Biosignatures Of Antidepressant Response Research Articles

Reward Behavior Disengagement, a Neuroeconomic Model-Based Objective Measure of Reward Pathology in Depression: Findings from the EMBARC Trial.

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS-SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS-SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward-behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants.

Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study

BackgroundMajor depressive disorder (MDD) may be associated with accelerated brain aging (higher brain age than chronological age). This report evaluated whether brain age is a clinically useful biomarker by checking its test-retest reliability using magnetic resonance imaging scans acquired 1 week apart and by evaluating the association of accelerated brain aging with symptom severity and antidepressant treatment outcomes. MethodsBrain age was estimated in participants of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study using T1-weighted structural magnetic resonance imaging (MDD n = 290; female n = 192; healthy control participants n = 39; female n = 24). Intraclass correlation coefficient was used for baseline-to-week-1 test-retest reliability. Association of baseline Δ brain age (brain age minus chronological age) with Hamilton Depression Rating Scale–17 and Concise Health Risk Tracking Self-Report domains (impulsivity, suicide propensity [measures: pessimism, helplessness, perceived lack of social support, and despair], and suicidal thoughts) were assessed at baseline (linear regression) and during 8-week-long treatment with either sertraline or placebo (repeated-measures mixed models). ResultsMean ± SD baseline chronological age, brain age, and Δ brain age were 37.1 ± 13.3, 40.6 ± 13.1, and 3.1 ± 6.1 years in MDD and 37.1 ± 14.7, 38.4 ± 12.9, and 0.6 ± 5.5 years in healthy control groups, respectively. Test-retest reliability was high (intraclass correlation coefficient = 0.98–1.00). Higher baseline Δ brain age in the MDD group was associated with higher baseline impulsivity and suicide propensity and predicted smaller baseline-to-week-8 reductions in Hamilton Depression Rating Scale–17, impulsivity, and suicide propensity with sertraline but not with placebo. ConclusionsBrain age is a reliable and potentially clinically useful biomarker that can prognosticate antidepressant treatment outcomes.

Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response

BackgroundDelivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. MethodsThe Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. ResultsDynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. ConclusionsDynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

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

BackgroundThe 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. MethodsA 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. ResultsDuring 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. ConclusionsDivergent 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.

Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial

BackgroundThe lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics. MethodsParticipants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models. ResultsThe predictive model for sertraline achieved R2 of 48% (95% CI, 33%–61%; p < 10−3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R2 of 28% (95% CI, 15%–42%; p < 10−3) and NNT of 2.95 in predicting response. The bupropion model achieved R2 of 34% (95% CI, 10%–59%, p < 10−3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion. ConclusionsThese findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

Journal Digest: Sertraline &amp; Depression; Cognitive Therapy &amp; Pregnancy; Clozaphine &amp; Schizofrenia; Despair &amp; Negative outcome; LGBTQ Teens &amp; Bullying; Exposure Therapy &amp; Drug Craving

Journal Digest: Sertraline &amp; Depression; Cognitive Therapy &amp; Pregnancy; Clozaphine &amp; Schizofrenia; Despair &amp; Negative outcome; LGBTQ Teens &amp; Bullying; Exposure Therapy &amp; Drug Craving

Dorsolateral Prefrontal Cortex and Subcallosal Cingulate Connectivity Show Preferential Antidepressant Response in Major Depressive Disorder

BackgroundMajor depressive disorder is associated with abnormal connectivity across emotion and reward circuits as well as other established circuits that may negatively impact treatment response. The goal of this study was to perform an exploratory reanalysis of archival data from a clinical trial to identify moderators of treatment outcome of sertraline over placebo. MethodsEMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study participants completed magnetic resonance imaging before randomization to either sertraline or placebo for 8 weeks (n = 279). Seed-based functional connectivity was computed using 4 bilateral seeds (2 spheres defined bilaterally): amygdala, dorsolateral prefrontal cortex (DLPFC), subcallosal cingulate cortex, and ventral striatum. Functional connectivity maps were generated, principal component analysis was performed, linear mixed effects models were used to determine moderators of treatment outcome, and post hoc analyses were used to determine level of connectivity (low and high, −1 and +1 SD from the mean) that was most sensitive to improved depression severity (baseline to week 8) based on treatment. ResultsGreater mean reduction in the 17-item Hamilton Rating Scale for Depression score by 8 weeks occurred with sertraline relative to placebo when connectivity in the DLPFC was low (3-way interaction test, p = .05). Conditional on low connectivity in the DLPFC and subcallosal cingulate cortex and high connectivity in the ventral striatum and amygdala, there was on average a 4.8-point greater reduction in the 17-item Hamilton Rating Scale for Depression score with sertraline relative to placebo (p = .003). ConclusionsThe level of functional connectivity seeded in both the DLPFC and the subcallosal cingulate cortex networks may play an important role in identifying a favorable response to sertraline over placebo.

Improvements in irritability with sertraline versus placebo: Findings from the EMBARC study

BackgroundThis report seeks to evaluate improvements in symptoms of irritability with sertraline (a selective serotonin reuptake inhibitor antidepressant) versus placebo. MethodsParticipants of Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study who were randomized to 8 weeks of treatment with either sertraline or placebo and completed 5-item irritability domain of Concise Associated Symptom Tracking scale (CAST-IRR) at baseline were included (n = 292). Repeated measures mixed model analysis with CAST-IRR as the outcome variable and treatment arm-by-time interaction as the independent variable of interest evaluated whether changes in irritability with treatment differed between sertraline and placebo arms. A separate analysis controlled for levels of overall depression severity (17-item Hamilton Depression Rating Scale). Covariates included age, sex, race, ethnicity, and site. ResultsThere was a significant treatment arm-by-time interaction (F = 6.96, df = 6, 1418, p <0.0001) suggesting that changes in irritability with sertraline differed from placebo. The magnitude of reduction in irritability from baseline to week-8 was greater with sertraline than with placebo (Cohen's d effect size = 0.56). After controlling for levels of overall depression severity at each visit, reduction in irritability with time continued to be significant with sertraline but not with placebo. LimitationsSecondary analysis, limited generalizability, lack of non-serotonergic antidepressants. DiscussionThere is greater improvement in irritability with sertraline than with placebo. Improvement in irritability with sertraline was independent of its effects on overall depression severity. Clinicaltrials.gov identifierNCT01407094

Psychometric Properties of the Concise Associated Symptom Tracking Scale and Validation of Clinical Utility in the EMBARC Study.

ObjectiveThe authors aimed to evaluate psychometric properties of the Concise Associated Symptom Tracking (CAST) Scale and validate the clinical utility of measuring irritability by updating and replicating a previously published outcome calculator from the Combining Medications to Enhance Depression Outcomes (CO‐MED) trial.MethodsParticipants were 292 adults from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study who had completed the CAST scale at baseline. The scale's five‐domain (irritability, anxiety, mania, insomnia, and panic) structure was evaluated with confirmatory factor analysis. Correlations with other clinical measures were used to confirm convergent and divergent validity. Logistic regression analyses from CO‐MED were used to estimate individual outcomes in EMBARC.ResultsCronbach's alpha for the CAST scale was 0.78. Model fit for the five‐domain structure was adequate (goodness of fit index=0.93, comparative fit index=0.92, root mean square error of approximation=0.06). Scores on irritability, anxiety, panic, insomnia, and mania were correlated with scores on the Anger Attack Questionnaire irritability item (rs=0.50), Hamilton Rating Scale for Depression anxiety subscale (rs=0.24), Mood and Anxiety Symptoms Questionnaire anxious arousal scale (rs=0.44), Quick Inventory of Depressive Symptomatology Self‐Report insomnia items (rs=0.38), and Altman Self‐Rating Mania Scale (rs=0.39), respectively. Individual outcomes of remission (area under the curve [AUC]=0.805) and no meaningful benefit (AUC=0.779) were predicted with high accuracy among EMBARC participants using their baseline and week 4 scores for depression and irritability and model estimates from CO‐MED.ConclusionsMeasuring irritability may help predict clinical course. The CAST scale is a valid measure of depression‐associated symptoms, including irritability.

Anger attacks are associated with persistently elevated irritability in MDD: findings from the EMBARC study.

This report tests the association of self-reported symptoms of irritability with overt behavior of anger attacks (uncharacteristic sudden bouts of anger that are disproportionate to situation and associated with autonomic activation). Participants of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care study who completed Massachusetts General Hospital Anger Attacks questionnaire were included (n = 293). At each visit, the 17-item Hamilton Depression Rating Scale and the 16-item Concise Associated Symptom Tracking scale were used to measure depression, anxiety, and irritability. In those with anger attacks present v. those without anger attacks, separate t tests and mixed model analyses compared afore-mentioned symptoms at baseline and changes with treatment respectively. As anger attacks may occur without aggressive behaviors, analyses were repeated based only on the presence of aggressive behaviors. At baseline, those with anger attacks (n = 109) v. those without anger attacks (n = 184) had similar levels of depression but higher levels of irritability [effect size (d) = 0.80] and anxiety (d = 0.32). With acute-phase treatment, participants with anger attacks experienced a greater reduction in irritability (p < 0.001) but not in depression (p = 0.813) or anxiety (p = 0.771) as compared to those without anger attacks. Yet, irritability levels at week-8 were higher in those with anger attacks (d = 0.32) than those without anger attacks. Similar results were found in participants with aggressive behaviors. The presence of anger attacks in outpatients with major depressive disorder may identify a sub-group of patients with persistently elevated irritability.

Could EEG Help Predict Antidepressant Response?

Could EEG Help Predict Antidepressant Response?

Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder

Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice. To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment. In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019. Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks. Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit. Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points. These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials. ClinicalTrials.gov identifier: NCT01407094.

Effect of Intrinsic Patterns of Functional Brain Connectivity in Moderating Antidepressant Treatment Response in Major Depression.

Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo. Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest-based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms. Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity. This study identified specific functional network-based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.

Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals.

Medications to treat major depressive disorder (MDD) are not equally effective across patients. Given that neural response to rewards is altered in MDD and given that reward-related circuitry is modulated by dopamine and serotonin, we examined, for the first time, whether reward-related neural activity moderated response to sertraline, an antidepressant medication that targets these neurotransmitters. 222 unmedicated adults with MDD randomized to receive sertraline (n=110) or placebo (n=112) in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study completed demographic and clinical assessments, and pre-treatment functional magnetic resonance imaging while performing a reward task. We tested whether an index of reward system function in the ventral striatum (VS), a key reward circuitry region, moderated differential response to sertraline versus placebo, assessed with the Hamilton Rating Scale for Depression over 8 weeks. We observed a significant moderation effect of the reward index, reflecting the temporal dynamics of VS activity, on Week-8 depression levels (Fs≥9.67,ps≤0.002). Specifically, VS responses that were abnormal with respect to predictions from reinforcement learning theory were associated with lower Week-8 depression symptoms in the sertraline versus placebo arms. Thus, a more abnormal pattern of pre-treatment VS dynamic response to reward expectancy (expected outcome value) and prediction error (difference between expected and actual outcome), likely reflecting serotonergic and dopaminergic deficits, was associated with better response to sertraline than placebo. Pre-treatment measures of reward-related VS activity may serve as objective neural markers to advance efforts to personalize interventions by guiding individual-level choice of antidepressant treatment.Trial Registration:NCT01407094; http://clinicaltrials.gov/show/NCT01407094

3193 Improving Individual Clinical Outcomes in a Sequential Multiple Assignment Randomized Trial (SMART)

OBJECTIVES/SPECIFIC AIMS: This work develops an algorithm that identifies patients in a Sequential Multiple Assignment Randomized Trial (SMART) who should switch treatments prior to the end of a stage because clinical effectiveness via their current intervention is unlikely. This algorithm uses as inputs patient baseline and interim measurements to assign a probability that a patient should switch or stay on their current intervention. First, the algorithm will be derived assuming both a linear and non-linear patient trajectory. Second, the performance of the algorithm will be assessed using trial data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care† (EMBARC) study. The primary objective of the algorithm is to switch treatment in patients who will not reach clinical effectiveness by the end of the stage, and the secondary objective is to avoid accidentally switching treatment in patients who will reach clinical effectiveness by the end of the stage. †Trivedi et al. Journal of Psychiatric Research 78 (2016) 11-23 METHODS/STUDY POPULATION: First, the algorithm was derived assuming a linear or non-linear trajectory. Next, performance of the algorithm was assessed using data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care† (EMBARC) study. This two-stage SMART design measured the effectiveness of sertraline in 242 patients with nonpsychotic Major Depressive Disorder (MDD). The algorithm was applied to baseline and interim measurements from the EMBARC study to predict end-stage Hamilton Depression (HAMD17) scores, the primary outcome of the study. True positive rate (TPR) and false positive rate (FPR) were used to measure respectively the primary study objective (switching treatment in patients who will not reach clinical effectiveness by the end of the stage), and the secondary study objective (avoiding accidentally switching treatment in patients who will reach clinical effectiveness by the end of the stage). TPR and FPR were calculated for the following prediction scenarios: (1) three separate two-point predictions: Baseline and Week 2, Baseline and Week 4, Baseline and Week 6, and (2) a single three-point prediction: Baseline and Weeks 2 and 6. †Trivedi et al. Journal of Psychiatric Research 78 (2016) 11-23 RESULTS/ANTICIPATED RESULTS: When using two-point prediction, we found TPR to increase and FPR to decrease as the interim measurements approached closer to the end of the stage. We also found TPR to increase when using a three-point prediction, but at the expense of FPR also increasing. Across these scenarios, TPR ranged between 70 and 90%, and FPR ranged between approximately 20 and 50%. DISCUSSION/SIGNIFICANCE OF IMPACT: Although SMART designs ultimately assign patients to more effective treatments, this process can take time and leave a patient (currently on an ineffective treatment) waiting until the end of a stage to try a potentially superior treatment. This disadvantage of the SMART design is currently addressed by this algorithm. By introducing a regression and likelihood approach to predict whether a patient should switch or stay on their current treatment, we move closer to the goal of designing rigorous, patient-centered studies. This work has the potential to improve individual clinical outcomes for patients enrolled in pragmatic clinical trials.

The Concise Health Risk Tracking-Self Report: Psychometrics within a placebo-controlled antidepressant trial among depressed outpatients.

While substantial prior research has evaluated the psychometric properties of the 12-item Concise Health Risk Tracking-Self Report (CHRT-SR12), a measure of suicide propensity and suicidal thoughts, no prior research has investigated its factor structure, sensitivity to change over time, and other psychometric properties in a placebo-controlled trial of antidepressant medication, nor determined whether symptoms change throughout treatment. Participants in the multi-site Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study ( n=278) provided data to evaluate the factor structure and sensitivity to change over time of the CHRT-SR12 through eight weeks of a clinical trial in which participants received either placebo or antidepressant medication (sertraline). Factor analysis confirmed two factors: propensity (comprised of first-order factors including pessimism, helplessness, social support, and despair) and suicidal thoughts. Internal consistency (α's ranged from 0.69-0.92) and external validity were both acceptable, with the total score and propensity factor scores significantly correlated with total scores and single-item suicidal-thoughts scores on the self-report Quick Inventory of Depressive Symptoms and the clinician-rated 17-item Hamilton Rating Scale for Depression. Through analyzing CHRT-SR12 changes over eight treatment weeks, the total score and both the factors decreased regardless of baseline suicidal thoughts. Change in clinician-rated suicidal thoughts was reflected by change in both the total score and propensity factor score. These results confirm the reliability, validity, and applicability of the CHRT-SR12 to a placebo-controlled clinical trial of depressed outpatients receiving antidepressant medication.

Anxiety and anhedonia in depression: Associations with neuroticism and cognitive control

BackgroundDespite the fact that higher levels of anxiety and anhedonia in Major Depressive Disorder (MDD) are linked to poorer treatment outcomes, mechanisms contributing to these clinical presentations remain unclear. Neuroticism, impaired cognitive control, and blunted reward learning may be critical processes involved in MDD and may help to explain symptoms of anxiety and anhedonia. MethodsUsing baseline data from patients with early-onset MDD (N = 296) in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) trial, we conducted a path analysis to model relationships between neuroticism, cognitive control, and reward learning to levels of anxiety and anhedonia. ResultsNeuroticism was positively associated with both anhedonia (standardized coefficient = 0.26, p < .001) and anxiety (standardized coefficient = 0.40, p < .001). Cognitive control was negatively associated with anxiety (standardized coefficient = -0.18, p < .05). Reward learning was not significantly associated with either anxiety or anhedonia. LimitationsExtraneous variables not included in the model may have even more influence in explaining symptoms of anxiety and anhedonia. Restricted range in these variables may have attenuated some of the hypothesized relationships. Most important, because this was a cross-sectional analysis in a currently depressed sample, we cannot draw any causal conclusions without experimental and longitudinal data. ConclusionsThese cross-sectional findings suggest that neuroticism may contribute to anxiety and anhedonia in patients with early onset and either chronic or recurrent MDD, while enhanced cognitive control may protect against anxiety.

Moderation of the Relationship Between Reward Expectancy and Prediction Error-Related Ventral Striatal Reactivity by Anhedonia in Unmedicated Major Depressive Disorder: Findings From the EMBARC Study.

Anhedonia, disrupted reward processing, is a core symptom of major depressive disorder. Recent findings demonstrate altered reward-related ventral striatal reactivity in depressed individuals, but the extent to which this is specific to anhedonia remains poorly understood. The authors examined the effect of anhedonia on reward expectancy (expected outcome value) and prediction error- (discrepancy between expected and actual outcome) related ventral striatal reactivity, as well as the relationship between these measures. A total of 148 unmedicated individuals with major depressive disorder and 31 healthy comparison individuals recruited for the multisite EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study underwent functional MRI during a well-validated reward task. Region of interest and whole-brain data were examined in the first- (N=78) and second- (N=70) recruited cohorts, as well as the total sample, of depressed individuals, and in healthy individuals. Healthy, but not depressed, individuals showed a significant inverse relationship between reward expectancy and prediction error-related right ventral striatal reactivity. Across all participants, and in depressed individuals only, greater anhedonia severity was associated with a reduced reward expectancy-prediction error inverse relationship, even after controlling for other symptoms. The normal reward expectancy and prediction error-related ventral striatal reactivity inverse relationship concords with conditioning models, predicting a shift in ventral striatal responding from reward outcomes to reward cues. This study shows, for the first time, an absence of this relationship in two cohorts of unmedicated depressed individuals and a moderation of this relationship by anhedonia, suggesting reduced reward-contingency learning with greater anhedonia. These findings help elucidate neural mechanisms of anhedonia, as a step toward identifying potential biosignatures of treatment response.