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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 FROM AUTHOR]

Chronic alcohol consumption leads to myocardial injury, ventricle dilation, and cardiac dysfunction, which is defined as alcoholic cardiomyopathy (ACM). To explore the induced myocardial injury and underlying mechanism of ACM, the Liber-DeCarli liquid diet was used to establish an animal model of ACM and histopathology, echocardiography, molecular biology, and metabolomics were employed. Hematoxylin-eosin and Masson's trichrome staining revealed disordered myocardial structure and local fibrosis in the ACM group. Echocardiography revealed thinning wall and dilation of the left ventricle and decreased cardiac function in the ACM group, with increased serum levels of brain natriuretic peptide (BNP) and expression of myocardial BNP mRNA measured through enzyme-linked immunosorbent assay and real-time quantitative polymerase chain reaction (PCR), respectively. Through metabolomic analysis of myocardium specimens, 297 differentially expressed metabolites were identified which were involved in KEGG pathways related to the biosynthesis of unsaturated fatty acids, vitamin digestion and absorption, oxidative phosphorylation, pentose phosphate, and purine and pyrimidine metabolism. The present study demonstrated chronic alcohol consumption caused disordered cardiomyocyte structure, thinning and dilation of the left ventricle, and decreased cardiac function. Metabolomic analysis of myocardium specimens and KEGG enrichment analysis further demonstrated that several differentially expressed metabolites and pathways were involved in the ACM group, which suggests potential causes of myocardial injury due to chronic alcohol exposure and provides insight for further research elucidating the underlying mechanisms of ACM. [ABSTRACT FROM AUTHOR]

Impulsivity is a multidimensional construct that is related to different aspects of alcohol use, abuse, and dependence. Inhibitory control, one facet of impulsivity, can be assayed using the stop-signal task (SST) and quantified behaviorally via the stop-signal reaction time (SSRT) and electrophysiologically using event-related potentials (ERPs). Research on the relationship between alcohol use and SSRTs, and between alcohol use and inhibitory-control ERPs, is mixed. Here, adult alcohol users (n = 79), with a wide range of scores on the Alcohol Use Disorders Identification Test (AUDIT), completed the SST under electroencephalography (EEG) (70% of participants had AUDIT total scores greater than or equal to 8). Other measures, including demographic, self-report, and task-based measures of impulsivity, personality, and psychological factors, were also recorded. A machine-learning method with penalized linear regression was used to correlate individual differences in alcohol use with impulsivity measures. Four separate models were tested, with out-of-sample validation used to quantify performance. ERPs alone statistically predicted alcohol use (cross-validated r = 0.28), with both early and late ERP components contributing to the model (larger N2, but smaller P3, amplitude). Behavioral data from a wide range of impulsivity measures were also associated with alcohol use (r = 0.37). SSRT was a relatively weak statistical predictor, whereas the Stroop interference effect was relatively strong. The addition of nonimpulsivity behavioral measures did not improve the correlation (r = 0.34) and was similar when ERPs were combined with non-ERP data (r = 0.29). These findings show that inhibitory control ERPs are robustly correlated individual differences in alcohol use. [ABSTRACT FROM AUTHOR]