A major challenge to treating drug addiction is understanding how learned associations to aversive reinforcers promote avoidant behavior and trigger the onset and relapse of drug use. Studies in non-human animals have begun to elucidate the neurobehavioral mechanisms of avoidance learning, but there have been few efforts to translate these findings to human populations. Neuroplastic brain mechanisms that support long-term memory formation are hypothesized to alter the representations of stimuli and strengthen contextual associations as a function of their incentive properties. The proposed research adopts a cognitive neuroscience perspective to characterize how motivational brain systems modulate declarative memory formation in humans and lead to behavioral avoidance. Although human declarative memory is traditionally probed using list- learning paradigms, recent advances in computer graphics interfaces and immersive virtual reality (VR) technology permit the development of novel navigational avoidance tasks that provide a tighter link with the animal literature and more closely model real-world avoidant behaviors exhibited by drug addicts in response to environmental stressors. Healthy participants will undergo a series of functional magnetic resonance imaging studies that present reinforcing stimuli within the context of both traditional list-learning and novel VR-based navigational learning and memory tasks. The first series of experiments compares the influence of appetitive versus aversive instrumental reinforcers on declarative memory systems. The second series of experiments determines how negative mood states amplify the mnemonic effects of the incentive properties of instrumental reinforcers and motivate reward-seeking ("relief") as a form of mood repair. The third series of experiments develops a multisensory, immersive VR paradigm that simulates stress-induced avoidance and escape on a naturalistic memory task that combines navigational and list-learning approaches. Functional connectivity modeling, in combination with multiple regression and independent components analyses, will characterize the interactions of motivational and memory systems and their relationship to individual differences in behavioral performance indices and trait markers of avoidance. The proposed studies thus represent a systematic and innovative approach to human avoidance learning that combines cutting-edge VR technology and functional neuroimaging methods. The research findings will bridge a translational gap in understanding how positive reinforcers, mood states, and stressors modify the impact of aversive behavioral consequences on learning and memory systems that help establish internal maps of salient features of the environment.

Public Health Relevance

In drug addiction, the experience and recall of environmental stressors can trigger the onset and relapse of drug use as a means to escape from life challenges. The proposed research will impact public health by advancing an understanding of how aversive reinforcers modulate memory systems in the brain and promote avoidant behavior. The research also develops new paradigms that could be incorporated into clinical trials to assess how rewards provide relief from negative moods, and to assess escape behavior in a stressful learning context using virtual reality simulations.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA027802-05
Application #
8515375
Study Section
Special Emphasis Panel (ZDA1-KXH-C (07))
Program Officer
Grant, Steven J
Project Start
2009-09-01
Project End
2014-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
5
Fiscal Year
2013
Total Cost
$359,536
Indirect Cost
$129,064
Name
Duke University
Department
Other Basic Sciences
Type
Schools of Arts and Sciences
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
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Green, Steven R; Kragel, Philip A; Fecteau, Matthew E et al. (2014) Development and validation of an unsupervised scoring system (Autonomate) for skin conductance response analysis. Int J Psychophysiol 91:186-93
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Dunsmoor, Joseph E; LaBar, Kevin S (2013) Effects of discrimination training on fear generalization gradients and perceptual classification in humans. Behav Neurosci 127:350-6
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Dunsmoor, Joseph E; LaBar, Kevin S (2012) Brain activity associated with omission of an aversive event reveals the effects of fear learning and generalization. Neurobiol Learn Mem 97:301-12
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Dunsmoor, Joseph E; Martin, Alex; LaBar, Kevin S (2012) Role of conceptual knowledge in learning and retention of conditioned fear. Biol Psychol 89:300-5
Whitford, T J; Mathalon, D H; Shenton, M E et al. (2011) Electrophysiological and diffusion tensor imaging evidence of delayed corollary discharges in patients with schizophrenia. Psychol Med 41:959-69

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