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Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Anxiety and related disorders are a significant public-health burden with rising prevalence in the wake of the COVID-19 pandemic. As demand for effective anxiety treatment increases, so too does the need for strategies to bolster treatment outcomes. Research on the mechanisms of exposure therapy, the frontline behavioral treatment, will be critically important for optimizing clinical outcomes. We outline an initial agenda for future research on the mechanisms of change of exposure therapy, developed in collaboration with a large international team of researchers through the Exposure Therapy Consortium. Key questions and recommendations for future research focus on four priority areas: conceptualization, measurement, study design/analysis, and individual/contextual differences. Rising to the challenge of addressing these questions will require coordinated action and availability of centralized tools that can be used across trials, settings, and research groups.

Estimates of the causal effects of hormonal contraceptives on psychological outcomes are likely distorted by contraceptive discontinuation (including starting or stopping a method and switching between methods) because of side effects. Few studies examine contraceptive discontinuation and most are based on correlational cross-sectional data. The literature suggests method- and sexuality-related variables as predictors of contraceptive discontinuation. The current study therefore analyzed type of contraceptive method, contraceptive satisfaction and usage duration, as well as sexual frequency and sexual satisfaction as predictors of contraceptive switches, which is one form of contraceptive discontinuation. In addition, we examined the sensitivity of the reported effects to unobserved selection effects. Analyses were based on longitudinal data from the Continuity and Change in Contraceptive Use Study with a sample of up to 1,993 women. We found substantial and robust effects of contraceptive method and contraceptive satisfaction on contraceptive switches, but no clear evidence on interaction effects between contraceptive method and contraceptive satisfaction. Furthermore, we found no effects of sexual satisfaction or frequency across all analyses.

Cognitive bias modification (CBM) has evolved from an experimental method testing cognitive mechanisms of psychopathology to a promising tool for accessible digital mental health care. While we are still discovering the conditions under which clinically relevant effects occur, the dire need for accessible, effective, and low-cost mental health tools underscores the need for implementation where such tools are available. Providing our expert opinion as Association for Cognitive Bias Modification members, we first discuss the readiness of different CBM approaches for clinical implementation, then discuss key considerations with regard to implementation. Evidence is robust for approach bias modification as an adjunctive intervention for alcohol use disorders and interpretation bias modification as a stand-alone intervention for anxiety disorders. Theoretical predictions regarding the mechanisms by which bias and symptom change occur await further testing. We propose that CBM interventions with demonstrated efficacy should be provided to the targeted populations. To facilitate this, we set a research agenda based on implementation frameworks, which includes feasibility and acceptability testing, co-creation with end-users, and collaboration with industry partners.