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Background and Aims: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies., Design and Setting: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years)., Cases: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected., Measurements: Graph theory metrics of segregation and integration were used to summarize SCN., Findings: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar., Conclusion: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology., (© 2023 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

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., (© 2022 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Methanol poisoning is responsible for high morbidity and mortality, and the elevated concentration of methanol in the body is the major criteria for forensic diagnosis of methanol poisoning. However, in cases with lower methanol concentrations, diagnosis is mainly dependent on highly variable postmortem manifestations.Herein, we report a fatal methanol poisoning cases that two subjects ingested the same amount of methanol simultaneously, yet the subject one presented only non-specific gastrointestinal and mild central nervous system symptoms, while the other subject exhibited typical toxic manifestations with the exception of visual compromise. In autopsy, subject number 1 did not show typical pathological changes caused by methanol poisoning, except for the elevated levels of methanol in body fluids. On the contrary, bilateral basal ganglia hemorrhage and necrosis caused by methanol-induced brain lesion was observed in case number 2. Due to the complex and multifactorial process of methanol intoxication, many factors, including comprehensive autopsy, quantitative detection of methanol and formic acid, and genotype analysis, participate in its metabolism and toxicity, and can impact the clinical symptoms, prognosis and postmortem manifestations. Therefore, a combination of multiple diagnosis methods may more accurately contribute to the forensic diagnosis of methanol poisoning and should be tailored on an individual basis. This case report also reviews forensic diagnosis literature on methanol poisoning to provide a reference for forensic pathologists., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant's alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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]