• Energy Research

AbstractAlcohol use disorder is characterised by disrupted reward learning, underpinned by dysfunctional cortico‐striatal reward pathways, although relatively little is known about the biology of reward processing in populations who engage in risky alcohol use. Cues that trigger reward anticipation can be categorized according to their learnt valence (i.e., positive vs. negative outcomes) and motivational salience (i.e., incentive vs. neutral cues). Separating EEG signals associated with these dimensions is challenging because of their inherent collinearity, but the recent application of machine learning methods to single EEG trials affords a solution. Here, the Alcohol Use Disorders Identification Test (AUDIT) was used to quantify risky alcohol use, with participants split into high alcohol (HA) (n = 22, mean AUDIT score: 13.82) and low alcohol (LA) (n = 22, mean AUDIT score: 5.77) groups. We applied machine learning multivariate single‐trial classification to the electroencephalography (EEG) data collected during reward anticipation. The LA group demonstrated significant valence discrimination in the early stages of reward anticipation within the cue‐P3 time window (400–550 ms), whereas the HA group was insensitive to valence within this time window. Notably, the LA, but not the HA group demonstrated a relationship between single‐trial variability in the early valence component and reaction times for gain and loss trials. This study evidences disrupted hypoactive valence sensitivity in the HA group, revealing potential neurophysiological markers for risky drinking behaviours which place individuals at‐risk of adverse health events.

AbstractAlcohol use disorder is characterised by disrupted reward learning, underpinned by dysfunctional cortico‐striatal reward pathways, although relatively little is known about the biology of reward processing in populations who engage in risky alcohol use. Cues that trigger reward anticipation can be categorized according to their learnt valence (i.e., positive vs. negative outcomes) and motivational salience (i.e., incentive vs. neutral cues). Separating EEG signals associated with these dimensions is challenging because of their inherent collinearity, but the recent application of machine learning methods to single EEG trials affords a solution. Here, the Alcohol Use Disorders Identification Test (AUDIT) was used to quantify risky alcohol use, with participants split into high alcohol (HA) (n = 22, mean AUDIT score: 13.82) and low alcohol (LA) (n = 22, mean AUDIT score: 5.77) groups. We applied machine learning multivariate single‐trial classification to the electroencephalography (EEG) data collected during reward anticipation. The LA group demonstrated significant valence discrimination in the early stages of reward anticipation within the cue‐P3 time window (400–550 ms), whereas the HA group was insensitive to valence within this time window. Notably, the LA, but not the HA group demonstrated a relationship between single‐trial variability in the early valence component and reaction times for gain and loss trials. This study evidences disrupted hypoactive valence sensitivity in the HA group, revealing potential neurophysiological markers for risky drinking behaviours which place individuals at‐risk of adverse health events.

Drug and alcohol dependence are global problems with substantial societal costs. There are few treatments for relapse prevention and therefore a pressing need for further study of brain mechanisms underpinning relapse circuitry. The Imperial College Cambridge Manchester (ICCAM) platform study is an experimental medicine approach to this problem: using functional magnetic resonance imaging (fMRI) techniques and selective pharmacological tools, it aims to explore the neuropharmacology of putative relapse pathways in cocaine, alcohol, opiate dependent, and healthy individuals to inform future drug development. Addiction studies typically involve small samples because of recruitment difficulties and attrition. We established the platform in three centres to assess the feasibility of a multisite approach to address these issues. Pharmacological modulation of reward, impulsivity and emotional reactivity were investigated in a monetary incentive delay task, an inhibitory control task, and an evocative images task, using selective antagonists for µ-opioid, dopamine D3 receptor (DRD3) and neurokinin 1 (NK1) receptors (naltrexone, GSK598809, vofopitant/aprepitant), in a placebo-controlled, randomised, crossover design. In two years, 609 scans were performed, with 155 individuals scanned at baseline. Attrition was low and the majority of individuals were sufficiently motivated to complete all five sessions ( n=87). We describe herein the study design, main aims, recruitment numbers, sample characteristics, and explain the test hypotheses and anticipated study outputs.

Drug and alcohol dependence are global problems with substantial societal costs. There are few treatments for relapse prevention and therefore a pressing need for further study of brain mechanisms underpinning relapse circuitry. The Imperial College Cambridge Manchester (ICCAM) platform study is an experimental medicine approach to this problem: using functional magnetic resonance imaging (fMRI) techniques and selective pharmacological tools, it aims to explore the neuropharmacology of putative relapse pathways in cocaine, alcohol, opiate dependent, and healthy individuals to inform future drug development. Addiction studies typically involve small samples because of recruitment difficulties and attrition. We established the platform in three centres to assess the feasibility of a multisite approach to address these issues. Pharmacological modulation of reward, impulsivity and emotional reactivity were investigated in a monetary incentive delay task, an inhibitory control task, and an evocative images task, using selective antagonists for µ-opioid, dopamine D3 receptor (DRD3) and neurokinin 1 (NK1) receptors (naltrexone, GSK598809, vofopitant/aprepitant), in a placebo-controlled, randomised, crossover design. In two years, 609 scans were performed, with 155 individuals scanned at baseline. Attrition was low and the majority of individuals were sufficiently motivated to complete all five sessions ( n=87). We describe herein the study design, main aims, recruitment numbers, sample characteristics, and explain the test hypotheses and anticipated study outputs.