Discovery and replication of cerebral blood flow differences in major depressive disorder.

Major depressive disorder (MDD) is a serious, heterogeneous disorder accompanied by brain-related changes, many of which are still to be discovered or refined. Arterial spin labeling (ASL) is a neuroimaging technique used to measure cerebral blood flow (CBF; perfusion) to understand brain function and detect differences among groups. CBF differences have been detected in MDD, and may reveal biosignatures of disease-state. The current work aimed to discover and replicate differences in CBF between MDD participants and healthy controls (HC) as part of the EMBARC study. Participants underwent neuroimaging at baseline, prior to starting study medication, to investigate biosignatures in MDD. Relative CBF (rCBF) was calculated and compared between 106 MDD and 36 HC EMBARC participants (whole-brain Discovery); and 58 MDD EMBARC participants and 58 HC from the DLBS study (region-of-interest Replication). Both analyses revealed reduced rCBF in the right parahippocampus, thalamus, fusiform and middle temporal gyri, as well as the left and right insula, for those with MDD relative to HC. Both samples also revealed increased rCBF in MDD relative to HC in both the left and right inferior parietal lobule, including the supramarginal and angular gyri. Cingulate and prefrontal regions did not fully replicate. Lastly, significant associations were detected between rCBF in replicated regions and clinical measures of MDD chronicity. These results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.

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. A total of 222 unmedicated adults with MDD randomized to receive sertraline (n = 110) or placebo (n = 112) in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study completed demographic and clinical assessments, and pretreatment 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 (HSRD) 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 pretreatment 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. Pretreatment 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.

Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. METHODS: Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. RESULTS: Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). CONCLUSIONS: A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Development and evaluation of a multimodal marker of major depressive disorder.

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial.

BACKGROUND: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. METHODS: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. RESULTS: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. CONCLUSION: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.

Cortical thickness is not associated with current depression in a clinical treatment study.

BACKGROUND: Reduced cortical thickness is a candidate biological marker of depression, although findings are inconsistent. This could reflect analytic heterogeneity, such as use of region-wise cortical thickness based on the Freesurfer Desikan-Killiany (DK) atlas or surface-based morphometry (SBM). The Freesurfer Destrieux (DS) atlas (more, smaller regions) has not been utilized in depression studies. This could also reflect differential gender and age effects. METHODS: Cortical thickness was collected from 170 currently depressed adults and 52 never-depressed adults. Visually inspected and approved Freesurfer-generated surfaces were used to extract cortical thickness estimates according to the DK atlas (68 regions) and DS atlas (148 regions) for region-wise analysis (216 total regions) and for SBM. RESULTS: Overall, except for small effects in a few regions, the two region-wise approaches generally failed to discriminate depressed adults from nondepressed adults or current episode severity. Differential effects by age and gender were also rare and small in magnitude. Using SBM, depressed adults showed a significantly thicker cluster in the left supramarginal gyrus than nondepressed adults (P = 0.047) but there were no associations with current episode severity. CONCLUSIONS: Three analytic approaches (i.e., DK atlas, DS atlas, and SBM) converge on the notion that cortical thickness is a relatively weak discriminator of current depression status. Differential age and gender effects do not appear to represent key moderators. Robust associations with demographic factors will likely hinder translation of cortical thickness into a clinically useful biomarker. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. Hum Brain Mapp 38:4370-4385, 2017. © 2017 Wiley Periodicals, Inc.

A COMPREHENSIVE EXAMINATION OF WHITE MATTER TRACTS AND CONNECTOMETRY IN MAJOR DEPRESSIVE DISORDER.

BACKGROUND: Major depressive disorder (MDD) is a debilitating disorder characterized by widespread brain abnormalities. The literature is mixed as to whether or not white matter abnormalities are associated with MDD. This study sought to examine fractional anisotropy (FA) in white matter tracts in individuals with MDD using diffusion tensor imaging (DTI). METHODS: 139 participants with MDD and 39 healthy controls (HC) in a multisite study were included. DTI scans were acquired in 64 directions and FA was determined in the brain using four methods: region of interest (ROI), tract-based spatial statistics (TBSS), and diffusion tractography. Diffusion connectometry was used to identify white matter pathways associated with MDD. RESULTS: There were no significant differences when comparing FA in MDD and HC groups using any method. In the MDD group, there was a significant relationship between depression severity and FA in the right medial orbitofrontal cortex, and between age of onset of MDD and FA in the right caudal anterior cingulate cortex using the ROI method. There was a significant relationship between age of onset and connectivity in the thalamocortical radiation, inferior longitudinal fasciculus, and cerebellar tracts using diffusion connectometry. CONCLUSIONS: The lack of group differences in FA and connectometry analysis may result from the clinically heterogenous nature of MDD. However, the relationship between FA and depression severity may suggest a state biomarker of depression that should be investigated as a potential indicator of response. Age of onset may also be a significant clinical feature to pursue when studying white matter tracts.

Test-retest reliability of freesurfer measurements within and between sites: Effects of visual approval process.

In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test-retest reliability of FreeSurfer-derived cortical measures in four groups of subjects-those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test-retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan-Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects' results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI-derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution.

Relationship between obesity and depression: characteristics and treatment outcomes with antidepressant medication.

OBJECTIVE: Obesity and major depressive disorder often co-occur. However, differences between obese and normal-weight depressed patients and the moderating effect of obesity on antidepressant treatment outcome are not well studied. METHODS: Adults (n = 662) with major depressive disorder in the Combining Medications to Enhance Depression Outcomes study were randomized to treatment with escitalopram plus placebo, bupropion plus escitalopram, or venlafaxine plus mirtazapine for a 12-week primary treatment phase and 16-week follow-up. Body mass index (BMI) was calculated at baseline and categorized according to World Health Organization criteria: normal or low weight (NW), overweight, Obese I and Obese II+. A repeated-effects model, unadjusted and adjusted for baseline variables, assessed outcomes. RESULTS: Obesity was common (46.2%), only 25.5% were NW. Higher BMI was associated with greater medical illness (p < .001), social phobia (p = .003), and bulimia (p = .026). Lower BMI was associated with more frequent post-traumatic stress disorder (p = .002) and drug abuse (p < .001). Treatment outcomes did not differ including Week 12 remission rates (NW 36%, overweight 40%, Obese I 43%, Obese II+ 37%; p = .69). Lower BMI was associated with more frequent (p = .024 [unadjusted] and .053 [adjusted]) and more severe (p = .008 [unadjusted] and .053 [adjusted]) adverse effects. CONCLUSIONS: BMI was related to clinical presentation and prevalence of comorbidities, but not antidepressant outcomes. Lower BMI classes had more psychiatric comorbidities, potentially obscuring the relationship between BMI and antidepressant effects. Trial Registration ClinicalTrials.gov identifier: NCT00590863.

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.

Depression treatment in patients with general medical conditions: results from the CO-MED trial.

PURPOSE: We studied the effect of 3 antidepressant treatments on outcomes (depressive severity, medication tolerability, and psychosocial functioning) in depressed patients having comorbid general medical conditions in the Combining Medications to Enhance Depression Outcomes (CO-MED) trial. METHODS: Adult outpatients who had chronic and/or recurrent major depressive disorder (MDD) with and without general medical conditions were randomly assigned in 1:1:1 ratio to 28 weeks of single-blind, placebo-controlled antidepressant treatment with (1) escitalopram plus placebo, (2) bupropion-SR plus escitalopram, or (3) venlafaxine-XR plus mirtazapine. At weeks 12 and 28, we compared response and tolerability between participants with 0, 1, 2, and 3 or more general medical conditions. RESULTS: Of the 665 evaluable patients, 49.5% reported having no treated general medical conditions, 23.8% reported having 1, 14.8% reported having 2, and 11.9% reported having at least 3. We found only minimal differences in antidepressant treatment response between these groups having different numbers of conditions; patients with 3 or more conditions reported higher rates of impairment in social and occupational functioning at week 12 but not at week 28. Additionally, we found no significant differences between the 3 antidepressant treatments across these groups. CONCLUSIONS: Patients with general medical conditions can be safely and effectively treated for MDD with antidepressants with no additional adverse effect or tolerability burden relative to their counterparts without such conditions. Combination therapy is not associated with an increased treatment response beyond that found with traditional monotherapy in patients with MDD, regardless of the presence and number of general medical conditions.

What are the clinical implications of new onset or worsening anxiety during the first two weeks of SSRI treatment for depression?

OBJECTIVE: To evaluate the prevalence of new onset or worsening of anxiety symptoms, as well as their clinical implications, during the first 2 weeks of Selective Serotonin Reuptake Inhibitor (SSRI) pharmacotherapy for depression. METHOD: Adult outpatients with nonpsychotic major depressive disorder were enrolled in an 8-week acute phase SSRI treatment trial at 15 clinical sites across the United States. Worsening anxiety was defined as a greater than 2-point increase on the Beck Anxiety Inventory (BAI) between baseline and Week 2. New onset of anxiety symptoms was ascribed when the BAI baseline rating was 0 and the Week 2 value was greater or equal to 2 points on the BAI. RESULTS: Overall, after 2 weeks of treatment, 48.8% (98 of 201 participants) reported improvement in anxiety symptoms, 36.3% (73 of 201) reported minimal symptom change, and 14.9% (30 of 201) reported worsening of anxiety symptoms. No association was found between change in anxiety symptoms within the first 2 weeks and change in depressive symptoms or remission at the end of 8 weeks of treatment. For participants with clinically meaningful anxiety symptoms at baseline, however, worsening of anxiety during the first 2 weeks of treatment was associated with worsening depressive symptoms by 8 weeks (P = .054). CONCLUSIONS: The trajectory of anxiety symptom change early in SSRI treatment is an important indicator of eventual outcome for outpatients with major depression and baseline anxiety symptoms.

Feasible evidence-based strategies to manage depression in primary care.

According to the World Health Organization, major depressive disorder (MDD) is a leading cause of disability-adjusted life years worldwide. However, recent evidence suggests depression remains undertreated in primary care settings. Measurement-based care (MBC) is an evidence-based strategy that can feasibly assist primary care physicians in managing patients with MDD. Utilizing health information technology tools, such as computer decision support software, can improve adherence to evidence-based treatment guidelines and MBC at the point of care.

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

BACKGROUND: The 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. METHODS: Participants 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. RESULTS: The 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. CONCLUSIONS: These findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

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

BACKGROUND: Major 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. METHODS: EMBARC (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. RESULTS: Greater 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). CONCLUSIONS: The 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.

Early adverse events and attrition in selective serotonin reuptake inhibitor treatment: a suicide assessment methodology study report.

Adverse events during selective serotonin reuptake inhibitor (SSRI) treatment are frequent and may lead to premature treatment discontinuation. If attrition is associated with early worsening of adverse effects or the frequency, intensity, or burden of adverse effects, interventions to maximize retention could be focused on patients with these events. Outpatient participants (n = 265) with nonpsychotic major depressive disorder entered an 8-week trial with an SSRI. At baseline and week 2, specific adverse effects were evaluated with the Systematic Assessment for Treatment Emergent Events--Systematic Inquiry, and at week 2, the Frequency, Intensity, and Burden of Side Effects Rating globally assessed adverse effects. Attrition was defined by those participants who left treatment after week 2 but before week 8. No specific week 2 adverse effect, either treatment-emergent or with worsening intensity, was independently associated with attrition. Global ratings of adverse effect frequency, intensity, or burden at week 2 were also not associated with subsequent attrition. Neither global ratings nor specific adverse effects at week 2 were related to patient attrition during SSRI treatment. Other factors seem to contribute to patient decisions about continuing with treatment.

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

A computerized decision support system for depression in primary care.

OBJECTIVE: In 2004, results from The Texas Medication Algorithm Project (TMAP) showed better clinical outcomes for patients whose physicians adhered to a paper-and-pencil algorithm compared to patients who received standard clinical treatment for major depressive disorder (MDD). However, implementation of and fidelity to the treatment algorithm among various providers was observed to be inadequate. A computerized decision support system (CDSS) for the implementation of the TMAP algorithm for depression has since been developed to improve fidelity and adherence to the algorithm. METHOD: This was a 2-group, parallel design, clinical trial (one patient group receiving MDD treatment from physicians using the CDSS and the other patient group receiving usual care) conducted at 2 separate primary care clinics in Texas from March 2005 through June 2006. Fifty-five patients with MDD (DSM-IV criteria) with no significant difference in disease characteristics were enrolled, 32 of whom were treated by physicians using CDSS and 23 were treated by physicians using usual care. The study's objective was to evaluate the feasibility and efficacy of implementing a CDSS to assist physicians acutely treating patients with MDD compared to usual care in primary care. Primary efficacy outcomes for depression symptom severity were based on the 17-item Hamilton Depression Rating Scale (HDRS(17)) evaluated by an independent rater. RESULTS: Patients treated by physicians employing CDSS had significantly greater symptom reduction, based on the HDRS(17), than patients treated with usual care (P < .001). CONCLUSIONS: The CDSS algorithm, utilizing measurement-based care, was superior to usual care for patients with MDD in primary care settings. Larger randomized controlled trials are needed to confirm these findings. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00551083.

Examining raphe-amygdala structural connectivity as a biological predictor of SSRI response.

BACKGROUND: Our lab has previously found that structural integrity in tracts from the raphe nucleus (RN) to the amygdala, measured by fractional anisotropy (FA), predicts remission to selective serotonin reuptake inhibitors (SSRIs) in major depressive disorder (MDD). This could potentially serve as a biomarker for remission that can guide clinical decision-making. To enhance repeatability and reproducibility, we replicated our study in a larger, more representative multi-site sample. METHODS: 64 direction DTI was collected in 144 medication-free patients with MDD from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study. We performed probabilistic tractography between the RN and bilateral amygdala and hippocampus and calculated weighted FA in these tracts. Patients were treated with either sertraline or placebo, and their change in Hamilton Depression Rating Scale (HDRS) score reported. Pretreatment weighted FA was compared between remitters and nonremitters, and correlation between FA and percent change in HDRS score was assessed. Exploratory moderator and voxel analyses were also performed. RESULTS: Contrary to our hypotheses, FA was greater in nonremitters than in remitters in RN-left and right amygdala tracts (p = 0.02 and 0.01, respectively). Pretreatment FA between the raphe and left amygdala correlated with greater, not reduced, HDRS (r = 0.18, p = 0.04). This finding was found to be greater in the placebo group. Moderator and voxel analyses yielded no significant findings. CONCLUSIONS: We found greater FA in nonremitters between the RN and amygdala than in remitters, and a correlation between FA and symptom worsening, particularly with placebo. These findings may help reveal more about the nature of MDD, as well as guide research methods involving placebo response.

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

BACKGROUND: Despite 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. METHODS: Using 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. RESULTS: Neuroticism 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. LIMITATIONS: Extraneous 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. CONCLUSIONS: These 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.