A treeless absolutely random forest with closed-form estimators of expected proximities.

We introduce a simple variant of a Purely Random Forest, an Absolute Random Forest (ARF) for clustering. At every node splits of units are determined by a randomly chosen feature and a random threshold drawn from a uniform distribution whose support, the range of the selected feature in the root node, does not change. This enables closed-form estimators of parameters, such as pairwise proximities, to be obtained without having to grow a forest. The probabilistic structure corresponding to an ARF is called a Treeless Absolute Random Forest (TARF). With high probability, the algorithm will split units whose feature vectors are far apart and keep together units whose feature vectors are similar. Thus, the underlying structure of the data drives the growth of the tree. The expected value of pairwise proximities is obtained for three pathway functions. One, a completely common pathway function, is an indicator of whether a pair of units follow the same path from the root to the leaf node. The properties of TARF-based proximity estimators for clustering and classification are compared to other methods in eight real-world data sets and in simulations. Results show substantial performance and computing efficiencies of particular value for large data sets.

Screening for PTSD and TBI in Veterans using Routine Clinical Laboratory Blood Tests.

Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

A General Iterative Clustering Algorithm.

The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. A RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a General Iterative Cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the Extremely Randomized Tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette Score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.

Percentage of Heavy Drinking Days Following Psilocybin-Assisted Psychotherapy vs Placebo in the Treatment of Adult Patients With Alcohol Use Disorder: A Randomized Clinical Trial.

Importance: Although classic psychedelic medications have shown promise in the treatment of alcohol use disorder (AUD), the efficacy of psilocybin remains unknown. Objective: To evaluate whether 2 administrations of high-dose psilocybin improve the percentage of heavy drinking days in patients with AUD undergoing psychotherapy relative to outcomes observed with active placebo medication and psychotherapy. Design, Setting, and Participants: In this double-blind randomized clinical trial, participants were offered 12 weeks of manualized psychotherapy and were randomly assigned to receive psilocybin vs diphenhydramine during 2 day-long medication sessions at weeks 4 and 8. Outcomes were assessed over the 32-week double-blind period following the first dose of study medication. The study was conducted at 2 academic centers in the US. Participants were recruited from the community between March 12, 2014, and March 19, 2020. Adults aged 25 to 65 years with a DSM-IV diagnosis of alcohol dependence and at least 4 heavy drinking days during the 30 days prior to screening were included. Exclusion criteria included major psychiatric and drug use disorders, hallucinogen use, medical conditions that contraindicated the study medications, use of exclusionary medications, and current treatment for AUD. Interventions: Study medications were psilocybin, 25 mg/70 kg, vs diphenhydramine, 50 mg (first session), and psilocybin, 25-40 mg/70 kg, vs diphenhydramine, 50-100 mg (second session). Psychotherapy included motivational enhancement therapy and cognitive behavioral therapy. Main Outcomes and Measures: The primary outcome was percentage of heavy drinking days, assessed using a timeline followback interview, contrasted between groups over the 32-week period following the first administration of study medication using multivariate repeated-measures analysis of variance. Results: A total of 95 participants (mean [SD] age, 46 [12] years; 42 [44.2%] female) were randomized (49 to psilocybin and 46 to diphenhydramine). One participant (1.1%) was American Indian/Alaska Native, 3 (3.2%) were Asian, 4 (4.2%) were Black, 14 (14.7%) were Hispanic, and 75 (78.9%) were non-Hispanic White. Of the 95 randomized participants, 93 received at least 1 dose of study medication and were included in the primary outcome analysis. Percentage of heavy drinking days during the 32-week double-blind period was 9.7% for the psilocybin group and 23.6% for the diphenhydramine group, a mean difference of 13.9%; (95% CI, 3.0-24.7; F1,86 = 6.43; P = .01). Mean daily alcohol consumption (number of standard drinks per day) was also lower in the psilocybin group. There were no serious adverse events among participants who received psilocybin. Conclusions and Relevance: Psilocybin administered in combination with psychotherapy produced robust decreases in percentage of heavy drinking days over and above those produced by active placebo and psychotherapy. These results provide support for further study of psilocybin-assisted treatment for AUD. Trial Registration: ClinicalTrials.gov Identifier: NCT02061293.

A likely responder approach for the analysis of randomized controlled trials.

OBJECTIVE: To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS: Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS: A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION: The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

Posttraumatic stress disorder symptom trajectories within the first year following emergency department admissions: pooled results from the International Consortium to predict PTSD.

BACKGROUND: Research exploring the longitudinal course of posttraumatic stress disorder (PTSD) symptoms has documented four modal trajectories (low, remitting, high, and delayed), with proportions varying across studies. Heterogeneity could be due to differences in trauma types and patient demographic characteristics. METHODS: This analysis pooled data from six longitudinal studies of adult survivors of civilian-related injuries admitted to general hospital emergency departments (EDs) in six countries (pooled N = 3083). Each study included at least three assessments of the clinician-administered PTSD scale in the first post-trauma year. Latent class growth analysis determined the proportion of participants exhibiting various PTSD symptom trajectories within and across the datasets. Multinomial logistic regression analyses examined demographic characteristics, type of event leading to the injury, and trauma history as predictors of trajectories differentiated by their initial severity and course. RESULTS: Five trajectories were found across the datasets: Low (64.5%), Remitting (16.9%), Moderate (6.7%), High (6.5%), and Delayed (5.5%). Female gender, non-white race, prior interpersonal trauma, and assaultive injuries were associated with increased risk for initial PTSD reactions. Female gender and assaultive injuries were associated with risk for membership in the Delayed (v. Low) trajectory, and lower education, prior interpersonal trauma, and assaultive injuries with risk for membership in the High (v. Remitting) trajectory. CONCLUSIONS: The results suggest that over 30% of civilian-related injury survivors admitted to EDs experience moderate-to-high levels of PTSD symptoms within the first post-trauma year, with those reporting assaultive violence at increased risk of both immediate and longer-term symptoms.

CRF serum levels differentiate PTSD from healthy controls and TBI in military veterans.

BACKGROUND AND OBJECTIVE: Posttraumatic stress disorder (PTSD) is a serious and frequently debilitating psychiatric condition that can occur in people who have experienced traumatic stessors, such as war, violence, sexual assault and other life-threatening events. Treatment of PTSD and traumatic brain injury (TBI) in veterans is challenged by diagnostic complexity, partially due to PTSD and TBI symptom overlap and to the fact that subjective self-report assessments may be influenced by a patient's willingness to share their traumatic experiences and resulting symptoms. Corticotropin-releasing factor (CRF) is one of the main mediators of hypothalamic pituitary adrenal (HPA)-axis responses in stress and anxiety. METHODS AND RESULTS: We analyzed serum CRF levels in 230 participants including heathy controls (64), and individuals with PTSD (53), TBI (70) or PTSD+TBI (43) by enzyme immunoassay (EIA). Significantly lower CRF levels were found in both the PTSD and PTSD+TBI groups compared to healthy control (PTSD vs Controls: P=0.0014, PTSD + TBI vs Controls: P=0.0011) and chronic TBI participants (PTSD vs TBI: P<0.0001PTSD + TBI vs TBI: P<0.0001) , suggesting a PTSD-related mechanism independent from TBI and associated with CRF reduction. CRF levels negatively correlated with PTSD severity on the CAPS-5 scale in the whole study group. CONCLUSIONS: Hyperactivation of the HPA axis has been classically identified in acute stress. However, the recognized enhanced feedback inhibition of the HPA axis in chronic stress supports our findings of lower CRF in PTSD patients. This study suggests that reduced serum CRF in PTSD should be further investigated. Future validation studies will establish if CRF is a possible blood biomarker for PTSD and/or for differentiating PTSD and chronic TBI symptomatology.

Gabapentin Enacarbil Extended-Release Versus Placebo: A Likely Responder Reanalysis of a Randomized Clinical Trial.

BACKGROUND: We reanalyzed a multisite 26-week randomized double-blind placebo-controlled clinical trial of 600 mg twice-a-day Gabapentin Enacarbil Extended-Release (GE-XR), a gabapentin prodrug, designed to evaluate safety and efficacy for treating alcohol use disorder. In the original analysis (n = 338), published in 2019, GE-XR did not differ from placebo. Our aim is to advance precision medicine by identifying likely responders to GE-XR from the trial data and to determine for likely responders if GE-XR is causally superior to placebo. METHODS: The primary outcome measure in the reanalysis is the reduction from baseline of the number of heavy drinking days (ΔHDD). Baseline features including measures of alcohol use, anxiety, depression, mood states, sleep, and impulsivity were used in a random forest (RF) model to predict ΔHDD to treatment with GE-XR based on those assigned to GE-XR. The resulting RF model was used to obtain predicted outcomes for those randomized to GE-XR and counterfactually to those randomized to placebo. Likely responders to GE-XR were defined as those predicted to have a reduction of 14 days or more. Tests of causal superiority of GE-XR to placebo were obtained for likely responders and for the whole sample. RESULTS: For likely responders, GE-XR was causally superior to placebo (p < 0.0033), while for the whole sample, there was no difference. Likely responders exhibited improved outcomes for the related outcomes of percent HDD and drinks per week. Compared with unlikely responders, at baseline likely responders had higher HDDs; lower levels of anxiety, depression, and general mood disturbances; and higher levels of cognitive and motor impulsivity. CONCLUSIONS: There are substantial causal benefits of treatment with GE-XR for a subset of patients predicted to be likely responders. The likely responder statistical paradigm is a promising approach for analyzing randomized clinical trials to advance personalized treatment.

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Predeployment neurocognitive functioning predicts postdeployment posttraumatic stress in Army personnel.

OBJECTIVE: The Fort Campbell Cohort study was designed to assess predeployment biological and behavioral markers and build predictive models to identify risk and resilience for posttraumatic stress disorder (PTSD) following deployment. This article addresses neurocognitive functioning variables as potential prospective predictors. METHOD: In a sample of 403 soldiers, we examined whether PTSD symptom severity (using the PTSD Checklist) as well as posttraumatic stress trajectories could be prospectively predicted by measures of executive functioning (using two web-based tasks from WebNeuro) assessed predeployment. RESULTS: Controlling for age, gender, education, prior number of deployments, childhood trauma exposure, and PTSD symptom severity at Phase 1, linear regression models revealed that predeployment sustained attention and inhibitory control performance were significantly associated with postdeployment PTSD symptom severity. We also identified two posttraumatic stress trajectories utilizing latent growth mixture models. The "resilient" group consisted of 90.9% of the soldiers who exhibited stable low levels of PTSD symptoms from pre- to postdeployment. The "increasing" group consisted of 9.1% of the soldiers, who exhibited an increase in PTSD symptoms following deployment, crossing a threshold for diagnosis based on PTSD Checklist scores. Logistic regression models predicting trajectory revealed a similar pattern of findings as the linear regression models, in which predeployment sustained attention (95% CI of odds ratio: 1.0109, 1.0558) and inhibitory control (95% CI: 1.0011, 1.0074) performance were significantly associated with postdeployment PTSD trajectory. CONCLUSIONS: These findings have clinical implications for understanding the pathogenesis of PTSD and building preventative programs for military personnel. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Electronic self-administered screening for substance use in adult primary care patients: feasibility and acceptability of the tobacco, alcohol, prescription medication, and other substance use (myTAPS) screening tool.

BACKGROUND: The TAPS Tool is a substance use screening and brief assessment instrument that was developed for use in primary care medical settings. It is one of the first screening instruments to provide rapid assessment of all commonly used substance classes, including illicit and prescription opioids, and is one of the only available screeners designed and validated in an electronic self-administered format (myTAPS). This secondary analysis of data from the TAPS Tool validation study describes the feasibility and acceptability of the myTAPS among primary care patients. METHODS: Adult patients (N = 2000) from five primary care clinics completed the TAPS Tool on a tablet computer (myTAPS), and in an interviewer-administered format. Requests for assistance and time required were tracked, and participants completed a survey on ease of use, utilization of audio guidance, and format preference. Logistic regression was used to examine outcomes in defined subpopulations, including groups that may have greater difficulty completing an electronic screener, and those that may prefer an electronic self-administered approach. RESULTS: Almost all participants (98.3%) reported that the myTAPS was easy to use. The median time to complete myTAPS screening was 4.0 min (mean 4.48, standard deviation 2.57). More time was required by participants who were older, Hispanic, Black, or reported non-medical prescription drug use, while less time was required by women. Assistance was requested by 25% of participants, and was more frequently requested by those who with lower education (OR = 2.08, 95% CI 1.62-2.67) or age > 65 years (OR = 2.79, 95% CI 1.98-3.93). Audio guidance was utilized by 18.3%, and was more frequently utilized by participants with lower education (OR = 2.01, 95% CI 1.54-2.63), age > 65 years (OR = 1.79, 95% CI 1.22-2.61), or Black race (OR = 1.30, 95% 1.01-1.68). The myTAPS format was preferred by women (OR = 1.29, 95% CI 1.00-1.66) and individuals with drug use (OR = 1.43, 95% CI 1.09-1.88), while participants with lower education preferred the interviewer-administered format (OR = 2.75, 95% CI 2.00-3.78). CONCLUSIONS: Overall, myTAPS screening was feasible and well accepted by adult primary care patients. Clinics adopting electronic screening should be prepared to offer assistance to some patients, particularly those who are older or less educated, and should have the capacity to use an interviewer-administered approach when required.

Extended-release vs. oral naltrexone for alcohol dependence treatment in primary care (XON).

BACKGROUND: Extended-release naltrexone (XR-NTX, Vivitrol®) and daily oral naltrexone tablets (O-NTX) are FDA-approved mu opioid receptor antagonist medications for alcohol dependence treatment. Despite the efficacy of O-NTX, non-adherence and poor treatment retention have limited its adoption into primary care. XR-NTX is a once-a-month injectable formulation that offers a potentially more effective treatment option in reducing alcohol consumption and heavy drinking episodes among persons with alcohol use disorders. METHODS: This pragmatic, open-label, randomized controlled trial examines the effectiveness of XR-NTX vs. O-NTX in producing a Good Clinical Outcome, defined as abstinence or moderate drinking (<2 drinks/day, men; <1 drink/day, women; and < 2 heavy drinking occasions/month) during the final 20 of 24 weeks of primary care-based Medical Management treatment for alcohol dependence. Secondary aims will estimate the cost effectiveness of XR-NTX vs. O-NTX, in conjunction with primary-care based Medical Management for both groups, and patient-level characteristics associated with effectiveness in both arms. Alcohol dependent persons are recruited from the community into treatment in a New York City public hospital primary care setting (Bellevue Hospital Center) for 24 weeks of either XR-NTX (n = 117) or O-NTX (n = 120). RESULTS: We describe the rationale, specific aims, design, and recruitment results to date. Alternative design considerations and secondary aims and outcomes are reported. CONCLUSIONS: XR-NTX treatment in a primary care setting is potentially more efficacious, feasible, and cost-effective than oral naltrexone when treating community-dwelling persons with alcohol use disorders. This study will estimate XR-NTX's treatment and cost effectiveness relative to oral naltrexone.

Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience.

Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF)1. Particularly for stress management, targeting deeply located limbic areas involved in stress processing2 has paved new paths for brain-guided interventions. However, the high cost and immobility of fMRI constitute a challenging drawback for the scalability (accessibility and cost-effectiveness) of the approach, particularly for clinical purposes3. The current study aimed to overcome the limited applicability of rt-fMRI by using an electroencephalography (EEG) model endowed with improved spatial resolution, derived from simultaneous EEG-fMRI, to target amygdala activity (termed amygdala electrical fingerprint (Amyg-EFP))4-6. Healthy individuals (n = 180) undergoing a stressful military training programme were randomly assigned to six Amyg-EFP-NF sessions or one of two controls (control-EEG-NF or NoNF), taking place at the military training base. The results demonstrated specificity of NF learning to the targeted Amyg-EFP signal, which led to reduced alexithymia and faster emotional Stroop, indicating better stress coping following Amyg-EFP-NF relative to controls. Neural target engagement was demonstrated in a follow-up fMRI-NF, showing greater amygdala blood-oxygen-level-dependent downregulation and amygdala-ventromedial prefrontal cortex functional connectivity following Amyg-EFP-NF relative to NoNF. Together, these results demonstrate limbic specificity and efficacy of Amyg-EFP-NF during a stressful period, pointing to a scalable non-pharmacological yet neuroscience-based training to prevent stress-induced psychopathology.

Author Correction: Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience.

The original and corrected Acknowledgements are shown in the accompanying Author Correction.

Speech-based markers for posttraumatic stress disorder in US veterans.

BACKGROUND: The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls. METHODS: Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm. RESULTS: The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders. CONCLUSIONS: This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP).

A timely determination of the risk of post-traumatic stress disorder (PTSD) is a prerequisite for efficient service delivery and prevention. We provide a risk estimate tool allowing a calculation of individuals' PTSD likelihood from early predictors. Members of the International Consortium to Predict PTSD (ICPP) shared individual participants' item-level data from ten longitudinal studies of civilian trauma survivors admitted to acute care centers in six countries. Eligible participants (N=2,473) completed an initial clinical assessment within 60 days of trauma exposure, and at least one follow-up assessment 4-15 months later. The Clinician-Administered PTSD Scale for DSM-IV (CAPS) evaluated PTSD symptom severity and diagnostic status at each assessment. Participants' education, prior lifetime trauma exposure, marital status and socio-economic status were assessed and harmonized across studies. The study's main outcome was the likelihood of a follow-up PTSD given early predictors. The prevalence of follow-up PTSD was 11.8% (9.2% for male participants and 16.4% for females). A logistic model using early PTSD symptom severity (initial CAPS total score) as a predictor produced remarkably accurate estimates of follow-up PTSD (predicted vs. raw probabilities: r=0.976). Adding respondents' female gender, lower education, and exposure to prior interpersonal trauma to the model yielded higher PTSD likelihood estimates, with similar model accuracy (predicted vs. raw probabilities: r=0.941). The current model could be adjusted for other traumatic circumstances and accommodate risk factors not captured by the ICPP (e.g., biological, social). In line with their use in general medicine, risk estimate models can inform clinical choices in psychiatry. It is hoped that quantifying individuals' PTSD risk will be a first step towards systematic prevention of the disorder.

Application of data pooling to longitudinal studies of early post-traumatic stress disorder (PTSD): the International Consortium to Predict PTSD (ICPP) project.

Background: Understanding the development of post-traumatic stress disorder (PTSD) is a precondition for efficient risk assessment and prevention planning. Studies to date have been site and sample specific. Towards developing generalizable models of PTSD development and prediction, the International Consortium to Predict PTSD (ICPP) compiled data from 13 longitudinal, acute-care based PTSD studies performed in six different countries. Objective: The objectives of this study were to describe the ICPP's approach to data pooling and harmonization, and present cross-study descriptive results informing the longitudinal course of PTSD after acute trauma. Methods: Item-level data from 13 longitudinal studies of adult civilian trauma survivors were collected. Constructs (e.g. PTSD, depression), measures (questions or scales), and time variables (days from trauma) were identified and harmonized, and those with inconsistent coding (e.g. education, lifetime trauma exposure) were recoded. Administered in 11 studies, the Clinician Administered PTSD Scale (CAPS) emerged as the main measure of PTSD diagnosis and severity. Results: The pooled data set included 6254 subjects (39.9% female). Studies' average retention rate was 87.0% (range 49.1-93.5%). Participants' baseline assessments took place within 2 months of trauma exposure. Follow-up durations ranged from 188 to 1110 days. Reflecting studies' inclusion criteria, the prevalence of baseline PTSD differed significantly between studies (range 3.1-61.6%), and similar differences were observed in subsequent assessments (4.3-38.2% and 3.8-27.0% for second and third assessments, respectively). Conclusion: Pooling data from independently collected studies requires careful curation of individual data sets for extracting and optimizing informative commonalities. However, it is an important step towards developing robust and generalizable prediction models for PTSD and can exceed findings of single studies. The large differences in prevalence of PTSD longitudinally cautions against using any individual study to infer trauma outcome. The multiplicity of instruments used in individual studies emphasizes the need for common data elements in future studies.

Antecedentes: Comprender el desarrollo del trastorno de estrés postraumático (TEPT) es una condición previa para una evaluación de riesgos y una planificación de prevención eficientes. Los estudios hasta la fecha han sido específicos del sitio y de la muestra. Hacia el desarrollo de modelos generalizables de desarrollo y predicción de TEPT, el Consorcio Internacional para Predecir el TEPT (ICPP) recopiló datos de 13 estudios de TEPT longitudinales basados en la atención aguda realizados en seis países diferentes.Objetivo: describir el enfoque del ICPP para la combinación de datos y la armonización, y presentar los resultados descriptivos entre estudios que informan el curso longitudinal del trastorno de estrés postraumático después del trauma agudo.Métodos: se recogieron datos a nivel de ítem de 13 estudios longitudinales de adultos sobrevivientes de trauma civil. Se identificaron y armonizaron constructos (por ejemplo, trastorno de estrés postraumático, depresión), medidas (preguntas o escalas) y variables de tiempo (días desde el trauma) y se recodificaron aquellas con codificación inconsistente (por ejemplo, educación, exposición de trauma a lo largo de la vida). Administrado en 11 estudios, la Escala de TEPT Administrada por el Clínico (CAPS) surgió como la principal medida del diagnóstico y la gravedad del TEPT.Resultados: el conjunto de datos agrupado incluyó 6254 sujetos (39,9% mujeres). La tasa promedio de retención de estudios fue 87.0% (rango: 49.1% a 93.5%). Las evaluaciones iniciales de los participantes se realizaron dentro de los dos meses posteriores a la exposición al trauma. Las duraciones de seguimiento variaron de 188 a 1110 días. Reflejando los criterios de inclusión de los estudios, la prevalencia de TEPT basal difirió significativamente entre los estudios (rango: 3,1% a 61,6%), y se observaron diferencias similares en las evaluaciones posteriores (respectivamente, 4,3% a 38,2% y 3,8% a 27,0% para la segunda y tercera evaluaciones).Conclusión: la agrupación de datos de los estudios recopilados de forma independiente requiere una cuidadosa conservación de los conjuntos de datos individuales para extraer y optimizar las comunalidades informativas. Sin embargo, es un paso importante hacia el desarrollo de un modelo de predicción robusto y generalizable para el TEPT y poder superar los hallazgos de estudios únicos. Las grandes diferencias en la prevalencia de TEPT advierten longitudinalmente contra el uso de cualquier estudio individual para inferir el resultado del trauma. La multiplicidad de instrumentos utilizados en estudios individuales enfatiza la necesidad de elementos de datos comunes en estudios futuros.