Faculty Bibliography

OBJECTIVE: The impact of multiple major life stressors is hypothesized to reduce the probability of resilience and increase rates of mortality. However, this hypothesis lacks strong empirical support because of the lack of prospective evidence. This study investigated whether experiencing multiple major health events diminishes rates of resilience and increases rates of mortality using a large population-based prospective cohort. METHOD: There were n = 1,395 individuals sampled from the Health and Retirement Study (HRS) and examined prospectively from 2 years before 4 years after either single or multiple health events (lung disease, heart disease, stroke, or cancer). Distinct depression and resilience trajectories were identified using latent growth mixture modeling (LGMM). These trajectories were compared on rates of mortality 4 years after the health events. RESULTS: Findings indicated that 4 trajectories best fit the data including resilience, emergent postevent depression, chronic pre-to-post depression, and depressed prior followed by improvement. Analyses demonstrate that multiple health events do not decrease rates of resilience but do increase the severity of symptoms among those on the emergent depression trajectory. Emergent depression increased mortality compared with all others but among those in this class, rates were not different in response to single versus multiple health events. CONCLUSIONS: Multiple major stressors do not reduce rates of resilience. The emergence of depression after health events does significantly increase risk for mortality regardless of the number of events. (PsycINFO Database Record

Suicide rates among recent veterans have led to interest in risk identification. Evidence of gender-and trauma-specific predictors of suicidal ideation necessitates the use of advanced computational methods capable of elucidating these important and complex associations. In this study, we used machine learning to examine gender-specific associations between predeployment and military factors, traumatic deployment experiences, and psychopathology and suicidal ideation (SI) in a national sample of veterans deployed during the Iraq and Afghanistan conflicts (n = 2,244). Classification, regression tree analyses, and random forests were used to identify associations with SI and determine their classification accuracy. Findings converged on several associations for men that included depression, posttraumatic stress disorder (PTSD), and somatic complaints. Sexual harassment during deployment emerged as a key factor that interacted with PTSD and depression and demonstrated a stronger association with SI among women. Classification accuracy for SI presence or absence was good based on the receiver operating characteristic area under the curve, men = .91, women = .92. The risk for SI was classifiable with good accuracy, with associations that varied by gender. The use of machine learning analyses allowed for the discovery of rich, nuanced results that should be replicated in other samples and may eventually be a basis for the development of gender-specific actuarial tools to assess SI risk among veterans.

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.

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.

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.

Deficits in fear extinction learning are hypothesized to underlie the development of posttraumatic stress disorder (PTSD). Such deficits may, in part, be due to genetic and epigenetic variation in the stress related gene FKBP5. Conversely, altering FKBP5 epigenetic responses during memory consolidation may rescue extinction deficits making it a target for acute intervention to prevent the development of PTSD. Study 1 (Humans) examines if FKBP5 single nucleotide polymorphisms (SNPs) and PTSD symptom domains (re-experiencing, avoidance/numbing, hyperarousal) are associated with abnormal fear extinction phenotypes identified using latent growth mixture modeling (LGMM). Study 2 (Mice) tests if increasing doses of dexamethasone administered prior to extinction alters Fkbp5 mRNA production in the amygdala after extinction and recall and prevents the development of abnormal extinction phenotypes. In humans, abnormal extinction was associated with the TT homozygous genotype of FKBP5 SNPs RS9470080 and RS1360780, and hyperarousal symptoms. In mice, dexamethasone 300 mug/kg was associated with increased amygdala Fkbp5 mRNA following extinction and robust extinction learning while lower doses were not associated with amygdala Fkbp5 mRNA or differences in extinction learning. Further, mice that extinguished on dexamethasone 300 mug/kg maintained low levels of freezing behavior during recall training while mRNA levels were no longer elevated. Together, findings indicate that FKBP5 confers risk for fear extinction deficits. However, this risk may be ameliorated by increasing fkbp5 mRNA expression in the amygdala during memory consolidation making this mechanism a plausible point of acute intervention to prevent the development of PTSD.

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

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.