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Steroid hormone dysregulations have frequently been implicated in posttraumatic stress disorder (PTSD) pathogenesis. However, the translation into naturalistic clinical settings as markers of symptomatology and treatment success remains complex. Particularly, there is little longitudinal data on steroid secretion over the course of interventions. This study examined the potential of long-term steroid hormone secretion assessed in hair as diagnostic and intervention-related biomarkers among medicated, multimorbid inpatients with PTSD. As part of a secondary analysis of a randomized controlled trial, 54 female inpatients with a primary diagnosis of PTSD receiving standardised treatment provided hair samples at pre-treatment, post-treatment, and 3-months follow-up. Cortisol, cortisone, and dehydroepiandrosterone (DHEA) were determined, alongside clinical assessments. Cross-sectional results showed a negative association of pre-treatment DHEA with anxiety symptoms and a trend-level association with lifetime trauma exposure. While inpatients improved in PTSD symptomatology during treatment, neither pre-treatment steroids, nor treatment-induced steroid changes predicted PTSD symptoms at post-treatment or follow-up. The study highlights the challenges of establishing biomarkers in naturalistic clinical populations. While the association of attenuated DHEA with anxiety symptoms warrants further exploration, our data points towards the potential necessity of patient sub-sample selection to understand, and in the long run clinically target, the endocrine mechanisms in PTSD.

Background: While available posttraumatic stress disorder (PTSD) treatments are generally successful, 30–40% of patients show limited improvement. The endocannabinoid system may play a role in the aftermath of trauma, in PTSD, and in extinction processes. Therefore, this secondary analysis of a randomized-controlled trial including PTSD inpatients over the course of trauma-focused treatment investigated whether a dysregulated endocannabinoid system is associated with symptom severity and treatment response. Methods: Fifty-four female PTSD inpatients provided hair samples and completed psychometric questionnaires at admission, discharge, and 3-month follow-up. Endocannabinoid (EC: AEA, 1AG/2AG) and N-acylethanolamine (NAE: SEA, OEA, PEA) concentrations were measured in scalp-near 3-cm hair segments. Results: At admission, higher hair AEA correlated with lower depressive and anxiety and higher PTSD symptoms (when controlling for depressive symptoms). Hair NAEs associated with more traumatic experiences. PTSD symptoms improved across treatment, remaining stable at follow-up, but were predicted neither by pre-treatment hair ECs/NAEs nor their changes. Subgroup analyses with those who received exposure treatment tentatively indicated a trend linking higher hair PEA and OEA to lower PTSD symptoms at discharge. Conclusions: Taken together, hair ECs/NAEs may relate differentially to trauma exposure and symptom severity, but not to PTSD inpatient treatment response. Larger-scale research is necessary to confirm this.

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.