Abstract Objective The primary objective of this review is to examine the literature assessing abnormalities in neural circuitry and cognition early in the course of major depressive disorder (MDD) ...and the impact of these features on treatment selection and long-term outcomes. Data Sources English language and peer-reviewed publications were obtained by PubMed / Medline ( www.pubmed.org ) searches using combinations of major depressive disorder, major depression, or unipolar depression and “first episode”, early, cognition, cognitive, executive function and memory. The terms bipolar and psychosis were excluded from the searches. These searches yielded 409 records. Study selection A total of 12 studies, systematic reviews and meta-analyses were selected that evaluated learning, memory and executive function in individuals with major depressive disorder. Additional publications meeting these criteria were identified from the bibliographies of the 12 selected articles and from the “related citations” section of PubMed. Results Difficulty in concentrating and indecisiveness are reported as among the most troubling symptoms by patients with MDD and may limit functional recovery. Cognitive deficits in memory and decision-making are present early in the course of MDD and may be accompanied by structural abnormalities in the hippocampus and prefrontal cortex involved in cognitive functions. Although resolution of cognitive symptoms of depression lags behind recovery from mood symptoms in many patients, preliminary evidence suggests they may improve with antidepressant therapy, but can also persist residually. Conclusions New strategies that target cognitive symptoms of depression in addition to mood symptoms are needed to improve long-term outcomes, particularly functional recovery.
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such ...unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
•Cortical thickness (CT) measurements are highly scanner specific.•Identifying scanner effects is crucial for inference and biomarker development.•We propose to use ComBat to harmonize cortical thickness values across scanners.
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, ...multi‐site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi‐site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi‐site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI‐derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi‐site fMRI neuroimaging studies.
Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with ...depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.
We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes COMED; ClinicalTrials.gov, number NCT00590863).
We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% SD 3·2; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.
Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.
Yale University.
Numerous placebo-controlled studies have demonstrated the ability of ketamine, an NMDA receptor antagonist, to induce rapid (within hours), transient antidepressant effects when administered ...intravenously (IV) at subanesthetic doses (0.5 mg/kg over 40 min). However, the optimal antidepressant dose remains unknown. We aimed to compare to active placebo the rapid acting antidepressant properties of a broad range of subanesthetic doses of IV ketamine among outpatients with treatment-resistant depression (TRD). A range of IV ketamine doses were compared to active placebo in the treatment of adult TRD over a 3-day period following a single infusion over 40 min. This was an outpatient study conducted across six US academic sites. Outpatients were 18-70 years old with TRD, defined as failure to achieve a satisfactory response (e.g., less than 50% improvement of depression symptoms) to at least two adequate treatment courses during the current depressive episode. Following a washout period, 99 eligible subjects were randomly assigned to one of the five arms in a 1:1:1:1:1 fashion: a single intravenous dose of ketamine 0.1 mg/kg (n = 18), a single dose of ketamine 0.2 mg/kg (n = 20), a single dose of ketamine 0.5 mg/kg (n = 22), a single dose of ketamine 1.0 mg/kg (n = 20), and a single dose of midazolam 0.045 mg/kg (active placebo) (n = 19). The study assessments (HAM-D-6, MADRS, SDQ, PAS, CGI-S, and CGI-I) were performed at days 0, 1, 3 (endpoint), 5, 7, 14, and 30 to assess the safety and efficacy. The overall group × time interaction effect was significant for the primary outcome measure, the HAM-D-6. In post hoc pairwise comparisons controlling for multiple comparisons, standard dose (0.5 mg/kg) and high dose (1 mg/kg) of intravenous ketamine were superior to active placebo; a low dose (0.1 mg/kg) was significant only prior to adjustment (p = 0.02, p-adj = 0.14, d = -0.82 at day 1). Most of the interaction effect was due to differences at day 1, with no significant adjusted pairwise differences at day 3. This pattern generally held for secondary outcomes. The infusions of ketamine were relatively well tolerated compared to active placebo, except for greater dissociative symptoms and transient blood pressure elevations with the higher doses. Our results suggest that there is evidence for the efficacy of the 0.5 mg/kg and 1.0 mg/kg subanesthetic doses of IV ketamine and no clear or consistent evidence for clinically meaningful efficacy of lower doses of IV ketamine. Trial Registration: NCT01920555.
Major depressive disorder (MDD) is a chronic condition that affects one in six adults in the US during their lifetime. The current practice of antidepressant medication prescription is a ...trial-and-error process. Additionally, over a third of patients with MDD fail to respond to two or more antidepressant treatments. There are no valid clinical markers to personalize currently available antidepressant medications, all of which have similar mechanisms targeting monoamine neurotransmission. The goal of this review is to summarize the recent findings of immune dysfunction in patients with MDD, the utility of inflammatory markers to personalize treatment selection, and the potential of targeting inflammation to develop novel antidepressant treatments. To personalize antidepressant prescription, a c-reactive protein (CRP)-matched treatment assignment can be rapidly implemented in clinical practice with point-of-care fingerstick tests. With this approach, 4.5 patients need to be treated for 1 additional remission as compared to a CRP-mismatched treatment assignment. Anti-cytokine treatments may be effective as novel antidepressants. Monoclonal antibodies against proinflammatory cytokines, such as interleukin 6, interleukin 17, and tumor necrosis factor α, have demonstrated antidepressant effects in patients with chronic inflammatory conditions who report significant depressive symptoms. Additional novel antidepressant strategies targeting inflammation include pharmaceutical agents that block the effect of systemic inflammation on the central nervous system. In conclusion, inflammatory markers offer the potential not only to personalize antidepressant prescription but also to guide the development of novel mechanistically-guided antidepressant treatments.
IMPORTANCE: Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary ...construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small. OBJECTIVES: To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups. DESIGN, SETTING, AND PARTICIPANTS: Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 4039) were used to identify clusters of symptoms in a depressive symptom checklist. The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n = 640). Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n = 4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n = 2515). Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016. MAIN OUTCOMES AND MEASURES: Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms. RESULTS: Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years. Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale. Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P < .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, −0.7 to 0.8; P = .94). CONCLUSIONS AND RELEVANCE: Two common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.
In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved ...treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures.
In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers.
Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field.
Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.
•We summarize current therapeutic interventions for depression and discuss the need for biomarker identification.•We discuss the utility of “omics” screening tools and their applicability to discovering biomarkers.•We review studies using blood based “omics” analyses with MDD and treatment response.
Objective:Despite significant advances in neuroscience and treatment development, no widely accepted biomarkers are available to inform diagnostics or identify preferred treatments for individuals ...with major depressive disorder.Method:In this critical review, the authors examine the extent to which multimodal neuroimaging techniques can identify biomarkers reflecting key pathophysiologic processes in depression and whether such biomarkers may act as predictors, moderators, and mediators of treatment response that might facilitate development of personalized treatments based on a better understanding of these processes.Results:The authors first highlight the most consistent findings from neuroimaging studies using different techniques in depression, including structural and functional abnormalities in two parallel neural circuits: serotonergically modulated implicit emotion regulation circuitry, centered on the amygdala and different regions in the medial prefrontal cortex; and dopaminergically modulated reward neural circuitry, centered on the ventral striatum and medial prefrontal cortex. They then describe key findings from the relatively small number of studies indicating that specific measures of regional function and, to a lesser extent, structure in these neural circuits predict treatment response in depression.Conclusions:Limitations of existing studies include small sample sizes, use of only one neuroimaging modality, and a focus on identifying predictors rather than moderators and mediators of differential treatment response. By addressing these limitations and, most importantly, capitalizing on the benefits of multimodal neuroimaging, future studies can yield moderators and mediators of treatment response in depression to facilitate significant improvements in shorter- and longer-term clinical and functional outcomes.
To describe the rates of elevated inflammation, obesity, and metabolic syndrome (MetS) within a large cohort of individuals with depression and to examine the interrelationships of inflammation and ...MetS in depressed individuals.
Analyses were conducted on study participants from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) with Patient Health Questionnaire (PHQ-9) depression scores ≥ 10 to (1) examine the relationship of inflammation (C-reactive protein; CRP) with demographic and clinical characteristics and (2) examine the prevalence of MetS criteria within CRP groups.
5,579 participants provided PHQ-9 data; of those, 606 had PHQ-9 scores ≥ 10 and were included in further analysis. Of the 606 depressed participants, 585 participants had valid CRP data; 275 participants (47.01%) had CRP levels ≥ 3.0 mg/L, while 170 (29.06%) had CRP levels ≥ 5.0 mg/L. Elevated inflammation was significantly correlated with body weight, waist circumference, body mass index, insulin, 2-hour glucose tolerance, and self-report general health (P values < .05). 112 subjects (41.18%) met American Heart Association/National Heart, Lung, and Blood Institute criteria for MetS. Those with elevated CRP were more likely to meet criteria for MetS (odds ratios of 2.81 for those with CRP levels ≥ 3.0 mg/L and 1.94 for those with CRP levels ≥ 5.0 mg/L).
Over 29% of depressed individuals had elevated levels of CRP, and 41% met criteria for MetS. Individuals with elevated inflammation are more likely to be obese and meet criteria for MetS. These results highlight the significant inflammatory and metabolic burden of individuals with depression.
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