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Brain asymmetry reflects left‐right hemispheric differentiation, which is a quantitative brain phenotype that develops with age and can vary with psychiatric diagnoses. Previous studies have shown that substance dependence is associated with altered brain structure and function. However, it is unknown whether structural brain asymmetries are different in individuals with substance dependence compared with nondependent participants. Here, a mega‐analysis was performed using a collection of 22 structural brain MRI datasets from the ENIGMA Addiction Working Group. Structural asymmetries of cortical and subcortical regions were compared between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis (n = 1,796) and nondependent participants (n = 996). Substance‐general and substance‐specific effects on structural asymmetry were examined using separate models. We found that substance dependence was significantly associated with differences in volume asymmetry of the nucleus accumbens (NAcc; less rightward; Cohen's d = 0.15). This effect was driven by differences from controls in individuals with alcohol dependence (less rightward; Cohen's d = 0.10) and nicotine dependence (less rightward; Cohen's d = 0.11). These findings suggest that disrupted structural asymmetry in the NAcc may be a characteristic of substance dependence. [ABSTRACT FROM AUTHOR]

Previous studies suggest that individuals with substance use disorder have abnormally large responses to unexpected outcomes (reward prediction errors [RPEs]). However, there is much less information on RPE in individuals at risk of alcohol misuse, prior to neurobiological adaptations that might result from sustained alcohol use. Here, participants (mean age 23.77 years, range 18-32 years) performed the electrophysiological monetary incentive delay task. This task involved responding to a target stimulus following reward incentive cues to win, or avoid losing, the cued reward while brain activity was recorded under 64-channel EEG. The Alcohol Use Disorders Identification Test (AUDIT) was used to quantify at-risk alcohol use, with high (n = 22, mean AUDIT score: 13.82) and low (n = 22, mean AUDIT score: 5.77) alcohol use groups. Trial-by-trial RPEs were estimated using a Rescorla-Wagner reinforcement model based on behavioral data. A single-trial analysis revealed that the feedback-related negativity (FRN) and feedback P3 (fb-P3) event-related potential components were significantly modulated by RPEs. There was increased RPE-related fb-P3 amplitude for those in the high alcohol use group. Next, the mean amplitude of ERPs elicited by positive and negative RPEs were compared between groups. We found that high alcohol use participants had attenuated FRN amplitude in contrast with low alcohol use participants for both positive and negative RPEs but enhanced fb-P3 for both positive and negative RPE. These results, with differences in RPE in an at-risk group, suggest that RPE a potential vulnerability marker for alcohol use disorder. [ABSTRACT FROM AUTHOR]

Background: The Lieber–DeCarli alcoholic liquid diet is a classical method for establishing animal models of alcoholic cardiomyopathy (ACM). No study has reported whether the AIN‐93 diet, which is widely used as a standard diet for both long‐term and short‐term studies with laboratory animals, could be used to construct the ACM animal model. The present study intended to investigate whether the AIN‐93 diet could be used to establish a mouse ACM model. Methods: Twenty‐four C57BL/6 male mice were randomly divided into 4 equally sized groups. In ethanol (EtOH)‐fed groups, mice were fed a 4%‐EtOH (w/v, 28% of total calories) alcoholic liquid diet of Lieber–DeCarli or the AIN‐93 diet for chronic alcohol exposure for 180 days. In control‐fed groups, mice were fed with non‐EtOH liquid diets with the same calories as EtOH‐fed groups. Morphological observations of the hearts and molecular investigation of the brain natriuretic peptide (BNP) were carried out by echocardiography, hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining, real‐time quantitative polymerase chain reaction (qPCR), and enzyme‐linked immunosorbent assay. Results: Echocardiography showed that mice fed with either the 4%‐EtOH Lieber–DeCarli diet or the 4%‐EtOH AIN‐93 diet had dilated ventricles and poor cardiac function. IHC staining of BNP, qPCR of BNP mRNA, and plasma concentration of BNP showed an up‐regulated expression in mice fed with both the 4%‐EtOH Lieber–DeCarli and 4%‐EtOH AIN‐93 diets. Less fatty liver was also observed in mice fed the AIN‐93 alcoholic diet than those fed the Lieber–DeCarli alcoholic diet. Conclusions: The AIN‐93 alcoholic liquid diet can be used to establish ACM animal models, as with the conventional Lieber–DeCarli alcoholic liquid diet. [ABSTRACT FROM AUTHOR]

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]

Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy‐drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design Cross‐sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non‐dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings The younger AD adults had lower network segregation and higher integration relative to non‐dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area‐under‐the‐curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P‐value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P‐value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P‐value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P‐value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P‐value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P‐value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P‐value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P‐value = 0.048). Conclusions: Cross‐sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy‐drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre‐existing risk factor for problematic drinking. [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]