Although military personnel may have vulnerabilities and susceptibilities for anxiety disorders similar to the non-military population, the extreme stressors of deployment and war time service enhance the likelihood of developing anxiety disorders such as post-traumatic stress disorder (PTSD). However, only a subset of individuals exposed to such stressors develops PTSD, leading to the concept of pre-existing vulnerability factors such that individuals with one or more of these vulnerability factors, who are then exposed to extreme stressors, are more likely to develop PTSD than individuals who receive equivalent exposure but have no (or fewer) vulnerability factors. Avoidance is a core symptom of PTSD;avoidance symptoms tend to increase rather than decrease over the course of the disorder, and may be particularly predictive of which trauma-exposed individuals are likely to develop PTSD. Vulnerability to PTSD may therefore partially reflect individual differences in an individual's pre-existing tendency to acquire, express, or maintain avoidant behaviors. Understanding the mechanisms by which such vulnerabilities translate into psychopathology in veterans is key for a better understanding of PTSD and also for designing effective therapeutic interventions that target these mechanisms. There is a rich tradition of studying avoidance learning in animals, including work from our lab and others, but work directly linking animal data to human avoidance has lagged behind. Here, we will employ a parallel program of computational modeling and human avoidance studies to begin to bridge the gap between human and animal studies. First, we will develop a computational model of avoidance learning to address data from animals, including data from a rat model of vulnerability to PTSD and anxiety that shows facilitated learning and persistence of avoidance learning. Second, in parallel with the modeling work, we will conduct studies of avoidance learning in veterans with varying degrees of PTSD avoidance symptoms, to determine whether veterans with severe avoidance symptoms show avoidance behavior in laboratory tasks that parallels the behaviors observed in the rats. These studies will provide valuable empirical data on avoidance learning in veterans, in addition to directly testing how closely the rat behavior mimics that observed in the humans. Third, we will apply the computational model to the veteran avoidance learning data, to determine whether the same computational mechanisms that sufficed to account for the rat data can also account for the human data. If so, this will increase our confidence in the translational potential of the rat model;if not, this will identify important limitations that can be studied in future animal and human work. In either case, the results from the computational modeling will begin to bridge the gap in our understanding of the mechanisms driving avoidance learning in rats and in veterans with and without PTSD avoidance symptoms. The long term goal of this program of work is to use computational modeling to better understand the processes by which vulnerability to avoidance translates into psychopathology in anxiety disorders including PTSD. Knowledge of these processes and their underlying mechanisms may guide future development of targeted therapies that modulate these mechanisms to affect the development and maintenance of aberrant avoidance, providing strategies to treat or prevent development of avoidance symptoms in PTSD.

Public Health Relevance

Public Health Relevance Statement Veterans are at heightened risk for PTSD and the associated health and personal costs (difficulty maintaining employment, family disruption, co-morbid alcohol and drug abuse, etc.) can be substantial for the individual, his/her family, and society. Avoidance is a core symptom of PTSD, and avoidance symptoms tend to increase rather than decrease over time with the disorder. Yet while there is a wealth of animal data on how avoidance is acquired and maintained, the translation of insights from animals to humans has lagged behind. This project will use computational modeling and studies of avoidance learning in veterans to bridge the gap between animal and human studies, to better understand the processes by which vulnerability to avoidance translates into psychopathology. Knowledge of these processes and their underlying mechanisms could help identify at-risk veterans before avoidance symptoms develop, and could aid in development of targeted therapies that treat or prevent development and maintenance of aberrant avoidance in PTSD.

Agency
National Institute of Health (NIH)
Institute
Veterans Affairs (VA)
Type
Non-HHS Research Projects (I01)
Project #
5I01CX000771-02
Application #
8595171
Study Section
Mental Health and Behavioral Science A (MHBA)
Project Start
2012-10-01
Project End
2016-09-30
Budget Start
2013-10-01
Budget End
2014-09-30
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
VA New Jersey Health Care System
Department
Type
DUNS #
087286308
City
East Orange
State
NJ
Country
United States
Zip Code
07018
Myers, Catherine E; Rego, Janice; Haber, Paul et al. (2017) Learning and generalization from reward and punishment in opioid addiction. Behav Brain Res 317:122-131
O'Connell, Garret; Myers, Catherine E; Hopkins, Ramona O et al. (2016) Amnesic patients show superior generalization in category learning. Neuropsychology 30:915-919
Scharfman, Helen E; Myers, Catherine E (2016) Corruption of the dentate gyrus by ""dominant"" granule cells: Implications for dentate gyrus function in health and disease. Neurobiol Learn Mem 129:69-82
Myers, Catherine E; Kostek, John A; Ekeh, Barbara et al. (2016) Watch what I do, not what I say I do: Computer-based avatars to assess behavioral inhibition, a vulnerability factor for anxiety disorders. Comput Human Behav 55 Pt B:804-816
Radell, Milen L; Myers, Catherine E; Beck, Kevin D et al. (2016) The Personality Trait of Intolerance to Uncertainty Affects Behavior in a Novel Computer-Based Conditioned Place Preference Task. Front Psychol 7:1175
Myers, Catherine E; Radell, Milen L; Shind, Christine et al. (2016) Beyond symptom self-report: use of a computer ""avatar"" to assess post-traumatic stress disorder (PTSD) symptoms. Stress 19:593-598
Radell, Milen L; Sanchez, Rosanna; Weinflash, Noah et al. (2016) The personality trait of behavioral inhibition modulates perceptions of moral character and performance during the trust game: behavioral results and computational modeling. PeerJ 4:e1631
Moustafa, Ahmed A; Sheynin, Jony; Myers, Catherine E (2015) The Role of Informative and Ambiguous Feedback in Avoidance Behavior: Empirical and Computational Findings. PLoS One 10:e0144083
Anastasides, Nicole; Beck, Kevin D; Pang, Kevin C H et al. (2015) Increased generalization of learned associations is related to re-experiencing symptoms in veterans with symptoms of post-traumatic stress. Stress 18:484-9
Radell, Milen L; Beck, Kevin D; Pang, Kevin C H et al. (2015) Using signals associated with safety in avoidance learning: computational model of sex differences. PeerJ 3:e1081

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