Alcohol-dependent individuals frequently have deficits in self-control, as measured by their excessive discounting of delayed rewards. The dual neurobehavioral decision systems model suggests that these deficits may result from a disruption in the regulatory balance between two interacting neurobiological systems. These systems are the executive system (prefrontal cortex and parietal cortex) which is responsible for valuing delayed rewards (i.e., long-term goals), and the impulsive system (limbic and paralimbic areas) which is associated with immediate rewards (i.e., instant gratification). Our recent research has demonstrated that working memory (WM) training repairs self-control, putatively by restoring regulatory balance between these systems. There is, however, still much to learn about repairing self-control.
In Aim 1, we will further explore the effects WM training on a range of WM, self-control, and clinically relevant (e.g., craving) measures. Additionally, the neural mechanisms of WM training will be explored though functional neuroimaging techniques. We will examine the dose-effect function of WM training by systematically varying the number of WM training sessions across several groups of alcohol-dependent individuals. The duration of these improvements in self-control will be tested by conducting follow-up assessments one month, three months, and six months after training.
In Aim 2 we plan to capitalize on the neurobiological knowledge gained in Aim 1 to test the effects of two variants of real time fMRI neuro-feedback on our suite of WM, self-control, and clinically significant measures. This novel exploration of neuro-feedback effects on neurocognitive and clinically significant measures will include a direct comparison of feedback techniques based on a specific brain region and across a distributed neural network. Long-term changes in neural function and/or our neurocognitive measures will be explored during a one-month follow-up visit. Successfully achieving our aims could allow us to both refine our current techniques (i.e., WM training), and possibly begin the process of developing novel techniques (i.e., neuro-feedback) for the repair of self-control in alcohol-dependent individuals. This application will contribute to personalized medicine approaches in alcohol dependence, where treatment is defined by documented self-control deficits. Furthermore, the functional neuroimaging data collected across both aims should provide unique insights into both patterns of neural disruption seen in alcohol dependence and any treatment associated changes in neural function.

Public Health Relevance

The proposed project will provide new information about the repair of alcohol-dependent individuals'self- control via working memory training and neurofeedback (real-time fMRI). By examining the brain regions that underlie impaired self-control before, during, and after intervention, we expect to provide novel insights into the neurobiology of self-control repair. We believe that a greater understanding of these neural mechanisms could lead to new and refined treatments for the pathologic decision-making seen in alcohol dependence.

Agency
National Institute of Health (NIH)
Institute
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Type
Research Project (R01)
Project #
5R01AA021529-02
Application #
8728704
Study Section
Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section (NPAS)
Program Officer
Matochik, John A
Project Start
2013-09-01
Project End
2018-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$563,091
Indirect Cost
$213,345
Name
Virginia Polytechnic Institute and State University
Department
None
Type
Organized Research Units
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
Bickel, W K; Moody, L; Quisenberry, A J et al. (2014) A Competing Neurobehavioral Decision Systems model of SES-related health and behavioral disparities. Prev Med 68:37-43
Bickel, Warren K; George Wilson, A; Franck, Christopher T et al. (2014) Using crowdsourcing to compare temporal, social temporal, and probability discounting among obese and non-obese individuals. Appetite 75:82-9