Resilience and Disaster: Flexible Adaptation in the Face of Uncertain Threat

Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.

Machine Learning and the Digital Measurement of Psychological Health

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings

OBJECTIVE

Predicting risk of posttraumatic stress disorder (PTSD) in the acute care setting is challenging given the pace and acute care demands in the emergency department (ED) and the infeasibility of using time-consuming assessments. Currently, no accurate brief screening for long-term PTSD risk is routinely used in the ED. One instrument widely used in the ED is the 27-item Immediate Stress Reaction Checklist (ISRC). The aim of this study was to develop a short screener using a machine learning approach and to investigate whether accurate PTSD prediction in the ED can be achieved with substantially fewer items than the IRSC.

METHOD

This prospective longitudinal cohort study examined the development and validation of a brief screening instrument in two independent samples, a model development sample (N = 253) and an external validation sample (N = 93). We used a feature selection algorithm to identify a minimal subset of features of the ISRC and tested this subset in a predictive model to investigate if we can accurately predict long-term PTSD outcomes.

RESULTS

We were able to identify a reduced subset of 5 highly predictive features of the ISRC in the model development sample (AUC = 0.80), and we were able to validate those findings in the external validation sample (AUC = 0.84) to discriminate non-remitting vs. resilient trajectories.

CONCLUSION

This study developed and validated a brief 5-item screener in the ED setting, which may help to improve the diagnostic process of PTSD in the acute care setting and help ED clinicians plan follow-up care when patients are still in contact with the healthcare system. This could reduce the burden on patients and decrease the risk of chronic PTSD.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

BACKGROUND

Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

METHODS

N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.

RESULTS

Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).

CONCLUSIONS

Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study

Background

Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage.

Objective

This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones.

Methods

Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale.

Results

Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity.

Conclusions

Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.

Discriminating Heterogeneous Trajectories of Resilience and Depression After Major Life Stressors Using Polygenic Scores

IMPORTANCE

Major life stressors, such as loss and trauma, increase the risk of depression. It is known that individuals show heterogeneous trajectories of depressive symptoms following major life stressors, including chronic depression, recovery, and resilience. Although common genetic variation has been associated with depression risk, genomic factors that could help discriminate trajectories of risk vs resilience following adversity have not been identified.

OBJECTIVE

To assess the discriminatory accuracy of a deep neural net combining joint information from 21 psychiatric and health-related multiple polygenic scores (PGSs) for discriminating resilience vs other longitudinal symptom trajectories with use of longitudinal, genetically informed data on adults exposed to major life stressors.

DESIGN, SETTING, AND PARTICIPANTS

The Health and Retirement Study is a longitudinal panel cohort study in US citizens older than 50 years, with data being collected once every 2 years between 1992 and 2010. A total of 2071 participants who were of European ancestry with available depressive symptom trajectory information after experiencing an index depressogenic major life stressor were included. Latent growth mixture modeling identified heterogeneous trajectories of depressive symptoms before and after major life stressors, including stable low symptoms (ie, resilience), as well as improving, emergent, and preexisting/chronic symptom patterns. Twenty-one PGSs were examined as factors distinctively associated with these heterogeneous trajectories. Local interpretable model-agnostic explanations were applied to examine PGSs associated with each trajectory. Data were analyzed using the DNN model from June to July 2020.

EXPOSURES

Development of depression and resilience were examined in older adults after a major life stressor, such as bereavement, divorce, and job loss, or major health events, such as myocardial infarction and cancer.

MAIN OUTCOMES AND MEASURES

Discriminatory accuracy of a deep neural net model trained for the multinomial classification of 4 distinct trajectories of depressive symptoms (Center for Epidemiologic Studies-Depression scale) based on 21 PGSs using supervised machine learning.

RESULTS

Of the 2071 participants, 1329 were women (64.2%); mean (SD) age was 55.96 (8.52) years. Of these, 1638 (79.1%) were classified as resilient, 160 (7.75) in recovery (improving), 159 (7.7%) with emerging depression, and 114 (5.5%) with preexisting/chronic depression symptoms. Deep neural nets distinguished these 4 trajectories with high discriminatory accuracy (multiclass micro-average area under the curve, 0.88; 95% CI, 0.87-0.89; multiclass macro-average area under the curve, 0.86; 95% CI, 0.85-0.87). Discriminatory accuracy was highest for preexisting/chronic depression (AUC 0.93), followed by emerging depression (AUC 0.88), recovery (AUC 0.87), resilience (AUC 0.75).

CONCLUSIONS AND RELEVANCE

The results of the longitudinal cohort study suggest that multivariate PGS profiles provide information to accurately distinguish between heterogeneous stress-related risk and resilience phenotypes.

Remote Digital Measurement of Facial and Vocal Markers of Major Depressive Disorder Severity and Treatment Response: A Pilot Study

Objectives: Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine uptake inhibitors (SNRIs). Methods: Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT (n = 18). MDD diagnosis was confirmed using the Mini-International Neuropsychiatric Interview (MINI), and the Montgomery-Åsberg Depression Rating Scale (MADRS) was collected concordantly to assess changes in MDD severity. Results: Patient responses to ADT demonstrated clinically and statistically significant changes in the MADRS [F (2, 34) = 51.62, p < 0.0001]. Additionally, patients demonstrated significant increases in multiple digital markers including facial expressivity, head movement, and amount of speech. Finally, patients demonstrated significantly decreased frequency of fear and anger facial expressions. Conclusion: Digital markers associated with MDD demonstrate validity as measures of treatment response.

Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors

Background

Alterations in multiple domains of cognition have been observed in individuals who have experienced a traumatic stressor. These domains may provide important insights in identifying underlying neurobiological dysfunction driving an individual's clinical response to trauma. However, such assessments are burdensome, costly, and time-consuming. To overcome barriers, efforts have emerged to measure multiple domains of cognitive functioning through the application of machine learning (ML) models to passive data sources.

Methods

We utilized automated computer vision and voice analysis methods to extract facial, movement, and speech characteristics from semi-structured clinical interviews in 81 trauma survivors who additionally completed a cognitive assessment battery. A ML-based regression framework was used to identify variance in visual and auditory measures that relate to multiple cognitive domains.

Results

Models derived from visual and auditory measures collectively accounted for a large variance in multiple domains of cognitive functioning, including motor coordination (R² = 0.52), processing speed (R² = 0.42), emotional bias (R² = 0.52), sustained attention (R² = 0.51), controlled attention (R² = 0.44), cognitive flexibility (R² = 0.43), cognitive inhibition (R² = 0.64), and executive functioning (R² = 0.63), consistent with the high test-retest reliability of traditional cognitive assessments. Face, voice, speech content, and movement have all significantly contributed to explaining the variance in predicting functioning in all cognitive domains.

Conclusions

The results demonstrate the feasibility of automated measurement of reliable proxies of cognitive functioning through low-burden passive patient evaluations. This makes it easier to monitor cognitive functions and to intervene earlier and at a lower threshold without requiring a time-consuming neurocognitive assessment by, for instance, a licensed psychologist with specialized training in neuropsychology.

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

Introduction

Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote "digital phenotyping" of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones.

Methods

Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed.

Results

The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; p = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (p = 0.04), primarily with negative symptoms of schizophrenia.

Conclusions

Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

Accuracy of machine learning-based prediction of medication adherence in clinical research

Medication non-adherence represents a significant barrier to treatment efficacy. Remote, real-time measurement of medication dosing can facilitate dynamic prediction of risk for medication non-adherence, which in-turn allows for proactive clinical intervention to optimize health outcomes. We examine the accuracy of dynamic prediction of non-adherence using data from remote real-time measurements of medication dosing. Participants across a large set of clinical trials (n = 4,182) were observed via a smartphone application that video records patients taking their prescribed medication. The patients' primary diagnosis, demographics, and prior indication of observed adherence/non-adherence were utilized to predict (1) adherence rates ≥ 80% across the clinical trial, (2) adherence ≥ 80% for the subsequent week, and (3) adherence the subsequent day using machine learning-based classification models. Empirically observed adherence was demonstrated to be the strongest predictor of future adherence/non-adherence. Collectively, the classification models accurately predicted adherence across the trial (AUC = 0.83), the subsequent week (AUC = 0.87) and the subsequent day (AUC = 0.87). Real-time measurement of dosing can be utilized to dynamically predict medication adherence with high accuracy.

Computational causal discovery for post-traumatic stress in police officers

This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline-the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)-was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure

BACKGROUND

Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction.

METHODS

N = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based.

RESULTS

Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors.

CONCLUSIONS

Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively.

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

Socioeconomic resources predict trajectories of depression and resilience following disability

OBJECTIVE

Adjustment to chronic disability is a topic of considerable focus in the rehabilitation sciences and constitutes an important public health problem given the adverse outcomes associated with maladjustment. While existing literature has established an association between disability onset and elevated rates of depression, resilience and alternative patterns of adjustment have received substantially less empirical inquiry. The current study sought to model heterogeneity in mental health responding to disability onset in later life while exploring the impact of socioeconomic resources on these latent patterns of adaptation.

METHOD

Latent growth mixture modeling was utilized to identify trajectories of depressive symptoms surrounding physical disability onset in a population sample of older adults. Individuals with verified disability onset (n = 3,204) were followed across four measurement points representing a 6-year period.

RESULTS

Four trajectories of depressive symptoms were identified: resilience (56.5%), emerging depression (17.2%), remitting depression (13.4%), and chronic depression (12.9%). Socioeconomic resources were then analyzed as predictors of trajectory membership. Prior education and financial assets at the time of disability onset robustly predicted class membership in the resilient class compared to all other classes.

CONCLUSION

The course of adjustment in response to disability onset is heterogeneous. Our results confirm the presence of multiple pathways of adjustment surrounding late-life disability, with the most common outcome being near-zero depressive symptoms for the duration of the study. Socioeconomic resources strongly predicted membership in the resilient class compared with all other classes, indicating that such resources may play a protective role during the stress of physical disability onset. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Trajectories of resilience and dysfunction following potential trauma: A review and statistical evaluation

Given the rapid proliferation of trajectory-based approaches to study clinical consequences to stress and potentially traumatic events (PTEs), there is a need to evaluate emerging findings. This review examined convergence/divergences across 54 studies in the nature and prevalence of response trajectories, and determined potential sources of bias to improve future research. Of the 67 cases that emerged from the 54 studies, the most consistently observed trajectories following PTEs were resilience (observed in: n = 63 cases), recovery (n = 49), chronic (n = 47), and delayed onset (n = 22). The resilience trajectory was the modal response across studies (average of 65.7% across populations, 95% CI [0.616, 0.698]), followed in prevalence by recovery (20.8% [0.162, 0.258]), chronicity (10.6%, [0.086, 0.127]), and delayed onset (8.9% [0.053, 0.133]). Sources of heterogeneity in estimates primarily resulted from substantive population differences rather than bias, which was observed when prospective data is lacking. Overall, prototypical trajectories have been identified across independent studies in relatively consistent proportions, with resilience being the modal response to adversity. Thus, trajectory models robustly identify clinically relevant patterns of response to potential trauma, and are important for studying determinants, consequences, and modifiers of course following potential trauma.

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals.

Heterogeneity in Trajectories of Depression in Response to Divorce is Associated with Differential Risk for Mortality

Divorce is a common stressful event associated with both increased rates of depression and mortality. Given evidence of significant individual differences in depression following major life stressors, we examined if heterogeneous depression responses confer differential risk for mortality. Data from a population based longitudinal study was utilized to identify individuals who experienced divorce (n=559). Prospective trajectories of depression severity from before to after divorce were identified using latent growth mixture modeling, and rates of mortality between trajectories were compared as a distal outcome. Four trajectories demonstrated strongest model fit: resilience (67%), emergent depression (10%), chronic pre-to-post divorce depression (12%), and decreasing depression (11%). Mortality base rate was 9.7% by 6 years post-event, and depression that emerged due to divorce was associated with significantly greater mortality risk compared to resilient (OR, 2.46; 95% CI, 1.05-5.81) and to married individuals, while chronic depression was not associated with greater risk.

Emergence of depression following job loss prospectively predicts lower rates of reemployment

Job loss has been associated with the emergence of depression and subsequent long-term diminished labor market participation. In a sample of 500 adults who lost their jobs, trajectories of depression severity from four years before to four years after job loss were identified using Latent Growth Mixture Modeling. Rates of unemployment by trajectory were compared at two and four years following job loss. Four trajectories demonstrated optimal model fit including resilience (72%), chronic pre-to-post job loss depression (9%), emergent depression (10%), and remitting depression (9%). Logistic regression comparing reemployment status by class while controlling for age, gender, and education at two-years post job loss revealed no significant differences by class. An identical logistic regression on four-year reemployment revealed significant differences by class with post-hoc analyses revealing emergent depression resulting in a 33.3% reemployment rate compared to resilient individuals (60.4%) together indicating that depression affects reemployment rather than lack of reemployment causing the emergence of depression. The emergence of depression following job loss significantly increases the risk of continued unemployment. However, observed high rates of resilience with resulting downstream benefits in reemployment mitigates significant concern about the effects of wide spread unemployment on ongoing global economic recovery following the Great Recession.

Amygdala Reactivity and Anterior Cingulate Habituation Predict Posttraumatic Stress Disorder Symptom Maintenance After Acute Civilian Trauma

BACKGROUND

Studies suggest that exaggerated amygdala reactivity is a vulnerability factor for posttraumatic stress disorder (PTSD); however, our understanding is limited by a paucity of prospective, longitudinal studies. Recent studies in healthy samples indicate that, relative to reactivity, habituation is a more reliable biomarker of individual differences in amygdala function. We investigated reactivity of the amygdala and cortical areas to repeated threat presentations in a prospective study of PTSD.

METHODS

Participants were recruited from the emergency department of a large level I trauma center within 24 hours of trauma. PTSD symptoms were assessed at baseline and approximately 1, 3, 6, and 12 months after trauma. Growth curve modeling was used to estimate symptom recovery trajectories. Thirty-one individuals participated in functional magnetic resonance imaging around the 1-month assessment, passively viewing fearful and neutral face stimuli. Reactivity (fearful > neutral) and habituation to fearful faces was examined.

RESULTS

Amygdala reactivity, but not habituation, 5 to 12 weeks after trauma was positively associated with the PTSD symptom intercept and predicted symptoms at 12 months after trauma. Habituation in the ventral anterior cingulate cortex was positively associated with the slope of PTSD symptoms, such that decreases in ventral anterior cingulate cortex activation over repeated presentations of fearful stimuli predicted increasing symptoms.

CONCLUSIONS

Findings point to neural signatures of risk for maintaining PTSD symptoms after trauma exposure. Specifically, chronic symptoms were predicted by amygdala hyperreactivity, and poor recovery was predicted by a failure to maintain ventral anterior cingulate cortex activation in response to fearful stimuli. The importance of identifying patients at risk after trauma exposure is discussed.

Elevated C-reactive protein and posttraumatic stress pathology among survivors of the 9/11 World Trade Center attacks

BACKGROUND

Systemic inflammation has emerged as a promising marker and potential mechanism underlying post-traumatic stress disorder (PTSD). The relationship between posttraumatic stress pathology and systemic inflammation has not, however, been consistently replicated and is potentially confounded by comorbid illness or injury, common complications of trauma exposure.

METHODS

We analyzed a large naturalistic cohort sharing a discrete physical and mental health trauma from the destruction of the World Trade Center (WTC) towers on September 11, 2001 (n = 641). We evaluated the relationship between multiple physical and mental health related indices collected through routine evaluations at the WTC Environmental Health Center (WTC EHC), a treatment program for community members exposed to the disaster. C-Reactive Protein (CRP), a marker of systemic inflammation, was examined in relation to scores for PTSD, PTSD symptom clusters (re-experiencing, avoidance, negative cognitions/mood, arousal), depression and anxiety, while controlling for WTC exposures, lower respiratory symptoms, age, sex, BMI and smoking as potential risks or confounders.

RESULTS

CRP was positively associated with PTSD severity (p < 0.001), trending toward association with depression (p = 0.06), but not with anxiety (p = 0.27). CRP was positively associated with re-experiencing (p < 0.001) and avoidance (p < 0.05) symptom clusters, and trended toward associations with negative cognitions/mood (p = 0.06) and arousal (p = 0.08).

CONCLUSIONS

In this large study of the relationship between CRP and posttraumatic stress pathology, we demonstrated an association between systemic inflammation and stress pathology (PTSD; trending with depression), which remained after adjusting for potentially confounding variables. These results contribute to research findings suggesting a salient relationship between inflammation and posttraumatic stress pathology.

It's Not So Easy to Make Resilience Go Away: Commentary on Infurna and Luthar (2016)

A large number of studies have identified trajectories of adjustment following acute and aversive life events. In these studies, a stable trajectory of positive health or resilience is almost always the modal outcome (Bonanno, 2004; Bonanno et al., 2011). Infurna and Luthar (2016, this issue) reported that they replicated findings from two early studies in which trajectories of subjective well-being were identified before and after divorce, widowhood, and unemployment (Galatzer-Levy, Bonanno, & Mancini, 2010; Mancini, Bonanno, & Clark, 2011) and then reanalyzed these data in such a way to conclude a decrease in the prevalence of resilience. In this commentary, we discuss three serious flaws in Infurna and Luthar's claims. First, they did not actually replicate our original analyses. They used different data, time points, and parameters. Second, the model specifications in their reanalyses were not optimal because they increased variance, reduced variability in response to the stressor, and had lower entropy, indicating that their models more poorly captured unique patterns of response. Third, their reanalyses were theoretically uninformative as they minimized both group differences and overall responses to the stressor event and thus failed to identify widely acknowledged populations, such as chronic stress reactivity.

Marijuana and other cannabinoids as a treatment for posttraumatic stress disorder: A literature review

Posttraumatic stress disorder (PTSD) is common in the general population, yet there are limitations to the effectiveness, tolerability, and acceptability of available first-line interventions. We review the extant knowledge on the effects of marijuana and other cannabinoids on PTSD. Potential therapeutic effects of these agents may largely derive from actions on the endocannabinoid system and we review major animal and human findings in this area. Preclinical and clinical studies generally support the biological plausibility for cannabinoids' potential therapeutic effects, but underscore heterogeneity in outcomes depending on dose, chemotype, and individual variation. Treatment outcome studies of whole plant marijuana and related cannabinoids on PTSD are limited and not methodologically rigorous, precluding conclusions about their potential therapeutic effects. Reported benefits for nightmares and sleep (particularly with synthetic cannabinoid nabilone) substantiate larger controlled trials to determine effectiveness and tolerability. Of concern, marijuana use has been linked to adverse psychiatric outcomes, including conditions commonly comorbid with PTSD such as depression, anxiety, psychosis, and substance misuse. Available evidence is stronger for marijuana's harmful effects on the development of psychosis and substance misuse than for the development of depression and anxiety. Marijuana use is also associated with worse treatment outcomes in naturalistic studies, and with maladaptive coping styles that may maintain PTSD symptoms. Known risks of marijuana thus currently outweigh unknown benefits for PTSD. Although controlled research on marijuana and other cannabinoids' effects on PTSD remains limited, rapid shifts in the legal landscape may now enable such studies, potentially opening new avenues in PTSD treatment research.

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

Impact of Cannabis Use on Treatment Outcomes among Adults Receiving Cognitive-Behavioral Treatment for PTSD and Substance Use Disorders

BACKGROUND

Research has demonstrated a strong link between trauma, posttraumatic stress disorder PTSD and substance use disorders (SUDs) in general and cannabis use disorders in particular. Yet, few studies have examined the impact of cannabis use on treatment outcomes for individuals with co-occurring PTSD and SUDs.

METHODS

Participants were 136 individuals who received cognitive-behavioral therapies for co-occurring PTSD and SUD. Multivariate regressions were utilized to examine the associations between baseline cannabis use and end-of-treatment outcomes. Multilevel linear growth models were fit to the data to examine the cross-lagged associations between weekly cannabis use and weekly PTSD symptom severity and primary substance use during treatment.

RESULTS

There were no significant positive nor negative associations between baseline cannabis use and end-of-treatment PTSD symptom severity and days of primary substance use. Cross-lagged models revealed that as cannabis use increased, subsequent primary substance use decreased and vice versa. Moreover, results revealed a crossover lagged effect, whereby higher cannabis use was associated with greater PTSD symptom severity early in treatment, but lower weekly PTSD symptom severity later in treatment.

CONCLUSION

Cannabis use was not associated with adverse outcomes in end-of-treatment PTSD and primary substance use, suggesting independent pathways of change. The theoretical and clinical implications of the reciprocal associations between weekly cannabis use and subsequent PTSD and primary substance use symptoms during treatment are discussed.

Is the intensive care unit traumatic? What we know and don't know about the intensive care unit and posttraumatic stress responses

The intensive care unit (ICU) has been portrayed as psychologically stressful, with a growing body of research substantiating elevated rates of depression, posttraumatic stress disorder (PTSD), and other psychological disruptions in populations of critical care survivors. To explain these psychopathology elevations, some have proposed a direct effect of ICU admission upon the later development of psychopathology, whereas others highlight the complex interaction between the trauma of a life-threatening illness or injury and the stressful life-saving interventions often administered in the ICU. However, the conclusion that the ICU is an independent causal factor in trauma-related psychological outcomes may be premature. Current ICU research suffers from important methodological problems including lack of true prospective data, failure to employ appropriate comparison groups, sampling bias, measurement issues, and problems with statistical methodology. In addition, the ICU literature has yet to investigate important risk and resilience factors that have been empirically validated in the broader stress-response literature. The authors propose the application of these important constructs to the unique setting of the ICU. This review focuses on multiple aspects of the important but complex research question of whether the ICU confers risk for psychological distress above and beyond the traumatic impact of the serious health events that necessitate ICU treatment. (PsycINFO Database Record

Trajectories of depression following spousal and child bereavement: A comparison of the heterogeneity in outcomes

Our understanding of how individuals react to the loss of a close loved one comes largely from studies of spousal bereavement. The extent to which findings are relevant to other bereavements is uncertain. A major methodological limitation of current studies has been a reliance on retrospective reporting of functioning and use of samples of individuals who have self-selected for participant in grief research. To address these limitations, in the current study we applied Latent Growth Mixture Modelling (LGMM) in a prospective population-based sample to identify trajectories of depression following spousal and child bereavement in later life. The sample consisted of 2512 individual bereaved adults who were assessed once before and three times after their loss. Four discrete trajectories were identified: Resilience (little or no depression; 68.2%), Chronic Grief (an onset of depression following loss; 13.2%), Depressed-Improved (high pre-loss depression that decreased following loss; 11.2%), and Pre-existing Chronic Depression (high depression at all assessments; 7.4%). These trajectories were present for both child and spousal loss. There was some evidence that child loss in later life was associated more strongly with the Chronic Grief trajectory and less strongly with the Resilience trajectory. However these differences disappeared when covariates were included in the model. Limitations of the analyses are discussed. These findings increase our understanding of the variety of outcomes following bereavement and underscore the importance of using prospective designs to map heterogeneity of response outcomes.

Bridging a translational gap: using machine learning to improve the prediction of PTSD

BACKGROUND

Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators.

METHODS

Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials.

RESULTS

The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets.

CONCLUSIONS

Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data

BACKGROUND

Scientific research into mental health outcomes following trauma is undergoing a revolution as scientists refocus their efforts to identify underlying dimensions of health and psychopathology. This effort is in stark contrast to the previous focus which was to characterize individuals based on Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic status (Insel et al., 2010). A significant unresolved issue underlying this shift is how to characterize clinically relevant populations without reliance on the categorical definitions provided by the DSM. Classifying individuals based on their pattern of stress adaptation over time holds significant promise for capturing inherent inter-individual heterogeneity as responses including chronicity, recovery, delayed onset, and resilience can only be determined longitudinally (Galatzer-Levy & Bryant, 2013) and then characterizing these patterns for future research (Depaoli, Van de Schoot, Van Loey, & Sijbrandij, 2015). Such an approach allows for the identification of phenominologically similar patterns of response to diverse extreme environmental stressors (Bonanno, Kennedy, Galatzer-Levy, Lude, & Elfstom, 2012; Galatzer-Levy & Bonanno, 2012; Galatzer-Levy, Brown, et al., 2013; Galatzer-Levy, Burton, & Bonanno, 2012) including translational animal models of stress adaptation (Galatzer-Levy, Bonanno, Bush, & LeDoux, 2013; Galatzer-Levy, Moscarello, et al., 2014). The empirical identification of heterogeneous stress response patterns can increase the identification of mechanisms (Galatzer-Levy, Steenkamp, et al., 2014), consequences (Galatzer-Levy & Bonanno, 2014), treatment effects (Galatzer-Levy, Ankri, et al., 2013), and prediction (Galatzer-Levy, Karstoft, Statnikov, & Shalev, 2014) of individual differences in response to trauma.

METHOD

METHODological and theoretical considerations for the application of Latent Growth Mixture Modeling (LGMM) and allied methods such as Latent Class Growth Analysis (LCGA) for the identification of heterogeneous populations defined by their pattern of change over time will be presented (Van De Schoot, 2015). Common pitfalls including non-identification, over identification, and issues related to model specification will be discussed as well as the benefits of applying such methods along with the theoretical grounding of such approaches.

CONCLUSIONS

LGMM and allied methods have significant potential for improving the science of stress pathology as well as our understanding of healthy adaptation (resilience).

Identifying profiles of recovery from reward devaluation in rats

In humans and other mammals, the unexpected loss of a resource can lead to emotional conflict. Consummatory successive negative contrast (cSNC) is a laboratory model of reward devaluation meant to capture that conflict. In this paradigm, animals are exposed to a sharp reduction in the sucrose concentration of a solution after several days of access. This downshift in sucrose content leads to behavioral responses such as the suppression of consumption and physiologic responses including elevation of corticosterone levels. However, response heterogeneity in cSNC has yet to be explored and may be relevant for increasing the validity of this model, as humans demonstrate clinically meaningful heterogeneity in response to resource loss. The current analysis applied latent growth mixture modeling to test for and characterize heterogeneity in recovery from cSNC among rats (N=262). Although most animals exhibited recovery of consummatory behavior after a sharp drop in consumption in the first postshift trial (Recovery class; 83%), two additional classes were identified including animals that did not change their consumption levels after downshift (No Contrast class; 6%), and animals that exhibited an initial response similar to that of the Recovery class but did not recover to preshift consumption levels (No Recovery class; 11%). These results indicate heterogeneity in recovery from reward loss among rats, which may increase the translatability of this animal model to understand diverse responses to loss among humans.

Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Fear load: The psychophysiological over-expression of fear as an intermediate phenotype associated with trauma reactions

Psychophysiological measures of fear expression provide observable intermediate phenotypes of fear-related symptoms. Research Domain Criteria (RDoC) advocate using neurobiological intermediate phenotypes that provide dimensional correlates of psychopathology. Negative Valence Systems in the RDoC matrix include the construct of acute threat, which can be measured on a physiological level using potentiation of the acoustic startle reflex assessed via electromyography recordings of the orbicularis oculi muscle. Impairments in extinction of fear-potentiated startle due to high levels of fear (termed fear load) during the early phases of extinction have been observed in posttraumatic stress disorder (PTSD). The goals of the current work were to examine dimensional associations between fear-related symptoms of PTSD and fear load variables to test their validity as an intermediate phenotype. We examined extinction of fear-potentiated startle in a cohort (n=269) of individuals with a broad range of civilian trauma exposure (range 0-13 traumatic events per person, mean=3.5). Based on previously reported findings, we hypothesized that fear load would be significantly associated with intrusion and fear memories of an index traumatic event. The results indicated that early extinction was correlated with intrusive thoughts (p=0.0007) and intense physiological reactions to trauma reminders (p=0.036). Degree of adult or childhood trauma exposure, and depression severity were not associated with fear load. After controlling for age, sex, race, income, level of prior trauma, and level of fear conditioning, fear load during extinction was still significantly predictive of intrusive thoughts (p=0.004). The significance of these findings is that they support dimensional associations with symptom severity rather than diagnostic category and, as such, fear load may emerge as a transdiagnostic intermediate phenotype expressed across fear-related disorders (e.g., specific phobia, social phobia).

Optimism and Death

The course of depression in relation to myocardial infarction (MI), commonly known as heart attack, and the consequences for mortality are not well characterized. Further, optimism may predict both the effects of MI on depression as well as mortality secondary to MI. In the current study, we utilized a large population-based prospective sample of older adults ( N = 2,147) to identify heterogeneous trajectories of depression from 6 years prior to their first-reported MI to 4 years after. Findings indicated that individuals were at significantly increased risk for mortality when depression emerged after their first-reported MI, compared with resilient individuals who had no significant post-MI elevation in depression symptomatology. Individuals with chronic depression and those demonstrating pre-event depression followed by recovery after MI were not at increased risk. Further, optimism, measured before MI, prospectively differentiated all depressed individuals from participants who were resilient.

Heterogeneity in signaled active avoidance learning: substantive and methodological relevance of diversity in instrumental defensive responses to threat cues

Individuals exposed to traumatic stressors follow divergent patterns including resilience and chronic stress. However, researchers utilizing animal models that examine learned or instrumental threat responses thought to have translational relevance for Posttraumatic Stress Disorder (PTSD) and resilience typically use central tendency statistics that assume population homogeneity. This approach potentially overlooks fundamental differences that can explain human diversity in response to traumatic stressors. The current study tests this assumption by identifying and replicating common heterogeneous patterns of response to signaled active avoidance (AA) training. In this paradigm, rats are trained to prevent an aversive outcome (shock) by performing a learned instrumental behavior (shuttling between chambers) during the presentation of a conditioned threat cue (tone). We test the hypothesis that heterogeneous trajectories of threat avoidance provide more accurate model fit compared to a single mean trajectory in two separate studies. Study 1 conducted 3 days of signaled AA training (n = 81 animals) and study 2 conducted 5 days of training (n = 186 animals). We found that four trajectories in both samples provided the strongest model fit. Identified populations included animals that acquired and retained avoidance behavior on the first day (Rapid Avoiders: 22 and 25%); those who never successfully acquired avoidance (Non-Avoiders; 20 and 16%); a modal class who acquired avoidance over 3 days (Modal Avoiders; 37 and 50%); and a population who demonstrated a slow pattern of avoidance, failed to fully acquire avoidance in study 1 and did acquire avoidance on days 4 and 5 in study 2 (Slow Avoiders; 22.0 and 9%). With the exception of the Slow Avoiders in Study 1, populations that acquired demonstrated rapid step-like increases leading to asymptotic levels of avoidance. These findings indicate that avoidance responses are heterogeneous in a way that may be informative for understanding both resilience and PTSD as well as the nature of instrumental behavior acquisition. Characterizing heterogeneous populations based on their response to threat cues would increase the accuracy and translatability of such models and potentially lead to new discoveries that explain diversity in instrumental defensive responses.

Cortisol response to an experimental stress paradigm prospectively predicts long-term distress and resilience trajectories in response to active police service

Heterogeneity in glucocorticoid response to experimental stress conditions has shown to differentiate individuals with healthy from maladaptive real-life stress responses in a number of distinct domains. However, it is not known if this heterogeneity influences the risk for developing stress related disorders or if it is a biological consequence of the stress response itself. Determining if glucocorticoid response to stress induction prospectively predicts psychological vulnerability to significant real life stressors can adjudicate this issue. To test this relationship, salivary cortisol as well as catecholamine responses to a laboratory stressor during academy training were examined as predictors of empirically identified distress trajectories through the subsequent 4 years of active duty among urban police officers routinely exposed to potentially traumatic events and routine life stressors (N = 234). During training, officers were exposed to a video vignette of police officers exposed to real-life trauma. Changes in salivary 3-methoxy-4-hydroxyphenylglycol (MHPG) and cortisol in response to this video challenge were examined as predictors of trajectory membership while controlling for age, gender, and baseline neuroendocrine levels. Officers who followed trajectories of resilience and recovery over 4 years mounted significant increases in cortisol in response to the experimental stressor, while those following a trajectory of chronic increasing distress had no significant cortisol change in response to the challenge. MHPG responses were not associated with distress trajectories. Cortisol response prospectively differentiated trajectories of distress response suggesting that a blunted cortisol response to a laboratory stressor is a risk factor for later vulnerability to distress following significant life stressors.

Heterogeneous depression responses to chronic pain onset among middle-aged adults: a prospective study

Studies on depression response to chronic pain are limited by lack of clarification of different forms of response patterns and cross-sectional measures. The current study examined heterogeneous long-term patterns of depression response to chronic pain onset prospectively using the mixture modeling technique. Depression symptoms prior to and following pain onset over a course of six years were charted in a nationally representative middle-aged sample. Four distinct depression symptom trajectories emerged. The resilience (72.0%) trajectory describes a pattern of no/minimal depression symptoms prior to and following pain onset. The post-pain depression trajectory (11.4%) describes a pattern of low depression at baseline and increasing symptoms following pain onset. The chronic depression (6.8%) trajectory is characterized by persistently high depression symptoms irrespective of pain onset. The prior depression improved (9.8%) trajectory describes a pattern of high depression at baseline and gradually declining symptoms following pain onset. Self-rated health at both baseline and following pain onset predicted the resilience trajectory. Baseline self-rated health distinguished the post-pain depression and chronic depression trajectories. Individuals in the prior depression improved trajectory were older and had more chronic illnesses at baseline but fewer illnesses following pain onset, compared to those in the resilience or post-pain depression trajectory.

636,120 Ways to Have Posttraumatic Stress Disorder

In an attempt to capture the variety of symptoms that emerge following traumatic stress, the revision of posttraumatic stress disorder (PTSD) criteria in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders ( DSM–5) has expanded to include additional symptom presentations. One consequence of this expansion is that it increases the amorphous nature of the classification. Using a binomial equation to elucidate possible symptom combinations, we demonstrate that the DSM–IV criteria listed for PTSD have a high level of symptom profile heterogeneity (79,794 combinations); the changes result in an eightfold expansion in the DSM–5, to 636,120 combinations. In this article, we use the example of PTSD to discuss the limitations of DSM-based diagnostic entities for classification in research by elucidating inherent flaws that are either specific artifacts from the history of the DSM or intrinsic to the underlying logic of the DSM’s method of classification. We discuss new directions in research that can provide better information regarding both clinical and nonclinical behavioral heterogeneity in response to potentially traumatic and common stressful life events. These empirical alternatives to an a priori classification system hold promise for answering questions about why diversity occurs in response to stressors.

Early PTSD symptom trajectories: persistence, recovery, and response to treatment: results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS)

Context

Uncovering heterogeneities in the progression of early PTSD symptoms can improve our understanding of the disorder's pathogenesis and prophylaxis.

Objectives

To describe discrete symptom trajectories and examine their relevance for preventive interventions.

Design

Latent Growth Mixture Modeling (LGMM) of data from a randomized controlled study of early treatment. LGMM identifies latent longitudinal trajectories by exploring discrete mixture distributions underlying observable data.

Setting

Hadassah Hospital unselectively receives trauma survivors from Jerusalem and vicinity.

Participants

Adult survivors of potentially traumatic events consecutively admitted to the hospital's emergency department (ED) were assessed ten days and one-, five-, nine- and fifteen months after ED admission. Participants with data at ten days and at least two additional assessments (n = 957) were included; 125 received cognitive behavioral therapy (CBT) between one and nine months.

Approach

We used LGMM to identify latent parameters of symptom progression and tested the effect of CBT on these parameters. CBT consisted of 12 weekly sessions of either cognitive therapy (n = 41) or prolonged exposure (PE, n = 49), starting 29.8±5.7 days after ED admission, or delayed PE (n = 35) starting at 151.8±42.4 days. CBT effectively reduced PTSD symptoms in the entire sample.

Main Outcome Measure

Latent trajectories of PTSD symptoms; effects of CBT on these trajectories.

Results

Three trajectories were identified: Rapid Remitting (rapid decrease in symptoms from 1- to 5-months; 56% of the sample), Slow Remitting (progressive decrease in symptoms over 15 months; 27%) and Non-Remitting (persistently elevated symptoms; 17%). CBT accelerated the recovery of the Slow Remitting class but did not affect the other classes.

Conclusions

The early course of PTSD symptoms is characterized by distinct and diverging response patterns that are centrally relevant to understanding the disorder and preventing its occurrence. Studies of the pathogenesis of PTSD may benefit from using clustered symptom trajectories as their dependent variables.

Heterogeneity in threat extinction learning: substantive and methodological considerations for identifying individual difference in response to stress

Pavlovian threat (fear) conditioning (PTC) is an experimental paradigm that couples innate aversive stimuli with neutral cues to elicit learned defensive behavior in response to the neutral cue. PTC is commonly used as a translational model to study neurobiological and behavioral aspects of fear and anxiety disorders including Posttraumatic Stress Disorder (PTSD). Though PTSD is a complex multi-faceted construct that cannot be fully captured in animals PTC is a conceptually valid model for studying the development and maintenance of learned threat responses. Thus, it can inform the understanding of PTSD symptomatology. However, there are significant individual differences in posttraumatic stress that are not as of yet accounted for in studies of PTC. Individuals exposed to danger have been shown to follow distinct patterns: some adapt rapidly and completely (resilience) others adapt slowly (recovery) and others failure to adapt (chronic stress response). Identifying similar behavioral outcomes in PTC increases the translatability of this model. In this report we present a flexible methodology for identifying individual differences in PTC by modeling latent subpopulations or classes characterized by defensive behavior during training. We provide evidence from a reanalysis of previously examined PTC learning and extinction data in rats to demonstrate the effectiveness of this methodology in identifying outcomes analogous to those observed in humans exposed to threat. By utilizing Latent Class Growth Analysis (LCGA) to test for heterogeneity in freezing behavior during threat conditioning and extinction learning in adult male outbred rats (n = 58) three outcomes were identified: rapid extinction (57.3%), slow extinction (32.3%), and failure to extinguish (10.3%) indicating that heterogeneity analogous to that in naturalistic human studies is present in experimental animal studies strengthening their translatability in understanding stress responses in humans.

Patterns of lifetime PTSD comorbidity: a latent class analysis

BACKGROUND

Posttraumatic stress disorder (PTSD) is associated with high rates of psychiatric comorbidity, most notably substance use disorders, major depression, and other anxiety disorders. However, little is known about how these disorders cluster together among people with PTSD, if disorder clusters have distinct etiologies in terms of trauma type, and if they confer greater burden over and above PTSD alone.

METHOD

Utilizing Latent Class Analysis, we tested for discrete patterns of lifetime comorbidity with PTSD following trauma exposure (n = 409). Diagnoses were based on the Structured Clinical Interview for DSM-IV (SCID). Next, we examined if gender, trauma type, symptom frequency, severity, and interference with everyday life were associated with the latent classes.

RESULTS

Three patterns of lifetime comorbidity with PTSD emerged: a class characterized by predominantly comorbid mood and anxiety disorders; a class characterized by predominantly comorbid mood, anxiety, and substance dependence; and a relatively pure low-comorbidity PTSD class. Individuals in both high comorbid classes had nearly two and a half times the rates of suicidal ideation, endorsed more PTSD symptom severity, and demonstrated a greater likelihood of intimate partner abuse compared to the low comorbidity class. Men were most likely to fall into the substance dependent class.

CONCLUSION

PTSD comorbidity clusters into a small number of common patterns. These patterns may represent an important area of study, as they confer distinct differences in risk and possibly etiology. Implications for research and treatment are discussed.

Positive and negative emotion prospectively predict trajectories of resilience and distress among high-exposure police officers

Responses to both potentially traumatic events and other significant life stressors have been shown to conform to discrete patterns of response such as resilience, anticipatory stress, initial distress with gradual recovery, and chronic distress. The etiology of these trajectories is still unclear. Individual differences in levels of negative and positive emotion are believed to play a role in determining risk and resilience following traumatic exposure. In the current investigation, we followed police officers prospectively from academy training through 48 months of active duty, assessing levels of distress every 12 months. Using latent class growth analysis, we identified 4 trajectories closely conforming to prototypical patterns. Furthermore, we found that lower levels of self-reported negative emotion during academy training prospectively predicted membership in the resilient trajectory compared with the more symptomatic trajectories following the initiation of active duty, whereas higher levels of positive emotion during academy training differentiated resilience from a trajectory that was equivalently low on distress during academy training but consistently grew in distress through 4 years of active duty. These findings emerging from a prospective longitudinal design provide evidence that resilience is predicted by both lower levels of negative emotion and higher levels of positive emotion prior to active duty stressor exposure.

Heterogeneous patterns of stress over the four years of college: associations with anxious attachment and ego-resiliency

OBJECTIVE

A growing body of literature suggests that college students display alarming rates of psychological distress. However, studies of responses to significant life stressors in other contexts have found that people respond in heterogeneous ways and that attachment style and ego-resiliency mitigate the effects of stressors on mental health.

METHOD

Individual differences in distress among a cohort of students (N = 157; Mean age = 18.8 years, 62.6% female) across the four years of college were analyzed using latent class growth analysis. Trajectories were then regressed on levels of anxious and avoidant attachment and ego-resiliency.

RESULTS

Four discrete patterns emerged characterized by healthy and maladaptive patterns of stress response, indicating that students respond to college in heterogeneous ways. Several patterns showed significant variability in distress by semester. Low levels of anxious but not avoidant attachment predicted membership in the stable-low distress or resilient class while ego-resiliency predicted membership in both the resilient and moderate distress classes.

CONCLUSIONS

Findings indicate that low levels of anxious attachment and the ability to flexibly cope with adversity may be associated with better mental health throughout college. Implications from stress response and developmental perspectives are discussed.

Positron emission tomography shows elevated cannabinoid CB1 receptor binding in men with alcohol dependence

BACKGROUND

Several lines of evidence link cannabinoid (CB) type 1 (CB (1) ) receptor-mediated endogenous CB (eCB) signaling to the etiology of alcohol dependence (AD). However, to date, only peripheral measures of eCB function have been collected in living humans with AD and no human in vivo data on the potentially critical role of the brain CB (1) receptor in AD have been published. This is an important gap in the literature, because recent therapeutic developments suggest that these receptors could be targeted for the treatment for AD.

METHODS

Medication-free participants were scanned during early abstinence 4 weeks after their last drink. Using positron emission tomography (PET) with a high-resolution research tomograph and the CB (1) receptor selective radiotracer [(11) C]OMAR, we determined [(11) C]OMAR volume of distribution ( V (T) ) values, a measure of CB (1) receptor density, in a priori selected brain regions in men with AD (n = 8, age 37.4 ± 7.9 years; 5 smokers) and healthy control (HC) men (n = 8, age 32.5 ± 6.9 years; all nonsmokers). PET images reconstructed using the MOLAR algorithm with hardware motion correction were rigidly aligned to the subject-specific magnetic resonance (MR) image, which in turn was warped to an MR template. Time-activity curves (TACs) were extracted from the dynamic PET data using a priori selected regions of interest delineated in the MR template space.

RESULTS

In AD relative to HC, [(11) C]OMAR V (T) values were elevated by approximately 20% (p = 0.023) in a circuit, including the amygdala, hippocampus, putamen, insula, anterior and posterior cingulate cortices, and orbitofrontal cortex. Age, body mass index, or smoking status did not influence the outcome.

CONCLUSIONS

These findings agree with preclinical evidence and provide the first, albeit still preliminary in vivo evidence suggesting a role for brain CB (1) receptors in AD. The current study design does not answer the important question of whether elevated CB (1) receptors are a preexisting vulnerability factor for AD or whether elevations develop as a consequence of AD.

Beyond normality in the study of bereavement: heterogeneity in depression outcomes following loss in older adults

Studies of individual differences in bereavement have revealed prototypical patterns of outcome. However, many of these studies were conducted prior to the advent of sophisticated contemporary data analytic techniques. For example, Bonanno et al. (2002) used rudimentary categorization procedures to identify unique trajectories of depression symptomatology from approximately 3 years prior to 4 years following conjugal loss in a representative sample of older American adults. In the current study, we revisited these same data using Latent Class Growth Analysis (LCGA) to derive trajectories and test predictors. LCGA is a technique well-suited for modeling empirically- and conceptually-derived heterogeneous longitudinal patterns while simultaneously modeling predictors of those longitudinal patterns. We uncovered four discrete trajectories similar in shape and proportion to the previous analyses: Resilience (characterized by little or no depression; 66.3%), Chronic Grief (characterized by depression following loss, alleviated by 4 years post-loss; 9.1%), _Pre-existing Chronic Depression (ongoing high pre- through post-loss depression; 14.5%), and Depressed-Improved (characterized by high pre-loss depression that decreases following loss; 10.1%). Using this analytic strategy, we were able to examine multiple hypotheses about bereavement simultaneously. Health, financial stress, and emotional stability emerged as strong predictors of variability in depression only for some trajectories, indicating that depression levels do not have a common etiology across all the bereaved. As such, we find that identifying distinct patterns informs both the course and etiology of depression in response to bereavement.