Faculty Bibliography

Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. However, individual differences models (e.g., latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy, even when a single animal is included in the data set (i.e., n = 1 level). We further show an online software that enables the easy application of our method to any SigAA data set.

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.

Importance/UNASSIGNED:Duration of untreated psychosis (DUP) has been associated with poor outcomes in schizophrenia, but the mechanism responsible for this association is not known. Objectives/UNASSIGNED:To determine whether hippocampal volume loss occurs during the initial 8 weeks of antipsychotic treatment and whether it is associated with DUP, and to examine molecular biomarkers in association with hippocampal volume loss and DUP. Design, Setting, and Participants/UNASSIGNED:A naturalistic longitudinal study with matched healthy controls was conducted at Shanghai Mental Health Center. Between March 5, 2013, and October 8, 2014, 71 medication-naive individuals with nonaffective first-episode psychosis (FEP) and 73 age- and sex-matched healthy controls were recruited. After approximately 8 weeks, 31 participants with FEP and 32 controls were reassessed. Exposures/UNASSIGNED:The participants with FEP were treated according to standard clinical practice with second-generation antipsychotics. Main Outcomes and Measures/UNASSIGNED:Hippocampal volumetric integrity (HVI) (an automated estimate of the parenchymal fraction in a standardized hippocampal volume of interest), DUP, 13 peripheral molecular biomarkers, and 14 single-nucleotide polymorphisms from 12 candidate genes were determined. Results/UNASSIGNED:The full sample consisted of 71 individuals with FEP (39 women and 32 men; mean [SD] age, 25.2 [7.7] years) and 73 healthy controls (40 women and 33 men; mean [SD] age, 23.9 [6.4] years). Baseline median left HVI was lower in the FEP group (n = 57) compared with the controls (n = 54) (0.9275 vs 0.9512; difference in point estimate, -0.020 [95% CI, -0.029 to -0.010]; P = .001). During approximately 8 weeks of antipsychotic treatment, left HVI decreased in 24 participants with FEP at a median annualized rate of -.03791 (-4.1% annualized change from baseline) compared with an increase of 0.00115 (0.13% annualized change from baseline) in 31 controls (difference in point estimate, -0.0424 [95% CI, -0.0707 to -0.0164]; P = .001). The change in left HVI was inversely associated with DUP (r = -0.61; P = .002). Similar results were found for right HVI, although the association between change in right HVI and DUP did not achieve statistical significance (r = -0.35; P = .10). Exploratory analyses restricted to the left HVI revealed an association between left HVI and markers of inflammation, oxidative stress, brain-derived neurotrophic factor, glial injury, and markers reflecting dopaminergic and glutamatergic transmission. Conclusions and Relevance/UNASSIGNED:An association between longer DUP and accelerated hippocampal atrophy during initial treatment suggests that psychosis may have persistent, possibly deleterious, effects on brain structure. Additional studies are needed to replicate these exploratory findings of molecular mechanisms by which untreated psychosis may affect hippocampal volume and to determine whether these effects account for the known association between longer DUP and poor outcome.

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.

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.

Posttraumatic stress disorder (PTSD) is associated with increased risk for Type 2 diabetes and cardiovascular disease (cardiometabolic disease), warranting research into targeted prevention strategies. In the present case-control study of 160 young (mean age 32.7 years) male military veterans, we aimed to assess whether PTSD status predicted increased markers of cardiometabolic risk in otherwise healthy individuals, and further, to explore biological pathways between PTSD and these increased markers of cardiometabolic risk. Toward these aims, we compared measures of cardiometabolic risk, namely insulin resistance (IR) (HOMA-IR), metabolic syndrome (MetS) and prediabetes, between 80 PTSD cases and 80 controls without PTSD. We then determined whether PTSD-associated increases in HOMA-IR were correlated with select biological variables from pathways previously hypothesized to link PTSD with cardiometabolic risk, including systemic inflammation (increased C-reactive protein, interleukin-6, and tumor necrosis factor alpha), sympathetic over-activity (increased resting heart rate), and neuroendocrine dysregulation (increased plasma cortisol or serum brain-derived neurotrophic factor (BDNF)). We found PTSD diagnosis was associated with substantially higher HOMA-IR (cases 4.3+/-4.3 vs controls 2.4+/-2.0; p<0.001), and a higher frequency of MetS (cases 21.3% vs controls 2.5%; p<0.001), but not prediabetes (cases 20.0% vs controls 18.8%; p>0.05). Cases also had increased pro-inflammatory cytokines (p<0.01), heart rate (p<0.001), and BDNF (p<0.001), which together predicted increased HOMA-IR (adjusted R2=0.68, p<0.001). Results show PTSD diagnosis in young male military veterans without cardiometabolic disease is associated with increased IR, predicted by biological alterations previously hypothesized to link PTSD to increased cardiometabolic risk. Findings support further research into early, targeted prevention of cardiometabolic disease in individuals with PTSD.

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.