Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.

There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD).

In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10–20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers—C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2)—measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response.

Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.

Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Adults with significant childhood trauma and/or serious mental illness may exhibit persistent structural brain changes within limbic structures, including the amygdala. Little is known about the structure of the amygdala prior to the onset of SMI, despite the relatively high prevalence of trauma in at-risk youth.

Data were gathered from the Canadian Psychiatric Risk and Outcome study. A total of 182 youth with a mean age of 18.3 years completed T1-weighted MRI scans along with clinical assessments that included questionnaires on symptoms of depression and anxiety. Participants also completed the Childhood Trauma and Abuse Scale. We used a novel subfield-specific amygdala segmentation workflow as a part of FreeSurfer 6.0 to examine amygdala structure.

Participants with higher trauma scores were more likely to have smaller amygdala volumes, particularly within the basal regions. Among various types of childhood trauma, sexual and physical abuse had the largest effects on amygdala subregions. Abuse-related differences in the right basal region mediated the severity of depression and anxiety symptoms, even though no participants met criteria for clinical diagnosis at the time of assessment.

The experience of physical or sexual abuse may leave detectable structural alterations in key regions of the amygdala, potentially mediating the risk of psychopathology in trauma-exposed youth.

Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.

Healthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM−, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM−, n = 80). Separate analyses in MDD participants who remitted were conducted.

DM+ had lower baseline global cognition, processing speed, and memory v. HM−, with no significant baseline differences amongst DM−, HM+, and HM− groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM−, scored significantly lower than HM− in working memory and processing speed.

Childhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.

In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD.

Participants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later.

Individuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment.

Findings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.

Cognitive dysfunction is common in major depressive disorder (MDD) and a critical determinant of health outcome. Anhedonia is a criterion item toward the diagnosis of a major depressive episode (MDE) and a well-characterized domain in MDD. We sought to determine the extent to which variability in self-reported cognitive function correlates with anhedonia.

A post hoc analysis was conducted using data from (N=369) participants with a Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR)-defined diagnosis of MDD who were enrolled in the International Mood Disorders Collaborative Project (IMDCP) between January 2008 and July 2013. The IMDCP is a collaborative research platform at the Mood Disorders Psychopharmacology Unit, University of Toronto, Toronto, Canada, and the Cleveland Clinic, Cleveland, Ohio. Measures of cognitive function, anhedonia, and depression severity were analyzed using linear regression equations.

A total of 369 adults with DSM-IV-TR–defined MDD were included in this analysis. Self-rated cognitive impairment [ie, as measured by the Adult ADHD Self-Report Scale (ASRS)] was significantly correlated with a proxy measure of anhedonia (r=0.131, p=0.012). Moreover, total depression symptom severity, as measured by the total Montgomery–Åsberg Depression Rating Scale (MADRS) score, was also significantly correlated with self-rated measures of cognitive dysfunction (r=0.147, p=0.005). The association between anhedonia and self-rated cognitive dysfunction remained significant after adjusting for illness severity (r=0.162, p=0.007).

These preliminary results provide empirical data for the testable hypothesis that anhedonia and self-reported cognitive function in MDD are correlated yet dissociable domains. The foregoing observation supports the hypothesis of overlapping yet discrete neurobiological substrates for these domains.

Major depressive disorder is associated with significant impairment in occupational functioning and reduced productivity, which represents a large part of the overall burden of depression.

To examine symptom-based and work functioning outcomes with combined pharmacotherapy and psychotherapy treatment of major depressive disorder.

Employed patients with a DSM-IV diagnosis of major depressive disorder were treated with escitalopram 10–20mg/day and randomised to: (a) telephone-administered cognitive-behavioural therapy (telephone CBT) (n = 48); or (b) adherence-reminder telephone calls (n = 51). Outcomes included the Montgomery-åsberg Depression Rating Scale (MADRS), administered by masked evaluators via telephone, and self-rated work functioning scales completed online. (Registered at clinicaltrials.gov: NCT00702598.)

After 12 weeks, there were no significant between-group differences in change in MADRS score or in response/remission rates. However, participants in the telephone-CBT group had significantly greater improvement on some measures of work functioning than the escitalopram-alone group.

Combined treatment with escitalopram and telephone- administered CBT significantly improved some self-reported work functioning outcomes, but not symptom-based outcomes, compared with escitalopram alone.

There is controversy about the implications of relatively small average drug–placebo differences observed in randomised controlled trials of antidepressant medications.

To investigate whether efficacy is better understood as a large effect in a subgroup of patients.

The mixture model was used to identify patient subgroups (patients benefiting or not benefiting from treatment) to directly model the skewness of Montgomery–åsberg Depression Rating Scale (MADRS) scores at week 8.

The MADRS scores improved by 15.9 points (95% CI 15.2–16.6) among patients who benefited from treatment. The proportion of patients who benefited from escitalopram and not from placebo treatment was 19.5%, corresponding to a number needed to treat of 5.

This model gave a considerably better fit to the data than the analysis of covariance model in which all patients were assumed to benefit from treatment. The small average antidepressant–placebo difference obscures a much larger effect in a clinically meaningful subgroup of patients.