UCLA

Aims

To advance translational studies of the role of reward prediction error (PE) in alcohol use disorder, the present study sought to develop and conduct an initial test of an alcohol-specific PE task paradigm using functional magnetic resonance imaging in humans.

Methods

Alcohol dependent or social drinkers received small tastes of their preferred alcohol beverage or control beverage, with preceding visual cues indicating whether alcohol (or water) would be delivered. To assess both positive and negative PE signals, expectancies were systematically violated in both positive (i.e. expecting water and receiving alcohol) and negative (i.e. expecting alcohol and receiving water) directions. Exploratory trial-by-trial analyses were conducted to explore temporal fluctuations of activation within a priori-defined regions of interest that have been implicated in cue reactivity and PE processing.

Results

Across the entire sample of participants, positive PE-related brain activation was found in a large cluster comprised of frontal lobe regions, as well as insular cortex, and motor/sensory cortices. Compared to social drinking subjects, alcohol dependent subjects had greater positive PE-related brain activity in left superior parietal lobule, lateral occipital cortex and postcentral gyrus. Exploratory trial-by-trial analyses indicated differences in activation specific to type of taste, mostly at earlier trials.

Conclusions

This task-development oriented pilot study found that PE signaling may not be detected in expected brain regions when image analyses average across all PE trials of the task. Rather, a trial-by-trial analysis approach may help detect sparse, temporally distinct PE signaling in expected reward processing regions.

Short summary

This fMRI study of reward prediction error found greater positive prediction error-related activity (i.e. expecting water taste, receiving alcohol taste) in alcohol dependent individuals relative to social drinkers in parietal and occipital cortices. Trial-by-trial analyses may be able to better detect sparse prediction error signaling in expected reward processing regions.