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. Author manuscript; available in PMC: 2018 Jun 1.
Published in final edited form as: J Child Psychol Psychiatry. 2017 Jul 17;59(6):618–627. doi: 10.1111/jcpp.12783

Research Review: What Have We Learned About Adolescent Substance Use?

Kevin M Gray 1, Lindsay M Squeglia 1
PMCID: PMC5771977  NIHMSID: NIHMS929935  PMID: 28714184

Abstract

Background

Adolescence is a critical biological, psychological, and social developmental stage involving heightened risk for substance use and associated adverse consequences. This review, synthesizing emerging findings on this complex topic, is intended to inform research and clinical care focused on adolescents.

Methods

Literature searches were conducted using PubMed, yielding a cross-section of observational and interventional studies focused on adolescent substance use. Findings were organized and categorized to cover key areas of epidemiology, neurobiology, prevention, and treatment.

Findings

Adolescent substance-related attitudes and use patterns have evolved over time, informed by adult and peer behaviors, public policy, media messaging, substance availability, and other variables. A number of risk and resiliency factors contribute to individual differences in substance use and related consequences. Advances in observational techniques have provided enhanced understanding of adolescent brain development, and its implications for substance use. Prevention efforts have yielded mixed results, and while a number of adolescent-targeted evidence-based treatments for substance use disorders have been developed, effect sizes are generally modest, indicating the need for further research to enhance prevention and treatment outcomes.

Conclusions

Substance use in adolescence is heterogeneous, ranging from normative to pathological, and can lead to significant acute and long-term morbidity and mortality. Understanding risk and resiliency factors, underlying neurobiology, and optimal developmentally-sensitive interventions is critical in addressing substance-associated problems in adolescence.

Keywords: adolescence, development, substance use, addiction, neurobiology, prevention, treatment

Introduction

Adolescence is a critical developmental phase involving significant physical, cognitive, emotional, social, and behavioral changes. The neurobiological alterations underlying these complex developmental processes may predispose adolescents to initiate substance use, to develop substance use disorders, and to experience potentially serious and long-lasting substance-related adverse consequences. Given the high prevalence of problematic substance use in this age range, and given the unique biopsychosocial context, research has increasingly focused on characterizing adolescent substance use disorders, with particular focus on optimizing and disseminating evidence-based prevention, assessment, and treatment interventions. The present review was conducted to provide an overview of recent clinically relevant advances in the field.

Methods

Literature searches were conducted via PubMed, and information was compiled to yield an overview of clinically relevant advances in knowledge regarding adolescent substance use, spanning epidemiology, neurobiology, assessment, and interventions.

Results

Epidemiology

Prevalence of Use

Substance use is typically initiated during adolescence. Alcohol is the most commonly used substance among adolescents, with 64% of 18 year olds endorsing lifetime alcohol use, followed by marijuana (45%) and cigarette use (31%) (Johnston et al., 2017). Overall, rates of adolescent substance use have remained relatively stable over the past several years, with a few notable exceptions. Cigarette use has declined dramatically over the past several decades, while e-cigarette use has become more prevalent in recent years. Thirteen percent of teens report using e-cigarettes in the past month, compared to 3% reporting cigarette use, with a concerning increase in the number of never-smoking youth reporting e-cigarette use (Bunnell et al., 2015). Another recent trend includes increased frequency of marijuana use, with 6% of 18 year olds reporting using marijuana daily (Johnston et al., 2017). Attitudes about marijuana use continue to move toward greater acceptance; the perception of risk about using marijuana is at the lowest point ever recorded, with a third of 18 year olds reporting that regular marijuana use is harmful. While overall rates of marijuana use have remained relatively stable over the past several years, decreased perception of harm typically corresponds with increased use. Other drug use is relatively uncommon, with less than 6% of adolescents reporting past month use of other illicit drugs (Johnston et al., 2017). A brief summary of rates of current substance use, by substance and by grade level, among adolescents in the United States is presented in Table 1 (Johnston et al., 2017).

Table 1.

Rates (%) of current (30-day) substance use among adolescents in the United States, by substance and grade level (Johnston et al., 2017).

8th Grade 10th Grade 12th Grade
Alcohol (any) 7.3 19.9 33.2
Alcohol (been drunk) 1.8 9.0 20.4
Marijuana 5.4 14.0 22.5
E-cigarettes 6.2 11.0 12.5
Cigarettes 2.6 4.9 10.5
Smokeless Tobacco 2.5 3.5 6.6
Inhalants 1.8 1.0 0.8
Hallucinogens 0.6 0.9 1.4
Ecstasy (MDMA) 0.3 0.5 0.9
Cocaine 0.3 0.4 0.9
Heroin 0.2 0.2 0.2
Narcotics other than Heroin NR NR 1.7
Amphetamines 1.7 2.7 3.0
Methamphetamine 0.3 0.2 0.3
Tranquilizers 0.8 1.5 1.9
Any Prescription Drug NR NR 5.4
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NR=Not Reported

Prevalence of Substance Use Disorders

Adolescent substance use is heterogeneous, ranging from normative early experimentation with substance use to heavier and higher-risk patterns of use. Most youth who use substances do not become addicted; however, the prevalence of substance use disorders is still quite high, with 15% of youth meeting diagnostic criteria for alcohol abuse and 16% for drug abuse by age 18 (Swendsen et al. 2012). Tobacco, alcohol, and marijuana are typically the first addictive substances that youth try. The likelihood of developing a substance use disorder increases significantly when individuals initiate alcohol and drug use during adolescence. Youth who begin drinking before age 15 have four to six times the rate of lifetime alcohol dependence than those who remain abstinent from alcohol use until age 21 (Grant & Dawson, 1997; SAMHSA, 2014). The majority of adults who have a substance use disorder started using before age 18 and develop their disorder by age 20, highlighting the need to delay initiation of substance use for as long as possible (Dennis et al., 2002).

Neurobiology

The brain undergoes significant neurodevelopment between childhood and young adulthood, with maturation continuing until around age 25 (Pfefferbaum et al., 1994; Giedd 2004). Brain gray matter, which includes mostly nerve cell bodies and dendrites, tends to decrease during adolescence via synaptic pruning and changes in the extracellular matrix (Gogtay et al., 2004; Paus, 2005; Petjanek et al., 2011; Raznahan et al., 2014; Sowell et al., 1999; Stiles & Jernigan, 2010). Concurrently, white-matter volume and white matter integrity increases over this time, which allows for more efficient and rapid communication between brain regions (Giedd, 2004; Lebel et al., 2011; 2012). Brain regions have time-varying developmental trajectories, with lower-order sensorimotor regions maturing first, and regions associated with higher-order cognitive functioning (e.g., frontal and subcortical brain regions) developing later in adolescence and young adulthood (Sowell et al., 1999, 2004; Shaw et al., 2008; Giedd and Rapoport, 2010; Stiles & Jernigan, 2010). Healthy brain development throughout adolescence is imperative, with even minor changes in neurodevelopmental trajectories affecting a range of cognitive, emotional, and social functioning (Nagy et al., 2004; Casey et al., 2008). Altered brain development due to exposure of neurotoxins during adolescence, particularly alcohol and other drug use, could set the stage for cognitive problems into adulthood, conferring functional consequences throughout life.

Adolescents are known to be particularly vulnerable, compared to children and adults, to initiation of substance use and progression to problematic use. Dopaminergic systems are significantly reorganized in the adolescent brain, with decreases in dopamine in striatal structures such as the nucleus accumbens, in the context of limited inhibitory control, potentially precipitating high-risk behaviors to compensate for dopaminergic void (Chambers et al., 2003; Spear, 2002). Theories have suggested an “imbalance” in brain development underlying a propensity for risk behavior, including substance use, during adolescence, with emotion and reward systems (e.g., amygdala, nucleus accumbens) developing before cognitive control systems (e.g., prefrontal cortex) (Casey et al., 2008; Somerville et al., 2010). Within the window of this imbalance, adolescents’ decisions may be based on brain processes favoring immediate reward over consideration of longer-term consequences (Casey & Jones, 2010). This theoretical model has been supported by a number of recent experimental studies (Baker et al., 2015; Mills et al., 2014; van Duijvenvoorde et al., 2016).

In the past 10 years, there has been a proliferation of neuroimaging and neurocognitive studies that have attempted to understand neural risk factors that predate adolescent substance use, as well as determine the effect of substance use on the developing brain. Because alcohol and marijuana are the two most commonly used substances, most existing research has focused on these substances. Several large-scale, multisite longitudinal studies are currently underway to help further understand the consequences of alcohol and marijuana use on cognitive functioning, as well as determine the effects of tobacco and other less frequently used drugs on adolescent brain development. These studies include the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) which is following >800 youth across 5 different sites in the US for at least 10 years (Brown et al., 2015); the IMAGEN study which has followed 2,000 youth from England, Ireland, Germany, and France for the past 8 years (https://imagen-europe.com/); and the recently launched Adolescent Brain Cognitive Development (ABCD; http://abcdstudy.org/), which will follow 11,500 youth across 21 US sites for at least 10 years. Existing studies have relatively small, homogeneous samples; therefore, these large scale studies will allow for a more complex understanding how demographic, social, genetic, and environmental factors play a role in the impact of substance use on brain development.

Several neurocognitive features have been identified as risk factors for initiation of alcohol and other drug use during adolescence (Squeglia & Gray, 2016; Squeglia & Cservenka, 2017). Findings suggest poorer performance on tasks of inhibition and working memory (Heitzeg et al., 2015; Khurana et al., 2013; López-Caneda et al., 2014; Squeglia et al., 2014a, 2017), smaller brain volumes in reward and cognitive control regions (Cheetham et al., 2012, 2014; Squeglia et al., 2014a; Urošević et al., 2015; Weiland et al., 2014; Whelan et al., 2014), less brain activation during executive functioning tasks, and heightened reward responsivity are important predictors of adolescent substance use (Dager et al., 2014; Heitzeg et al., 2014; Mahmood et al., 2013; Norman et al., 2011; Ramage et al., 2015; Squeglia et al., 2012, 2017; Wetherill et al., 2013a; Whelan et al., 2014).

Alcohol and marijuana use during adolescence has been associated with poorer performance on a range of cognitive domains. In a sample of 234 healthy adolescents, greater alcohol and marijuana use between approximately ages 13 to 17 was associated with poorer verbal memory, visuospatial functioning, and psychomotor speed (Nguyen-Louie et al., 2015). Gender specific effects have also been found, with heavy-drinking girls showing worsening performance on tests of visuospatial functioning compared to non-using girls, and alcohol-using boys showing poorer attention compared to alcohol-naïve boys (Squeglia et al., 2009). In a 10-year longitudinal study, treatment-seeking youth who continued to use alcohol and other substances showed poorer verbal learning and memory, visuospatial functioning, and working memory and attention by age 25 (Hanson et al., 2011a, 2011b). A dose-dependent relationship was found, with heavier use patterns and greater hangover and withdrawal symptoms relating to poorer cognitive functioning. Youth who were in remission from alcohol and drug use performed similarly to those who continued to meet criteria for a substance use disorder, suggesting substance use during adolescence could have persisting effects into adulthood (Hanson et al., 2011b).

There is some suggestion that cognitive domains are differentially impacted by marijuana use in adolescence, with attention, declarative memory, and cognitive control particularly affected (Randolph et al., 2013). In a longitudinal study of marijuana and alcohol-using youth, substance use was related to worsening performance on several cognitive domains when compared to non-using youth, including worse performance on tests of complex attention, memory, processing speed, and visuospatial functioning, with early use (before age 16) relating to worse performance (Jacobus et al., 2015). A large longitudinal birth cohort study from New Zealand (N=1,037) found that persistent adolescent-onset marijuana use was associated with an IQ decline of more than 5 points in the most persistent marijuana-use group, with deficits persisting into adulthood (Meier et al., 2012). However, a recent longitudinal twin study found that IQ deficits observed in marijuana users may be attributable to confounding factors like familial and environmental influences rather than the direct neurotoxic effect of marijuana (Jackson et al., 2016). The follow-up assessment periods for these studies differed, with the Meier study ending at age 38 and the Jackson study following youth until age ~20; regular use over a prolonged period may result in more deleterious effects.

Adolescent alcohol and marijuana use has also been associated with a range of structural and functional brain changes. In the largest prospective study to date on this topic (N=134), alcohol-using adolescents showed abnormal neurodevelopmental trajectories when compared to continuously non-using controls, including accelerated decreases in gray matter volume (particularly in frontal and temporal regions) and attenuated increases in white matter volume over the ~5 year follow-up (Squeglia et al., 2015), replicating earlier studies with smaller sample sizes (Luciana et al., 2013; Squeglia et al., 2014b). These findings suggest potentially non-beneficial pruning or, alternatively, premature cortical gray matter decline in alcohol-using youth. In a study examining white matter development, adolescents with extensive marijuana- and alcohol-use histories showed worsening white matter integrity over an 18 month (Baba et al., 2013) and 3-year follow-up (Jacobus et al., 2013a, 2013b) in a number of important white matter tracts when compared to non-using youth, as well as poorer performance on tests of neurocognitive functioning. In conjunction with structural changes, adolescent alcohol and marijuana use appears to also affect brain functioning. On several functional MRI studies, youth who initiated heavy alcohol use during adolescence have shown increasing brain activation over time on tests of visual working memory (Squeglia et al., 2012) and inhibition (Wetherill et al., 2013a, 2013b), when compared to non-using peers.

Risk Factors

Several factors increase the likelihood of an individual developing a substance use problem, including familial, social, and individual risk factors (Whitesell et al., 2013). Vulnerability for developing substance-related problems is especially heightened among individuals with a family history of substance use disorder (Cservenka, 2016). A recent meta-analysis of twin and adoption studies found that alcohol use disorder is approximately 50% heritable (Verhulst, Neale, & Kendler, 2015); however, identifying specific genes has been challenging, highlighting the complexity and heterogeneity of the disorder (Hart & Kranzler, 2015; Tawa, Hall, & Lohoff, 2016). Findings suggest there are a number of genes, each with relatively small effects, that interact with each other and with the environment (Enoch, 2012) to make an individual more or less susceptible to having a substance use disorder (Meyers & Dick, 2010). While specific genes have not been consistently identified, youth with a family history of alcohol use disorder are 3–5 times more likely to develop an alcohol use disorder than youth without a family history of alcoholism (Cotton, 1979).

Males tend to have higher rates of substance use than females (SAMSHA, 2014). Other important risk factors for adolescent substance use include environmental factors such as early exposure to traumatic life events (Dube et al., 2006), prenatal exposure to alcohol and other drugs (Enoch et al., 2011), lack of parental supervision and monitoring (Nash et al., 2005), sleep problems (Shibely et al., 2008), being involved in romantic relationships (Squeglia et al., 2017; Whelan et al., 2014), and peer substance use (Leung et al., 2014). Co-occurring psychopathology, including ADHD and depression have been shown to significantly increase the risk of adolescent alcohol use (Charach et al, 2011; Lee et al., 2011; Libby et al., 2005; Rao et al., 1999; Taylor, 2011; Wu et al., 2008). Additionally, childhood depression and conduct disorder symptoms predict persistence of substance dependence in adulthood (Meier et al., 2016). Other research suggests that externalizing, but not internalizing, mental health problems in childhood predict later substance use in males and females, while among females adolescent substance use predicts internalizing disorders in adulthood (Miettunen et al., 2014).

Prevention

Prevention science is grounded in the premise that modulation of risk and protective factors may affect the probability of later problems (Coie et al., 1993). Applied to adolescent substance use, this has been implemented across a number of modalities and settings, with the goal of reducing modifiable risk factors and enhancing/reinforcing modifiable protective factors (Harrop & Catalano, 2016). Most commonly, programs have been developed and evaluated in school, family, and community settings. Evidence is mixed amid heterogeneity of methodology and outcomes between studies, but there is some support for parenting-focused (Allen et al., 2016), school-based teacher-led (Lize et al., 2017), and peer-led prevention programs (MacArthur et al., 2016).

Screening and Assessment

Despite the serious implications of adolescent substance use, many clinical providers do not conduct routine screening, and most that do fail to utilize a validated screening method (Harris et al., 2012). In light of this, considerable recent effort has focused on developing and disseminating efficient and reliable screening methods for adolescent substance use (Levy et al., 2016). Screening tools can potentially be used across a number of settings where adolescents are present, though to date most research in this area has focused on primary care practice. The ideal tool would be both sensitive and specific to substance use and related problems, and would guide subsequent in-depth assessment and intervention when appropriate. This must be balanced with the need for time efficiency amid busy clinical practice in which a wide variety of health screening assessments are indicated.

Two brief screeners have strong evidence of achieving the balance of the aforementioned goals, and can be delivered via interview or electronic administration. The Screening to Brief Intervention (S2BI), which queries the adolescent regarding frequency of using 8 types of drugs in the past year, yields high sensitivity and specificity for identifying use and substance use disorders (Levy et al., 2014). Similarly, the Brief Screener for Tobacco, Alcohol, and Other Drugs (BSTAD) queries frequency of use in the past year, with optimal cutoff points for identifying substance use disorders as ≥6 days of tobacco use, ≥2 days of alcohol use, and ≥2 days of marijuana use (Kelly et al., 2014). Both screeners can be delivered electronically, which is a preferred method for adolescents. In practice, adolescent patients can complete the screener via mobile electronic device in the waiting area, and the results can be delivered to the provider in anticipation of the visit and interview.

Initial screeners such as the S2BI and BSTAD can be complemented with subsequent brief assessments to evaluate level of substance involvement and severity of substance-related problems. These assessments can help determine the level and modality of treatment that may be needed, spanning from psychoeducation and/or brief intervention in the primary care office to more intensive service referrals. The CRAFFT (Car, Relax, Alone, Friends/Family, Forget, Trouble), ASSIST (Alcohol, Smoking and Substance Involvement Screening Test) and GAIN (Global Appraisal of Individual Needs) are all validated for this purpose. The CRAFFT and ASSIST serve as adolescent-validated analogues to the adult screener CAGE, the latter of which is not developmentally appropriate for adolescents (Gryczynski et al., 2015; Knight et al., 1999). The GAIN assesses for both substance use disorders and potentially associated mental health disorders, including four subscales assessing substance use, internalizing disorders, externalizing disorders, and crime/violence (Dennis et al., 2006).

When more thorough and formal evaluation is indicated (i.e., when brief screening is highly suggestive of substance-related problems requiring intervention), diagnostic evaluation via the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), may be undertaken (American Psychiatric Association, 2013). Within this framework, problematic substance use may be diagnostically categorized as a substance use disorder (e.g., Cannabis Use Disorder), with severity of mild, moderate, or severe, depending on the number of substance use disorder symptoms endorsed for a given substance. Diagnostic evaluation should be complemented by functional behavioral analysis, in which substance use is evaluated as a problem behavior with antecedents and consequences which may be unique to a given adolescent’s substance use. The functional analysis framework is used to determine what antecedents and consequences are potentially modifiable to reduce the likelihood of ongoing problem behavior (Randall et al., 2001).

Laboratory testing, most commonly urine drug testing, is often used to complement self-report when evaluating adolescent substance use; it is, however, not generally supported as a standalone screening or assessment for substance use (Hadland & Levy, 2016). Particularly in situations when trust has been eroded within a family and the veracity of an adolescent’s self-report is in doubt, urine testing may serve as a useful, though imperfect, method for objective assessment. With some exceptions, most studies have shown reasonable agreement between adolescent self-report and urine drug test results (Gignac et al., 2005). Parental report is comparatively less consistent with urine testing, reflecting that parents may not often be aware of the adolescent’s day-to-day substance use patterns. Variations in detection times due to substance, dose, chronicity of use, cutoff used, and metabolism of user, should be noted as potential limits of urine testing. Additionally, some “designer” and synthetic substances may not be included among commonly available urine drug testing panels. Home drug testing kits are widely available, but often lack informational guidance to inform parents/guardians of empirically supported strategies to incorporate them as a part of treatment and monitoring, indicating that clear guidance by the provider is critical when considering drug testing at home rather than in clinic (Washio et al., 2014).

Treatment

Treatment development for adolescent substance use disorders has focused primarily on psychosocial interventions, spanning individual, group, and family modalities. The intensity and duration of investigated treatments has varied from brief (even single-session) interventions to extended multimodal strategies. The majority of studies have evaluated office-based outpatient interventions, many of which were developmentally adapted from established adult-targeted treatments.

Despite the considerable appeal of brief interventions as part of the Screening, Brief Intervention, and Referral to Treatment (SBIRT) model, standalone brief interventions have demonstrated very limited effects when provided to adolescents with substance use disorders (Young et al., 2014). This is similarly the case with brief school-based interventions (Carney et al., 2016) and brief standalone motivational interviewing approaches (Li et al., 2016). These strategies appear more useful when provided as a component of multimodal intervention with increased overall intensity and duration.

A recent comprehensive review indicated that the most well established efficacious treatments include ecological family-based treatment, group cognitive-behavioral therapy (CBT), and individual CBT, while fair evidence supports behavioral family therapy and motivational enhancement therapy (MET) (Hogue et al., 2014). Substantial evidence supports combined treatment approaches, incorporating elements from the aforementioned modalities and others to enhance outcomes. Given that adolescent substance use disorders are heterogeneous and multidetermined, treatment approaches that address multiple biopsychosocial targets are often indicated. Among combined treatments, strong evidence supports combined MET and CBT, as well as combined MET, CBT, and behavioral family-based treatment (Hogue et al., 2014). Outcomes are further enhanced by complementing the aforementioned treatments with contingency management (CM), a behavioral treatment based on operant conditioning principles, in which tangible rewards are provided for objectively confirmed desired behaviors (e.g., token provided for negative urine drug test) (Stanger et al., 2015, 2016). In general, evaluated treatments in large-scale trials have demonstrated short-term substance reduction and/or cessation, but adolescents with substance use disorders, even with the best evidence-based care, rarely achieve long-term abstinence (Dennis et al., 2004; Hogue et al., 2014; Waldron & Turner, 2008).

Pharmacotherapy is an established complement to psychosocial treatment for adults with substance use disorders, with several medications receiving United States Food and Drug Administration (FDA) approval for treatment of alcohol, tobacco, and opioid use disorders. While the neuropathology of adolescent substance use disorders is increasingly recognized, relatively little work has focused on developing pharmacotherapies for this age group. To date, only buprenorphine-naloxone possesses FDA approval for opioid use disorder in youth ages 16 and older. Other medications have been studied at least preliminarily for youth alcohol, tobacco, cannabis, and opioid use disorders, yielding mixed results (Hammond & Gray, 2016). Findings from randomized controlled trials, though not yet exhaustive, indicate that select pharmacotherapies may be considered as adjunctive treatments to psychosocial interventions: buprenorphine-naloxone maintenance to improve treatment retention and outcomes for opioid use disorder (Marsch et al., 2005; Woody et al., 2008), N-acetylycysteine as an adjunct to cessation counseling and contingency management for cannabis use disorder (Gray et al., 2012), and nicotine patch or bupropion SR to improve tobacco cessation rates (Gray et al., 2011, Moolchan et al., 2005; Muramoto et al., 2007).

Given the ubiquity of mobile technology among adolescents, and the considerable logistical barriers to office-based care (e.g., transportation, accessibility), recent research has focused on translating evidence-based care for delivery via electronic platforms. A meta-analysis revealed a small positive effect size for text messaging interventions for youth with substance use problems (Mason et al., 2015), and a pilot study demonstrated the feasibility of using smartphones to provide recovery support for adolescents after discharge from residential treatment (Dennis et al., 2015). Ongoing work is focused on combining ecological momentary assessment and smartphone application intervention in adolescents with substance use and co-occurring psychiatric disorders (Benarous et al., 2016).

Limitations

This review provides a general overview of a broad, complex topic area, with multiple lines of ongoing research. The methodology of the review was by design not structured or meta-analytic, but rather a general cross-section of recent advances and findings. Additionally, the review focused largely, but not exclusively, on data derived from United States samples. As such, caution should be taken in interpreting findings, acknowledging the potential for limitations in generalizability across populations, policies, and cultural and environmental factors.

Conclusions

Recent research has significantly advanced the understanding of substance use in adolescence. Survey-based epidemiological studies have identified trends in substance-related attitudes and rates of use of various substances, while emerging longitudinal neuroimaging studies have elucidated the neurobiology underlying adolescents’ propensity for substance initiation and progression to substance use disorders, with potentially lasting substance-related adverse consequences. A variety of prevention programs have been implemented across settings with varying degrees of success. Novel screening and assessment instruments have been developed to efficiently identify youth with particular risk for serious substance-related problems, and a number of evidence-based treatment approaches have been shown to be effective in reducing substance use and substance-related problems. Unfortunately, adolescents with substance use disorders rarely achieve long-term abstinence even with the most robust evidence-based treatments, indicating that further work is needed to optimize methods to understand and address this important public health issue.

Key Points.

  • ▪

    Adolescents, amid rapid biopsychosocial development, are more prone than children or adults to initiating substance use and suffering lasting substance-related adverse consequences

  • ▪

    Recent research has elucidated the neurobiological processes underlying substance use risk in adolescents, as well as substance-related neuropathology among adolescents with substance use disorders

  • ▪

    Advances in prevention, screening, assessment, and treatment research have yielded evidence-based interventions to address adolescent substance use disorders

  • ▪

    Further research is needed to enhance outcomes and reduce the considerable public health burden of adolescent substance use disorders

Acknowledgments

Dr. Gray has received research support (medication only) from Pfizer.

Footnotes

Dr. Squeglia reports no competing interests.

References

  1. Allen ML, Garcia-Huidobro D, Porta C, Curran D, Patel R, Miller J, Borowsky I. Effective parenting interventions to reduce youth substance use: A systematic review. Pediatrics. 2016;138(2):e20154425. doi: 10.1542/peds.2015-4425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5. Washington, DC: Author; 2013. [Google Scholar]
  3. Baker ST, Lubman DI, Yücel M, Allen NB, Whittle S, Fulcher BD, Zalesky A, Fornito A. Developmental changes in brain network hub connectivity in late adolescence. Journal of Neuroscience. 2015;35(24):9078–9087. doi: 10.1523/JNEUROSCI.5043-14.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bava S, Jacobus J, Thayer RE, Tapert SF. Longitudinal changes in white matter integrity among adolescent substance users. Alcoholism: Clinical and Experimental Research. 2013;37(Suppl 1):E181–189. doi: 10.1111/j.1530-0277.2012.01920.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Benarous X, Edel Y, Consoli A, Brunelle J, Etter JF, Cohen D, Khazaal Y. Ecological momentary assessment and smartphone application intervention in adolescents with substance use and comorbid severe psychiatric disorders: Study protocol. Frontiers in Psychiatry. 2016;7:157. doi: 10.3389/fpsyt.2016.00157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brown SA, Brumback T, Tomlinson K, Cummins K, Thompson WK, Nagel BJ, De Bellis MD, Hooper SR, Clark DB, Chung T, Hasler BP, Colrain IM, Baker FB, Prouty D, Pfefferbaum A, Sullivan EV, Pohl KM, Rohlfing T, Nichols BN, Chu W, Tapert SF. The National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA): A multi-site study of adolescent development and substance use. Journal of Studies on Alcohol and Drugs. 2015;76:895–908. doi: 10.15288/jsad.2015.76.895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bunnell R, Agaku IT, Arrazola RA, Apelberg BJ, Caraballo RS, Corey CG, Coleman BN, Dube SR, King BA. Intentions to smoke cigarettes among never-smoking US middle and high school electronic cigarette users: National Youth Tobacco Survey, 2011–2013. Nicotine & Tobacco Research. 2015;17(2):228–235. doi: 10.1093/ntr/ntu166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carney T, Myers BJ, Louw J, Okwundu CI. Brief school-based interventions and behavioural outcomes for substance-using adolescents. Cochrane Database of Systematic Reviews. 2016:CD008969. doi: 10.1002/14651858.CD008969.pub3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Casey BJ, Jones RM. Neurobiology of the adolescent brain and behavior: Implications for substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry. 2010;49(12):1189–1201. doi: 10.1016/j.jaac.2010.08.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Casey BJ, Jones RM, Hare TA. The adolescent brain. Annals of the New York Academy of Sciences. 2008;1124:111–126. doi: 10.1196/annals.1440.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: A critical period of addiction vulnerability. American Journal of Psychiatry. 2003;160(6):1041–1052. doi: 10.1176/appi.ajp.160.6.1041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Charach A, Yeung E, Climans T, Lillie E. Childhood attention-deficit/hyperactivity disorder and future substance use disorders: Comparative meta-analyses. Journal of the American Academy of Child and Adolescent Psychiatry. 2011;50(1):9–21. doi: 10.1016/j.jaac.2010.09.019. [DOI] [PubMed] [Google Scholar]
  13. Cheetham A, Allen NB, Whittle S, Simmons J, Yücel M, Lubman DI. Volumetric differences in the anterior cingulate cortex prospectively predict alcohol-related problems in adolescence. Psychopharmacology. 2014;231:1731–1742. doi: 10.1007/s00213-014-3483-8. [DOI] [PubMed] [Google Scholar]
  14. Cheetham A, Allen NB, Whittle S, Simmons JG, Yücel M, Lubman DI. Orbitofrontal volumes in early adolescence predict initiation of cannabis use: A 4-year longitudinal and prospective study. Biological Psychiatry. 2012;71:684–692. doi: 10.1016/j.biopsych.2011.10.029. [DOI] [PubMed] [Google Scholar]
  15. Coie JD, Watt NF, West SG, Hawkins JD, Asarnow JR, Markman HJ, Ramey SL, Shure MB, Long B. The science of prevention: A conceptual framework and some directions for a national research program. American Psychologist. 1993;48(10):1013–1022. doi: 10.1037//0003-066x.48.10.1013. [DOI] [PubMed] [Google Scholar]
  16. Cotton NS. The familial incidence of alcoholism: A review. Journal of Studies on Alcohol. 1979;40:89–116. doi: 10.15288/jsa.1979.40.89. [DOI] [PubMed] [Google Scholar]
  17. Cservenka A. Neurobiological phenotypes associated with a family history of alcoholism. Drug and Alcohol Dependence. 2016;158:8–21. doi: 10.1016/j.drugalcdep.2015.10.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dager AD, Anderson BM, Rosen R, Khadka S, Sawyer B, Jiantonio-Kelly RE, Austad CS, Raskin SA, Tennen H, Wood RM, Fallahi CR, Pearlson GD. Functional magnetic resonance imaging (fMRI) response to alcohol pictures predicts subsequent transition to heavy drinking in college students. Addiction. 2014;109:585–595. doi: 10.1111/add.12437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dennis M, Babor TF, Roebuck C, Donaldson J. Changing the focus: The case for recognizing and treating cannabis use disorders. Addiction. 2002;97(s1):4–15. doi: 10.1046/j.1360-0443.97.s01.10.x. [DOI] [PubMed] [Google Scholar]
  20. Dennis ML, Chan YF, Funk RR. Development and validation of the GAIN Short Screener (GSS) for internalizing, externalizing and substance use disorders and crime/violence problems among adolescents and adults. American Journal on Addiction. 2006;15(suppl 1):80–91. doi: 10.1080/10550490601006055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dennis M, Godley SH, Diamond G, Tims FM, Babor T, Donaldson J, Liddle H, Titus JC, Kaminer Y, Webb C, Hamilton N, Funk R. The Cannabis Youth Treatment (CYT) Study: Main findings from two randomized trials. Journal of Substance Abuse Treatment. 2004;27(3):197–213. doi: 10.1016/j.jsat.2003.09.005. [DOI] [PubMed] [Google Scholar]
  22. Dennis ML, Scott CK, Funk RR, Nicholson L. A pilot study to examine the feasibility of potential effectiveness of using smartphones to provide recovery support for adolescents. Substance Abuse. 2015;36(4):486–492. doi: 10.1080/08897077.2014.970323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Meyers JL, Dick DM. Genetic and environmental risk factors for adolescent-onset substance use disorders. Child & Adolescent Psychiatric Clinics of North America. 2010;19(3):465–477. doi: 10.1016/j.chc.2010.03.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Dube SR, Miller JW, Brown DW, Giles WH, Felitti VJ, Dong M, Anda RF. Adverse childhood experiences and the association with ever using alcohol and initiating alcohol use during adolescence. Journal of Adolescent Health. 2006;38(4):444.e1–10. doi: 10.1016/j.jadohealth.2005.06.006. [DOI] [PubMed] [Google Scholar]
  25. Enoch MA. The role of early life stress as a predictor for alcohol and drug dependence. Psychopharmacology (Berl) 2011;214(1):17–31. doi: 10.1007/s00213-010-1916-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Enoch MA. The influence of gene-environment interactions on the development of alcoholism and drug dependence. Current Psychiatry Reports. 2012;14(2):150–158. doi: 10.1007/s11920-011-0252-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Giedd JN. Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences. 2004;1021:77–85. doi: 10.1196/annals.1308.009. [DOI] [PubMed] [Google Scholar]
  28. Giedd JN, Rapoport JL. Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 2010;67:728–734. doi: 10.1016/j.neuron.2010.08.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gignac M, Wilens TE, Biederman J, Kwon A, Mick E, Swezey A. Assessing cannabis use in adolescents and young adults: What do urine screen and parental report tell you? Journal of Child and Adolescent Psychopharmacology. 2005;15:742–750. doi: 10.1089/cap.2005.15.742. [DOI] [PubMed] [Google Scholar]
  30. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF, 3rd, Herman DH, Clasen LS, Toga AW, Rapaport JL, Thompson PM. Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(21):8174–8179. doi: 10.1073/pnas.0402680101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Grant BF, Dawson DA. Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse. 1997;9:103–110. doi: 10.1016/s0899-3289(97)90009-2. [DOI] [PubMed] [Google Scholar]
  32. Gray KM, Carpenter MJ, Baker NL, Hartwell KJ, Lewis AL, Hiott DW, Deas D, Upadhyaya HP. Bupropion SR and contingency management for adolescent smoking cessation. Journal of Substance Abuse Treatment. 2011;40:77–86. doi: 10.1016/j.jsat.2010.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gray KM, Carpenter MJ, Baker NL, DeSantis SM, Kryway E, Hartwell KJ, McRae-Clark AL, Brady KT. A double-blind randomized controlled trial of N-acetylcysteine in cannabis-dependent adolescents. American Journal of Psychiatry. 2012;169:805–812. doi: 10.1176/appi.ajp.2012.12010055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gryczynski J, Kelly SM, Mitchell SG, Kirk A, O’Grady KE, Schwartz RP. Validation and performance of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) among adolescent primary care patients. Addiction. 2015;110(2):240–247. doi: 10.1111/add.12767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hadland SE, Levy S. Objective testing: urine and other drug tests. Child and Adolescent Psychiatric Clinics of North America. 2016;25(3):549–565. doi: 10.1016/j.chc.2016.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hammond CJ, Gray KM. Pharmacotherapy for substance use disorders in youths. Journal of Child and Adolescent Substance Abuse. 2016;25(4):292–316. doi: 10.1080/1067828X.2015.1037517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hanson KL, Cummins K, Tapert SF, Brown SA. Changes in neuropsychological functioning over 10 years following adolescent substance abuse treatment. Psychology of Addictive Behaviors. 2011a;25(1):127–142. doi: 10.1037/a0022350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hanson KL, Medina KL, Padula CB, Tapert SF, Brown SA. Impact of adolescent alcohol and drug use on neuropsychological functioning in young adulthood: 10-year outcomes. Journal of Child and Adolescent Substance Abuse. 2011b;20(2):135–154. doi: 10.1080/1067828X.2011.555272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Harris SK, Herr-Zaya K, Weinstein Z, Whelton K, Perfas F, Jr, Castro-Donlan C, Straus J, Schoneman K, Botticelli M, Levy S. Results of a statewide survey of adolescent substance use screening rates and practices in primary care. Substance Abuse. 2012;33:321–326. doi: 10.1080/08897077.2011.645950. [DOI] [PubMed] [Google Scholar]
  40. Harrop E, Catalano RF. Evidence-based prevention for adolescent substance use. Child and Adolescent Psychiatric Clinics of North America. 2016;25:387–410. doi: 10.1016/j.chc.2016.03.001. [DOI] [PubMed] [Google Scholar]
  41. Hart AB, Kranzler HR. Alcohol dependence genetics: Lessons learned from genome-wide association studies (GWAS) and post-GWAS analyses. Alcoholism: Clinical and Experimental Research. 2015;39(8):1312–1327. doi: 10.1111/acer.12792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Heitzeg MM, Cope LM, Martz ME, Hardee JE. Neuroimaging risk markers for substance abuse: Recent findings on inhibitory control and reward system functioning. Current Addiction Reports. 2015;2:91–103. doi: 10.1007/s40429-015-0048-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Heitzeg MM, Nigg JT, Hardee JE, Soules M, Steinberg D, Zubieta JK, Zucker RA. Left middle frontal gyrus response to inhibitory errors in children prospectively predicts early problem substance use. Drug and Alcohol Dependence. 2014;141:51–57. doi: 10.1016/j.drugalcdep.2014.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hogue A, Henderson CE, Ozechowski TJ, Robbins MS. Evidence base on outpatient behavioral treatments for adolescent substance use: Updates and recommendations 2007–2013. Journal of Clinical Child & Adolescent Psychology. 2014;43(5):695–720. doi: 10.1080/15374416.2014.915550. [DOI] [PubMed] [Google Scholar]
  45. Jacobus J, Squeglia LM, Infante MA, Castro N, Brumback T, Meruelo AD, Tapert SF. Neuropsychological performance in adolescent marijuana users with co-occurring alcohol use: A three-year longitudinal study. Neuropsychology. 2015;29(6):829–843. doi: 10.1037/neu0000203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Khurana A, Romer D, Betancourt LM, Brodsky NL, Giannetta JM, Hurt H. Working memory ability predicts trajectories of early alcohol use in adolescents: The mediational role of impulsivity. Addiction. 2013;108:506–515. doi: 10.1111/add.12001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Jackson NJ, Isen JD, Khoddam R, Irons D, Tuvblad C, Iacono WG, McGue M, Raine A, Baker LA. Impact of adolescent marijuana use on intelligence: Results from two longitudinal twin studies. Proceedings of the National Academy of Sciences of the United States of America. 2016;113(5):E500–E508. doi: 10.1073/pnas.1516648113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Jacobus J, Squeglia LM, Bava S, Tapert SF. White matter characterization of adolescent binge drinking with and without co-occurring marijuana use: A 3-year investigation. Psychiatry Research. 2013a;214(3):374–381. doi: 10.1016/j.pscychresns.2013.07.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Jacobus J, Squeglia LM, Infante MA, Bava S, Tapert SF. White matter integrity pre- and post marijuana and alcohol initiation in adolescence. Brain Sciences. 2013b;3(1):396–414. doi: 10.3390/brainsci3010396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Johnston LD, O’Malley PM, Miech RA, Bachman JG, Schulenberg JE. Monitoring the Future national survey results on drug use, 1975–2016: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, The University of Michigan; 2017. [Google Scholar]
  51. Kelly SM, Gryczynski J, Mitchell SG, Kirk A, O’Grady KE, Schwartz RP. Validity of brief screening instrument for adolescent tobacco, alcohol, and drug use. Pediatrics. 2014;133(5):819–826. doi: 10.1542/peds.2013-2346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Knight JR, Shrier LA, Bravender TD, Farrell M, Vander Bilt J, Shaffer HJ. A new brief screen for adolescent substance abuse. Archives of Pediatrics and Adolescent Medicine. 1999;153(6):591–596. doi: 10.1001/archpedi.153.6.591. [DOI] [PubMed] [Google Scholar]
  53. Lebel C, Beaulieu C. Longitudinal development of human brain wiring continues from childhood into adulthood. Journal of Neuroscience. 2011;31:10937–10947. doi: 10.1523/JNEUROSCI.5302-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage. 2012;60:340–352. doi: 10.1016/j.neuroimage.2011.11.094. [DOI] [PubMed] [Google Scholar]
  55. Lee SS, Humphreys KL, Flory K, Liu R, Glass K. Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: A meta-analytic review. Clinical Psychology Review. 2011;31(3):328–341. doi: 10.1016/j.cpr.2011.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Leung RK, Toumbourou JW, Hemphill SA. The effect of peer influence and selection processes on adolescent alcohol use: A systematic review of longitudinal studies. Health Psychology Review. 2014;8(4):426–457. doi: 10.1080/17437199.2011.587961. [DOI] [PubMed] [Google Scholar]
  57. Levy S, Weiss R, Sherritt L, Ziemnik R, Spalding A, Van Hook S, Shrier LA. An electronic screen for triaging adolescent substance use by risk levels. JAMA Pediatrics. 2014;168(9):822–828. doi: 10.1001/jamapediatrics.2014.774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Levy SJ, Williams JF AAP Committee on Substance Use and Prevention. Substance use screening, brief intervention, and referral to treatment. Pediatrics. 2016;138(1):e20161211. doi: 10.1542/peds.2016-1211. [DOI] [PubMed] [Google Scholar]
  59. Li L, Zhu S, Tse N, Tse S, Wong P. Effectiveness of motivational interviewing to reduce illicit drug use in adolescents: A systematic review and meta-analysis. Addiction. 2016;111:795–805. doi: 10.1111/add.13285. [DOI] [PubMed] [Google Scholar]
  60. Libby AM, Orton HD, Stover SK, Riggs PD. What came first, major depression or substance use disorder? Clinical characteristics and substance use comparing teens in a treatment cohort. Addictive Behaviors. 2005;30(9):1649–1662. doi: 10.1016/j.addbeh.2005.07.012. [DOI] [PubMed] [Google Scholar]
  61. Lize SE, Iachini AL, Tang W, Tucker J, Seay KD, Clone S, DeHart D, Browne T. A meta-analysis of the effectiveness of interactive middle school cannabis prevention programs. Prevention Science. 2017;18:50–60. doi: 10.1007/s11121-016-0723-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. López-Caneda E, Rodríguez Holguín S, Cadaveira F, Corral M, Doallo S. Impact of alcohol use on inhibitory control (and vice versa) during adolescence and young adulthood: A review. Alcohol and Alcoholism. 2015;49:173–181. doi: 10.1093/alcalc/agt168. [DOI] [PubMed] [Google Scholar]
  63. Luciana M, Collins PF, Muetzel RL, Lim KO. Effects of alcohol use initiation on brain structure in typically developing adolescents. American Journal of Drug and Alcohol Abuse. 2013;39(6):345–355. doi: 10.3109/00952990.2013.837057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. MacArthur GJ, Harrison S, Caldwell DM, Hickman M, Campbell R. Peer-led interventions to prevent tobacco, alcohol and/or drug use among young people aged 11–21 years: A systematic review and meta-analysis. Addiction. 2016;111:391–407. doi: 10.1111/add.13224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Mahmood OM, Goldenberg D, Thayer R, Migliorini R, Simmons AN, Tapert SF. Adolescents’ fMRI activation to a response inhibition task predicts future substance use. Addictive Behaviors. 2013;38:1435–1441. doi: 10.1016/j.addbeh.2012.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Marsch LA, Bickel WK, Badger GJ, Stothart ME, Quesnel KJ, Stanger C, Brooklyn J. Comparison of pharmacological treatments for opioid-dependent adolescents. Archives of General Psychiatry. 2005;62:1157–1164. doi: 10.1001/archpsyc.62.10.1157. [DOI] [PubMed] [Google Scholar]
  67. Mason M, Ola B, Zaharakis N, Zhang J. Text messaging interventions for adolescent and young adult substance use: A meta-analysis. Prevention Science. 2015;16:181–188. doi: 10.1007/s11121-014-0498-7. [DOI] [PubMed] [Google Scholar]
  68. Meier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RS, McDonald K, Ward A, Poulton R, Moffitt TE. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proceedings of the National Academ of Sciences of the United States of America. 2012;109:E2657–E2664. doi: 10.1073/pnas.1206820109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Meier MH, Hall W, Caspi A, Belsky DW, Cerdá M, Harrington HL, Houts R, Poulton R, Moffitt TE. Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment. Psychological Medicine. 2016;46:877–889. doi: 10.1017/S0033291715002482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Miettunen J, Murray GK, Jones PB, Mäki P, Ebeling H, Taanila A, Joukamaa M, Savolainen J, Törmänsen S, Järvelin MR, Veijola J, Moilanen I. Longitudinal associations between childhood and adulthood externalizing and internalizing psychopathology and adolescent substance use. Psychological Medicine. 2014;44(8):1727–1738. doi: 10.1017/S0033291713002328. [DOI] [PubMed] [Google Scholar]
  71. Mills KL, Goddings AL, Clasen LS, Giedd JN, Blakemore SJ. The developmental mismatch in structural brain maturation during adolescence. Developmental Neuroscience. 2014;36(3–4):147–160. doi: 10.1159/000362328. [DOI] [PubMed] [Google Scholar]
  72. Moolchan ET, Robinson ML, Ernst M, Cadet JL, Pickworth WB, Heishman SJ, Schroeder JR. Safety and efficacy of the nicotine patch and gum for the treatment of adolescent tobacco addiction. Pediatrics. 2005;115:e407–e414. doi: 10.1542/peds.2004-1894. [DOI] [PubMed] [Google Scholar]
  73. Muramoto ML, Leischow SJ, Sherrill D, Matthews E, Strayer LJ. Randomized, double-blind, placebo-controlled trial of 2 dosages of sustained-release bupropion for adolescent smoking cessation. Archives of Pediatrics and Adolescent Medicine. 2007;161:1068–1074. doi: 10.1001/archpedi.161.11.1068. [DOI] [PubMed] [Google Scholar]
  74. Nagy Z, Westerberg H, Klingberg T. Maturation of white matter is associated with the development of cognitive functions during childhood. Journal of Cognitive Neuroscience. 2004;16:1227–1233. doi: 10.1162/0898929041920441. [DOI] [PubMed] [Google Scholar]
  75. Nash SG, McQueen A, Bray JH. Pathways to adolescent alcohol use: Family environment, peer influence, and parental expectations. Journal of Adolescent Health. 2005;37(1):19–28. doi: 10.1016/j.jadohealth.2004.06.004. [DOI] [PubMed] [Google Scholar]
  76. Nguyen-Louie TT, Castro N, Matt GE, Squeglia LM, Brumback T, Tapert SF. Effects of emerging alcohol and marijuana use behaviors on adolescents’ neuropsychological functioning over four years. Journal of Studies on Alcohol and Drugs. 2015;76(5):738–748. doi: 10.15288/jsad.2015.76.738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Norman AL, Pulido C, Squeglia LM, Spadoni AD, Paulus MP, Tapert SF. Neural activation during inhibition predicts initiation of substance use in adolescence. Drug and Alcohol Dependence. 2011;119:216–223. doi: 10.1016/j.drugalcdep.2011.06.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Paus T. Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Science. 2005;9(2):60–68. doi: 10.1016/j.tics.2004.12.008. [DOI] [PubMed] [Google Scholar]
  79. Petanjek Z, Judas M, Simic G, Rasin MR, Uylings HB, Rakic P, Kostovic I. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(32):13281–13286. doi: 10.1073/pnas.1105108108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO. A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of Neurology. 1994;51:874–887. doi: 10.1001/archneur.1994.00540210046012. [DOI] [PubMed] [Google Scholar]
  81. Ramage AE, Lin AL, Olvera RL, Fox PT, Williamson DE. Resting- state regional cerebral blood flow during adolescence: associations with initiation of substance use and prediction of future use disorders. Drug and Alcohol Dependence. 2015;149:40–48. doi: 10.1016/j.drugalcdep.2015.01.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Randall J, Henggeler SW, Cunningham PB, Rowland MD, Swenson CC. Adapting multisystemic therapy to treat adolescent substance abuse more effectively. Cognitive and Behavioral Practice. 2001;8(4):359–366. [Google Scholar]
  83. Randolph K, Turull P, Margolis A, Tau G. Cannabis and cognitive systems in adolescents. Adolescent Psychiatry. 2013;3(2):135–147. [Google Scholar]
  84. Rao U, Ryan ND, Dahl RE, Birmaher B, Williamson DE, Perel JM. Factors associated with the development of substance use disorder in depressed adolescents. Journal of the American Academy of Child and Adolescent Psychiatry. 1999;38(9):1109–1117. doi: 10.1097/00004583-199909000-00014. [DOI] [PubMed] [Google Scholar]
  85. Raznahan A, Shaw PW, Lerch JP, Clasen LS, Greenstein D, Berman R, Pipitone J, Chakravarty MM, Giedd JN. Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(4):1592–1597. doi: 10.1073/pnas.1316911111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, Greenstein D, Clasen L, Evans A, Rapaport JL, Giedd JN, Wise SP. Neurodevelopmental trajectories of the human cerebral cortex. Journal of Neuroscience. 2008;28:3586–3594. doi: 10.1523/JNEUROSCI.5309-07.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Shibley HL, Malcolm RJ, Veatch LM. Adolescents with insomnia and substance abuse: Consequences and comorbidities. Journal of Psychiatric Practice. 2008;14:146–153. doi: 10.1097/01.pra.0000320113.30811.46. [DOI] [PubMed] [Google Scholar]
  88. Somerville LH, Jones RM, Casey BJ. A time of change: Behavioral and neural correlates of adolescent sensitivity to appetitive and aversive emotional cues. Brain and Cognition. 2010;72(1):124–133. doi: 10.1016/j.bandc.2009.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neuroscience. 1999;2(10):859–861. doi: 10.1038/13154. [DOI] [PubMed] [Google Scholar]
  90. Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post adolescent brain maturation in frontal and striatal regions. Nature Neuroscience. 1999;2:859–861. doi: 10.1038/13154. [DOI] [PubMed] [Google Scholar]
  91. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. Journal of Neuroscience. 2004;24:8223–8231. doi: 10.1523/JNEUROSCI.1798-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Spear LP. The adolescent brain and the college drinker: Biological basis of propensity to use and misuse alcohol. Journal of Studies on Alcohol Supplement. 2002;14:71–81. doi: 10.15288/jsas.2002.s14.71. [DOI] [PubMed] [Google Scholar]
  93. Squeglia LM, Ball TM, Jacobus J, Brumback T, McKenna BS, Nguyen-Louie TT, Sorg SF, Paulus MP, Tapert SF. Neural predictors of alcohol use initiation during adolescence. American Journal of Psychiatry. 2017;174(2):172–185. doi: 10.1176/appi.ajp.2016.15121587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Squeglia LM, Cservenka A. Adolescence and drug use vulnerability: Findings from neuroimaging. Current Opinion in Behavioral Sciences. 2017;13:164–170. doi: 10.1016/j.cobeha.2016.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Squeglia LM, Gray KM. Alcohol and drug use and the developing brain. Current Psychiatry Reports. 2016;18(5):46. doi: 10.1007/s11920-016-0689-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Squeglia LM, Jacobus J, Nguyen-Louie TT, Tapert SF. Inhibition during early adolescence predicts alcohol and marijuana use by late adolescence. Neuropsychology. 2014;28:782–790. doi: 10.1037/neu0000083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Squeglia LM, Pulido C, Wetherill RR, Jacobus J, Brown GG, Tapert SF. Brain response to working memory over three years of adolescence: influence of initiating heavy drinking. Journal of Studies on Alcohol and Drugs. 2012;73:749–760. doi: 10.15288/jsad.2012.73.749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Squeglia LM, Rinker DA, Bartsch H, Castro N, Chung Y, Dale AM, Jernigan TL, Tapert SF. Brain volume reductions in adolescent heavy drinkers. Developmental Cognitive Neuroscience. 2014b;9:117–125. doi: 10.1016/j.dcn.2014.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Squeglia LM, Spadoni AD, Infante MA, Myers MG, Tapert SF. Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys. Psychology of Addictive Behaviors. 2009;23(4):715–722. doi: 10.1037/a0016516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Squeglia LM, Tapert SF, Sullivan EV, Jacobus J, Meloy MJ, Rohlfing T, Pfefferbaum A. Brain development in heavy-drinking adolescents. American Journal of Psychiatry. 2015;172(6):531–542. doi: 10.1176/appi.ajp.2015.14101249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Stanger C, Lansing AH, Budney AJ. Advances in research on contingency management for adolescent substance use. Child and Adolescent Psychiatric Clinics of North America. 2016;25(4):645–659. doi: 10.1016/j.chc.2016.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Stanger C, Ryan SR, Scherer EA, Norton GE, Budney AJ. Clinic- and home-based contingency management plus parent training for adolescent cannabis use disorders. Journal of the American Academy of Child and Adolescent Psychiatry. 2015;54(6):445–453. doi: 10.1016/j.jaac.2015.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Stiles J, Jernigan TL. The basics of brain development. Neuropsychology Review. 2010;20(4):327–348. doi: 10.1007/s11065-010-9148-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Substance Abuse and Mental Health Services Administration. Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings [PDF-3.2MB]NSDUH Series H-48, HHS Publication No(SMA) 14-4863. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2014. [Google Scholar]
  105. Swendsen J, Burstein M, Case B, Conway KP, Dierker L, He J, Merikangas KR. Use and abuse of alcohol and illicit drugs in US adolescents: Results of the National Comorbidity Survey-Adolescent Supplement. Archives of General Psychiatry. 2012;69(4):390–398. doi: 10.1001/archgenpsychiatry.2011.1503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Tawa EA, Hall SD, Lohoff FW. Overview of the genetics of alcohol use disorder. Alcohol and Alcoholism. 2016;51(50):507–514. doi: 10.1093/alcalc/agw046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Taylor OD. Adolescent depression as a contributing factor to the development of substance use disorders. Journal of Human Behavior in the Social Environment. 2011;21(6):696–710. [Google Scholar]
  108. Urošević S, Collins P, Muetzel R, Schissel A, Lim K, Luciana M. Effects of reward sensitivity and regional brain volumes on substance use initiation in adolescence. Social Cognitive and Affective Neuroscience. 2015;10:106–113. doi: 10.1093/scan/nsu022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Van Duijvenvoorde AC, Achterberg M, Braams BR, Peters S, Crone EA. Testing a dual-systems model of adolescent brain development using resting-state connectivity analysis. Neuroimage. 2016;124:409–420. doi: 10.1016/j.neuroimage.2015.04.069. [DOI] [PubMed] [Google Scholar]
  110. Verhulst B, Neale MC, Kendler KS. The heritability of alcohol use disorders: A meta-analysis of twin and adoption studies. Psychological Medicine. 2015;45(5):1061–1072. doi: 10.1017/S0033291714002165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Waldron HB, Turner CW. Evidence-based psychosocial treatments for adolescent substance abuse. Journal of Clinical Child and Adolescent Psychology. 2008;37(1):238–261. doi: 10.1080/15374410701820133. [DOI] [PubMed] [Google Scholar]
  112. Washio Y, Fairfax-Columbo J, Ball E, Cassey H, Arria AM, Bresani E, Curtis BL, Kirby KC. A review of guidelines on home drug testing web sites for parents. Journal of Addiction Medicine. 2014;8(4):258–263. doi: 10.1097/ADM.0000000000000042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Weiland BJ, Korycinski ST, Soules M, Zubieta JK, Zucker RA, Heitzeg MM. Substance abuse risk in emerging adults associated with smaller frontal gray matter volumes and higher externalizing behaviors. Drug and Alcohol Dependence. 2014;137:68–75. doi: 10.1016/j.drugalcdep.2014.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Wetherill RR, Castro N, Squeglia LM, Tapert SF. Atypical neural activity during inhibitory processing in substance-naïve youth who later experience alcohol-induced blackouts. Drug and Alcohol Dependence. 2013;128:243–249. doi: 10.1016/j.drugalcdep.2012.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. Wetherill RR, Squeglia LM, Yang TT, Tapert SF. A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking. Psychopharmacology (Berl) 2013;230(4):663–671. doi: 10.1007/s00213-013-3198-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, Barker GJ, Bokde AL, Büchel C, Carvalho FM, Conrod PJ, Flor H, Fauth-Bühler M, Frouin V, Gallinat J, Gan G, Gowland P, Heinz A, Ittermann B, Lawrence C, Mann K, Martinot JL, Nees F, Ortiz N, Paillère-Martinot ML, Paus T, Pausova Z, Rietschel M, Robbins TW, Smolka MN, Ströhle A, Schumann G, Garavan H IMAGEN Consortium. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014;512:185–189. doi: 10.1038/nature13402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Whitesell M, Bachand A, Brown M. Familial, social, and individual factors contributing to risk for adolescent substance use. Journal of Addiction. 2013;2013:579310. doi: 10.1155/2013/579310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Woody GE, Poole SA, Subramaniam G, Duosh K, Bogenschu M, Abbott P, Patkar A, Publicker M, McCain K, Potter JS, Forman R, Vetter V, McNicholas L, Blaine J, Lynch KG, Fudala P. Extended versus short-term buprenorphine-naloxone for treatment of opioid-addicted youth. Journal of the American Medical Association. 2008;300(17):2003–2011. doi: 10.1001/jama.2008.574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Wu P, Hoven CW, Okezie N, Fuller CJ, Cohen P. Alcohol abuse and depression in children and adolescents. Journal of Child and Adolescent Substance Abuse. 2008;17(2):51–69. [Google Scholar]
  120. Young MM, Stevens A, Galipeau J, Pirie T, Garritty C, Singh K, Yazdi F, Golfam M, Pratt M, Turner L, Porath-Waller A, Arratoon C, Haley N, Leslie K, Reardon R, Sproule B, Grimshaw J, Moher D. Effectiveness of brief interventions as part of the Screening, Brief Intervention and Referral to Treatment (SBIRT) model for reducing the nonmedical use of psychoactive substances: A systematic Review. Systematic Reviews. 2014;3:50. doi: 10.1186/2046-4053-3-50. [DOI] [PMC free article] [PubMed] [Google Scholar]

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