Sensitivity, specificity, and predictive power of the "Brief Risk-resilience Index for SCreening," a brief pan-diagnostic web screen for emotional health

Attributional negativity bias and acute stress disorder symptoms mediate the association between trauma history and future posttraumatic stress disorder

Individuals who have experienced more trauma throughout their life have a heightened risk of developing posttraumatic stress disorder (PTSD) following injury. Although trauma history cannot be retroactively modified, identifying the mechanism(s) by which preinjury life events influence future PTSD symptoms may help clinicians mitigate the detrimental effects of past adversity. The current study proposed attributional negativity bias, the tendency to perceive stimuli/events as negative, as a potential intermediary in PTSD development. We hypothesized an association between trauma history and PTSD symptom severity following a new index trauma via heightened negativity bias and acute stress disorder (ASD) symptoms. Recent trauma survivors (N =189, 55.5% women, 58.7% African American/Black) completed assessments of ASD, negativity bias, and lifetime trauma 2-weeks postinjury; PTSD symptoms were assessed 6 months later. A parallel mediation model was tested with bootstrapping (10,000 resamples). Both negativity bias, Path b1 : β = -.24, t(187) = -2.88, p = .004, and ASD symptoms, Path b2 : β = .30, t(187) = 3.71, p < .001, fully mediated the association between trauma history and 6-month PTSD symptoms, full model: F(6, 182) = 10.95, p < .001, R 2 = .27; Path c': β = .04, t(187) = 0.54, p = .587. These results suggest that negativity bias may reflect an individual cognitive difference that can be further activated by acute trauma. Moreover, negativity bias may be an important, modifiable treatment target, and interventions addressing both acute symptoms and negativity bias in the early posttrauma period may weaken the link between trauma history and new-onset PTSD.

Association of COVID-19 impact with outcomes of an integrated obesity and depression intervention: Posthoc analysis of an RCT

OBJECTIVE

To examine the association between COVID-19 impact and clinical outcomes of an integrated collaborative care intervention for adults with obesity and comorbid depression.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

The ENGAGE-2 study: Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes in a randomized controlled trial (Phase 2)

Despite evidence for effective integrated behavior therapy for treating comorbid obesity and depression, treatment response is highly variable and the underlying neurobiological mechanisms remain unknown. This hampers efforts to identify mechanistic targets in order to optimize treatment precision and potency. Funded within the NIH Science of Behavior Change (SOBC) Research Network, the 2-phased ENGAGE research project applies an experimental precision medicine approach to address this gap. The Phase 1 study focused on demonstrating technical feasibility, target engagement and potential neural mechanisms of responses to an integrated behavior therapy. This therapy combines a video-based behavioral weight loss program and problem-solving therapy for depression, with as-needed intensification of antidepressant medications, and its clinical effectiveness was demonstrated within a parent randomized clinical trial. Here, we describe the ENGAGE Phase 2 (ENGAGE-2) study protocol which builds on Phase 1 in 2 ways: (1) pilot testing of an motivational interviewing-enhanced, integrated behavior therapy in an independent, primarily minority patient sample, and (2) evaluation of a priori defined neural targets, specifically the negative affect (threat and sadness) circuits which demonstrated engagement and malleability in Phase 1, as mediators of therapeutic outcomes. Additionally, the Phase 2 study includes a conceptual and methodological extension to explore the role of microbiome-gut-brain and systemic immunological pathways in integrated behavioral treatment of obesity and depression. This protocol paper documents the conceptualization, design and the transdisciplinary methodologies in ENGAGE-2, which can inform future clinical and translational research in experimental precision medicine for behavior change and chronic disease management. Trial registration: ClinicalTrials.gov #NCT 03,841,682.

Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome: an iSPOT-D report

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.

Development of a computerized adaptive screening tool for overall psychopathology ("p")

A substantial body of work supports the existence of a general psychopathology factor ("p"). Psychometrically, this is important because it implies that there is a psychological phenomenon (overall psychopathology) that can be measured and potentially used in clinical research or treatment. The present study aimed to construct, calibrate, and begin to validate a computerized adaptive (CAT) screener for "p". In a large community sample (N = 4544; age 11-21), we modeled 114 clinical items using a bifactor multidimensional item response theory (MIRT) model and constructed a fully functional (and public) CAT for assessing "p" called the Overall mental illness (OMI) screener. In a random, non-overlapping sample (N = 1019) with extended phenotyping (neuroimaging) from the same community cohort, adaptive versions of the OMI screener (10-, 20-, and 40-item) were simulated and compared to the full 114-item test in their ability to predict demographic characteristics, common mental disorders, and brain parameters. The OMI screener performed almost as well as the full test, despite being only a small fraction of the length. For prediction of 13 mental disorders, the mid-length (20-item) adaptive version showed mean area under the receiver operating characteristic curve of 0.76, compared to 0.79 for the full version. For prediction of brain parameters, mean absolute standardized relationship was 0.06 for the 20-item adaptive version, compared to 0.07 for the full form. This brief, public tool may facilitate the rapid and accurate measurement of overall psychopathology in large-scale studies and in clinical practice.

Characterising anxiety in major depressive disorder and its use in predicting antidepressant treatment outcome: An iSPOT-D report

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.

Increased chronic stress predicts greater emotional negativity bias and poorer social skills but not cognitive functioning in healthy adults

Background and Objectives: Chronically stressed individuals report deficits spanning cognitive and emotional functioning. However, limitations to clinical populations and measures of stress have impeded the generalisability and scope of results. This study investigated whether chronic stress predicted cognitive and emotional functioning, and whether these relationships differed between males and females, in a large representative sample of healthy participants. Design: Cross-sectional study. Method: 1883 healthy adults sampled from the Brain Resource International Database reported stress using the 21-item Depression Anxiety Stress Scales. Participants then completed a cognitive and emotional assessment battery (IntegNeuro), as well as questionnaires related to sleep, emotional functioning, and self-regulation. Results: In contrast to previously reported results, chronic stress did not predict cognitive functioning. However, higher stress predicted a greater negativity bias and poorer social skills, confirming previous research identifying these links. Conclusions: Cognitive deficits related to stress are absent in healthy participants when stress is measured using the 21-items Depression Anxiety Stress Scales. Identifying how chronic stress is associated with aspects of emotional functioning can lead to personalized interventions for individuals to better manage the negative outcomes resulting from stress.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders

IMPORTANCE

The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.

OBJECTIVE

To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.

MAIN OUTCOMES AND MEASURES

We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.

RESULTS

Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171).

CONCLUSIONS AND RELEVANCE

These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model

Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases.

Impairment and distress patterns distinguishing the melancholic depression subtype: an iSPOT-D report

BACKGROUND

This study seeks to provide a comprehensive and systematic evaluation of baseline clinical and psychological features and treatment response characteristics that differentiate Major Depressive Disorder (MDD) outpatients with and without melancholic features. Reflecting the emphasis in DSM-5, we also include impairment and distress.

METHODS

Participants were assessed pre-treatment on clinical features (severity, risk factors, comorbid conditions, illness course), psychological profile (personality, emotion regulation), functional capacity (social and occupational function, quality of life) and distress/coping (negativity bias, emotional resilience, social skills, satisfaction with life). Participants were randomized to sertraline, escitalopram or venlafaxine extended-release and re-assessed post-treatment at 8 weeks regarding remission, response, and change in impairment and distress.

RESULTS

Patients with melancholic features (n=339; 33.7%) were distinguished clinically from non-melancholics by more severe depressive symptoms and greater exposure to abuse in childhood. Psychologically, melancholic patients were defined by introversion, and a greater use of suppression to regulate negative emotion. Melancholics also had poorer capacity for social and occupational function, and physical and psychological quality of life, along with poorer coping, reflected in less emotional resilience and capacity for social skills. Post-treatment, melancholic patients had lower remission and response, but some of this effect was due to the more severe symptoms pre-treatment. The distress/coping outcome measure of capacity for social skills remained significantly lower for melancholic participants.

LIMITATIONS

Due to the cross-sectional nature of this study, causal pathways cannot be concluded.

CONCLUSIONS

Findings provide new insights into a melancholic profile of reduced ability to function interpersonally or effectively deal with one׳s emotions. This distinctly poorer capacity for social skills remained post-treatment. The pre-treatment profile may account for some of the difficulty in achieving remission or response with treatment.

A model for streamlining psychotherapy in the RDoC era: the example of 'Engage'

A critical task for psychotherapy research is to create treatments that can be used by community clinicians. Streamlining of psychotherapies is a necessary first step for this purpose. We suggest that neurobiological knowledge has reached the point of providing biologically meaningful behavioral targets, thus guiding the development of effective, simplified psychotherapies. This view is supported by the Research Domain Criteria (RDoC) Project, which reflects the field's consensus and recognizes the readiness of neurobiology to guide research in treatment development. 'Engage' is an example of such a streamlined therapy. It targets behavioral domains of late-life depression grounded on RDoC constructs using efficacious behavioral strategies selected for their simplicity. 'Reward exposure' targeting the behavioral expression of positive valence systems' dysfunction is the principal therapeutic vehicle of 'Engage'. Its first three sessions consist of direct 'reward exposure', but the therapists search for barriers in three behavioral domains, that is, 'negativity bias' (negative valence), 'apathy' (arousal) and 'emotional dysregulation' (cognitive control), and add strategies targeting these domains when needed. The end result is a structured, stepped approach using neurobiological constructs as targets and as a guide to personalization. We argue that the 'reduction' process needed in order to arrive to simplified effective therapies can be achieved in three steps: (1) identify RDoC constructs driving the syndrome's psychopathology; (2) create a structured intervention utilizing behavioral and ecosystem modification techniques targeting behaviors related to these constructs; (3) examine whether the efficacy of the new intervention is mediated by change in behaviors related to the targeted RDoC constructs.

Using multiple methods to characterize the phenotype of individuals with a family history of major depressive disorder

BACKGROUND

Unaffected relatives (URs) of individuals with major depressive disorder (MDD) are biologically more vulnerable to depression. We compare healthy URs and controls at the level of phenotype (symptoms and functioning) and endophenotype (negative emotion bias), and further investigate the interrelation between these and the contribution of environmental early life stress.

METHODS

URs (n=101), identified using Family History Screen interview methods and matched controls completed written and interview questions assessing symptoms of depression and anxiety, negative cognitive style, life functioning and early life stress. Biases in emotion processing were measured using a facial expression of emotion identification paradigm.

RESULTS

Compared to controls, URs reported higher levels of depression and anxiety, a stronger negative cognitive bias, and poorer functioning and lower satisfaction with life. URs were slower to correctly identify fear and sad facial expressions. A slower response time to identify sad faces was correlated with lower quality of life in the social domain. Early life stress (ELS) did not contribute significantly to any outcome.

LIMITATIONS

The methodology relies on accurate reporting of participants' own psychiatric history and that of their family members. The degree of vulnerability varies among URs.

CONCLUSIONS

A family history of depression accounts for subtle differences in symptom levels and functioning without a necessary role of ELS. A negative emotion bias in processing emotion may be one vulnerability marker for MDD. Biological markers may affect functioning measures before symptoms at the level of experience.