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Background: Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model). Methods: We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored re-calibrating the model to account for differences in population prevalence. Results: The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999. Conclusion: The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life., 25 pages

Supplemental material, sj-docx-1-asm-10.1177_10731911231164627 for Differential Item Functioning in Reports of Delinquent Behavior Between Black and White Youth: Evidence of Measurement Bias in Self-Reports of Arrest in the Adolescent Brain Cognitive Development Study by Sarah J. Brislin, D. Angus Clark, Duncan B. Clark, C. Emily Durbin, Ashley C. Parr, Lia Ahonen, Kaston D. Anderson-Carpenter, Mary M. Heitzeg, Beatriz Luna, Chandra Sripada, Robert A. Zucker and Brian M. Hicks in Assessment

Supplemental material, sj-docx-2-asm-10.1177_10731911231164627 for Differential Item Functioning in Reports of Delinquent Behavior Between Black and White Youth: Evidence of Measurement Bias in Self-Reports of Arrest in the Adolescent Brain Cognitive Development Study by Sarah J. Brislin, D. Angus Clark, Duncan B. Clark, C. Emily Durbin, Ashley C. Parr, Lia Ahonen, Kaston D. Anderson-Carpenter, Mary M. Heitzeg, Beatriz Luna, Chandra Sripada, Robert A. Zucker and Brian M. Hicks in Assessment

Contains fulltext : 200963.pdf (Publisher’s version ) (Closed access) Objective: Although 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. Method: Brain 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. Results: Lower 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. Conclusions: The 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. 10 p.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

The ABCD study is recruiting and following the brain development and health of over 10,000 9-10 year olds through adolescence. The imaging component of the study was developed by the ABCD Data Analysis and Informatics Center (DAIC) and the ABCD Imaging Acquisition Workgroup. Imaging methods and assessments were selected, optimized and harmonized across all 21 sites to measure brain structure and function relevant to adolescent development and addiction. This article provides an overview of the imaging procedures of the ABCD study, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature.

SUMMARY Emotional behavior is in part heritable and often disrupted in psychopathology. Identification of specific genetic variants that drive this heritability may provide important new insight into molecular and neurobiological mechanisms involved in emotionality. Our results demonstrate that the presynaptic vesicular monoamine transporter 1 (VMAT1) Thr136Ile (rs1390938) polymorphism is functional in vitro, with the Ile allele leading to increased monoamine transport into presynaptic vesicles. Moreover, we show that the Thr136Ile variant predicts differential responses in emotional brain circuits consistent with its effects in vitro. Lastly, deep sequencing of bipolar disorder (BPD) patients and controls identified several rare novel VMAT1 variants. The variant Phe84Ser was only present in individuals with BPD and leads to marked increase monoamine transport in vitro. Taken together, our data show that VMAT1 polymorphisms influence monoamine signaling, the functional response of emotional brain circuits, and risk for psychopathology.

Dopamine receptor concentrations, primarily in the striatum, are hypothesized to contribute to a developmental imbalance between subcortical and prefrontal control systems in emerging adulthood potentially biasing motivation and increasing risky behaviors. Positron emission tomography (PET) studies have found significant reductions in striatal dopamine D2 receptors, and blunted amphetamine-induced dopamine release, in substance users compared to healthy controls. Extant literature is limited and inconsistent concerning vulnerability associated with having a family history of substance abuse (FH+). Some studies have reported familial liability associated with higher dopamine receptor levels, reduced dopamine response to stimulant challenges and decreased response to oral alcohol. However other reports have failed to find group differences based on family history. We explored the interaction of familial liability and behavioral risk with multi-modal molecular and neural imaging of the dopaminergic system. Forty-four young adult male subjects performed monetary incentive delay tasks during both [11C]raclopride PET and functional magnetic resonance imaging (fMRI) scans. FH+ subjects were identified as Low (n=24) or High Risk (n=9) based on early initiation of drunkenness. FH+ High Risk subjects exhibited heightened striatal dopamine response to monetary reward but did not differ in neural activations compared with FH+ Low Risk subjects and controls with no familial loading (FH-, n=11). Across all subjects, a negative relationship was found between dopamine release and age of first drunkenness and a positive relationship with neural response to reward receipt. These results suggest that in at-risk individuals higher dopamine transmission associated with monetary reward may represent a particularly useful neurobiological phenotype.

Contains fulltext : 157596.pdf (Publisher’s version ) (Closed access) Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction. 21 p.