Studies investigating the long-term effect of attention bias modification (ABM) in clinical samples are lacking. This study investigates the 6-months follow-up effect of ABM on depressive symptoms in participant with major depressive disorder with and without comorbid disorders.

We conducted a double-blind randomized sham-controlled trial in 101 participants between 19 November 2019, and 17 August 2021. Follow-up ended 3 April 2022. Participants were allocated to ABM or sham condition twice daily for 14 consecutive days. Primary outcomes were the total score on the Beck Depression Inventory-II (BDI-II) at 6 months, mean Brief State Rumination Inventory (BSRI) score post-treatment and reduction in BSRI post-treatment. Secondary outcome was change in attentional bias (AB). The trial was preregistered in ClinicalTrials.gov (#NCT 04137367).

A total of 118 patients aged 18–65 years were assessed for eligibility, and 101 were randomized and subjected to intention-to-treat analyses. At 6 months, ABM had no effect on depression and anxiety compared to a sham condition. While rumination decreased during the intervention, there was no effect of condition on rumination and AB. Predictor analysis did not reveal differences between participants with ongoing major depressive episode or comorbid anxiety.

Compared to sham training, there was no effect of ABM on depressive symptoms at 6-months follow-up. Since the intervention failed at modifying AB, it is unclear whether changes in AB are related to long-term outcomes.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Childhood maltreatment (CM) and exposure to community violence (ETV) are correlated with physical/mental health and psychosocial problems. Typically, CM and ETV are examined separately, by subtypes within category, or collapsed across both into one category of adversity. Consequently, research is limited in identifying subgroups of individuals with different amounts of exposure to both CM and ETV. Accordingly, we lack sufficient understanding of the extent to which problems associated with CM and ETV vary based on the amount (i.e. dose) of exposure to both of these experiences.

We used 20 samples (28,300 individuals) to estimate person-centered profiles of CM and ETV occurrence and co-occurrence within each sample. An individual data multilevel meta-analytic framework was used to determine the average effect size across samples for different profiles and conditional probability correlations within sociodemographic, neighborhood, health, mental health, and psychosocial domains.

The profile characterized by high levels of CM and high levels of ETV correlated with stressful life events, depression and anxiety symptoms, and general indicators of externalizing behaviors. CM predominant profiles were associated with mental health diagnoses and treatment. ETV predominant profiles associated with risk-taking/violent behavior and neighborhood-level disadvantage. However, nuance based on the dose of CM or ETV was evident.

It is important to identify subgroups based on the amount of exposure to CM and ETV. These subgroups have differential relationships with correlates across domains. Greater delineation and description of the lived experience will allow for more precision in addressing the burden of childhood adversity.

This is the first report on the association between trauma exposure and depression from the Advancing Understanding of RecOvery afteR traumA(AURORA) multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience.

We focus on participants presenting at EDs after a motor vehicle collision (MVC), which characterizes most AURORA participants, and examine associations of participant socio-demographics and MVC characteristics with 8-week depression as mediated through peritraumatic symptoms and 2-week depression.

Eight-week depression prevalence was relatively high (27.8%) and associated with several MVC characteristics (being passenger v. driver; injuries to other people). Peritraumatic distress was associated with 2-week but not 8-week depression. Most of these associations held when controlling for peritraumatic symptoms and, to a lesser degree, depressive symptoms at 2-weeks post-trauma.

These observations, coupled with substantial variation in the relative strength of the mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated in more in-depth analyses of the rich and evolving AURORA database to find new targets for intervention and new tools for risk-based stratification following trauma exposure.