The initiation of cannabis use in adolescence is predicted by sex-specific psychosocial and neurobiological features

[Factors Influencing Sexual Experiences in Adolescents Using a Random Forest Model: Secondary Data Analysis of the 2019~2021 Korea Youth Risk Behavior Web-based Survey Data]

PURPOSE

The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance."

METHODS

The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix.

RESULTS

The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol.

CONCLUSION

Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.

Cannabis use and dependence among festival attendees: results from the French OCTOPUS survey

BACKGROUND

Chronic use of cannabis is associated with an increased risk of psychosocial, mental and physical health impairments. Sociohealth institutions reach a very limited proportion of cannabis users in need of treatment. Using data collected from festival attendees, this study aimed to estimate the prevalence of dependent cannabis users and to characterize cannabis dependence.

METHODS

We used data from the cross-sectional OCTOPUS survey carried out at 13 music events in the French department of Loire-Atlantique between July 2017 and July 2018. 383 participants aged 18 or older underwent a face-to-face interview about their basic sociodemographics, tobacco use, alcohol use and past-year substance use. Using the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria, we estimated the prevalence of dependent cannabis users and characterized their dependence.

RESULTS

More than two-thirds of participants reported that they had used cannabis in the past 12 months. Among 194 regular cannabis users (at least monthly), 63.4% were dependent. At least 40% of regular users reported health and/or social consequences of cannabis use. Compared to nondependent cannabis users, dependent cannabis users were more likely to be stimulant users and hallucinogen users.

CONCLUSIONS

Dependent cannabis use is common among festival attendees, especially among stimulant or hallucinogen users. Festival settings may be important arenas for i) implementing efficient harm reduction measures to prevent dependence and ii) providing information on care structures and promoting the use of care to dependent users. In addition, healthcare professionals should be aware of trends in polysubstance use among dependent cannabis users.

Effects of Stimulant Treatment on Changes in Brain Activation During Reward Notifications in Drug Naïve Youth With ADHD

BACKGROUND

Research examining the potential effects of stimulant exposure in childhood on subsequent development of substance use disorder (SUD) have focused on differences in the brain reward system as a function of risk.

METHODS

18 drug naïve children ages 7 to 12 years (11 High Risk [ADHD + ODD/CD]; 7 Low Risk [ADHD only]), underwent fMRI scans before and after treatment with mixed amphetamine salts, extended release (MAS-XR). We examined correlations between clinical ratings and fMRI activation at baseline and following treatment as a function of risk status.

RESULTS

High Risk children had higher activation than Low Risk children at baseline during both the Reward and Surprising Non-Reward conditions. Treatment produced strong differential effects on brain activation pertinent to group and reward outcome.

CONCLUSIONS

Findings support the hypothesized role of reward mechanisms in SUD risk, and suggest that stimulant treatment may have differential effects on reward processing in relation to SUD risk.

Impact of Adolescent Cannabis Use on Neurocognitive and Brain Development

Research examining associations between frequent cannabis use in adolescence and brain-behavior outcomes has increased substantially over the past 2 decades. This review attempts to synthesize the state of evidence in this area of research while acknowledging challenges in interpretation. Although there is converging evidence that ongoing, frequent cannabis use in adolescence is associated with small reductions in cognitive functioning, there is still significant debate regarding the persistence of reductions after a period of abstinence. Similarly, there is controversy regarding the replicability of structural and functional neuroimaging findings related to frequent cannabis use in adolescence. Larger studies with informative designs are needed.

Factors associated with cannabis use in early adolescence

INTRODUCTION

We examined whether factors identified as associated with cannabis use at age 14 to 16 years are also associated with ever use at age 12.

METHODS

Participants in the AdoQuest study (n = 1852) were recruited in 2005 from among Grade 5 students in 29 French-language elementary schools in Montréal, Canada. Self-report data were collected from participants in Grade 5 (spring 2005) and 6 (fall 2005 and spring 2006) and from parents/guardians in 2006/07. Inclusion in the analytic sample (n = 1076; mean age [SD] = 10.7 [0.5]) required data from participant and parental questionnaires and data on cannabis use in Grade 6 (mean age [SD] = 11.7 [0.4]). We estimated associations between ever use at age 12 with 33 potential correlates, separately in unadjusted and adjusted logistic regression models.

RESULTS

Fifty-three participants (4.9%) reported ever use at age 12. Factors associated with higher odds of ever use included older age, identifying as male, lower household income, more weekly spending money, ever tried cigarettes or other tobacco products, ever drank alcohol or binge drank, ever gambled, friends or siblings smoke cigarettes, greater nicotine dependence, higher depressive symptoms and greater impulsivity. Protective factors included higher levels of parental/guardian monitoring and greater self-esteem and school connectedness.

CONCLUSION

Factors associated with cannabis use at later ages are also associated with ever use at age 12. Our findings suggest that surveillance for and interventions to prevent cannabis use are warranted before age 12.

Predictors of problematic adult alcohol, cannabis, and other substance use: A longitudinal study of two samples

This study examined whether a key set of adolescent and early adulthood risk factors predicts problematic alcohol, cannabis, and other substance use in established adulthood. Two independent samples from the Child Development Project (CDP; n = 585; 48% girls; 81% White, 17% Black, 2% other race/ethnicity) and Fast Track (FT; n = 463; 45% girls; 52% White, 43% Black, 5% other race/ethnicity) were recruited in childhood and followed through age 34 (CDP) or 32 (FT). Predictors of substance use were assessed in adolescence based on adolescent and parent reports and in early adulthood based on adult self-reports. Adults reported their own problematic substance use in established adulthood. In both samples, more risk factors from adolescence and early adulthood predicted problematic alcohol use in established adulthood (compared to problematic cannabis use and other substance use). Externalizing behaviors and prior substance use in early adulthood were consistent predictors of problematic alcohol and cannabis misuse in established adulthood across samples; other predictors were specific to the sample and type of substance misuse. Prevention efforts might benefit from tailoring to address risk factors for specific substances, but prioritizing prevention of externalizing behaviors holds promise for preventing both alcohol and cannabis misuse in established adulthood.

Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction

Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.

Impact of Adolescent Cannabis Use on Neurocognitive and Brain Development

Research examining associations between frequent cannabis use in adolescence and brain-behavior outcomes has increased substantially over the past 2 decades. This review attempts to synthesize the state of evidence in this area of research while acknowledging challenges in interpretation. Although there is converging evidence that ongoing, frequent cannabis use in adolescence is associated with small reductions in cognitive functioning, there is still significant debate regarding the persistence of reductions after a period of abstinence. Similarly, there is controversy regarding the replicability of structural and functional neuroimaging findings related to frequent cannabis use in adolescence. Larger studies with informative designs are needed.

The brain in social context: A systematic review of substance use and social processing from adolescence to young adulthood

Substance use escalates between adolescence and young adulthood, and most experimentation occurs among peers. To understand underlying mechanisms, research has focused on neural response during relevant psychological processes. Functional magnetic resonance imaging (fMRI) research provides a wealth of information about brain activity when processing monetary rewards; however, most studies have used tasks devoid of social stimuli. Given that adolescent neurodevelopment is sculpted by the push-and-pull of peers and emotions, identifying neural substrates is important for intervention. We systematically reviewed 28 fMRI studies examining substance use and neural responses to stimuli including social reward, emotional faces, social influence, and social stressors. We found substance use was positively associated with social-reward activity (e.g., in the ventral striatum), and negatively with social-stress activity (e.g., in the amygdala). For emotion, findings were mixed with more use linked to heightened response (e.g., in amygdala), but also with decreased response (e.g., in insula). For social influence, evidence supported both positive (e.g., cannabis and nucleus accumbens during conformity) and negative (e.g., polydrug and ventromedial PFC during peers' choices) relations between activity and use. Based on the literature, we offer recommendations for future research on the neural processing of social information to better identify risks for substance use.

The ABCD stop signal data: Response to Bissett et al

This paper responds to a recent critique by Bissett et al. of the fMRI Stop task used in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®). The critique focuses primarily on a task design feature related to race model assumptions (i.e., that the Go and Stop processes are fully independent). In response, we note that the race model is quite robust against violations of its assumptions. Most importantly, while Bissett raises conceptual concerns with the task we focus here on analyzes of the task data and conclude that the concerns appear to have minimal impact on the neuroimaging data (the validity of which do not rely on race model assumptions) and have far less of an impact on the performance data than the critique suggests. We note that Bissett did not apply any performance-based exclusions to the data they analyzed, a number of the trial coding errors they flagged were already identified and corrected in ABCD annual data releases, a number of their secondary concerns reflect sensible design decisions and, indeed, their own computational modeling of the ABCD Stop task suggests the problems they identify have just a modest impact on the rank ordering of individual differences in subject performance.

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) "women," "men," or "sex"; (b) "big data," "artificial intelligence," or "NLP"; and (c) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in healthresearch, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review

In the last decade, extensive research has emerged on the predictive value of brain morphology for substance use initiation and related problems during adolescence. This systematic review provides an overview of longitudinal studies on pre-existing brain variations and later initiation of alcohol, tobacco, and cannabis use (N = 18). Adolescent structural neuroimaging studies that started before substance use initiation suggest that a smaller anterior cingulate cortex (ACC) volume, thicker or smaller superior frontal gyrus, and larger nucleus accumbens (NAcc) volume are associated with future alcohol use. Also, both smaller and larger orbitofrontal cortex (OFC) volumes were associated with future cannabis and combined alcohol/cannabis use. Smaller amygdala volumes were related to future daily tobacco smoking. These findings could point to specific vulnerabilities for adolescent substance use, as these brain areas are involved in cognitive control (ACC), reward (NAcc), motivation (OFC), and emotional memory (amygdala). However, the reported findings were inconsistent in directionality and laterality, and the largest study on alcohol use predictors reported null findings. Therefore, large population-based longitudinal studies should investigate the robustness and mechanisms of these associations. We suggested future research directions regarding sample selection, timing of baseline and follow-up measurements, and a harmonization approach of study methods.

Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants

Cannabis use peaks in adolescence, and adolescents may be more vulnerable to the neural effects of cannabis and cannabis-related harms due to ongoing brain development during this period. In light of ongoing cannabis policy changes, increased availability, reduced perceptions of harm, heightened interest in medicinal applications of cannabis, and drastic increases in cannabis potency, it is essential to establish an understanding of cannabis effects on the developing adolescent brain. This systematic review aims to: (1) synthesize extant literature on functional and structural neural alterations associated with cannabis use during adolescence and emerging adulthood; (2) identify gaps in the literature that critically impede our ability to accurately assess the effect of cannabis on adolescent brain function and development; and (3) provide recommendations for future research to bridge these gaps and elucidate the mechanisms underlying cannabis-related harms in adolescence and emerging adulthood, with the long-term goal of facilitating the development of improved prevention, early intervention, and treatment approaches targeting adolescent cannabis users (CU). Based on a systematic search of Medline and PsycInfo and other non-systematic sources, we identified 90 studies including 9441 adolescents and emerging adults (n = 3924 CU, n = 5517 non-CU), which provide preliminary evidence for functional and structural alterations in frontoparietal, frontolimbic, frontostriatal, and cerebellar regions among adolescent cannabis users. Larger, more rigorous studies are essential to reconcile divergent results, assess potential moderators of cannabis effects on the developing brain, disentangle risk factors for use from consequences of exposure, and elucidate the extent to which cannabis effects are reversible with abstinence. Guidelines for conducting this work are provided.

Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use

Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a "common SUB genetic factor" and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Precision Mental Health Care for Cannabis Use Disorder: Utility of a bioSocial Cognitive Theory to Inform Treatment

Globally, cannabis is the most frequently used controlled substance after alcohol and tobacco. Rates of cannabis use are steadily increasing in many countries and there is emerging evidence that there is likely to be greater risk due to increased concentrations of delta-9-tetrahydrocannabinol (THC). Cannabis use and Cannabis Use Disorder (CUD) has been linked to a wide range of adverse health outcomes. Several biological, psychological, and social risk factors are potential targets for effective evidence-based treatments for CUD. There are no effective medications for CUD and psychological interventions are the main form of treatment. Psychological treatments based on Social Cognitive Theory (SCT) emphasize the importance of targeting 2 keys psychological mechanisms: drug outcome expectancies and low drug refusal self-efficacy. This mini-review summarizes the evidence on the role of these mechanisms in the initiation, maintenance, and cessation of cannabis use. It also reviews recent evidence showing how these psychological mechanisms are affected by social and biologically-based risk factors. A new bioSocial Cognitive Theory (bSCT) is outlined that integrates these findings and implications for psychological cannabis interventions are discussed. Preliminary evidence supports the application of bSCT to improve intervention outcomes through better targeted treatment.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.

The IMAGEN study: a decade of imaging genetics in adolescents

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype 'drug use' to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

Longitudinal associations between amygdala reactivity and cannabis use in a large sample of adolescents

RATIONALE

The amygdala is a key brain structure to study in relation to cannabis use as reflected by its high-density of cannabinoid receptors and functional reactivity to processes relevant to drug use. Previously, we identified a correlation between cannabis use in early adolescence and amygdala hyper-reactivity to angry faces (Spechler et al. 2015).

OBJECTIVES

Here, we leveraged the longitudinal aspect of the same dataset (the IMAGEN study) to determine (1) if amygdala hyper-reactivity predicts future cannabis use and (2) if amygdala reactivity is affected by prolonged cannabis exposure during adolescence.

METHODS

First, linear regressions predicted the level of cannabis use by age 19 using amygdala reactivity to angry faces measured at age 14 prior to cannabis exposure in a sample of 1119 participants. Next, we evaluated the time course of amygdala functional development from age 14 to 19 for angry face processing and how it might be associated with protracted cannabis use throughout this developmental window. We compared the sample from Spechler et al. 2015, the majority of whom escalated their use over the 5-year interval, to a matched sample of non-users.

RESULTS

Right amygdala reactivity to angry faces significantly predicted cannabis use 5 years later in a dose-response fashion. Cannabis-naïve adolescents demonstrated the lowest levels of amygdala reactivity. No such predictive relationship was identified for alcohol or cigarette use. Next, follow-up analyses indicated a significant group-by-time interaction for the right amygdala.

CONCLUSIONS

(1) Right amygdala hyper-reactivity is predictive of future cannabis use, and (2) protracted cannabis exposure during adolescence may alter the rate of neurotypical functional development.

Modeling the Comorbidity of Cannabis Abuse and Conduct Disorder/Conduct Problems from a Cognitive Neuroscience Perspective

Objective: A cognitive neuroscience perspective seeks to understand behavior, in this case the comorbidity of cannabis abuse and conduct disorder/conduct problems, in terms of dysfunction in cognitive processes underpinned by neural processes. The goal of this review is to articulate a cognitive neuroscience account of this comorbidity. Methods: Literature on the following issues will be reviewed: (i) the longitudinal relationship between cannabis abuse and conduct disorder/conduct problems (CD/CP); (ii) the extent to which there are genetic and environmental (specifically maltreatment) factors that underpin this relationship; (iii) forms of neurocognitive function that are reported dysfunctional in CD/CP and also, when dysfunctional, appear to be risk factors for future cannabis abuse; and (iv) the extent to which cannabis abuse may further compromise these systems leading to increased future abuse and greater conduct problems. Results: CD/CP typically predate cannabis abuse. There appear to be shared genetic factors that contribute to the relationship between CD/CP and cannabis abuse. Moreover, trauma exposure increases risk for both cannabis abuse and CP/CD. One form of neurocognitive dysfunction, response disinhibition, that likely exacerbates the symptomatology of many individuals with CD also appears to increase the risk for cannabis abuse. The literature with respect to other forms of neurocognitive dysfunction remains inconclusive. Conclusions: Based on the literature, a causal model of the comorbidity of cannabis abuse and CD/CP is developed.

Cannabis Use in Adolescence: A Review of Neuroimaging Findings

Objective: Shifting policies and widespread acceptance of cannabis for medical and/or recreational purposes have fueled worries of increased cannabis initiation and use in adolescents. In particular, the adolescent period is thought to be associated with an increased susceptibility to the potential harms of repeated cannabis use, due to being a critical period for neuromaturational events in the brain. This review investigates the neuroimaging evidence of brain harms attributable to adolescent cannabis use. Methods: PubMed and Scopus searches were conducted for empirical articles that examined neuroimaging effects in both adolescent cannabis users and adult user studies that explored the effect of age at cannabis use onset on the brain. Results: We found 43 studies that examined brain effect (structural and functional magnetic resonance imaging) in adolescent cannabis users and 20 that examined the link between onset age of cannabis use and brain effects in adult users. Studies on adolescent cannabis users relative to nonusers mainly implicate frontal and parietal regions and associated brain activation in relation to inhibitory control, reward, and memory. However, studies in adults are more mixed, many of which did not observe an effect of onset age of cannabis use on brain imaging metrics. Conclusions: While there is some evidence of compromised frontoparietal structure and function in adolescent cannabis use, it remains unclear whether the observed effects are specifically attributable to adolescent onset of use or general cannabis use-related factors such as depressive symptoms. The relative contribution of adolescent onset of cannabis use and use chronicity will have to be more comprehensively examined in prospective, longitudinal studies with more rigorous measures of cannabis use (dosage, exposure, dependence, constituent compounds such as the relative cannabinoid levels).

Examining the association between exposure to various screen time sedentary behaviours and cannabis use among youth in the COMPASS study

Background

Canadian youth are among the top users of cannabis globally. The Canadian federal government identified protecting youth from cannabis-related harms as a key public health objective aligned with the legalization and strict regulation of cannabis. While there are well-established associations between screen time sedentary behaviour (STSB) and alcohol and tobacco use, the association with cannabis use is understudied. The purpose of this study is to examine the association between various types of STSBs and cannabis use in a large sample of Canadian youth.

Methods

Using cross-sectional data from 46,957 grade 9 to 12 students participating in year 5 of the COMPASS host study (2016–2017), four gender-stratified ordinal logistic regression models were used to examine how total STSB and four different types of STSBs (watching/streaming TV shows/movies, playing video games, Internet use, emailing/messaging/texting) are associated with frequency of cannabis use.

Results

One-quarter of participants (24.9%) reported using cannabis in past 12 months; the largest proportion of this group (37.9%) reported rare/sporadic use. Overall, participants spent an average 7.45 ( ±5.26) hours/day on STSBs. Total STSB was positively associated with more frequent cannabis use, and when separated by type, internet use and messaging were significant. Playing video games and watching TV/movies were also significantly associated with more frequent cannabis use, but only for females.

Conclusions

The associations between frequency of cannabis use and various measures of STSBs suggest that screen time may be a risk factor for cannabis use among youth. This association may be mediated by youths’ mental wellbeing, given emerging evidence that STSB is a risk factor for poor mental health, and the tendency for individuals to use substances as a coping mechanism. Further, the ubiquity of pro-substance use content on the internet may also contribute to increased exposure to and normalization of cannabis, further promoting its use.

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Youth average of overall STSBs was 7.48 h per day.

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Greater hours of overall STSB increased likelihood of higher frequency cannabis use.

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Internet use and messaging STSBs were both positively associated with cannabis use.

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Gender differences present for TV watching and video gaming STSBs.

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Cyber-bullying victims show increased likelihood for higher frequency cannabis use.

Cannabis and the developing brain: What does the evidence say?

Cannabis use during adolescence has been linked to deleterious effects on brain integrity. This article summarizes findings from two prospective investigations (3 and 6 years, on average) on adolescent cannabis use from our laboratory that utilize structural neuroimaging and neurocognitive assessment approaches. Across most studies, findings suggest recency, frequency, and age of onset of cannabis use are likely key variables in predicting poorer neural health outcomes. There is some evidence that preexisting differences in brain architecture may also contribute to vulnerability and outcome differences. Ongoing large-scale prospective studies of youth will be able to disentangle how both cannabis use as well as pre and postexposure differences play a role in divergent outcomes among youth who use cannabis.