Antidepressant Response For Clinical Care Research Articles

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Early-treatment cerebral blood flow change as a predictive biomarker of antidepressant treatment response: evidence from the EMBARC clinical trial.

Major depressive disorder (MDD) is one of the most prevalent and disabling illnesses worldwide. Treatment of MDD typically relies on trial-and-error to find an effective approach. Identifying early response-related biomarkers that predict response to antidepressants would help clinicians to decide, as early as possible, whether a particular treatment might be suitable for a given patient. Data were from the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A whole-brain, voxel-wise, mixed-effects model was applied to identify early-treatment cerebral blood flow (CBF) changes as biomarkers of treatment response. We examined changes in CBF measured with arterial spin labeling 1-week after initiating double-masked sertraline/placebo. We tested whether these early 1-week scans could be used to predict response observed after 8-weeks of treatment. Response to 8-week placebo treatment was associated with increased cerebral perfusion in temporal cortex and reduced cerebral perfusion in postcentral region captured at 1-week of treatment. Additionally, CBF response in these brain regions was significantly correlated with improvement in Hamilton Depression Rating Scale score in the placebo group. No significant associations were found for selective serotonin reuptake inhibitor treatment. We conclude that early CBF responses to placebo administration in multiple brain regions represent candidate neural biomarkers of longer-term antidepressant effects.

Individual deviations from normative electroencephalographic connectivity predict antidepressant response

BackgroundAntidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. MethodsWe built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. ResultsWe successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. ConclusionsOur findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. Trial registrationEstablishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.

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

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

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.

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

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

BackgroundTo address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes. MethodsPretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group. ResultsThree subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline. LimitationsDue to the exploratory nature of these analyses, there were no adjustments for multiple comparisons. ConclusionsBaseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

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.

Fibroblast growth factor 21 (FGF21) is increased in MDD and interacts with body mass index (BMI) to affect depression trajectory

Fibroblast growth factor 21 (FGF21) is a key regulator of metabolic function and nutrient preference. It also affects biological pathways associated with major depressive disorder (MDD), including corticotrophin-releasing hormone (CRH), leptin, and sympathetic activity. Lower levels of cerebrospinal fluid FGF21 have been associated with higher Beck Depression Inventory scores. FGF21 was examined as a metabolic marker that could be associated with MDD and evaluated as a biomarker of antidepressant treatment response in a large, randomized placebo-controlled trial in chronic, early-onset MDD participants. FGF21 levels at baseline and during treatment were determined for participants in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study. FGF21 was analyzed by ELISA in individuals with chronic, early-onset MDD (first major depressive episode before 30 years) compared to healthy control participants. Participants with MDD had higher levels of FGF21 compared to healthy controls (HCs), even after controlling for baseline age, sex, race, Hispanic ethnicity, BMI, and site (β-coefficient = 1.20, p < 0.0001, Cohen’s d = 0.60). FGF21 did not change over time nor differ between treatment groups. Interestingly though, those with normal BMI and lower FGF21 levels showed a reduction in depression severity over time compared to all other groups. In conclusion, depression is associated with higher levels of FGF21 compared to healthy controls and those with lower levels of FGF21 (25th percentile of the sample) in the context of normal-weight BMI seem to have improved depression severity over time.

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.

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

Objective: To identify data-driven subgroups in Major Depressive Disorder (MDD) in order to elucidate underlying neural correlates and determine if these subgroups have utility in predicting response to antidepressant versus placebo. Methods: Using 27 clinical measures at baseline of Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study, participants with MDD (n=244) were sub grouped using principal component (PC) analysis. Baseline-to-week-8 changes in depression severity with sertraline versus placebo were compared in these subgroups. Resting-state functional connectivity of these subgroups were compared to those of healthy controls (n=38). Results: Eight subgroups were identified from four PCs: (PC1) severity of depression-associated symptoms, (PC2) sub-threshold mania and anhedonia, (PC3) childhood trauma, medical comorbidities, and sexual dysfunction, and (PC4) personality traits of openness and agreeableness. Participants with high childhood trauma experienced greater improvement with sertraline (Cohen's d=0.87), whereas those with either higher levels of subthreshold hypomanic symptoms (Cohen's d=0.67) or with lower levels of agreeableness and openness experienced greater improvement with placebo (Cohen's d=0.71). Participants with high childhood trauma had greater connectivity between salience and dorsal attention networks, whereas those with higher levels of subthreshold hypomanic symptoms and lower levels of agreeableness and openness had greater connectivity within limbic network and that of visual network with hippocampus and dorsal attention network. Conclusion: Assessing history of childhood trauma, presence of subthreshold hypomanic symptoms and personality traits may help to identify subgroups of patients with MDD who respond differentially to sertraline or placebo and have distinct neural signatures.

Association of anger attacks with suicidal ideation in adults with major depressive disorder: Findings from the EMBARC study.

This report evaluates whether anger attacks (sudden uncharacteristic bouts of anger that are associated with autonomic arousal and/or aggression) in patients with major depressive disorder (MDD) are associated with elevated suicidal ideation (SI; active suicidal thoughts and plans). Participants of Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study who completed Massachusetts General Hospital Anger Attack Questionnaire (AAQ) at baseline were included (n = 293). Levels of SI (suicidal thoughts factor of Concise Health Risk Tracking) were compared at baseline with generalized linear models, and during Stage 1 (baseline-to-week-8) and Stage 2 (week-8-to-week-16) with repeated-measures mixed model analyses. Covariates included age, sex, race, ethnicity, site, and treatment arm. At baseline, participants with (n = 109) versus without anger attacks (n = 184) had higher levels of SI (Cohen's d effect size [d] = 1.20). Those with ≥9 anger attacks in the past month had significantly higher SI than those with 1-2 (d = 1.21), 3-4 (d = 1.48), and 5-8 (d = 0.94) anger attacks in the past month. Furthermore, participants with anger attacks at baseline reported higher SI at each post-baseline visit (both Stages 1 and 2) of EMBARC study (d = 0.39-0.77; all p < .05). Associations between anger attacks and SI were significant even after controlling for irritability, hostility, anxious arousal, depression, suicide propensity, and self-reported pain at baseline and lifetime suicidal tendencies. Similar results were found in participants with aggressive behaviors. Anger attacks in outpatients with MDD may be associated with chronically elevated SI. Clinical Trials Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC); NCT01407094; https://clinicaltrials.gov/ct2/show/NCT01407094.

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.

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