Improving Exposure Therapy: Rationale and Design of an International Consortium.

The Exposure Therapy Consortium (ETC) was established to advance the science and practice of exposure therapy. To encourage participation from researchers and clinicians, this article describes the organizational structure and activities of the ETC. Initial research working group experiences and a proof-of-principle study underscore the potential of team science and larger-scale collaborative research in this area. Clinical working groups have begun to identify opportunities to enhance access to helpful resources for implementing exposure therapy effectively. This article discusses directions for expanding the consortium's activities and its impact on a global scale.

Isradipine augmentation of virtual reality cue exposure therapy for tobacco craving: a triple-blind randomized controlled trial.

Preclinical research with rodents suggests that the L-type calcium channel blocker isradipine can enhance long-term extinction of conditioned place preference for addictive substances when it is administered in conjunction with extinction training. Although isradipine alone, which is FDA-approved for hypertension, has not shown a direct effect on craving in human drug users, its potential to augment behavioral treatments designed to reduce craving remains unknown. We conducted a triple-blind, randomized placebo-controlled pilot clinical trial of isradipine combined with a novel virtual reality cue exposure therapy (VR-CET) approach with multimodal cues that targeted craving. After 24 hours of abstinence, 78 adults with an ongoing history of daily cigarette use received isradipine (n = 40) or placebo (n = 38) and reported craving levels after each of 10 trials of VR-CET. Consistent with pre-registered hypotheses, the isradipine group had significantly lower mean craving across cue exposure trials at the medication-free 24-hour follow-up (d = -0.42, p = 0.046). There were no serious adverse events; however, side effects such as headache and dizziness occurred more frequently in the isradipine group. The findings of the current study support follow-up clinical trials that specifically test the efficacy of isradipine-augmented VR-CET for reducing smoking relapse rates after an initial quit attempt. clinicaltrials.gov: NCT03083353.

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.

Importance: Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective: To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants: This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures: Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results: The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance: In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk.

BACKGROUND: Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI). METHODS: We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA. RESULTS: In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI. CONCLUSIONS: PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA. LIMITATIONS: At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.

Project harmony: A systematic review and network meta-analysis of psychotherapy and pharmacologic trials for comorbid posttraumatic stress, alcohol, and other drug use disorders.

We conducted a systematic review and network meta-analyses (NMA) of psychotherapy and pharmacologic treatments for individuals with co-occurring posttraumatic stress disorder (PTSD) and alcohol or other drug use disorder (AOD). A comprehensive search spanning 1995-2019 yielded a pool of 39 studies for systematic review, including 24 randomized controlled trials for the NMA. Study interventions were grouped by target of treatment (PTSD + AOD, PTSD-only, and AOD-only) and approach (psychotherapy or medication). Standardized mean differences (SMD) from the NMA yielded evidence that at the end of treatment, integrated, trauma-focused therapy for PTSD + AOD was more effective at reducing PTSD symptoms than integrated, non-trauma-focused therapy (SMD = -0.30), AOD-focused psychotherapy (SMD = -0.29), and other control psychotherapies (SMD = -0.43). End-of-treatment alcohol use severity was less for AOD medication compared to placebo medication (SMD = -0.36) and trauma-focused therapy for PTSD + placebo medication (SMD = -0.67), and less for trauma-focused psychotherapy + AOD medication compared to PTSD medication (SMD = -0.53), placebo medication (SMD = -0.50), and trauma-focused psychotherapy + placebo medication (SMD = -0.81). Key limitations include the small number of studies in the NMA for pharmacologic treatments and the lack of demographic diversity apparent in the existing literature. Findings suggest room for new studies that can address limitations in study sample composition, sample sizes, retention, and apply new techniques for conducting comparative effectiveness in PTSD + AOD treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis.

A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.

Using pre-treatment de novo threat conditioning outcomes to predict treatment response to DCS augmentation of exposure-based CBT.

BACKGROUND: Over a decade and a half of research has resulted in inconsistent evidence for the efficacy of d-cycloserine (DCS), a partial glutamatergic N-methyl-D-aspartate agonist, for augmenting exposure-based cognitive behavioral therapy (CBT) for anxiety- and fear-based disorders. These variable findings have motivated the search for moderators of DCS augmentation efficacy. METHODS: In this secondary analysis of a previous randomized clinical trial, we evaluated the value of de novo threat conditioning outcomes-degree of threat acquisition, extinction, and extinction retention-for predicting treatment response to exposure-based CBT for social anxiety disorder, applied with and without DCS augmentation in a sample of 59 outpatients. RESULTS: We found that average differential skin conductance response (SCR) during extinction and extinction retention significantly moderated the prediction of clinical response to DCS: participants with poorer extinction and extinction retention showed relatively improved treatment response with DCS. No such effects were found for expectancy ratings, consistent with accounts of DCS selectively aiding lower-order but not higher-order extinction learning. CONCLUSIONS: These findings provide support for extinction and extinction retention outcomes from threat conditioning as potential pre-treatment biomarkers for DCS augmentation benefits. Independent of DCS augmentation, the current study did not support threat conditioning outcomes as useful for predicting response to exposure-based CBT.

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints.

BACKGROUND: Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use. METHOD: In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints. RESULTS: After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07-0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impacts. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints. CONCLUSION: Alcohol, nicotine, and cannabis use had unique associations to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links.

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment.

Importance: Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective: To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants: This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures: Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results: This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance: In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.

Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.

Recently developed measures of genetic liability to suicide attempt may convey unique information regarding an individual's risk of suicidal behavior. We calculated a polygenic risk score for suicide attempt (SA-PRS) for soldiers of European ancestry who participated in the Army STARRS New Soldier Study (NSS; n = 6573) or Pre/Post Deployment Study (PPDS; n = 4900). Multivariable logistic regression models were fit within each sample to estimate the association of SA-PRS with lifetime suicide attempt (LSA), and to examine whether SA-PRS displayed additive or interactive effects with environmental and behavioral risk/protective factors (lifetime trauma burden, childhood maltreatment, negative urgency impulsivity, social network size, perceived mattering, and dispositional optimism). Age, sex, and within-ancestry variation were included as covariates. Observed prevalence of LSA was 6.3% and 4.2% in the NSS and PPDS samples, respectively. In the NSS model, SA-PRS and environmental/behavioral factors displayed strictly additive effects on odds of LSA. Results indicated an estimated 21% increase in odds of LSA per 1 SD increase in SA-PRS [adjusted odds ratio (AOR; 95% CI) = 1.21 (1.09-1.35)]. In PPDS, the effect of SA-PRS varied by reports of optimism [AOR = 0.85 (0.74-0.98) for SA-PRS x optimism effect]. Individuals reporting low and average optimism had 37% and 16% increased odds of LSA per 1 SD increase in SA-PRS, respectively, whereas SA-PRS was not associated with LSA in those reporting high optimism. Overall, results suggested the SA-PRS had predictive value over and above several environmental and behavioral risk factors for LSA. Moreover, elevated SA-PRS may be more concerning in the presence of environmental and behavioral risk factors (e.g., high trauma burden; low optimism). Given the relatively small effect magnitudes, the cost and incremental benefits of utilizing SA-PRS for risk targeting must also be considered in future work.

Associations of polygenic risk scores with posttraumatic stress symptom trajectories following combat deployment.

BACKGROUND: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. METHOD: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. RESULTS: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06-1.43) and 1.18 (1.02-1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01-1.25) and 1.16 (1.04-1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03-1.31)]. No other associations were statistically significant. CONCLUSIONS: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.

Performance of a Prediction Model of Suicide Attempts Across Race and Ethnicity.

This study examines whether race disparities exist in the prediction of suicide attempts and if have they have detrimental effects on individuals and health care systems.

Posttraumatic stress disorder symptom trajectories in a 16-month COVID-19 pandemic period.

COVID-19 pandemic presents an unheralded opportunity to better understand trajectories of posttraumatic stress disorder (PTSD) symptoms across a prolonged period of social disruption and stress. We tracked PTSD symptoms among trauma-exposed individuals in the United States and sought to identify population-based variability in PTSD symptom trajectories and understand what, if any, early pandemic experiences predicted membership in one trajectory versus others. As part of a longitudinal study of U.S. residents during the pandemic, participants who reported at least one potentially traumatic experience in their lifetime (N = 1,206) at Wave 1 (April 2020) were included in the current study. PTSD symptoms were assessed using the PCL-5 at four time points extending to July 2021. Latent growth mixture modeling was used to identify heterogeneous symptom trajectories. Trajectory membership was regressed on experiences from the early stage of the pandemic as measured using the Epidemic-Pandemic Impacts Inventory in a model that controlled for variables with documented associations to PTSD trajectories, including age, sex, income, and trauma history. Four trajectories were identified, categorized as resilient (73.0%), recurring (13.3%), recovering (8.3%), and chronic (5.5%). Emotional and physical health problems and positive changes associated with the early phase of the pandemic were each significant predictors of trajectory membership over and above all other variables in the model. Predictors primarily differentiated the resilient trajectory from each of the other three trajectories. Distinct PTSD symptom trajectories during the COVID-19 pandemic suggest a need for targeted efforts to help individuals at most risk for ongoing distress.

Singular and combined effects of transcranial infrared laser stimulation and exposure therapy on pathological fear: a randomized clinical trial.

BACKGROUND: Preclinical findings suggest that transcranial infrared laser stimulation (TILS) improves fear extinction learning and cognitive function by enhancing prefrontal cortex (PFC) oxygen metabolism. These findings prompted our investigation of treating pathological fear using this non-invasive stimulation approach either alone to the dorsolateral PFC (dlPFC), or to the ventromedial PFC (vmPFC) in combination with exposure therapy. METHODS: Volunteers with pathological fear of either enclosed spaces, contamination, public speaking, or anxiety-related bodily sensations were recruited for this randomized, single-blind, sham-controlled trial with four arms: (a) Exposure + TILS_vmPFC (n = 29), (b) Exposure + sham TILS_vmPFC (n = 29), (c) TILS_dlPFC alone (n = 26), or (d) Sham TILS _dlPFC alone (n = 28). Post-treatment assessments occurred immediately following treatment. Follow-up assessments occurred 2 weeks after treatment. RESULTS: A total of 112 participants were randomized [age range: 18-63 years; 96 females (85.71%)]. Significant interactions of Group × Time and Group × Context indicated differential treatment effects on retention (i.e. between time-points, averaged across contexts) and on generalization (i.e. between contexts, averaged across time-points), respectively. Among the monotherapies, TILS_dlPFC outperformed SHAM_dlPFC in the initial context, b = -13.44, 95% CI (-25.73 to -1.15), p = 0.03. Among the combined treatments, differences between EX + TILS_vmPFC and EX + SHAM_vmPFC were non-significant across all contrasts. CONCLUSIONS: TILS to the dlPFC, one of the PFC regions implicated in emotion regulation, resulted in a context-specific benefit as a monotherapy for reducing fear. Contrary to prediction, TILS to the vmPFC, a region implicated in fear extinction memory consolidation, did not enhance exposure therapy outcome.

Comparing the effectiveness of a brief intervention to reduce unhealthy alcohol use among adult primary care patients with and without depression: A machine learning approach with augmented inverse probability weighting.

BACKGROUND: The combination of unhealthy alcohol use and depression is associated with adverse outcomes including higher rates of alcohol use disorder and poorer depression course. Therefore, addressing alcohol use among individuals with depression may have a substantial public health impact. We compared the effectiveness of a brief intervention (BI) for unhealthy alcohol use among patients with and without depression. METHOD: This observational study included 312,056 adult primary care patients at Kaiser Permanente Northern California who screened positive for unhealthy drinking between 2014 and 2017. Approximately half (48%) received a BI for alcohol use and 9% had depression. We examined 12-month changes in heavy drinking days in the previous three months, drinking days per week, drinks per drinking day, and drinks per week. Machine learning was used to estimate BI propensity, follow-up participation, and alcohol outcomes for an augmented inverse probability weighting (AIPW) estimator of the average treatment (BI) effect. This approach does not depend on the strong parametric assumptions of traditional logistic regression, making it more robust to model misspecification. RESULTS: BI had a significant effect on each alcohol use outcome in the non-depressed subgroup (-0.41 to -0.05, all ps < .003), but not in the depressed subgroup (-0.33 to -0.01, all ps > .28). However, differences between subgroups were nonsignificant (0.00 to 0.11, all ps > .44). CONCLUSION: On average, BI is an effective approach to reducing unhealthy drinking, but more research is necessary to understand its impact on patients with depression. AIPW with machine learning provides a robust method for comparing intervention effectiveness across subgroups.

Use of Cannabidiol (CBD) oil in the treatment of PTSD: Study design and rationale for a placebo-controlled randomized clinical trial.

BACKGROUND: The burden of illness for PTSD is staggering and confers significant interference in work, social functioning, as well as increased risk for other physical and mental health problems. Recently, there's been considerable attention paid to the potential therapeutic use of cannabidiol (CBD) products in the treatment of a variety of physical and mental health problems. The endocannabinoid system (ECS) is a logical therapeutic target for combating PTSD and other fear-based disorders given that cannabinoid receptors and other molecular mediators crucial for ECS signaling are richly expressed in a variety of brain regions that govern the regulation of learned fear and defensive behavior. METHODS: This is an 8-week single-site Phase II randomized double-blind placebo-controlled fixed dose clinical trial. Participants recruited throughout the United States (N = 150) meeting DSM-5 criteria for posttraumatic stress disorder are randomly assigned to one of three treatment arms: (a) 300 mg CBD Isolate; (b) 300 mg CBD Broad Spectrum; and (c) Placebo oil. The primary outcome is PTSD symptom severity as indexed by the PTSD Checklist for DSM-5 (PCL-5) assessed at post treatment (Week 9) and follow-up (Week 13). Secondary outcomes including patient-rated depression, overall disability, anxiety, quality of life, and alcohol use are assessed weekly throughout the trial. Safety and CBD adherence are assessed daily throughout the trial. CONCLUSION: This is the first placebo-controlled clinical trial investigating (a) CBD for the treatment of PTSD; and (b) the first study to test the relative efficacy of CBD Isolate vs CBD Broad Spectrum. Trial registration ClinicalTrials.gov registered (12/12/2019), trial identifier NCT04197102. PROTOCOL VERSION: issued 08/04/2022, protocol amendment number #2019-05-0123.

Impact of cannabidiol-rich hemp extract oil on reconsolidation disruption of naturalistic interoceptive aversive memory in humans: Protocol for a randomized clinical trial.

BACKGROUND: Preclinical experiments with rodents demonstrate that cannabidiol (CBD), the non-psychotomimetic constituent of the Cannabis sativa plant, disrupts reconsolidation of aversive memories conditioned in the laboratory when administered within the memory reconsolidation window (< 6 h. post-retrieval) by indirectly activating cannabinoid type-1 (CB1) receptors in the dorsal anterior cingulate cortex (dACC). Based on these findings, we aim to test whether administration of 300 mg CBD-rich hemp extract oil following fear reactivation of an aversive interoceptive threat memory can disrupt reconsolidation of naturalistic aversive memories in humans. More specifically, naturalistic interoceptive aversive memories, a form of transdiagnostic fear memory that contributes to the pathogenesis of fear-related disorders such as panic disorder, posttraumatic stress disorder (PTSD), and illness anxiety disorder. METHODS: For this proof-of-concept, placebo-controlled double-blind trial, volunteers (n = 99) reporting elevated fears of somatic sensations will be stratified on biological sex and randomized to one of three intervention arms: (a). CBD-rich oil administered within the reconsolidation window, (b) Placebo oil administered within the reconsolidation window; or (c) CBD-rich oil administered outside of the reconsolidation window. Change in emotional reactivity to a 35% CO2 challenge from baseline to two-week follow-up will serve as our primary outcome. CONCLUSION: Study findings may contribute towards the development of a novel brief transdiagnostic intervention guided by reconsolidation theory for individuals prone to fear-related psychiatric disorders. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04726475.

Frustrative nonreward and cannabinoid receptors: Chronic (but not acute) WIN 55,212-2 treatment increased resistance to change in two reward downshift tasks.

Assessing the role of cannabinoid (CB) receptors in behavior is relevant given the trend toward the legalization of medicinal and recreational marijuana. The present research aims at bridging a gap in our understanding of CB-receptor function in animal models of frustrative nonreward. These experiments were designed to (1) determine the effects of chronic administration of the nonselective CB1-receptor agonist WIN 55,212-2 (WIN) on reward downshift in rats and (2) determine whether the effects of chronic WIN were reducible to acute effects. In Experiment 1, chronic WIN (7 daily injections, 10 mg/kg, ip) accelerated the recovery of consummatory behavior after a 32-to-4% sucrose downshift relative to vehicle controls. In addition, chronic WIN eliminated the preference for an unshifted lever when the other lever was subject to a 12-to-2 pellet downshift in free-choice trials, but only in animals with previous experience with a sucrose downshift. In Experiment 2, acute WIN (1 mg/kg, ip) reduced consummatory behavior, but did not affect recovery from a 32-to-4% sucrose downshift. The antagonist SR 141716A (3 mg/kg, ip) also failed to interfere with recovery after the sucrose downshift. In Experiment 3, acute WIN administration (1 mg/kg, ip) did not affect free-choice behavior after a pellet downshift, although it reduced lever pressing and increased magazine entries relative to vehicle controls. The effects of chronic WIN on frustrative nonreward were not reducible to acute effects of the drug. Chronic WIN treatment in rats, like chronic marijuana use in humans, seems to increase resistance to the effects of frustrative nonreward.

Distinct trajectories of depression symptoms in early and middle adolescence: Preliminary evidence from longitudinal network analysis.

Adolescent depression is a clinically relevant concern that has major implications for mental and physical health. The trajectory of depressive symptoms among adolescents suggests that there is likely variability throughout this developmental period. The aim of the study was to assess the longitudinal relationship between individual symptoms of depression among early and middle adolescents to provide guidance for future research investigating targeted intervention efforts. Data were extracted from electronic medical records (2015-2017) from a pediatric primary care clinic in an urban setting. Cross-Lagged Panel Network analysis was used to evaluate symptoms of depression measured with the Patient Health Questionnaire (PHQ-9) measured twice over a 1-year period among early adolescents (ages 11-13 years; n = 309) and middle adolescents (ages 14-16 years; n = 255). The sample was predominantly Hispanic (90%) and 56% female. The analyses highlighted key differences and similarities between early and middle adolescence, largely focused on the role of suicidal ideation and tightly linked with feelings of failure and appetitive disturbance. In early adolescence suicidal ideation was highly likely to lead to other symptoms. In middle adolescence, however, suicidal ideation no longer had connections to other symptoms and instead the strongest connections were toward suicidal ideation. Interestingly, across both early and middle adolescence feelings of failure and appetitive disturbance were highly likely to lead to suicidal ideation. These exploratory findings highlight several longitudinal associations between early and middle adolescence that provide insight into differences and similarities regarding how symptoms might progress within those developmental periods. Taken together these results can provide direction for future research to evaluate brief, targeted interventions for adolescents.

Exploratory and Confirmatory Bayesian Networks Identify the Central Role of Non-judging in Symptoms of Depression.

OBJECTIVES: Depression is a highly heterogeneous disorder, and meta-analyses of mindfulness-based interventions show moderate efficacy for reducing depressive symptoms. However, the mechanisms governing their efficacy remain unclear, highlighting the need for hypothesis-generating analyses to guide future research. METHODS: We used Bayesian network analysis in three cross-sectional samples (N = 1135) of undergraduates and participants from the community to identify links between individual symptoms of depression and specific facets of mindfulness. In two exploratory studies, we assessed depression using the Patient Health Questionnaire (n = 384) or the Depression Anxiety and Stress Scale (n = 350) and mindfulness using the Five-Facet Mindfulness Scale. RESULTS: Across these samples and measures, exploratory analyses indicated that non-judging was a central bridge between facets of mindfulness and symptoms of depression. We confirmed this finding in a pre-registered replication (n = 401) using a recently developed confirmatory testing framework for network analysis. Non-judging was consistently a central bridge in the networks and specifically linked to the symptoms of depression related to feelings of failure and worthlessness. CONCLUSIONS: These findings provide strong evidence that non-judging is an essential feature of mindfulness in the context of depression and provides direction for future research testing mindfulness-oriented treatment prescriptions for depression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12671-021-01726-1.