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BackgroundHigh rates of cannabis and illicit drug use are experienced by young people during the final stages of neurodevelopment (aged 15-24 years), a period characterized by high neuroplasticity. Frequent drug use during this time may interfere with neurophysiological and neuropsychological development pathways, potentially leading to ongoing unfavorable neuroadaptations. The dose-response relationship between illicit drug use, exposure, and individual neurodevelopmental variation is unknown but salient with global shifts in the legal landscape and increasingly liberal attitudes and perceptions of the harm caused by cannabis and illicit drugs. ObjectiveThis systematic review aims to synthesize longitudinal studies that investigate the effects of illicit drug use on structural, functional, and cognitive brain domains in individuals under the neural age of adulthood (25 years). This protocol outlines prospective methods that will facilitate an exhaustive review of the literature exploring pre- and post-drug use brain abnormalities arising during neurodevelopment. MethodsFive electronic databases (Medline, Embase, PsycINFO, ProQuest Central, and Web of Science) will be systematically searched between 1990 and 2019. The search terms will be a combination of MeSH (Medical Subject Headings), with keywords adapted to each database. Study reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and if relevant, study quality will be assessed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. Eligible studies are those that sampled youth exposed to cannabis or illicit drugs and employed neurophysiological or neuropsychological assessment techniques. Studies will be excluded if participants had been clinically diagnosed with any psychiatric, neurological, or pharmacological condition. ResultsThis is an ongoing review. As of February 2020, papers are in full-text screening, with results predicted to be complete by July 2020. ConclusionsIntegrating data collected on the three brain domains will enable an assessment of the links between structural, functional, and cognitive brain health across individuals and may support the early detection and prevention of neurodevelopmental harm. Trial RegistrationPROSPERO CRD42020151442; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=151442 International Registered Report Identifier (IRRID)PRR1-10.2196/18349

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohens d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

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.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Many studies have reported that heavy substance use is associated with impaired response inhibition. Studies typically focused on associations with a single substance, while polysubstance use is common. Further, most studies compared heavy users with light/non-users, though substance use occurs along a continuum. The current mega-analysis accounted for these issues by aggregating individual data from 43 studies (3610 adult participants) that used the Go/No-Go (GNG) or Stop-signal task (SST) to assess inhibition among mostly "recreational" substance users (i.e., the rate of substance use disorders was low). Main and interaction effects of substance use, demographics, and task-characteristics were entered in a linear mixed model. Contrary to many studies and reviews in the field, we found that only lifetime cannabis use was associated with impaired response inhibition in the SST. An interaction effect was also observed: the relationship between tobacco use and response inhibition (in the SST) differed between cannabis users and non-users, with a negative association between tobacco use and inhibition in the cannabis non-users. In addition, participants' age, education level, and some task characteristics influenced inhibition outcomes. Overall, we found limited support for impaired inhibition among substance users when controlling for demographics and task-characteristics.

BackgroundError processing and inhibitory control enable the adjustment of behaviors to meet task demands. Functional magnetic resonance imaging studies report brain activation abnormalities in patients with obsessive-compulsive disorder (OCD) during both processes. However, conclusions are limited by inconsistencies in the literature and small sample sizes. Therefore, the aim here was to perform a meta-analysis of the existing literature using unthresholded statistical maps from previous studies.MethodsA voxelwise seed-based d mapping meta-analysis was performed using t-maps from studies comparing patients with OCD and healthy control subjects (HCs) during error processing and inhibitory control. For the error processing analysis, 239 patients with OCD (120 male; 79 medicated) and 229 HCs (129 male) were included, while the inhibitory control analysis included 245 patients with OCD (120 male; 91 medicated) and 239 HCs (135 male).ResultsPatients with OCD, relative to HCs, showed longer inhibitory control reaction time (standardized mean difference = 0.20, p = .03, 95% confidence interval = 0.016, 0.393) and more inhibitory control errors (standardized mean difference = 0.22, p = .02, 95% confidence interval = 0.039, 0.399). In the brain, patients showed hyperactivation in the bilateral dorsal anterior cingulate cortex, supplementary motor area, and pre-supplementary motor area as well as right anterior insula/frontal operculum and anterior lateral prefrontal cortex during error processing but showed hypoactivation during inhibitory control in the rostral and ventral anterior cingulate cortices and bilateral thalamus/caudate, as well as the right anterior insula/frontal operculum, supramarginal gyrus, and medial orbitofrontal cortex (all seed-based d mapping z value >2, p < .001).ConclusionsA hyperactive error processing mechanism in conjunction with impairments in implementing inhibitory control may underlie deficits in stopping unwanted compulsive behaviors in the disorder.

ObjectiveAlthough lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes.MethodBrain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings.ResultsLower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects.ConclusionsThe results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Cognitive control and reward-related abnormalities are centrally implicated in addiction. However, findings from longitudinal studies addressing neurocognitive predictors of addictive behaviors are mixed. Further, little work has been conducted predicting non-substance-related addictive behaviors. Our study aimed to assess predictors of substance and non-substance addictive behaviors in a community sample, systematically evaluating each neurocognitive function's independent influence on addictive behavior. Australians (N = 294; 51.7% female; M[SD] age = 24.8[4.7] years) completed online neurocognitive tasks and surveys at baseline and 3-month follow-up. Self-report scales assessed problematic alcohol use, addictive eating (AE), problematic pornography use (PPU), and problematic internet use (PUI) at 3- and 6-month follow-ups. Linear regressions with bootstrapping assessed neurocognitive predictors for each addictive behavior across a 6-month period. Neurocognition at baseline did not predict AE or PUI severity at 6-month follow-up. Less delay discounting at baseline predicted higher PPU at 6-month follow-up (β = −0.16, p = 0.005). Poorer performance monitoring at baseline predicted higher AE at 3-month follow-up (β = −0.16, p = 0.004), and more reward-related attentional capture at 3-months predicted higher AE at 6-month follow-up (β = 0.14, p = 0.033). Less reward-related attentional capture (β = −0.14, p = 0.003) and less risk-taking under ambiguity (β = −0.11, p = 0.029) at baseline predicted higher PUI at 3-month follow-up. All findings were of small effect size. None of the neurocognitive variables predicted problematic alcohol use. We were unable to identify a core set of specific neurocognitive functions that reliably predict multiple addictive behavior types. However, our findings indicate both cognitive control and reward-related functions predict non-substance addictive behaviors in different ways. Findings suggest that there may be partially distinct neurocognitive mechanisms contributing to addiction depending on the specific addictive behavior. [ABSTRACT FROM AUTHOR]

Drug use is a choice with immediate positive outcomes, but long-term negative consequences. Thus, the repeated use of drugs in the face of negative consequences suggests dysfunction in the cognitive mechanisms underpinning decision-making. This cognitive dysfunction can be mapped into three stages: the formation of preferences involving valuation of decision options; choice implementation including motivation, self-regulation and inhibitory processes; and feedback processing implicating reinforcement learning. This article reviews behavioral studies that have examined alterations in these three stages of decision-making in people with substance use disorders. Relative to healthy individuals, those with alcohol, cannabis, stimulant and opioid use disorders value risky options more highly during the formation of preferences; have a greater appetite for superficially attractive rewards during choice implementation; and are both more efficient in learning from rewards and less efficient in learning from losses during feedback processing. These observed decision-making deficits are most likely due to both premorbid factors and drug-induced effects. Because decision-making deficits have been prospectively associated with a greater risk of drug relapse, we advocate for greater research on modulating the component stages that give rise to dysfunctional decision-making in disorders of addiction.