Faculty Bibliography

OBJECTIVE/UNASSIGNED:Although several reports have documented heightened systemic inflammation in posttraumatic stress disorder (PTSD), few studies have assessed whether inflammatory markers serve as prospective biomarkers for PTSD risk. The present study aimed to characterize whether peripheral immune factors measured in blood samples collected in an emergency department immediately after trauma exposure would predict later chronic development of PTSD. METHODS/UNASSIGNED:Participants (N=505) were recruited from a hospital emergency department and underwent a 1.5-hour assessment. Blood samples were drawn, on average, about 3 hours after trauma exposure. Follow-up assessments were conducted 1, 3, 6, and 12 months after trauma exposure. Latent growth mixture modeling was used to identify classes of PTSD symptom trajectories. RESULTS/UNASSIGNED:Three distinct classes of PTSD symptom trajectories were identified: chronic (N=28), resilient (N=160), and recovery (N=85). Multivariate analyses of covariance revealed a significant multivariate main effect of PTSD symptom trajectory class membership on proinflammatory cytokines. Univariate analyses showed a significant main effect of trajectory class membership on plasma concentrations of proinflammatory tumor necrosis factor α (TNFα) and interferon-γ (IFNγ). Concentrations of proinflammatory TNFα and IFNγ were significantly lower in individuals in the chronic PTSD class compared with those in the recovery and resilient classes. There were no significant differences in interleukin (IL) 1β and IL-6 concentrations by PTSD symptom trajectory class. Anti-inflammatory and other cytokines, as well as chemokines and growth factor concentrations, were not associated with development of chronic PTSD. CONCLUSIONS/UNASSIGNED:Overall, the study findings suggest that assessing the proinflammatory immune response to trauma exposure immediately after trauma exposure, in the emergency department, may help identify individuals most at risk for developing chronic PTSD in the aftermath of trauma.

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.

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/METHODS:= 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/CONCLUSIONS: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).

Background/UNASSIGNED:Exposure to a traumatic event leads to posttraumatic stress disorder (PTSD) in 10-20% of exposed individuals. Predictors of risk are needed to target early interventions to those who are most vulnerable. The objective of the study was to test whether a noninvasive mobile device that measures a physiological biomarker of autonomic nervous system activation could predict future PTSD symptoms. Methods/UNASSIGNED:Skin conductance response (SCR) was collected during a trauma interview in the emergency department within hours of exposure to trauma in 95 individuals. Trajectories of PTSD symptoms over 12 months post-trauma were identified using Latent Growth Mixture Modeling. Results/UNASSIGNED:<0.00001). Conclusions/UNASSIGNED:The current study is the first prospective study of PTSD showing SCR in the immediate aftermath of trauma predicts subsequent development of chronic PTSD. This finding points to an easily obtained, and neurobiologically informative, biomarker in emergency departments that can be disseminated to predict the development of PTSD.

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.