Abstract
Alcohol-related morbidities and mortality are highly prevalent
increasing the burden to societies and health systems, with three
million deaths globally each year in young adults directly attributable
to alcohol. Cue induced alcohol craving has been formulated as a type of
aberrant associative learning, modelled using temporal difference
theory with expected reward value (ERV) linked to craving. Clinically,
whilst harmful use of alcohol is associated with increased time spent
obtaining and using alcohol, it is also associated with self-neglect.
The latter implies that the motivational aspects of non-alcohol stimuli
are blunted. Using an instrumental learning task with non-alcohol
related stimuli, here we tested hypotheses that the encoding of cue
signals (ERV) predicting reward delivery would be blunted in binge
alcohol drinkers in both sexes. We also predicted that for the binge
drinking group alone, ratings of problematic alcohol use would correlate
with abnormal ERV signals consistent with between groups (i.e. binge
drinkers vs controls) abnormalities. Our results support our hypotheses
with the ERV (non-alcohol cue) signal blunted in binge drinkers and with
the magnitude of the abnormality correlating with ratings of
problematic alcohol use. This implies that consistent with hypotheses,
the motivational aspects of non-alcohol related stimuli are blunted in
binge drinkers. Better understanding of the mechanisms of harmful
alcohol use will, in time, facilitate the development of more effective
interventions, which should aim to decrease the motivational value of
alcohol and increase the motivational value of non-alcohol related
stimuli.
Original language | English |
---|---|
Pages (from-to) | 5685-5692 |
Number of pages | 8 |
Journal | The Journal of Neuroscience |
Volume | 43 |
Issue number | 31 |
Early online date | 30 Jan 2023 |
DOIs | |
Publication status | Published - 2 Aug 2023 |
Keywords
- Amygdala-hippocampal complex
- Binge drinking
- Model-based fMRI
- Prediction error signal
- Orbitofrontal
- Reinforcement learning
- Value