At‐risk alcohol users have disrupted valence discrimination during reward anticipation. (7th April 2022)

• Record Type: Journal Article
• Authors: Komarnyckyj, Mica; Retzler, Chris; Cao, Zhipeng; Ganis, Giorgio; Murphy, Anna; Whelan, Robert; Fouragnan, Elsa Florence
• Is Part Of: Addiction biology. Volume 27:Number 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-07
• Subjects: alcohol -- AUDIT -- EEG -- humans -- machine learning -- reward; Substance abuse -- Periodicals; Substance abuse -- Physiological aspects -- Periodicals; Substance-Related Disorders -- periodicals; 616.86
• DOI: 10.1111/adb.13174
• Languages: English
• ISSNs: 1355-6215

Abstract

Abstract: Alcohol 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. Abstract : Here, we used a machine learning approach to disentangle two dimensions of reward anticipation—valence and salience—in young adults with and without hazardous drinking behaviours. Within the cue‐P3 time window, valence discriminator performance (i.e., Az values) was significantly higher in the low risk alcohol use (LA) group compared with the hazardous drinking (HA) group. This study evidences disrupted valence but intact salience sensitivity in the at‐risk group, revealing potential neurophysiological markers for vulnerability to AD.