Neural Mechanisms of High-Risk, Appetitive Decisions in Alcohol Dependent Women

Thesis (Ph.D.) - Indiana University, Psychological & Brain Sciences, 2014A defining feature of alcohol dependence (AD) is continuing to drink despite the risk of severe negative consequences. Currently, it is not known if the pattern of disordered activation in AD is more compatible with an over-sensitive reward system, a deficit in control systems or a combination of both to produce the high risk-taking behavior observed in alcohol dependents. Here, fMRI was used to examine neural mechanisms that drive high-risk behavior in alcohol dependent women (ADs). A novel ecological task was developed to assess high- and low-risk decisions to drink alcohol, have sex, eat food, and buy items in ADs and control women. In this dissertation, neural correlates of high-risk decisions to drink (Study 1), neural correlates of high-risk decisions to have sex (Study 2), and functional connectivity (fC) during high-risk decisions to drink using psychophysiological interactions (Study 3) are examined. Across these studies, the focus was on 1.) determining if a specific pattern of activation or fC drives high-risk behavior in ADs, and 2.) determining if neural patterns of activation or fC are specific to high-risk decisions to drink or if they generalize to other appetitive decisions in ADs. The results showed that for high-risk decisions to drink, ADs were significantly more likely to drink high-risk beverages compared to controls, and a specific pattern of activation was associated with high-risk decisions to drink compared to other appetitive decisions, in ADs compared to controls. ADs also had significantly reduced fC compared to controls during high-risk decisions to drink. However, for sexual decisions, there were no behavioral differences between ADs and controls, yet a significant difference in neural activation was observed. Overall, the results suggest that disordered activation and fC in ADs observed during this task may be due to a problem with switching between different neural networks