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Background: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. Aims: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. Method: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. Results: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I 2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I 2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. Conclusions: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated. [ABSTRACT FROM AUTHOR]

To examine the association between COVID-19 impact and clinical outcomes of an integrated collaborative care intervention for adults with obesity and comorbid depression. Latent class analysis identified clusters of self-reported COVID-19 impact. Cluster characteristics were examined using Fishers' least significant difference method and canonical discriminant analysis. Intervention vs. usual care effects on primary (body mass index [BMI], depressive symptoms) and secondary (anxiety symptoms and other psychosocial) outcomes stratified by cluster were examined using linear mixed models. Three clusters were identified: mental health and sleep impact (cluster 1, n = 37), economic impact (cluster 2, n = 18), and less overall impact (cluster 3, n = 20). Clusters differed in age, income, diet, and baseline coping skills. The intervention led to improvements across several health outcomes compared with usual care, with medium to large effects on functional impairments (standardized mean difference, −0.7 [95% CI: −1.3, −0.1]) in cluster 1, depressive symptoms (−1.1 [95% CI: −2.0, −0.1]) and obesity-related problems (−1.6 [95% CI: −2.8, −0.4]) in cluster 2, and anxiety (−1.1 [95% CI: −1.9, −0.3]) in cluster 3. People with obesity and comorbid depression may have varied intervention responses based on COVID-19 impact. Interventions tailored to specific COVID-19 impact clusters may restore post-pandemic health. [ABSTRACT FROM AUTHOR]

Background: Sex influences health and related behaviors due to biological and psychosocial/socioeconomic factors. Assessing sex-specific responses to integrated treatment for comorbid obesity and depression could inform intervention targeting.Purpose: To test (a) whether sex moderates the effects of integrated collaborative care on weight and depression outcomes through 24 months and (b) whether treatment response at 6 months predicts 12 and 24 month outcomes by sex.Methods: Secondary data analyses on weight and depression severity (SCL-20) measured over 24 months among 409 adults with obesity and depression in the Research Aimed at Improving Both Mood and Weight trial.Results: Men achieved significantly greater weight reductions in intervention versus usual care than women, whereas women achieved significantly greater percentage reductions in SCL-20 than men at both 12 and 24 months. In logistic models, at 80% specificity for correctly identifying participants not achieving clinically significant long-term outcomes, women who lost <3.0% weight and men who lost <4.1% weight at 6 months had ≥84% probability of not meeting 5% weight loss at 24 months. Similarly, at 80% specificity, women who reduced SCL-20 by <39.5% and men who reduced by <53.0% at 6 months had ≥82% probability of not meeting 50% decrease in SCL-20 at 24 months.Conclusions: Sex modified the integrated treatment effects for obesity and depression. Sex-specific responses at 6 months predicted clinically significant weight loss and depression outcomes through 24 months. Based on early responses, interventions may need to be tailored to address sex-specific barriers and facilitators to achieving healthy weight and depression outcomes at later time points.Clinical Trial Registration: NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413). [ABSTRACT FROM AUTHOR]

Background Depression hinders obesity treatment; elucidating mechanisms may enable treatment enhancements. Objectives The aim was to investigate whether changes in neural targets in the negative affect circuit following psychotherapy mediate subsequent changes in weight and behaviors. Methods Adults (n  = 108) with obesity and depression were randomly assigned to usual care or an intervention that delivered problem-solving therapy (PST) for depression over 2 mo. fMRI for brain imaging was performed at baseline and 2 mo. BMI, physical activity, and diet were measured at baseline and 12 mo. Mediation analysis assessed between-group differences in neural target changes using t test and correlations between neural target changes and outcome changes (simple and interaction effect) using ordinary least-squares regression. Results Compared with usual care, PST led to reductions in left amygdala activation (−0.75; 95% CI: −1.49, −0.01) and global scores of the negative affect circuit (−0.43; −0.81, −0.06), engaged by threat stimuli. Increases in amygdala activation and global circuit scores at 2 mo correlated with decreases in physical activity outcomes at 12 mo in the usual-care group; these relations were altered by PST. In relation to change in leisure-time physical activity, standardized β-coefficients were −0.67 in usual care and −0.01 in the intervention (between-group difference: 0.66; 0.02, 1.30) for change in left amygdala activation and −2.02 in usual care and −0.11 in the intervention (difference: 1.92; 0.64, 3.20) for change in global circuit scores. In relation to change in total energy expenditure, standardized β-coefficients were −0.65 in usual care and 0.08 in the intervention (difference: 0.73; 0.29, 1.16) for change in left amygdala activation and −1.65 in usual care and 0.08 in the intervention (difference: 1.74; 0.85, 2.63) for change in global circuit scores. Results were null for BMI and diet. Conclusions Short-term changes in the negative affect circuit engaged by threat stimuli following PST for depression mediated longer-term changes in physical activity. This trial was registered at www.clinicaltrials.gov as NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413). [ABSTRACT FROM AUTHOR]

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety. [ABSTRACT FROM AUTHOR]

Background: Although repetitive transcranial magnetic stimulation ('TMS') is becoming a gold standard treatment for pharmacoresistant depression, we lack neural target biomarkers for identifying who is most likely to respond to TMS and why. To address this gap in knowledge we evaluate neural targets defined by activation and functional connectivity of the dorsolateral prefrontal cortex-anchored cognitive control circuit, regions of the default mode network and attention circuit, and interactions with the subgenual anterior cingulate. We evaluate whether these targets and interactions between them change in a dose-dependent manner, whether changes in these neural targets correspond to changes in cognitive behavioral performance, and whether baseline and early change in neural target and cognitive behavioral performance predict subsequent symptom severity, suicidality, and quality of life outcomes. This study is designed as a pragmatic, mechanistic trial partnering with the National Clinical TMS Program of the Veteran's Health Administration. Methods: Target enrollment consists of 100 veterans with pharmacoresistant Major Depressive Disorder (MDD). All veterans will receive a clinical course of TMS and will be assessed at 'baseline' pre-TMS commencement, 'first week' after initiation of TMS (targeting five sessions) and 'post-treatment' at the completion of TMS (targeting 30 sessions). Veterans will be assessed using functional magnetic resonance imaging (fMRI), a cognitive behavioral performance battery, and established questionnaires. Multivariate linear mixed models will be used to assess whether neural targets change with TMS as a function of dose (Aim 1), whether extent and change of neural target relates to and predicts extent of behavioral performance (Aim 3), and whether extent of neural target change predicts improvement in symptom severity, suicidality, and quality of life (Aim 3). For all three aims, we will also assess the contribution of baseline moderators such as biological sex and age. Discussion: To our knowledge, our study will be the first pragmatic, mechanistic observational trial to use fMRI imaging and cognitive-behavioral performance as biomarkers of TMS treatment response in pharmacoresistant MDD. The results of this trial will allow providers to select suitable candidates for TMS treatment and better predict treatment response by assessing circuit connectivity and cognitive-behavioral performance at baseline and during early treatment. Trial registration: ClinicalTrials.gov NCT04663481, December 5th, 2020, retrospectively registered. The first veteran was enrolled October 30th, 2020. [ABSTRACT FROM AUTHOR]

Background: Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD. Methods: Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity. Results: Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8–13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems. Conclusions: These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD. [ABSTRACT FROM AUTHOR]

Objectives: Dysfunction of cognitive control is a feature of both bipolar disorder (BP) and major depression (MDD) and persists through to remission. However, it is unknown whether these disorders are characterized by common or distinct disruptions of cognitive control function and its neural basis. We investigated this gap in knowledge in asymptomatic BP and MDD participants, interpreted within a framework of normative function. Methods: Participants underwent fMRI scans engaging cognitive control through a working memory task and completed a cognitive battery evaluating performance across multiple subdomains of cognitive control, including attention, impulsivity, processing speed, executive function, and memory. Analysis was performed in two stages: (i) cognitive control‐related brain activation and deactivation were correlated with cognitive control performance in 115 healthy controls (HCs), then, (ii) significantly correlated regions from (i) were compared between 25 asymptomatic BP, 25 remitted MDD, and with 25 different HCs, matched for age and gender. Results: Impulsivity and executive function performance were significantly worse in BP compared to both MDD and HCs. Both BP and MDD had significantly poorer memory performance compared to HCs. Greater deactivation of the medial prefrontal cortex (MPFC) during the fMRI task was associated with better executive function in healthy controls. Significantly less deactivation in this region was present in both BP and MDD compared to HCs. Conclusions: Failure to deactivate the MPFC, a key region of the default mode network, during working memory processing is a shared neural feature present in both bipolar and major depression and could be a source of common cognitive dysfunction. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) commonly co-occurs with clinically significant levels of anxiety. However, anxiety symptoms are varied and have been inconsistently associated with clinical, functional, and antidepressant treatment outcomes. We aimed to identify and characterise dimensions of anxiety in people with MDD and their use in predicting antidepressant treatment outcome. Method: 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic, MDD were assessed at baseline on clinical features and cognitive/physiological functioning. Participants were then randomised to one of three commonly prescribed antidepressants and reassessed at 8 weeks regarding symptom change, as well as remission and response, on the 17-item Hamilton Rating Scale Depression (HRSD17) and the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16). Exploratory factor analysis was used on items from scales assessing anxiety symptoms, and resulting factors were assessed against clinical features and cognitive/physiological functioning. Factors were also assessed on their ability to predict treatment outcome. Results: Three factors emerged relating to stress, cognitive anxiety, and somatic anxiety. All factors showed high internal consistency, minimal cross-loadings, and unique clinical and functional profiles. Furthermore, only higher somatic anxiety was associated with poorer QIDS-SR16 remission, even after adjusting for covariates and multiple comparisons. Conclusions: Anxiety symptoms in people with MDD can be separated onto distinct factors that differentially respond to treatment outcome. Furthermore, these factors do not align with subscales of established measures of anxiety. Future research should consider cognitive and somatic symptoms of anxiety separately when assessing anxiety in MDD and their use in predicting treatment outcome. [ABSTRACT FROM AUTHOR]

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging–derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice. [ABSTRACT FROM AUTHOR]

Objective: Major depressive disorder commonly co-occurs with one or more anxiety disorders or with clinically significant levels of anxiety symptoms. Although evidence suggests that anxious forms of depression are prognostic of poorer antidepressant outcomes, there is no clear definition of anxious depression, and inferences about clinical outcomes are thus limited. Our objective was to compare and evaluate definitions of anxious depression and anxiety-related scales according to clinical and antidepressant outcome criteria. Method: A total of 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic, major depressive disorder were assessed at baseline on clinical features. Participants were then randomised to one of three antidepressants and reassessed at 8 weeks regarding remission and response of the 17-item Hamilton Rating Scale Depression (HRSD17) and the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16). Anxious depression was defined as major depressive disorder with one or more anxiety disorders or major depressive disorder with a HRSD17 anxiety/somatisation factor score ⩾7. Anxiety-related scales included the HRSD17 anxiety/somatisation factor and the 42-item Depression Anxiety Stress Scales (DASS42) anxiety and stress subscales. Results: Anxious depression definitions showed poor agreement (κ = 0.15) and the HRSD17 anxiety/somatisation factor was weakly correlated with both DASS42 anxiety (r = 0.24) and stress subscales (r = 0.20). Anxious depression definitions were also associated with few impairments on clinical features and did not predict poorer antidepressant treatment outcome. However, higher DASS42 anxiety predicted poorer HRSD17 and QIDS-SR16 remission, and item-level analysis found higher scores on items 9 (situational anxiety) and 23 (somatic anxiety) of the DASS42 predicted poorer treatment outcome, even after adjusting for covariates and multiple comparisons. Conclusion: Common definitions of anxious depression show poor agreement and do not predict poorer treatment outcome. Anxiety symptoms may be better characterised dimensionally using DASS42 when predicting treatment outcome. [ABSTRACT FROM AUTHOR]

Importance: Treatment-resistant major depression (TRMD) in veterans is a major clinical challenge given the high risk for suicidality in these patients. Repetitive transcranial magnetic stimulation (rTMS) offers the potential for a novel treatment modality for these veterans.Objective: To determine the efficacy of rTMS in the treatment of TRMD in veterans.Design, Setting, and Participants: A double-blind, sham-controlled randomized clinical trial was conducted from September 1, 2012, to December 31, 2016, in 9 Veterans Affairs medical centers. A total of 164 veterans with TRD participated.Interventions: Participants were randomized to either left prefrontal rTMS treatment (10 Hz, 120% motor threshold, 4000 pulses/session) or to sham (control) rTMS treatment for up to 30 treatment sessions.Main Outcomes and Measures: The primary dependent measure of the intention-to-treat analysis was remission rate (Hamilton Rating Scale for Depression score ≤10, indicating that depression is in remission and not a clinically significant burden), and secondary analyses were conducted on other indices of posttraumatic stress disorder, depression, hopelessness, suicidality, and quality of life.Results: The 164 participants had a mean (SD) age of 55.2 (12.4) years, 132 (80.5%) were men, and 126 (76.8%) were of white race. Of these, 81 were randomized to receive active rTMS and 83 to receive sham. For the primary analysis of remission, there was no significant effect of treatment (odds ratio, 1.16; 95% CI, 0.59-2.26; P = .67). At the end of the acute treatment phase, 33 of 81 (40.7%) of those in the active treatment group achieved remission of depressive symptoms compared with 31 of 83 (37.4%) of those in the sham treatment group. Overall, 64 of 164 (39.0%) of the participants achieved remission.Conclusions and Relevance: A total of 39.0% of the veterans who participated in this trial experienced clinically significant improvement resulting in remission of depressive symptoms; however, there was no evidence of difference in remission rates between the active and sham treatments. These findings may reflect the importance of close clinical surveillance, rigorous monitoring of concomitant medication, and regular interaction with clinic staff in bringing about significant improvement in this treatment-resistant population.Trial Registration: ClinicalTrials.gov Identifier: NCT01191333. [ABSTRACT FROM AUTHOR]

Currently there is a very limited understanding of how mental wellbeing versus anxiety and depression symptoms are associated with emotion processing behaviour. For the first time, we examined these associations using a behavioural emotion task of positive and negative facial expressions in 1668 healthy adult twins. Linear mixed model results suggested faster reaction times to happy facial expressions was associated with higher wellbeing scores, and slower reaction times with higher depression and anxiety scores. Multivariate twin modelling identified a significant genetic correlation between depression and anxiety symptoms and reaction time to happy facial expressions, in the absence of any significant correlations with wellbeing. We also found a significant negative phenotypic relationship between depression and anxiety symptoms and accuracy for identifying neutral emotions, although the genetic or environment correlations were not significant in the multivariate model. Overall, the phenotypic relationships between speed of identifying happy facial expressions and wellbeing on the one hand, versus depression and anxiety symptoms on the other, were in opposing directions. Twin modelling revealed a small common genetic correlation between response to happy faces and depression and anxiety symptoms alone, suggesting that wellbeing and depression and anxiety symptoms show largely independent relationships with emotion processing at the behavioral level. [ABSTRACT FROM AUTHOR]

Objective: Genetic variation within the hypothalamic-pituitary-adrenal (HPA) axis has been linked to risk for depression and antidepressant response. However, these associations have yet to produce clinical gains that inform treatment decisions. The authors investigated whether variation within HPA axis genes predicts antidepressant outcomes within two large clinical trials.Method: The test sample comprised 636 patients from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) who completed baseline and 8-week follow-up visits and for whom complete genotyping data were available. The authors tested the relationship between genotype at 16 candidate HPA axis single-nucleotide polymorphisms (SNPs) and treatment outcomes for three commonly used antidepressants (escitalopram, sertraline, and extended-release venlafaxine), using multivariable linear and logistic regression with Bonferroni correction. Response and remission were defined using the Hamilton Depression Rating Scale. Findings were then validated using the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study of outcome predictors in treatment-naive patients with major depression.Results: The authors found that the rs28365143 variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs28365143 had greater remission rates, response rates, and symptom reductions. These effects were specific to drug class. Patients homozygous for the G allele responded significantly better to the selective serotonin reuptake inhibitors escitalopram and sertraline than did A allele carriers. In contrast, rs28365143 genotype was not associated with treatment outcomes for the serotonin norepinephrine reuptake inhibitor venlafaxine. When patients were stratified by race, the overall effect of genotype on treatment response remained. In the validation sample, the GG genotype was again associated with favorable antidepressant outcomes, with comparable effect sizes.Conclusions: These findings suggest that a specific CRHBP SNP, rs28365143, may have a role in predicting which patients will improve with antidepressants and which type of antidepressant may be most effective. The results add to the foundational knowledge needed to advance a precision approach to personalized antidepressant choices. [ABSTRACT FROM AUTHOR]

Alterations to cognitive function are often reported with depression and anxiety symptoms, yet few studies have examined the same associations with mental well-being. This study examined the association between mental well-being, depression and anxiety symptoms and cognitive function in 1502 healthy adult monozygotic (MZ) and dizygotic (DZ) twins, and the shared/unique contribution of genetic (G) and environmental (E) variance. Using linear mixed models, mental well-being was positively associated (p < .01) with sustained attention (β = 0.127), inhibition (β = 0.096), cognitive flexibility (β = 0.149), motor coordination (β = 0.114) and working memory (β = 0.156), whereas depression and anxiety symptoms were associated (p < .01) with poorer sustained attention (β = −0.134), inhibition (β = −0.139), cognitive flexibility (β = −0.116) and executive function (β = −0.139). Bivariate twin modelling showed well-being shared a small environmental correlation with motor coordination and a small genetic correlation with working memory. Trivariate twin modelling showed well-being shared a small genetic correlation with inhibition, whereas depression and anxiety symptoms shared a small environmental correlation with inhibition. The remaining variance was mostly driven by unique G and/or E variance. Overall, well-being and depression and anxiety symptoms show both independent and shared relationships with cognitive functions but this is largely attributable to unique G or E variance and small shared G/E variance between pairs of variables. [ABSTRACT FROM PUBLISHER]

Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlines the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (default mode), detection of "salience," affective processing ("threat" and "reward"), "attention," and "cognitive control." Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders is reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined "biotypes." In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world. [ABSTRACT FROM AUTHOR]

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person. [ABSTRACT FROM AUTHOR]

Background: The prevalence of depression and anxiety symptoms and their comorbidity varies between males and females for reasons still unknown. This study aims to test whether differences between males and females in self-reported symptoms and their covariation are caused by variations in the magnitude of genetic and environmental factors.Methods: 750 monozygotic and dizygotic healthy twin pairs (18-60 years; M=39.77 years) participated in the TWIN-E project. Univariate and multivariate genetic modelling was undertaken using the Depression Anxiety Stress Scale (DASS-42).Results: Additive genetics and unique environment contributed to self-reported depression (heritability, h(2): 34%), anxiety (h(2): 30%) and stress (h(2): 34%) scores in univariate models, and to the common latent factor (h(2): 39%) in the multivariate model. No sex differences in magnitude of estimates for DASS-42 scores were found in the univariate model. However when considering correlated depression and anxiety symptomatology only shared genetic factors between depression and anxiety contributed to depression scores in males, but both specific and shared genetic factors contributed to depression scores in females.Limitations: The results are limited to the sample of healthy, community, adult, same sex twin pairs who participated in the study.Conclusions: Differences in males and females in genetic aetiology of self-reported dimensions of depression are only apparent when taking into consideration the covariation with self-reported anxiety. This difference is highlighted by the finding that both common and specific genetic factors contribute to self-reported depression in females but not males. This novel finding may help explain the increased incidence of depression symptoms in females. [ABSTRACT FROM AUTHOR]

Although the cost of poor treatment outcomes of depression is staggering, we do not yet have clinically useful methods for selecting the most effective antidepressant for each depressed person. Emotional brain activation is altered in major depressive disorder (MDD) and implicated in treatment response. Identifying which aspects of emotional brain activation are predictive of general and specific responses to antidepressants may help clinicians and patients when making treatment decisions. We examined whether amygdala activation probed by emotion stimuli is a general or differential predictor of response to three commonly prescribed antidepressants, using functional magnetic resonance imaging (fMRI). A test-retest design was used to assess patients with MDD in an academic setting as part of the International Study to Predict Optimized Treatment in Depression. A total of 80 MDD outpatients were scanned prior to treatment and 8 weeks after randomization to the selective serotonin reuptake inhibitors escitalopram and sertraline and the serotonin-norepinephrine reuptake inhibitor, venlafaxine-extended release (XR). A total of 34 matched controls were scanned at the same timepoints. We quantified the blood oxygen level-dependent signal of the amygdala during subliminal and supraliminal viewing of facial expressions of emotion. Response to treatment was defined by ⩾50% symptom improvement on the 17-item Hamilton Depression Rating Scale. Pre-treatment amygdala hypo-reactivity to subliminal happy and threat was a general predictor of treatment response, regardless of medication type (Cohen's d effect size 0.63 to 0.77; classification accuracy, 75%). Responders showed hypo-reactivity compared to controls at baseline, and an increase toward 'normalization' post-treatment. Pre-treatment amygdala reactivity to subliminal sadness was a differential moderator of non-response to venlafaxine-XR (Cohen's d effect size 1.5; classification accuracy, 81%). Non-responders to venlafaxine-XR showed pre-treatment hyper-reactivity, which progressed to hypo-reactivity rather than normalization post-treatment, and hypo-reactivity post-treatment was abnormal compared to controls. Impaired amygdala activation has not previously been highlighted in the general vs differential prediction of antidepressant outcomes. Amygdala hypo-reactivity to emotions signaling reward and threat predicts the general capacity to respond to antidepressants. Amygdala hyper-reactivity to sad emotion is involved in a specific non-response to a serotonin-norepinephrine reuptake inhibitor. The findings suggest amygdala probes may help inform the personal selection of antidepressant treatments. [ABSTRACT FROM AUTHOR]

Background Childhood maltreatment (CM) history has been associated with poor treatment response in major depressive disorder (MDD), but the mechanisms underlying this relationship remain opaque. Dysfunction in the neural circuits for executive cognition is a putative neurobiological consequence of CM that may contribute importantly to adverse clinical outcomes. We used behavioral and neuroimaging measures of executive functioning to assess their contribution to the relationship between CM and antidepressant response in MDD patients. Methods Ninety eight medication-free MDD outpatients participating in the International Study to Predict Optimized Treatment in Depression were assessed at baseline on behavioral neurocognitive measures and functional magnetic resonance imaging during tasks probing working memory (continuous performance task, CPT) and inhibition (Go/No-go). Seventy seven patients completed 8 weeks of antidepressant treatment. Baseline behavioral and neuroimaging measures were assessed in relation to CM (history of childhood physical, sexual, and/or emotional abuse) and posttreatment depression outcomes. Results Patients with maltreatment exhibited decreased modulation of right dorsolateral prefrontal cortex (DLPFC) activity during working memory updating on the CPT, and a corresponding impairment in CPT behavioral performance outside the scanner. No between-group differences were found for imaging or behavior on the Go/No-go test of inhibition. Greater DLPFC activity during CPT significantly predicted posttreatment symptom improvement in patients without maltreatment, whereas the relationship between DLPFC activity and symptom change was nonsignificant, and in the opposite direction, in patients with maltreatment. Conclusions The effect of CM on prefrontal circuitry involved in executive function is a potential predictor of antidepressant outcomes. [ABSTRACT FROM AUTHOR]

Depression involves impairments in a range of cognitive and emotional capacities. It is unknown whether these functions can inform medication choice when considered as a composite predictive biomarker. We tested whether behavioral tests, grounded in the neurobiology of cognitive and emotional functions, predict outcome with common antidepressants. Medication-free outpatients with nonpsychotic major depressive disorder (N=1008; 665 completers) were assessed before treatment using 13 computerized tests of psychomotor, executive, memory-attention, processing speed, inhibitory, and emotional functions. Matched healthy controls (N=336) provided a normative reference sample for test performance. Depressed participants were then randomized to escitalopram, sertraline, or venlafaxine-extended release, and were assessed using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) and the 17-item Hamilton Rating Scale for Depression. Given the heterogeneity of depression, analyses were furthermore stratified by pretreatment performance. We then used pattern classification with cross-validation to determine individual patient-level composite predictive biomarkers of antidepressant outcome based on test performance. A subgroup of depressed participants (approximately one-quarter of patients) were found to be impaired across most cognitive tests relative to the healthy norm, from which they could be discriminated with 91% accuracy. These patients with generally impaired cognitive task performance had poorer treatment outcomes. For this impaired subgroup, task performance furthermore predicted remission on the QIDS-SR16 at 72% accuracy specifically following treatment with escitalopram but not the other medications. Therefore, tests of cognitive and emotional functions can form a clinically meaningful composite biomarker that may help drive general treatment outcome prediction for optimal treatment selection in depression, particularly for escitalopram. [ABSTRACT FROM AUTHOR]

Background Oxytocin is a neuropeptide that is involved in the regulation of mood, anxiety and social biology. Genetic variation in the oxytocin receptor gene (OXTR) has been implicated in anxiety, depression and related stress phenotypes. It is not yet known whether OXTR interacts with other risk factors such as early life trauma to heighten the severity of experienced anxiety and depression. Methods In this study, we examined genotypes in 653 individuals and tested whether SNP variation in OXTR correlates with severity of features of self-reported experience on the Depression Anxiety and Stress Scale (DASS), and whether this correlation is enhanced when early life trauma is taken into account. We also assessed the effects of OXTR SNPs on RNA expression levels in two separate brain tissue cohorts totaling 365 samples. Results A significant effect of OXTR genotype on DASS anxiety, stress and depression scores was found and ELS events, in combination with several different OXTR SNPs, were significantly associated with differences in DASS scores with one SNP (rs139832701) showing significant association or a trend towards association for all three measures. Several OXTR SNPs were correlated with alterations in OXTR RNA expression and rs3831817 replicated across both sets of tissues. Conclusions These results support the hypothesis that the oxytocin system plays a role in the pathophysiology of mood and anxiety disorders. [ABSTRACT FROM AUTHOR]

Background Functional neuroimaging studies implicate anterior cingulate and limbic dysfunction in major depressive disorder (MDD) and responsiveness to antidepressants. Diffusion tensor imaging (DTI) enables characterisation of white matter tracts that relate to these regions. Aims To examine whether DTI measures of anterior cingulate and limbic white matter are useful prognostic biomarkers for MDD. Method Of the 102 MDD out-patients from the International Study to Predict Optimized Treatment for Depression (iSPOT-D) who provided baseline magnetic resonance imaging (MRI) data, 74 completed an 8-week course of antidepressant medication (randomised to escitalopram, sertraline or extended-release venlafaxine) and were included in the present analyses. Thirty-four matched controls also provided DTI data. Fractional anisotropy was measured for five anterior cingulate-limbic white matter tracts: cingulum cingulate and hippocampus bundle, fornix, stria terminate and uncinate fasciculus. (Trial registered at ClinicalTriais.gov: NCT00693849.) Results A cross-validated logistic regression model demonstrated that altered connectivity for the cingulum part of the cingulate and stria terminalis tracts significantly predicted remission independent of demographic and clinical measures with 62% accuracy. Prediction improved to 74% when age was added to this model. Conclusions Anterior cingulate-limbic white matter is a useful predictor of antidepressant treatment outcome in MDD. Declaration of interest L.M.W. is a small equity holder in Brain Resource Ltd. S.M.G. and L.M.W. have also previously received consultancy fees from Brain Resource Ltd. [ABSTRACT FROM AUTHOR]

Background: The brainstem is the main region that innervates neurotransmitter release to the Hypothalamic-Pituitary Adrenal (HPA) axis and fronto-limbic circuits, two key brain circuits found to be dysfunctional in Major Depressive Disorder (MDD). However, the brainstem’s role in MDD has only been evaluated in limited reports. Using Diffusion Tensor Imaging (DTI), we investigated whether major brainstem white matter tracts that relate to these two circuits differ in MDD patients compared to healthy controls. Methods: MDD patients (n = 95) and age- and gender-matched controls (n = 34) were assessed using probabilistic tractography of DTI to delineate three distinct brainstem tracts: the nigrostriatal tract (connecting brainstem to striatum), solitary tract (connecting brainstem to amygdala) and corticospinal tract (connecting brainstem to precentral cortex). Fractional anisotropy (FA) was used to measure the white matter integrity of these tracts, and measures were compared between MDD and control participants. Results: MDD participants were characterized by a significant and specific decrease in white matter integrity of the right solitary tract (p<0.009 using independent t-test), which is a “bottom up” afferent pathway that connects the brainstem to the amygdala. This decrease was not related to symptom severity. Conclusions: The results provide new evidence to suggest that structural connectivity between the brainstem and the amygdala is altered in MDD. These results are interesting in light of predominant theories regarding amygdala-mediated emotional reactivity observed in functional imaging studies of MDD. The characterization of altered white matter integrity in the solitary tract in MDD supports the possibility of dysfunctional brainstem-amygdala connectivity impacting vulnerable circuits in MDD. [ABSTRACT FROM AUTHOR]

Background: Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD.Methods/design: The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n=102), test the findings in the second half, and then extend the analyses to the total sample.Trial Registration: International Study to Predict Optimized Treatment--in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849. [ABSTRACT FROM AUTHOR]

Functional neuroimaging studies have implicated dysregulation of prefrontal circuits in major depressive disorder (MDD), and these circuits are a viable target for predicting treatment outcomes. However, because of the heterogeneity of tasks and samples used in studies to date, it is unclear whether the central dysfunction is one of prefrontal hyperreactivity or hyporeactivity. We used a standardized battery of tasks and protocols for functional magnetic resonance imaging, to identify the common vs the specific prefrontal circuits engaged by these tasks in the same 30 outpatients with MDD compared with 30 matched, healthy control participants, recruited as part of the International Study to Predict Optimized Treatment in Depression (iSPOT-D). Reflecting cognitive neuroscience theory and established evidence, the battery included cognitive tasks designed to assess functions of selective attention, sustained attention-working memory and response inhibition, and emotion tasks to assess explicit conscious and implicit nonconscious viewing of facial emotion. MDD participants were distinguished by a distinctive biosignature of: hypoactivation of the dorsolateral prefrontal cortex during working memory updating and during conscious negative emotion processing; hyperactivation of the dorsomedial prefrontal cortex during working memory and response inhibition cognitive tasks and hypoactivation of the dorsomedial prefrontal during conscious processing of positive emotion. These results show that the use of standardized tasks in the same participants provides a way to tease out prefrontal circuitry dysfunction related to cognitive and emotional functions, and not to methodological or sample variations. These findings provide the frame of reference for identifying prefrontal biomarker predictors of treatment outcomes in MDD. [ABSTRACT FROM AUTHOR]

Abstract: Exposure to early life trauma is a known risk factor for depression and anxiety disorders in adulthood. This study aimed to evaluate the relative contributions of early life versus adult trauma in predicting levels of depressive and anxiety symptoms in nonclinical community adults. 1209 nonclinical community adults (18–70 years; 45% male) were assessed for mental health status, early life stressors, lifetime trauma exposure, and self-reported levels of depressive and anxiety symptoms. A subset of the full sample subjected to group comparisons (n = 1088) indicated that early life stressor exposure primarily accounted for significantly higher depressive and anxiety symptom scores when compared against adults reporting to be free of childhood stressor or adult trauma exposure. Subsequent hierarchical multiple regression analyses of this subset using five distinct early life stressor types, namely ‘Interpersonal violation’, ‘Family breakup’, ‘Disasters/war’, ‘Familial health trauma/death’ and ‘Personal health trauma’ derived from principal component analysis of a wide range of self-reported early stressor events in the full sample, showed childhood ‘Interpersonal violation’ differentially predicted higher self-reported depressive and anxiety symptom scores in both males and females. Adult trauma exposure did not significantly predict these symptom scores. These findings underline the relative importance of exposure to ‘interpersonal violation’ relative to other types of early life stressors and adult trauma in the risk of depressive and anxiety symptoms in nonclinical community adults. [Copyright &y& Elsevier]

Background: Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. Methods/Design: The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multicentre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and eventrelated potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and selfreported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. Discussion: First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. Trial registration: International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849 URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment +for+Depression&rank=1 [ABSTRACT FROM AUTHOR]

Background: The risk for mental illnesses such as depression is increasingly conceptualized as the product of gene–environment interactions and their impact on brain structure and function. The role of serotonin 3A receptor gene (HTR3A -42C>T polymorphism) and its interaction with early life stress (ELS) was investigated in view of the receptor's localization to brain regions central to emotion processing. Methods: Fronto-limbic grey matter (GM) loss was measured using magnetic resonance imaging and assessed using voxel-based morphometry analysis in 397 nonclinical individuals from the Brain Resource International Database. Negative mood symptoms were also assessed. Results: The HTR3A CC genotype group, compared to the T carriers, demonstrated comparative loss to GM in hippocampal structures, which extended to the frontal cortices for those CC genotype individuals also exposed to ELS. Elevations in depressed mood were also evident. Conclusions: These findings suggest that the HTR3A CC genotype may be associated with alterations in brain structures central to emotion processing, particularly when exposed to stress, and further highlight the potential role of the serotonin system in the pathophysiology of affective disorders. In contrast, those individuals with the T allele, in particular the TT genotype, may be more protected from such alterations combined with minimal exposure to ELS events. Depression and Anxiety, 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The valence-arousal (W. Heller, 1993) and approach-withdrawal (R. J. Davidson, 1998a) models hypothesize that particular patterns of hemispheric brain activity are associated with specific motivational tendencies and psychopathologies. We tested several of these predictions in two groups—a posttraumatic stress disorder (PTSD) and a “supercontrol” group, selected to be maximally different from those with PTSD. Contrary to almost all hypotheses, individuals with PTSD did not differ from controls on resting electroencephalogram (EEG) asymmetry. Particular aspects of PTSD were also not related to EEG hemisphere differences. Our null findings are consistent with the few studies that have examined resting EEG asymmetries in PTSD and suggest that PTSD may be associated with different processes than psychopathologies previously examined in studies of hemispheric brain activity (e.g., major depressive disorder, panic disorder). [ABSTRACT FROM AUTHOR]

Although biases toward signals of fear may be an evolutionary adaptation necessary for survival, heightened biases may be maladaptive and associated with anxiety or depression. In this study, event-related potentials (ERPs) were used to examine the time course of neural responses to facial fear stimuli (versus neutral) presented overtly (for 500 msec with conscious attention) and covertly (for 10 msec with immediate masking to preclude conscious awareness) in 257 nonclinical subjects. We also examined the impact of trait anxiety and depression, assessed using psychometric ratings, on the time course of ERPs. In the total subject group, controlled biases to overtly processed fear were reflected in an enhancement of ERPs associated with structural encoding (120–220 msec) and sustained evaluation persisting from 250 msec and beyond, following a temporo-occipital to frontal topography. By contrast, covert fear processing elicited automatic biases, reflected in an enhancement of ERPs prior to structural encoding (80–180 msec) and again in the period associated with automatic orienting and emotion encoding (230–330 msec), which followed the reverse frontal to temporo-occipital topography. Higher levels of trait anxiety (in the clinical range) were distinguished by a heightened bias to covert fear (speeding of early ERPs), compared to higher depression which was associated with an opposing bias to overt fear (slowing of later ERPs). Anxiety also heightened early responses to covert fear, and depression to overt fear, with subsequent deficits in emotion encoding in each case. These findings are consistent with neural biases to signals of fear which operate automatically and during controlled processing, feasibly supported by parallel networks. Heightened automatic biases in anxiety may contribute to a cycle of hypervigilance and anxious thoughts, whereas depression may represent a ‘burnt out’ emotional state in which evaluation of fear stimuli is prolonged only when conscious attention is allocated. [ABSTRACT FROM AUTHOR]

Aims: Following an integrative neuroscience perspective, we propose that cognitive and emotional functions are integrally linked, and that genetic polymorphisms which impact upon neural processes may have complementary effects on these functions. The brain-derived neurotrophic factor (BDNF) 66Met allele may contribute to both cognitive and emotional aspects of the depression phenotype. Methods: In 374 nonclinical subjects, BDNF genotype differences in task-related ERPs, emotion, memory, and EEG cortical arousal were examined. Results: Using path modeling, higher negative affect in Met homozygotes was predicted by slow-wave EEG via the mediating effects of neuroticism. Both negative affect and working memory deficits were predicted by disturbances in emotion- and cognitive-related ERPs. This model held across groups with varying levels of depressed mood. Discussion: Since impairments in emotion and working memory are core features of major depression, the BDNF Met allele may contribute to vulnerability for this disorder. An integrative approach in which genotypes are considered in combination with brain function and behavioral measures may be important in identifying profile markers of depression. Integrative Significance: This study directly demonstrates that cognitive and emotional neural networks are not parallel independent systems, but rather highly integrated with effects on both cognitive performance and emotional behavior. [ABSTRACT FROM AUTHOR]

Introduction: Depression is characterized by disturbances in affect, cognition, brain and body function, yet studies have tended to focus on single domains of dysfunction. An integrated approach may provide a more complete profile of the range of deficits characterized by depressed individuals, but it is unclear whether this approach is able to predict depression severity over and above that predicted by single tasks or domains of function. In this study, we examined the value of combining multiple domains of function in predicting depression severity. Methods: Participants contained in the International Brain Database, ( ) had completed three testing components including a web-based questionnaire of Personal History, the Brain Resource Cognition battery of Neuropsychological tests, Personality assessment and Psychophysiological testing. Two hundred and sixty six of these participants were able to be classified as either non-depressed, mild-moderately or severely (non-clinically) depressed, based on a depression screening questionnaire. Analysis of variance identified variables on which the categorized participants differed. Significant variables were then entered into a series of stepwise regressions to examine their ability to predict depression scores. Results: An integrated model including measures of affect (increased Neuroticism; decreased Emotional Intelligence), cognition (increased variability of reaction time during a working memory task; decreased "name the word component score" in the verbal interference task), brain (decreased left-lateralized P150 ERP component during a working memory task) and body function (increased negative skin conductance level gradient) were found to predict more of the variation in depression severity than any single domain of function. Discussion: On the basis of behavioral as well as Psychophysiological findings reported in this study, it was suggested that deficits in subclinically depressed individuals are more pronounced during automatic stages of stimulus processing, and that performance in these individuals may improve (to the level displayed by controls) when task demands are increased. Findings also suggest that it is important to consider disturbances across different domains of function in order to elucidate depression severity. Each domain may contribute unique explanatory information consistent with an integrative model of depression, taking into account the role of both behavior and underlying neural changes. [ABSTRACT FROM AUTHOR]

Clinical depression is one of the most common psychiatric disorders in adults, yet non-clinical depression in the community may go unnoticed, despite high prevalence rates and significant psychosocial impairment. The aim of the current study was to classify 1,226 individuals from a community sample on the basis of depression scores (using the Depression Anxiety Stress Scales, DASS) and to determine whether depression in a non-clinical sample differed significantly from healthy controls on a profile of multimodal measures. The data analyzed in this study included personality, emotional intelligence, cognition and psychophysiology. It was predicted that non-clinically depressed participants would differ from healthy controls on measures of personality (increased neuroticism; decreased extraversion), emotional intelligence (decreased), cognition (impairments in executive dysfunction and memory impairment), psychophysiology (increased resting-state, right-frontal activation; diminished skin conductance) after controlling for gender, age, handedness and years of education. Findings provide support for the majority of hypotheses, though no evidence was found for memory impairment or frontal hemispheric asymmetry. Longitudinal studies are needed to determine the extent to which of these findings will have utility for the prediction of depression onset and treatment response/non-response. [ABSTRACT FROM AUTHOR]

Over the last decade, an increasing number of research studies have focused on the construct of Emotional Intelligence (EI), which may be broadly defined as the capacity to perceive and regulate emotions in oneself as well as those of others. Researchers have generally adopted an organizational or management focus to the study of EI, however studies which adopt a more integrated perspective by combining psychological with biological measures, may help in further elucidating this relatively abstract construct. The first objective of this paper was to report on the psychometric properties of a brief, self-report measure of EI (Brain Resource Inventory for Emotional intelligence Factors or BRIEF), comprising internal emotional capacity (IEC), external emotional capacity (EEC) and self concept (SELF). Second, we further explored the validity of the measure by assessing the relationships between the BRIEF and variables considered relevant to the understanding of EI (including gender, age, personality, cognitive intelligence and resting state electroencephalography, EEG). The BRIEF possessed sound psychometric properties (internal consistency, r = 0.68 - 0.81; test-retest reliability, r = 0.92; construct validity with the Self Report Emotional Intelligence Test, r = 0.70). As hypothesized, females were found to score higher than males on EI. EI was associated more with personality than with cognitive ability, and EEG was found to explain a significant portion of the variance in EI scores. The finding that low EI is related to underarousal of the left-frontal cortex (increased theta EEG) is consistent with research on patients with depression, as well as attention deficit hyperactivity disorder. Although EI did not display age-related increases, this might relate to the exclusion of adolescents from our sample. In conclusion, examination of the way in which EI measures relate to a complementary range of psychological and biological measures may help to further elucidate this construct. [ABSTRACT FROM AUTHOR]

The author reflects on the study "Randomized clinical trial of realtime fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall," by K. D. Young and colleagues. He discusses the use of amygdala-focused functional MRI (fMRI) neurofeedback for assessing major depression. He highlights how the new strategy shows the integration of informative neural substrates in clinical interventions.

Facial expressions signaling threat and mood-congruent loss have been used to probe abnormal neural reactivity in major depressive disorder (MDD) and may be implicated in genetic vulnerability to MDD. This study investigated electro-cortical reactivity to facial expressions in 101 unaffected, adult first-degree relatives of probands with MDD and non-relative controls (n = 101). We investigated event-related potentials (ERPs) to five facial expressions of basic emotion: fear, anger, disgust, sadness and happiness under both subliminal (masked) and conscious (unmasked) presentation conditions, and the source localization of group differences. In the conscious condition, controls showed a distinctly positive-going shift in responsive to negative versus happy faces, reflected in a greater positivity for the VPP frontally and the P300 parietally, and less negativity for the N200. By contrast, relatives showed less differentiation of emotions, reflected in less VPP and P300 positivity, particularly for anger and disgust, which produced an enhanced N200 for sadness. These group differences were consistently source localized to the anterior cingulate cortex. The findings contribute new evidence for neural disruptions underlying the differentiation of salient emotions in familial risk for depression. These disruptions occur to the appraisal (∼200 ms post-stimulus) through to the context evaluation (∼300 ms+ post-stimulus) phases of emotion processing, consistent with theories that risk for depression involves biased or attenuated processing of emotion. [ABSTRACT FROM AUTHOR]

Ketamine intravenous therapy (KIT) appears effective for treating depression in controlled trials testing a short series of infusions. A rapidly proliferating number of clinics offer KIT for depression and anxiety, using protocols without a strong evidence basis. Controlled comparison of mood and anxiety from real-world KIT clinics, and the stability of outcomes, is lacking. We performed a retrospective controlled analysis on patients treated with KIT in ten community clinics across the US, between 08/2017‐03/2020. Depression and anxiety symptoms were evaluated using the Quick Inventory of Depressive Symptomatology-Self Report 16-item (QIDS) and the Generalized Anxiety Disorder 7-item (GAD-7) scales, respectively. Comparison data sets from patients who did not undergo KIT were obtained from previously published real-world studies. Of 2758 patients treated, 714 and 836 met criteria for analysis of KIT induction and maintenance outcomes, respectively. Patients exhibited significant and concordant reduction in both anxiety and depression symptoms after induction (Cohen's d = -1.17 and d = -1.56, respectively). Compared to two external datasets of KIT-naive depressed patients or patients starting standard antidepressant therapy, KIT patients experienced a significantly greater reduction in depression symptoms at eight weeks (Cohen's d = -1.03 and d = -0.62 respectively). Furthermore, we identified a subpopulation of late-responders. During maintenance, up to a year post-induction, increases in symptoms were minimal. Due to the retrospective nature of the analyses, interpreting this dataset is limited by incomplete patient information and sample attrition. KIT treatment elicited robust symptomatic relief that remained stable up to one year of follow-up. • Depression and anxiety outcomes data for KIT are highly concordant and stable over time. • Eighty percent of the patients had SI pretreatment and 50% had no SI at all after KIT induction. [ABSTRACT FROM AUTHOR]

Although we have effective treatments for depression and anxiety, we lack mechanistic understanding or evidence-based strategies to tailor these treatments in the context of major comorbidities such as obesity. The current feasibility study uses functional neuroimaging and biospecimen data to determine if changes in inflammatory markers, fecal short-chain fatty acids, and neural circuit-based targets can predict depression and anxiety outcomes among participants with comorbid obesity. Blood and stool samples and functional magnetic resonance imaging data were obtained at baseline and 2 months, during the parent ENGAGE-2 trial. From 30 participants with both biospecimen and fMRI data, this subsample study explored the relationship among changes in inflammatory markers and fecal short-chain fatty acids and changes in neural targets, and their joint relationship with depression and anxiety symptoms. Bivariate and partial correlation, canonical correlation, and partial least squares analyses were conducted, with adjustments for age, sex, and treatment group. Initial correlation analyses revealed three inflammatory markers (IL-1RA, IL-6, and TNF-α) and five neural targets (in Negative Affect, Positive Affect, and Default Mode Circuits) with significantly associated changes at 2 months. Partial least squares analyses then showed that changes in IL-1RA and TNF-α and changes in three neural targets (in Negative Affect and Positive Affect Circuits) at 2 months were associated with changes in depression and anxiety symptoms at 6 months. This study sheds light on the plausibility of incorporation of inflammatory and gastrointestinal biomarkers with neural targets as predictors of depression and comorbid anxiety outcomes among patients with obesity. • Changes at 2 months in immune/short-chain fatty acid biomarkers were correlated with changes in specific neural targets. • Correlations were found between Default Mode and Negative/Positive Affect neural targets; and IL-1RA, IL-6, and TNF-α. • Changes in IL-1RA and TNA-α and in Negative/Positive Affect neural targets were predictors of depression and anxiety. [ABSTRACT FROM AUTHOR]

Mental wellbeing and mental illness symptoms are typically conceptualized as opposite ends of a continuum, despite only sharing about a quarter in common variance. We investigated the normative variation in measures of wellbeing and of depression and anxiety in 1486 twins who did not meet clinical criteria for an overt diagnosis. We quantified the shared versus distinct genetic and environmental variance between wellbeing and depression and anxiety symptoms. The majority of participants (93%) reported levels of depression and anxiety symptoms within the healthy range, yet only 23% reported a wellbeing score within the “flourishing” range: the remainder were within the ranges of “moderate” (67%) or “languishing” (10%). In twin models, measures of wellbeing and of depression and anxiety shared 50.09% of variance due to genetic factors and 18.27% due to environmental factors; the rest of the variance was due to unique variation impacting wellbeing or depression and anxiety symptoms. These findings suggest that an absence of clinically-significant symptoms of depression and anxiety does not necessarily indicate that an individual is flourishing. Both unique and shared genetic and environmental factors may determine why some individuals flourish in the absence of symptoms while others do not. [ABSTRACT FROM AUTHOR]

Background Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome. Methods We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization. Results Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures. Conclusions This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD. [ABSTRACT FROM AUTHOR]

Abstract: Background: Antidepressant medication (ADM) is thought to reduce depressive symptoms by altering emotion-generative brain systems. However, it is unknown whether successful ADM treatment is associated with changes in psychobehavioral strategies used to regulate emotions. We examined depressive symptoms and emotion regulation strategies before and after ADM in the international Study to Predict Optimized Treatment in Depression (iSPOT-D). Methods: The study enrolled 1008 adult patients with MDD (18–65 years old) from 18 primary and psychiatric care sites worldwide. Patients were randomly assigned to an 8-week course of escitalopram, sertraline, or venlafaxine-extended-release. We examined whether ADM is associated with changes in suppression, usually associated with maladaptive outcomes, and reappraisal, usually associated with adaptive outcomes. We also tested whether changes in emotion regulation predict changes in depressive symptoms following ADM. Results: We observed more adaptive emotion regulation (decreased use of suppression and increased use of reappraisal) following ADM. Furthermore, the largest improvements in emotion regulation were associated with the best treatment outcomes. Limitations: Because we assessed acute outcomes, it is not yet known if the effects of ADM on emotion regulation would persist over time. Conclusions: ADMs are associated with acute, adaptive changes in the psychobehavioral strategies used to regulate emotions. [Copyright &y& Elsevier]

Background: Psychotherapy is a standard depression treatment; however, determining a patient's prognosis with therapy relies on clinical judgment that is subject to trial-and-error and provider variability.Purpose: To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST).Method: Using data from the treatment arm of 2 randomized trials, ML models were trained and validated on ENGAGE-2 (ClinicalTrials.gov, #NCT03841682) and tested on RAINBOW (ClinicalTrials.gov, #NCT02246413) for predictions at baseline and at 2-months. Primary outcome was depression remission using the Depression Symptom Checklist (SCL-20) score < 0.5 at 6 months. Predictor variables included baseline characteristics (sociodemographic, behavioral, clinical, psychosocial) and intervention engagement through 2-months.Results: Of the 26 candidate variables, 8 for baseline and 11 for 2-months were predictive of depression remission, and used to train the models. The best-performing model predicted remission with an accuracy significantly greater than chance in internal validation using the ENGAGE-2 cohort, at baseline [72.6% (SD = 3.6%), p < 0.0001] and at 2-months [72.3% (5.1%), p < 0.0001], and in external validation with the RAINBOW cohort at baseline [58.3% (0%), p < 0.0001] and at 2-months [62.3% (0%), p < 0.0001]. Model-agnostic explanations highlighted key predictors of depression remission at the cohort and patient levels, including female sex, lower self-reported sleep disturbance, lower sleep-related impairment, and lower negative problem orientation.Conclusions: ML models using clinical and patient-reported data can predict depression remission for patients undergoing PST, affording opportunities for prospective identification of likely responders, and for developing personalized early treatment optimization along the patient care trajectory. [ABSTRACT FROM AUTHOR]

Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making. We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions. In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases—core characteristics that transcend diagnoses—and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205). Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry. [ABSTRACT FROM AUTHOR]

Whilst alterations in emotional face processing, as indicated by event-related potentials (ERPs), are associated with depression and anxiety symptoms in clinical and non-clinical samples, it has remained unclear whether they are related to mental wellbeing. The current study aimed to address this question in a non-clinical sample. The analysis included 402 adult twins from the TWIN-E study. The COMPAS-W and the Depression Anxiety Stress Scale (DASS-42) were used to measure mental wellbeing and depression/anxiety symptoms, respectively. Participants viewed facial expressions under Unmasked (conscious) and Masked (subliminal) conditions while ERPs were recorded. The associations of emotion processing with mental wellbeing and depression/anxiety symptoms were assessed using multivariate linear mixed models. There was a strong association between depression/anxiety symptoms and the N170 amplitude difference for the Fear – Happy contrast in the Masked condition after controlling for wellbeing scores (B = 0.34, p <.001). Specifically, higher depression/anxiety symptoms were associated with a lack of differentiation between fearful and happy faces. No associations were found between emotional face processing and mental wellbeing scores. These results indicate that even within a non-clinical sample, alterations in emotional ERPs, namely the N170, reflect differences in depression/anxiety symptoms rather than differences in wellbeing. Furthermore, this effect was limited to automatic processing, rather than conscious processing of emotional stimuli, suggesting the observed differences apply only to the subconscious pathway. [ABSTRACT FROM AUTHOR]