant response in a subgroup of depression. Clinical Trial Registration number: GENDEP is registered at EudraCT2004-001723-38 (http://eudract.emea.europa.eu) and ISRCTN03693000 (www.controlled-trials.com).

The subcomponents of affect scale (SAS): validating a widely used affect scale

OBJECTIVE

There is a need for a brief affect scale that also encompasses different components of affect relevant for researchers interested in physiological and health outcomes. The Subcomponents of Affect Scale (SAS) meets this need. This 18-item scale has nine positive and nine negative affect items encompassing six subscales (calm, well-being, vigour, depression, anxiety, anger). Previous research using the SAS has demonstrated its predictive validity, but no work has tested its subscale structure or longitudinal validity.

DESIGN

Data from the Common Cold Project in which individuals (N = 610) completed the SAS over the course of seven days were used.

RESULTS

Confirmatory factor analysis demonstrated the reliability of the subscale structure of the SAS across seven days (positive affect subscale structure: CFIs ≥ 0.98; negative affect subscale structure: CFIs ≥ 0.94 with day 6 CFI = 0.91) and tests of factorial invariance showed the scale is valid to use over time.

CONCLUSIONS

These results confirm the psychometric validity of the subscale structure of the SAS and imply that the subscales can be used longitudinally, allowing for its use in health research as well as non-health research that can benefit from its subscale structure and longitudinal capabilities.

N-acetylcysteine as an adjunctive treatment for bipolar depression: A systematic review and meta-analysis of randomized controlled trials

OBJECTIVES

Previous studies and meta-analyses suggested that N-acetylcysteine (NAC) was superior to placebo in improving depression in bipolar disorder. However, more recent data, including two larger trials, found that NAC was no more effective than placebo. We conducted a meta-analysis to appraise the possible efficacy of NAC in treating bipolar depression.

METHODS

A systematic review and meta-analysis of double-blind, placebo-controlled trials of NAC as a treatment augmentation strategy for bipolar depression was carried out in PubMed (1966-2020). We utilized random-effect analysis to evaluate improvement in depressive symptoms from baseline to endpoint as the primary efficacy measure.

RESULTS

Six trials including 248 patients were included. Treatment augmentation with NAC showed a moderate effect size favoring NAC over placebo (d = 0.45, 95% C.I.: 0.06-0.84). There was substantial heterogeneity (I²  = 49%). Meta-regression analyses did not identify any moderator that might explain variation in heterogeneity, including baseline depressive symptom scores, mean NAC dose, or duration of study.

CONCLUSIONS

Results from six clinical trials suggest that treatment augmentation with NAC for bipolar depression appears to be superior to placebo, with a moderate effect size, but a large confidence interval. Larger clinical trials, investigating possible moderating factors, such as NAC dose, treatment duration, baseline depression severity, or chronicity of illness, are warranted.

EffECTively Treating Depression: A Pilot Study Examining Manualized Group CBT as Follow-Up Treatment After ECT

Background: There is an urgent need for effective follow-up treatments after acute electroconvulsive therapy (ECT) in depressed patients. Preliminary evidence suggests psychotherapeutic interventions to be a feasible and efficacious follow-up treatment. However, there is a need for research on the long-term usefulness of such psychotherapeutic offers in a naturalistic setting that is more representative of routine clinical practice. Therefore, the aim of the current pilot study was to investigate the effects of a half-open continuous group cognitive behavioral therapy (CBT) with cognitive behavioral analysis system of psychotherapy elements as a follow-up treatment for all ECT patients, regardless of response status after ECT, on reducing depressive symptoms and promoting psychosocial functioning.

Method: Group CBT was designed to support patients during the often-difficult transition from inpatient to outpatient treatment. In a non-controlled pilot trial, patients were offered 15weekly sessions of manualized group CBT (called EffECTiv 2.0). The Montgomery-Åsberg Depression Rating Scale was assessed as primary outcome; the Beck Depression Inventory, WHO Quality of Life Questionnaire–BREF, and the Cognitive Emotion Regulation Questionnaire were assessed as secondary outcomes. Measurements took place before individual group start, after individual group end, and 6months after individual group end.

Results: During group CBT, Post-ECT symptom reduction was not only maintained but there was a tendency toward a further decrease in depression severity. This reduction could be sustained 6months after end of the group, regardless of response status after ECT treatment. Aspects of quality of life and emotion regulation strategies improved during group CBT, and these improvements were maintained 6months after the end of the group.

Conclusion: Even though the interpretability of the results is limited by the small sample and the non-controlled design, they indicate that manualized group CBT with cognitive behavioral analysis system of psychotherapy elements might pose a recommendable follow-up treatment option after acute ECT for depressed patients, regardless of response status after ECT. This approach might not only help to further reduce depressive symptoms and prevent relapse, but also promote long-term psychosocial functioning by improving emotion regulation strategies and psychological quality of life and thus could be considered as a valuable addition to clinical routine after future validation.

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals' trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning.

Discrepancies between self-rated depression and observed depression severity: The effects of personality and dysfunctional attitudes

BACKGROUND

Patient self-reports and clinician ratings of depression severity can differ substantially. The aim of the current study was to explore factors associated with discrepancies between depressed patients' Patient Health Questionnaire (PHQ-9) self-reports and clinicians' Hamilton Rating Scale for Depression (HAMD-17) ratings.

METHODS

We first computed discrepancy scores defined as the standardized weighted HAMD-17 total score minus the standardized PHQ-9 total score. To assess correlates of inconsistent scores, results of patients with similar standardized scores were removed (i.e., within ½ standard deviation, n = 270). Positive values indicate underreporting (HAMD-17 > PHQ-9), i.e., the underreporting group (n = 200); and negative discrepancy scores indicate overreporting (PHQ-9 > HAMD-17), i.e., the overreporting group (n = 221). We examined the relationship of demographic, dysfunctional attitudes and personality variables to the discrepancies between self and observer rated depression.

RESULTS

There were significant differences in extraversion, psychoticism, neuroticism, dysfunctional attitudes and occupation between the underreporting group and the overreporting group (all p < .05). When controlling for potential confounding variables, being a working professional and having high neuroticism and dysfunctional attitudes were significantly associated with overestimating symptoms of depression (e.g., professional: OR, 2.89; 95% CI, 1.67-5.00; p < .001; high neuroticism: OR, 7.08; 95% CI, 1.47-34.08; p < .001;dysfunctional attitudes: OR, 1.01; 95% CI, 1.00-1.02; p = .030). People with average, or high extraversion tended to underestimate scores (average extraversion: OR, 0.59; 95% CI, 0.37-0.95; high extraversion: OR, 0.48; 95% CI, 0.24-0.98).

CONCLUSIONS

This study is the first to use PHQ-9 and HAMD-17 to explore the discrepancies between self and observer rated depression. Discrepancies occurred between the PHQ-9 score and HAMD-17 score, which were related to neuroticism, extraversion, dysfunctional attitudes and being a working professional. Future research should clarify the relationship between these factors and therapeutic effects of treatments, including adverse outcomes.

Influence of anhedonic symptom severity on reward circuit connectivity in PTSD

Anhedonia, marked by deficits in reward processing, is a prominent symptom of several psychiatric conditions and has been shown to influence functional connectivity between reward-related regions. However, the unique influence of anhedonia severity on reward circuit connectivity in posttraumatic stress disorder (PTSD) remains unclear. To address this, we examined resting-state functional connectivity (rsFC) of the ventral striatum as a function of anhedonia for individuals with PTSD. Resting-state functional MRI scans and behavioral assessments were collected for 71 women diagnosed with PTSD. Seed-based voxelwise rsFC analyses for left and right nucleus accumbens (NAcc) seed regions of interest were performed. Voxelwise regression analyses were conducted to examine the relationship between anhedonia severity and rsFC of left and right NAcc. Results indicated that greater anhedonia severity was associated with reduced rsFC between the left NAcc and a cluster in the left caudate extending to the thalamus. This relationship between anhedonia and rsFC remained significant after controlling for PTSD symptom severity or depression severity. Our findings suggest that reward circuit dysfunction at rest is associated with anhedonia in PTSD. These results further contribute to our understanding of the neural correlates of anhedonia in psychiatric conditions.

Interest-activity symptom severity predicts response to ketamine infusion in treatment-resistant depression

BACKGROUND

Interest and activity are part of the positive mood domain. Evidence suggests the symptom domain of interest-activity at baseline as a clinical predictor for treatment response to traditional antidepressants. However, whether this domain is related to the response to a single low-dose ketamine infusion remains unclear.

METHODS

Seventy-one patients with treatment-resistant depression were randomized to 3 treatment groups: a single 0.5 or 0.2 mg/kg ketamine or normal saline placebo infusion. Depressive symptoms were measured using the 17-item Hamilton Depression Rating Scale before infusions and at postinfusion period (at 40 min and up to 2 weeks). Low (mild) versus medium versus high (severe) interest-activity symptom domain groups were classified on the basis of the cutoff point of ± 0.4 standard deviation. The effect of baseline interest-activity symptoms on outcomes was tested using generalized estimating equation models.

RESULTS

The interest-activity symptom domain as a continuous variable (β = 8.413, p = .016) was related to the trajectory of depressive symptoms. Stratified by levels of the interest-activity symptom domain, in the low interest-activity, 0.2 mg/kg ketamine infusion (β = 0.013) demonstrated the greatest antidepressant effect (p < .01) compared with 0.5 mg/kg ketamine (β = 0.739) and placebo infusions; however, in the high interest-activity, 0.5 mg/kg ketamine infusion (β = 0.001) demonstrated the best antidepressant effect (p < .01) compared with 0.2 mg/kg ketamine (β = 1.372) and placebo infusions.

DISCUSSION

The symptom domain of interest-activity was an independent predictor for the treatment response to a single low-dose ketamine infusion.

Prevalence and factors associated with postpartum depression during the COVID-19 pandemic among women in Guangzhou, China: a cross-sectional study

BACKGROUND

The 2019 coronavirus disease (COVID-19) is a public health emergency of international concern. To date, there are limited studies that have investigated the impact of COVID-19 pandemic on mental health among female population. Therefore, the study aims to investigate the prevalence of postpartum depression (PPD) and it's related factors among women in Guangzhou, China, during the COVID-19 pandemic.

METHODS

A cross-sectional study was performed from 30th March 2020 to 13th April 2020 using anonymous online questionnaire among 864 women at 6-12 weeks postpartum. The Chinese version of Edinburgh Postnatal Depression Scale and a questionnaire regarding associated factors were administered to all participants. Multivariate logistic regression was used to determine factors that were significantly associated with PPD.

RESULTS

The prevalence of PPD among women at 6-12 weeks postpartum was 30.0%. A multivariate logistic regression model identified significant factors as: immigrant women, persistent fever, poor social support, concerns about contracting COVID-19 and certain precautionary measures.

CONCLUSIONS

The findings suggest the need for policies and interventions to not only mitigate the psychological impacts but also target disadvantaged sub-groups of women following childbirth during the COVID-19 pandemic.

The clinical characterization of the adult patient with depression aimed at personalization of management

Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial-and-error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta-analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self-report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above-mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

Is it time to change the gold standard of obsessive-compulsive disorder severity assessment? Factor structure of the Yale-Brown Obsessive-Compulsive Scale

OBJECTIVES

The Yale-Brown Obsessive-Compulsive Scale has been considered the gold standard scale to assess obsessive-compulsive disorder severity. Previous studies using exploratory factor analysis and confirmatory factor analysis with this scale showed mixed findings in terms of factor structure and fit of models. Therefore, we used confirmatory factor analysis to compare different Yale-Brown Obsessive-Compulsive Scale models in a large sample aiming to identify the best model fit.

METHODS

We assessed adult obsessive-compulsive disorder patients (n = 955) using three measures: Yale-Brown Obsessive-Compulsive Scale severity ratings, the Dimensional Yale-Brown Obsessive-Compulsive Scale and the clinical global impression scale. We tested all factor structures reported by previous studies to investigate which model best fitted the data: one-factor, two-factor, three-factor and their equivalent high-order solutions. We also investigated Yale-Brown Obsessive-Compulsive Scale items correlations with scores from the other measures of obsessive-compulsive disorder severity.

RESULTS

Confirmatory factor analysis models presented mediocre to fair goodness-of-fit indexes. Severity items related to resistance to obsessions and compulsions presented low factor loadings. The model with the best fit indexes was a high-order model without obsessive-compulsive disorder resistance items. These items also presented small correlations with other obsessive-compulsive disorder severity measures.

CONCLUSION

The obsessive-compulsive disorder field needs to discuss further improvements in the Yale-Brown Obsessive-Compulsive Scale and/or continue to search for better measures of obsessive-compulsive disorder severity.

Concordance between clinician-rated and patient reported outcome measures of depressive symptoms in treatment resistant depression

BACKGROUND

Calls to implement measurement-based care (MBC) in psychiatry are increasing. A recent Cochrane meta-analysis concluded that there is insufficient evidence that routine application of patient reported outcomes (PROs) improves treatment outcomes for common psychiatric disorders. There is a particular paucity of this information in patients with treatment resistant depression (TRD).

METHODS

A TRD sample (n = 302) and a treatment-naïve sample with major depression (n = 344) were assessed for the level of agreement in depression severity between two PROs (the Beck Depression Inventory, BDI, and the Quick Inventory of Depressive Symptomatology Self-report, QIDS-SR) and two Clinician Rated (CRs) measures (Hamilton Depression Rating Scale, HDRS, and the Montgomery-Asberg Depression Rating Scale, MADRS).

RESULTS

Correlations between CR and PRO total scores in the TRD sample ranged from 0.57 (HDRS-QIDS-SR) to 0.68 (MADRS-BDI), reflecting a moderate-to-strong relationship between assessment tools. Correlations in the treatment naïve sample were non-significantly lower for most comparisons, ranging from 0.51 (HDRS-QIDS-SR) to 0.64 (MADRS-BDI). Few predictors of discordance between CRs and PROs were identified, though chronicity of the current episode in treatment-naïve patients was associated with greater agreement.

LIMITATIONS

Inter-rater reliability of the clinician interviews was conducted separately within the two studies so we could not determine the reliability between the two groups of raters used in the studies.

CONCLUSION

Findings generally supported acceptably high levels of agreement between patient and clinician ratings of baseline depression severity. More work is needed to determine the extent to which PROs can improve outcomes in MBC for depression and, more specifically, TRD.

Inflammation and the dimensions of depression: A review

Patients with depressive disorders show a wide range of clinical manifestations including cognitive and neurovegetative symptoms. Importantly, these symptoms can differ in terms of biological etiology, and deconstructing depression into specific symptoms may provide valuable insight into the underlying neurobiology. Little research has examined inflammation in the context of depressive dimensions. Here we conduct a narrative review of the existing literature (21 studies) to elucidate whether the depression-inflammation link is symptom specific. Overall, there is evidence that an association exists between neurovegetative symptoms of depression and inflammation, independent of cognitive symptoms. The same cannot be said of cognitive symptoms and inflammation. There is also some evidence of gender differences in the directionality of the relationship between depression and inflammation. Potential explanations for these findings, limitations of the existing literature and recommendations for future research design are discussed.

[Angedonia in the structure of affective disorders: therapeutic opportunities]

Anhedonia is one of the core features of depression. The article considers the place of anhedonia in the structure of affective disorders, its influence on the prognosis and effectiveness of therapy. The authors stress that various manifestations of anhedonia must be considered in correlation with the basic ability to feel pleasure. Therapy of anhedonia is not always effective. According to literature, agomelatin occupies a leading position among the drugs that reduce anhedonia.

From laboratory to life: associating brain reward processing with real-life motivated behaviour and symptoms of depression in non-help-seeking young adults

BACKGROUND

Depression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed - but non-help-seeking and unmedicated - individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.

METHODS

Individuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.

RESULTS

Significant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = -0.017, p = 0.006) and putamen (b = -0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).

CONCLUSIONS

Striatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.

Psilocybin for depression: Considerations for clinical trial design

Background and aims

Given the enormous global burden of depressive illness, there is an urgent need to develop novel and more effective treatments for major depressive disorder (MDD). Recent findings have suggested that psychedelic drugs may have a role in the treatment of depressive symptoms, and a number of groups are in the process of developing protocols to study this question systematically. Given the subjective quality of both the psychedelic experience and depressive symptomatology, great care must be taken when designing a protocol to study the clinical efficacy of psychedelic drugs. This study will discuss many factors to consider when designing a clinical trial of psilocybin for MDD.

Methods

We provide a thorough review of pertinent research into antidepressant clinical trial methodology and review practical considerations that are relevant to the study of psychedelic-assisted treatment for depression.

Results

We discuss participant selection (including diagnostic accuracy, exclusion criteria, characteristics of the depressive episode, and the use of concurrent medications), study interventions (including dosing regimens, placebo selection, non-pharmacological components of treatment, and the importance of blinding), trial duration, outcome measures, and safety considerations.

Conclusions

Careful and transparent study design and data analysis will maximize the likelihood of generating meaningful, reproducible results, and identifying a treatment-specific effect. Meeting the highest standards for contemporary trial design may also broaden the acceptance of psychedelic research in the scientific community at large.

Testing for response shift in treatment evaluation of change in self-reported psychopathology amongst secondary psychiatric care outpatients

OBJECTIVES

If patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called "response shifts," may pose a threat to the measurement of therapeutic change in patients. Therefore, it is important to test the occurrence of response shift in patients across their treatment.

METHODS

This study focused on self-reported psychological distress/psychopathology in a naturalistic sample of 206 psychiatric outpatients. Longitudinal measurement invariance tests were computed across treatment in order to detect response shifts.

RESULTS

Compared with before treatment, post-treatment psychopathology scores showed an increase in model fit and factor loading, suggesting that symptoms became more coherently interrelated within their psychopathology domains. Reconceptualization (depression/mood) and reprioritization (somatic and cognitive problems) response shift types were found in several items. We found no recalibration response shift.

CONCLUSION

This study provides further evidence that response shift can occur in adult psychiatric patients across their mental health treatment. Future research is needed to determine whether response shift implies an unwanted potential bias in treatment evaluation or a desired cognitive change intended by treatment.

Body mass index (BMI) in major depressive disorder and its effects on depressive symptomatology and antidepressant response

BACKGROUND

Obesity is one of the most prevalent somatic comorbidities of Major Depressive Disorder (MDD). We aimed to investigate the relationship between body mass index (BMI) and MDD, the symptomatology of the disorder as well as the outcome of antidepressant treatment.

METHODS

Early medication change (EMC) trial participants with BMI measurement (n = 811) were categorized according to WHO-criteria in normal or low weight (BMI < 25), overweight (25-< 30), and obese (≥30). Depression severity and BMI was assessed in weekly intervals up to 8 weeks. BMI at baseline and course of BMI during the study were investigated in linear regression models as possible moderators of therapy response. Possible moderators such as plasma concentrations of applied drugs, sex, comorbidities or age were controlled.

RESULTS

388 (48%) patients showed normal weight, 251 (31%) were overweight and 172 (21%) obese. Linear regression analyses revealed an association between BMI and antidepressant therapy outcome: Overweight patients showed the best response to antidepressant treatment. BMI at baseline was significantly correlated with improvement in neurovegetative and cognitive symptoms of depression. Furthermore, weight gain during the study was associated with better therapy response, independent of symptom complex. Other moderators including serum concentrations of drugs were not able to explain the differences between the BMI groups.

LIMITATIONS

Secondary exploratory analysis. No investigation of visceral fat.

CONCLUSION

We showed for the first time that patients with higher initial increase in BMI showed larger decrease in depression severity during study. The underlying mechanisms are unclear and require further investigation.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.