In response to NSF 08-514 (CRCNS), we propose a new collaborative project to develop a computational model of the interaction of hippocampus, amygdala, and ventromedial prefrontal cortex in conditioning, extinction, and contextual processing. The model will be applied to data collected from patients with post-traumatic stress disorder (PTSD), in the hopes of elucidating the brain substrates of this disorder. Parallel empirical studies, in healthy adults and in patients with PTSD, will be conducted to generate further data to constrain the model, while the model itself will generate new predictions that may drive further empirical studies. We thus anticipate an ongoing process in which the model is revised to account for new data while producing new predictions to guide further empirical studies, which in turn may lead to new diagnostic tools. In addition, the computational model will allow us to investigate the possibility that there may be different subtypes of PTSD that involve different nodes of brain dysfunction contributing to a common symptomatology. The inability to conduct controlled studies of the disorder in humans (e.g. via experimentally-induced trauma), and the resulting focus on individuals who have already developed the disorder, have to some extent hindered research into causes vs. effects of the disorder. Although animal models have had some success, there is also a potential here for computational modeling to examine how damage to or dysfunction of various brain systems (alone or in combination) might contribute to PTSD symptomatology. The project represents a new collaboration among experts on computational neuroscience of the hippocampus in conditioning and contextual processing (Myers), on the structural, functional, and behavioral abnormalities in PTSD (Gilbertson, Orr), and on classical fear conditioning in humans and animal models of anxiety (Servatius). Intellectual Merit The outcome of this research will provide the field with a deeper understanding of the role of several critical brain structures in normal learning and memory, as well as in PTSD. The empirical work will expand our understanding of generalized learning deficits in PTSD, and allow us to test model predictions, as well as producing new data to constrain the model. The project brings together researchers from several disciplines - computational neuroscience, experimental neuropsychology, clinical psychology, psychophysiology, and animal models of human anxiety disorders -- and builds on the expertise of each. The senior personnel have significant prior experience in the research methodologies involved and have strong publication records as well as commitments to teaching and mentoring. Broader Impact PTSD may affect over 6% of the US population at some point during their lifetimes, with accompanying health care burdens as well as societal costs due to lost participation in professional and personal activities. The proposed work will increase our understanding of PTSD and examine the idea that it may not be a unified disorder, but a family of pathologies that share clusters of common symptoms with each other and also with the broader spectrum of anxiety disorders. There will be implications for prevention, through better understanding of pre-existing risk factors, and for optimizing treatment that targets possible PTSD subtypes. Computational modeling and pilot empirical work will take place at Rutgers-Newark, which is consistently ranked as the #1 most diverse public university in the nation. Myers trains 4-6 undergraduate interns each semester, many of whom are underrepresented minorities, often the first in their families to go to college. Three of her current undergraduate interns have presented their research at national scientific conferences. Myers herself is one of the few female researchers represented in the field of computational neuroscience. The program will also fund a graduate student and 1-2 undergraduate researchers, who will play key roles in experimental design, empirical testing, data analysis, and report writing.

National Institute of Health (NIH)
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-IFCN-B (50))
Program Officer
Matochik, John A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Veterans Biomedical Research Institute
East Orange
United States
Zip Code
O'Connell, Garret; Myers, Catherine E; Hopkins, Ramona O et al. (2016) Amnesic patients show superior generalization in category learning. Neuropsychology 30:915-919
Sheynin, Jony; Moustafa, Ahmed A; Beck, Kevin D et al. (2016) Exaggerated acquisition and resistance to extinction of avoidance behavior in treated heroin-dependent men. J Clin Psychiatry 77:386-94
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
Myers, Catherine E; Sheynin, Jony; Balsdon, Tarryn et al. (2016) Probabilistic reward- and punishment-based learning in opioid addiction: Experimental and computational data. Behav Brain Res 296:240-248
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
Moustafa, Ahmed A; Gluck, Mark A; Herzallah, Mohammad M et al. (2015) The influence of trial order on learning from reward vs. punishment in a probabilistic categorization task: experimental and computational analyses. Front Behav Neurosci 9:153
Sheynin, Jony; Moustafa, Ahmed A; Beck, Kevin D et al. (2015) Testing the role of reward and punishment sensitivity in avoidance behavior: a computational modeling approach. Behav Brain Res 283:121-38
Sheynin, Jony; Beck, Kevin D; Servatius, Richard J et al. (2014) Acquisition and extinction of human avoidance behavior: attenuating effect of safety signals and associations with anxiety vulnerabilities. Front Behav Neurosci 8:323

Showing the most recent 10 out of 22 publications