Sensitive periods of substance abuse: Early risk for the transition to dependence (2017)

. Author manuscript; available in PMC 2017 Jun 20.

PMCID: PMC5410194



Early adolescent substance use dramatically increases the risk of lifelong substance use disorder (SUD). An adolescent sensitive period evolved to allow the development of risk-taking traits that aid in survival; today these may manifest as a vulnerability to drugs of abuse. Early substance use interferes with ongoing neurodevelopment to induce neurobiological changes that further augment SUD risk. Although many individuals use drugs recreationally, only a small percentage transition to SUD. Current theories on the etiology of addiction can lend insights into the risk factors that increase vulnerability from early recreational use to addiction. Building on the work of others, we suggest individual risk for SUD emerges from an immature PFC combined with hyper-reactivity of reward salience, habit, and stress systems. Early identification of risk factors is critical to reducing the occurrence of SUD. We suggest preventative interventions for SUD that can be either tailored to individual risk profiles and/or implemented broadly, prior to the sensitive adolescent period, to maximize resilience to developing substance dependence. Recommendations for future research include a focus on the juvenile and adolescent periods as well as on sex differences to better understand early risk and identify the most efficacious preventions for SUD.

Keywords: Abuse, Adolescence, Addiction, Substance dependence, Sensitive periods, Vulnerability

1. Introduction

Adolescence is a developmental period that evolved to maximize survival and reproductive fitness. Adolescence is defined by the maturation of secondary sexual characteristics and the development of adult-like psychological and social behaviors (; ; ). Risk-taking and subsequent drug experimentation during this developmental period increases the likelihood of developing a lifelong addiction. The 2010–2011 National Survey on Substance Use and Health reports an estimated 16.6% of 25.1 million adolescents in the U.S. aged 12–17 drank alcohol or experimented with illicit drugs for the first time (). This statistic represents approximately 4 million teenagers who are at increased risk for developing substance dependence. However, the teens that initiate substance use before the age of 14 years are at greatest risk for substance dependence (Fig. 1) and have a 34% prevalence rate of lifetime substance use (; SAMHSA, 2015a,). As individuals continue to mature between 13 and 21 years, the likelihood of lifetime substance abuse and dependence drops 4–5% for each year that initiation of substance use is delayed (; SAMHSA, 2015a,), further suggesting early drug use conveys the greatest risk. While it is probable that individuals who initiate substance use early have an underlying predisposition to use (), individual risk factors can interact with a specific maturational state of vulnerability, known as a sensitive period, to substantially increase the risk of addiction. Here, we integrate what is known about adolescent development with existing theories on the etiology of SUD to inform prevention efforts.

Fig. 1 

Early initiation of substance use increases the risk of substance abuse or dependence. Substance abuse or dependence among persons aged 18 or older (black bars) is plotted by age at first substance use for A) nicotine, B) alcohol, and C) illicit drugs

Substance use disorder is characterized by drug craving and loss of control over drug consumption, including inordinate amounts of time spent pursuing or using the drug and continued use despite negative consequences. Consequences of SUD involve a failure to fulfill work, school, and home obligations, and the development of social and interpersonal problems, physical or psychological harm, and tolerance and withdrawal symptoms (; ). While many adolescents experiment with drugs, the transition to dependence is marked by compulsive and habitual substance use (; ). In the present review we use the term addiction or substance dependence in reference to more severe forms of SUD, which are characterized by chronic drug seeking and drug use (; ).

2. An evolutionary understanding of adolescent risk behaviors

To understand how the developing brain can become vulnerable to drugs of abuse during adolescence, we first turn to evolution and the adaptive role of reward and risk-related behaviors. Our tenet is that the adaptive adolescent strategies, which evolved for survival, manifest today as risk behaviors that can be commuted to substance use disorder (SUD) in vulnerable individuals. Adolescence is maturational period unique to mammals, during which time puberty occurs before peripheral and neurological growth is complete (). Gonadal hormones released during puberty stimulate the development of adult social behaviors (). The adolescent stage allows individuals to practice more complex physical and social skills before adulthood is reached, to increase survival and reproductive fitness (; ).

Behaviors that emerged during adolescence to promote survival and reproduction may no longer be adaptive, but instead can increase an individual’s likelihood to experiment with, use, and become dependent on drugs (; ; ; ; ; ; ). For example, aggression and risk-taking in males can be a competitive strategy that increases reproductive fitness by increasing mating opportunities and genetic diversity (). Yet, data from the National Epidemiological Study of Alcohol and Related Conditions (a survey of n = 43,084 individuals 18 years and older) shows that violent behavior increases risk of SUD 2.42-fold (). Other traits, including hyperactivity, novelty seeking, and impulsivity were advantageous to early humans by promoting exploration of the environment and acquisition of resources (), but are also associated with substance abuse (; ; ; ; ; ).

The early-onset of puberty may represent a unique risk factor for substance abuse due to early initiation of adolescent risk behaviors. As a risk factor early puberty is of particular concern for females, who on average mature up to two years earlier than males (). Early puberty onset is associated with earlier initiation and increased frequency of nicotine and alcohol use in adolescent males and females (; ; ). Today puberty occurs at increasingly earlier ages, up to 3 years earlier than 100 years ago (). Earlier onset has been attributed to a number of factors, including improved nutrition, lower rates of disease in childhood, reduced early mortality, exposure to growth hormones through cow’s milk, other endocrine-disrupting toxins (i.e., bisphenol A), genetic polymorphisms, and childhood obesity (; ; ). Regardless of the cause, earlier-onset puberty has resulted in increasingly wider gaps between an individuals’ cognitive and reproductive maturity (). In some cases, interventions aimed at limiting factors that accelerate puberty may therefore be protective against SUD risk ().

3. Advantages and limitations of animal studies

Animal models, in particular rodents, represent an opportunity to investigate the contribution of behavioral and biological risk factors to substance dependence. Environment, genetics, and neurobiology can be manipulated in laboratory animals to determine mechanistic contributions to individual responses to drugs of abuse (; ; ; ). More broadly, behaviors related to substance dependence can be studied systematically using place conditioning or self-administration paradigms.

Limitations to animal studies exist. The relatively brief adolescent period in rodents () enables rapid assessments (days/weeks in rodents vs. months/years in humans), but necessitates quick tests to study substance abuse. Place conditioning assays animals’ preferences for a drug-associated environment over the course of 4–12 days (; ; ; ). However, in place conditioning drug delivery is non-contingent, i.e., drugs are administered by the experimenter. In contrast, self-administration paradigms allow rodents to respond voluntarily for drugs, allowing assessment of drug-seeking and drug-taking behaviors, but require weeks to months of training (; ; , ; ; ; ). Drug studies in adolescent versus adult rats are reviewed further in Section 5.2.2. Another limitation to animal studies is that non-human primates, and particularly rodents, do not exhibit cortical gyriification as complex as humans (). However, working within the constraints of animal models, drug studies can be designed to study discrete stages of exposure to identify sensitive periods of risk for SUD.

4. Sensitive periods of substance abuse

Sensitive periods are stages when an individual is more responsive to particular environmental input or can more readily acquire a behavior relative to other developmental stages (). As shown in Fig. 1, early substance use (before age 14) is associated with the highest risk of developing SUD (; SAMHSA, suggesting the concept of sensitive periods applies to drug addiction (, ). Well-known examples of sensitive periods in development include second language acquisition and musical and athletic abilities. For example, children more readily achieve fluency in a second language and acquire musical and athletic skills than adults (; ; ). Early language and musical skill acquisition is associated with increased cortical grey matter density and white matter connectivity in the corpus callosum compared to later skill acquisition (; ). These and other observations suggest that sensitive periods result from elevated plasticity in the brain (). Repeated activation of a neural circuit during a sensitive period produces in long-lasting increases in the responsivity of those circuits to the stimulating environmental input (). Drug use during a sensitive period can therefore have important long-term impact on neural development.

4.1. Evidence for sensitive periods of substance abuse in humans

Evidence indicates that drug exposure beginning in early adolescence can increase the risk of SUD long-term (; ). Predisposing risk factors, including impulsivity, exposure to early adversity, or other pre-existing conditions (such as attention deficit hyperactivity disorder [ADHD] and conduct disorder) may lead to early-onset drug use if not addressed (; ; ). However, individuals with ADHD who receive early treatment show the same age-related elevated rates of SUD as age-matched community controls (; ; ). In other words, medication does not seem to increase risk of substance use when initiated early (; ). While these former results have been shown in longitudinal studies, cross-sectional studies demonstrate a different relationship between impulsivity and marijuana use, such that early-onset use (<16 years of age) may be associated with elevated impulsivity (). Epidemiology studies further indicate that adolescent use of alcohol, marijuana, and cocaine adolescent increase the risk of substance dependence (). Findings such as these raise more questions—does early drug use lead to impulsivity? Do different drugs have different long-term effects on the brain and subsequent SUD vulnerability? The prospective ABCD initiative of the NIH ( will help answer some of these issues surrounding early drug exposure.

Disentangling the cause-and-effect of SUD from individual risk factors is difficult due to shared neural substrates. Adolescent networks that underlie impulsivity risk factors are the same as those affected by illicit drugs (; ; ; ). The prefrontal cortex (PFC) does not mature fully until late adolescence or early adulthood (; ; ; ; ; see Section 5.1), and is pivotal for underlying SUD risk. Substance use during adolescence can induce changes in PFC activity and PFC projections to subcortical regions that persist in adulthood (). Brain regions that are influenced by drug exposure depend on their state of maturation when drug exposure occurs (; ). For example, adolescent marijuana users show reduced cortical thickness in middle, superior frontal and insular cortices, but increased thickness in more posterior cortical regions such as the superior temporal and inferior parietal cortices, compared to non-users (). Moreover, early-onset marijuana use (<16 years) is associated with reduced white matter fiber tract integrity in the corpus callosum compared to later-onset marijuana use (>16 years; ).

4.2. Evidence for sensitive periods of substance abuse in animals

Animal studies have demonstrated that timing of drug exposure matters. Periods of increased vulnerability to stimulant use are evident in rodent models as further evidence for a sensitive adolescent period to substance abuse (; ; , ; ; , ; ; ; ; ; ). For example, in animal models of ADHD, which is often comorbid with SUD in humans (; ), treatment with stimulant drugs during adolescence (post-natal days [P] 28–55) enhanced the rate to acquire cocaine self-administration, and increases the efficacy and motivating influence of cocaine reinforcement (; ; ). provide further review on the long-term effects of adolescent drug exposure.

One mechanism by which adolescent drug exposure may increase the risk of SUD is by altering the developmental trajectory of the PFC and its connections with subcortical regions. In rodents, cocaine exposure in adolescence, but not adulthood, produces a long-lasting attenuation of medial PFC (mPFC) GABAergic activity and parvalbumin cell expression that remains evident in adulthood (). Moreover, binge-like alcohol exposure in adolescent rats reduces adult hippocampus, thalamus, dorsal striatum (STR), and cortex volumes compared to littermate controls (; see for further review). Taken together, evidence from both humans and rodents indicates that substance use during the sensitive adolescent period can further exacerbate vulnerability to developing SUD, with long-term impact on cortical and subcortical development.

4.3. Prevention measures: promoting invulnerability to substance abuse

With respect to substance abuse and dependence, an individual may also experience periods of relative invulnerability to the long-term effects of drugs, such as during the juvenile or prepubertal periods (, ; ). Studies both in humans (; ; ) and in rodents (; ; ; ; ) suggest that childhood or prepubertal exposure to stimulants reduces the rewarding properties of drugs of abuse and may protect against SUD later in life. In pre-pubertal children stimulants do not produce rewarding effects (). Moreover, in pre-pubertal children exposure to methylphenidate produces an enduring increase in methylphenidate-stimulated blood flow in the STR and thalamus, with no significant change observed in adult-exposed subjects (). Similar brain changes were evident in rodent males that were exposed pre-pubertally (P20-35) to methylphenidate (). Under these drug exposure conditions, exposure to methylphenidate induced aversions to cocaine-associated environments in a place preference paradigm that is evident in adulthood (; , but see ). In animals, pre-pubertally established ‘aversions’ to cocaine manifest as a deactivation of the amygdala in response to cocaine-conditioned odors (; discussed further in Section 5.2). Exposure to psychostimulants may also affect brain morphometry in regions relevant for SUD. In a longitudinal study of cerebral cortex thickness, psychostimulant treatment normalized the ADHD-associated excess cortical thinning during adolescence (, ; ). Age-dependent effects of methylphenidate treatment on brain morphometry in animals depend on the age of exposure, with a greater impact on corpus callosum white matter and striatal volume following adolescent exposure compared to adults (). Together, these data suggest that there is a pre-pubertal window of invulnerability to stimulants, and exposure to stimulants during this window may be protective against the rewarding effects of drugs later in life.

The juvenile period may represent an opportunity to institute preventative interventions for SUD. Pharmacotherapeutic interventions, such as pre-pubertal methylphenidate exposure, can reduce the rewarding properties of drugs later in life (; ; ; ; ). However, caution must be exercised as pharmacotherapies are not without side effects, and variables such as age, sex and duration of treatment can negatively impact SUD vulnerability (; , ; ; ; ; ; ). In particular, there is a greater need for research in females. Preclinical research suggests that females experience different long-term effects following pre-pubertal (), pubertal, or even adult exposure to drugs ().

In contrast to pharmacotherapies, behavioral interventions can be broadly applied to young populations with little concern for side effects, and can also be combined with medication to further increase efficacy. We propose that the prevailing theories of the etiology of SUD can inform effective interventions for at-risk individuals. Below we review four SUD theories and suggest behavioral interventions (Table 1) that can be implemented alone or in combination to address specific risk factors for the transition to substance dependence.

Table 1 

Summary of substance dependence etiology and relevance to adolescents.

5. Etiology of substance abuse and relevance to adolescence

Nearly 8000 teenagers initiate substance use each day (SAMHSA, 2015a), but only 5–14% of those who try drugs develop SUD (Fig. 1; ), suggesting early risk factors interact with the sensitive adolescent period to mediate the transition from substance use to dependence. Currently prevailing theories on the etiology of SUD conceptualize addiction as 1) an executive function/inhibitory control deficit (e.g., ; ), 2) increased incentive salience attributed to drug-related stimuli (), 3) a compulsive habit (), and 4) a hyperactive stress system and removal of negative reinforcement (). Building on the work of others, we suggest that early risk for SUD emerges from an immature prefrontal control system (; ), combined with hyper-reactivity of reward salience (; ; ; ; ), habit, and stress systems (; ; ; ).

5.1. Executive immaturity in adolescence

Substance use disorder is thought to arise in part from a reduced ability to inhibit or control the desire to pursue the rewarding effects of drugs, known as an executive function deficit (). Brain regions associated with executive function include the dorsolateral PFC, the dorsomedial PFC (), the pre-supplementary motor area () and the ventrolateral PFC (; Fig. 2). In the adult brain, the PFC plays an important inhibitory role on subcortical reward and motivational systems (; ), including interactions with the striatum (STR) and subthalamic nucleus (STN; ; Fig. 2).

Fig. 2 

Neural circuitry underlying adolescent vulnerability to substance use disorder (SUD). Current theories on the etiology of SUD indicate addiction results from an executive function deficit (A), increased incentive salience of drug-related cues (B), and

5.1.1. Evidence from humans

In drug-abusing and addicted adults, subregions of the PFC are hyper-reactive to environmental cues associated with substance use, but hypo-reactive during inhibitory control tasks (). With executive dysfunction as a framework for SUD, adolescence represents a developmentally sensitive period of heightened reactivity to drugs of abuse and the transition to addiction (). The frontal cortex does not complete development until the end of adolescence or as late as the mid-twenties (; ; ; ). Cognitive maturation results in improved integration between inhibitory networks and salience networks (Section 5.2; ) due, in large part, to increased myelination and connectivity between regions. For example, imaging studies show that white matter increases more or less linearly from childhood through early adulthood (; ), whereas grey matter volume in the frontal lobe peaks in late childhood or early adolescence, and declines post-adolescence (; ).

Functional MRI (fMRI) studies show that adolescents overall exhibit hypoactivity in the ventrolateral PFC, orbitofrontal cortex (OFC), and dorsal anterior cingulate cortex (ACC) compared to adults during decision-making tasks (; ). These cortical regions provide top-down inhibitory control of subcortical regions, including the amygdala, NAc, and dorsal STR (). As a result of an immature PFC, adolescents exhibit reduced cortical inhibition and are more subject to subcortically driven, reward-based decision-making (; ; ; ). The imbalance of adolescent cortical and subcortical systems, with predominance of mature subcortical reward-processing circuitry, has been conceptualized as the triadic model of motivated behavior (; ) and is hypothesized to play a role in adolescent risk for SUD.

5.1.2. Evidence from animals

The classic study of Goldman and Alexander was among the first to show that PFC development is delayed. Specifically, early cryogenic studies in adolescent, non-human primates show that the PFC becomes functional with sexual maturity (). The development of executive function in animals is limited due to the complexity of behavioral tasks, which often require more training time than the brief adolescent period allows (Section 3). In rodents, found that adolescents behave less flexibly in a attentional set-shifting task than adults, but were not different in the ability to learn the initial attentional set. Structurally, the rodent brain exhibits adolescent changes mirroring observations in humans. Increases in dendritic spine density in the PFC are evident through the juvenile through early adolescent periods, and thereafter decline (prune) from mid-adolescence to adulthood (). Conversely, in subcortical structures such as the amygdala, dendritic spine density matures before adolescence and remains relatively stable from puberty through adulthood (). Amygdalar dendritic spines, however, are sensitive to pubertal increases in gonadal hormones (). Developmental sex differences are described in more detail by . Maturational trajectories of other subcortical structures, such as the STR, are reviewed in subsequent sections.

5.1.3. Prevention measures: promoting executive maturity in adolescence

Promotion of executive maturity may be an effective intervention for adolescents at risk for SUD (). A number of PFC-mediated risk behaviors are measurable in both human and animal models, such as in stop-signal and go/no go paradigms (; ; ), although in rodents these paradigms require training that extends beyond adolescence. Mindfulness-based activities like meditation, yoga, or practicing martial arts improve inhibitory control, sustained attention, and emotional regulation (; ; ; ; ). These activities also increase activity, grey matter density, and cortical thickness in mPFC, ACC and insular cortex (, ; ; , ). Mindfulness-based interventions have some success in treating SUD (; ; ), but there is a need for research on mindfulness as a preventative intervention in at-risk youth.

5.2. Incentive salience and sensitization

A second theory on the etiology of substance dependence describes a key process in addiction: incentive salience, or the “wanting” or motivated desire attributed by the brain to a rewarding stimulus in the environment (; , ). During the transition from substance use to dependence, greater incentive salience is attributed to drug-related cues than to other reinforcing environmental cues or conditions (e.g., food, social cues, etc). Thus, over time, motivation to pursue the drug eclipses other needs and drug cues increasingly drive behavior. The salience network has been identified by resting state connectivity fMRI studies, and includes dorsal ACC, OFC, and insular cortex with their strong connectivity to subcortical and limbic structures (). Other important nodes within the salience network include subcortical sites for emotion, home-ostatic regulation, and reward (see Fig. 2; ; ). The amygdala in particular plays an integral role in encoding salience, and also maintains conditioned effects after repeated pairing of internal drug sensations with external environmental stimuli (; ; ). Over time, conditioned drug cues gain further salience by activating cortical sites. In turn, cortical sites impinge upon reward-associated regions of the NAc, which is associated with wanting of the drug, and the STR, which is associated with habitual drug-seeking/taking behavior.

5.2.1. Evidence from humans

Adolescence is characterized by unique patterns of neural activity and changes in innervation and myelination within brain regions that contribute to heightened incentive salience at this developmental stage (, ; ). In fMRI studies, OFC activation patterns in adolescents (aged 13–17 years) more closely resemble those of children (aged 7–11 years) than adults (aged 23–29 years; ). In contrast, adolescent NAc responses to anticipated reward more closely resemble those of adults than children, although the adolescent NAc may be more reactive overall compared to both other age groups (). Adolescents also exhibit greater amydalar activation to fearful faces (; ), a region that encodes the magnitude of cue salience ().

Functional connections between amygdala and mPFC do not emerge until age 10 years, and continue to mature through at least through 23 years of age (). Accordingly, adolescent males and females (ages 10–16) show reduced resting state connectivity in amygdala-PFC networks, and almost no coupling between the basolateral amygdala (BLA) and PFC compared to adults, further suggesting that cortico-amygdalar pathways are not yet fully developed (). Adolescents may therefore be less able to functionally recruit regions like the NAc and amygdala during reward-based tasks compared to adults (; ). In contrast to the development of cortical/subcortical connectivity, positive functional connectivity between the amygdala and other subcortical regions, including the NAc and dorsal STR (caudate/putamen), is observed in childhood and remains largely stable through adulthood (). Altogether, these data further indicate that subcortical systems are mature or even hyper-reactive to reward salience during adolescence, while cortical systems require more time to develop adult patterns of activity.

5.2.2. Evidence from animals

In contrast to executive function, incentive salience can be readily assessed during the brief adolescent period. Adolescents attribute greater incentive salience to rewarding stimuli, including drug-related cues, compared to juveniles or adults. Adolescent rodents form preferences for environments associated with lower doses of cocaine than juveniles or adults (; ; ) are more resistant to extinction of cocaine-associated cues, and reinstate cocaine place preferences to a greater degree than adults (; ). Young adolescent rodents also form place preferences for nicotine-associated environments after a single drug-environment pairing, whereas late adolescent and adult rats may not form preferences even after repeated pairings (; ; ). Similarly, self-administration paradigms show that, compared to adults, adolescent rats acquire cocaine self-administration faster (), earn more cocaine infusions, are more resistant to extinction and more readily reinstate cocaine seeking (; ; ). Furthermore, adolescent male and female rats self-administer more nicotine than adults (, ), and adolescent male rats self-administer greater amounts of heroin than adults (). Together, these findings suggest that heightened incentive or motivational salience during adolescence contributes to important characteristics of substance dependence, including augmented drug seeking, extinction resistance, and relapse behaviors.

Developing circuitry and dopaminergic markers may help to explain heightened incentive salience during adolescence (; ). Lesion and inactivation studies demonstrate the importance of the NAc in encoding the initial salience of the primary reward-related cue, while the BLA appears necessary for maintaining salience encoding over time (; ). The attribution of motivational salience to drug-related cues is mediated by elevated D1 receptor expression on excitatory input from the PFC to the NAc (; ; ). Over time, salient drug-related cues release dopamine in the NAc even in the absence of drug taking (; ).

Altered PFC ←–→ BLA and PFC → NAc connectivity in adolescence provide additional mechanisms by which reward-related cues acquire heightened incentive salience, relative to the juvenile or adult periods. The density of axonal projections increases with age in BLA → PFC (, ) and PFC → NAc () pathways until late adolescence/young adulthood. Within the BLA itself, dendritic spine density, length, and complexity increase locally from the juvenile period through late adolescence, and stabilize in adulthood (). Dendritic density also increases on long-range projections from the BLA → mPFC from the juvenile period through adulthood (). Inhibitory GABAergic interneurons in the mPFC are a primary target of BLA projections (), suggesting growing BLA → mPFC projections closes a sensitive period of development for the PFC. Excitatory BLA projections increase cortical interneuron excitation and ultimately augment PFC inhibitory tone, which may have downstream effects on driving NAc and other subcortical activity. Axonal projections from the PFC → BLA prune after adolescence (), suggesting further fine-tuning of activity.

Pharmacological changes also occur during adolescence that help to explain age differences in salience attribution (). For example, our work (; ), and others () shows that dopamine receptors are transiently overproduced and pruned over the course of adolescence in a regional- and sex-dependent manner that seems to be independent of pubertal hormone increases (,, ). More specifically, dopamine D1 and D2 receptors in the STR rise to higher levels in males than females during adolescence, and D1 remains higher in males during adulthood despite some pruning (). In contrast, dopamine D1 and D2 receptors in the NAc do not show this same pattern, suggesting NAc plasticity may be more adaptive to changing needs of the reward system ().

Dopamine receptors in the mPFC are also differentially expressed across transitions between childhood, adolescence, and adulthood (,; ; ). For example, D2 receptors switch from inhibitory to excitatory on parvalbumin interneurons in the mPFC during adolescence (). Notably, developing signaling mechanisms are not uniform across brain regions, as initially reported in non-human primates (). Rather, signaling mechanisms within individual circuit develop independently. For example, we find that D1 receptors are overproduced on glutamatergic, but not GABAergic, neurons in the mPFC → NAc projections (). Elevated D1 on excitatory mPFC projection neurons is associated with increased drug seeking, taking, and drug-cue salience, as well as addiction-related behaviors such as novelty seeking, sexual activity, preferences for sweet taste, and impulsivity (; ; ; ). As suggested by Fig. 3, we predict that subjects with elevated motivational salience at an early age may be most vulnerable to developing SUD.

Fig. 3 

Risk for the transition to substance use disorder (SUD). Substance use before age 14 is associated with the greatest risk of developing substance abuse or dependence later in life. However, while many individuals try drugs, only a small percentage transition

Taken together, these findings suggest that increases in PFC ←–→ BLA and PFC → NAc signaling and connectivity during adolescence may underlie elevated incentive salience of drug-related cues. We propose that theory of incentive salience helps capture the early phases of adolescent drug experimentation, while vulnerability to habit development (Section 5.3) reflects underlying risk to the transition to addiction.

5.2.3. Prevention measures: promoting ‘Selective’ salience in adolescence

Incentive salience can be assessed on an individual basis by quantifying hedonic pleasure, craving and preferences for rewards and associated cues (; ). Interventions recently studied in adolescents involve text messaging during periods of high craving to reduce nicotine consumption (), in part by re-directing behavior to other salient cues. Somewhat counter-intuitively, exposure to novel experiences and stimuli reduces reward sensitivity and the incentive salience of reward or drug-related cues, and, we propose, may represent opportunities for prevention of SUD. Novelty exposure as a SUD prevention has not been well investigated in humans. However, exposure to enriched and novel environments during the juvenile and adolescent periods in animals reduces the rewarding effects of drugs of abuse (; ; ), in part by reducing incentive salience of reward-related cues () and reactivity to novelty (). From a signal-to-noise perspective, experience of novel environments and stimuli may raise the threshold of salience attribution, thereby reducing sensitivity to drug reward and the potential impact of drug-related cues in motivating behavior.

5.3. Habit formation

An alternative theory proposes that addiction reflects a shift in the neural control of behavior from a goal-directed learning mechanism to a habit-based mechanism (). Goal-directed learning and decision making describes choices made based upon environmental input and the affective value of the expected outcome (; ). In contrast, habit formation maintains behaviors regardless of motivation or goals (; ), such that behaviors are initiated more or less “automatically” (). In substance users, drug seeking is driven initially by desire for the rewarding effects of the drug, a goal-directed behavior. After repeated drug pairings with the environment, drug-associated cues become behavioral triggers that ultimately lead to compulsive and habitual abuse. As use transitions to abuse, projections from limbic to associative to sensorimotor cortex gradually recruit involvement from the ventromedial striatum to progressively greater involvement of the dorsomedial to the dorsolateral striatal regions (Fig. 2; ; ; ; ; ).

5.3.1. Evidence from humans

The habit model provides a valuable framework for predicting early vulnerability to the transition from substance use to dependence. Habits such as playing music and sports are easily formed before adolescence, when the brain regions underlying these skills are still maturing. However, the same concept may also apply to drug addiction. Habits that are physically harmful, such as excessive television viewing and sugar consumption, are more persistent when established at a young age (; ). Although substance dependence often develops after age 18, as we show in Fig. 1, early substance use (<14 years; ; , 2015a,; ) is associated with the highest risk of developing SUD.

Early substance use may facilitate the transition to SUD due to early activation of habit-related circuitry in the brain. The transition to SUD is mediated by a shift in neural control of behavior from the ventral STR (NAc) to the dorsal STR, considered the “habit region” of the brain (). In drug-dependent humans, drug cues consistently increase BOLD responses in the STR, BLA, VTA, PFC, hippocampus, and NAc (; ; ; ). In chronic substance abusers, drug-related cues activate and increase dopamine release in the dorsal STR (; ), a finding associated with greater addiction severity ().

5.3.2. Evidence from animals

Animal models provide evidence of a propensity to habit formation and STR reactivity during adolescence. One approach to studying habit in animals is to examine punished responding, which models the cost of addiction by training rats to take drug in the presence of a small electric shock (). Only ~20% of the rats continue to respond for drug when delivery is paired with shock, which is consistent with the overall percentage of individuals who are likely to develop an addiction (). However, this paradigm can be difficult to implement in developing rodents. Other animal studies of habit formation involve over-training to respond for reinforcement, which is then devalued prior to a test session (). The term “devalued” refers to the removal of motivation to pursue the reinforcer; for example, if the subject is satiated or nauseous, it will no longer be motivated to work for food. Continued responding in the absence of motivation is considered outcome insensitive, or habitual. Adolescents are less sensitive to reward devaluation than adults (; ; ). Insensitivity to reward devaluation, in conjunction with resistance to extinction (; ; ), suggest an enhanced propensity to habit formation in adolescence. Once a habit is established, environmental cues associated with the behavior serve as triggers for the behavior. Augmented salience of environmental cues during adolescence interact with a propensity to habit formation, rendering young subjects increasingly vulnerable to SUD when substance use is initiated early.

Animal studies, like human studies, show an increasing role of the dorsal STR as habitual, compulsive substance use emerges. Tract-tracing studies reveal ascending spiral-like connections linking the ventromedial NAc shell and core to more dorsolateral STR (; ; ). In the primate brain, anterior portions of the dorsal STR receive projections from multiple regions of the PFC, including the mPFC, OFC, and ACC, suggesting the dorsal STR may be a critical node for integrating cortical and subcortical processing (). While acquisition of cocaine taking is associated with metabolic changes in the ventral STR, chronic, more habitual cocaine self-administration is associated with increasingly greater activity and dopamine transporter (DAT) density in the dorsal STR in adult primates (; ).

Functional MRI responses to drug-associated cues in adult rodents after chronic cocaine exposure show remarkable faithfulness to human and other primate fMRI changes, including elevated responses in the dorsal STR, NAc, mPFC, and insular cortex (; ). Similar changes in blood flow in response to cocaine-associated cues are found when a mechanism underlying salience (PFC D1 receptors; ) is increased in the PFC in young rats (). Like primates, repeated drug taking in rodents increases dopamine release in the dorsal STR in response to drug-related cues (). Inhibition of the dorsolateral STR, but not the NAc, impairs cue-induced cocaine seeking and prevents the reinstatement of seeking after prolonged abstinence (; ; ). Similarly, disrupting functional connectivity between the NAc and dorsolateral STR decreases cocaine-seeking maintained by a second-order schedule, but does not affect acquisition of self-administration (). Taken together, converging evidence across species implicates the dorsal STR as critical for the transition to habitual, compulsive substance abuse.

More studies are needed to determine the role of the dorsal STR in adolescent drug seeking. However, as with the other brain regions, the dorsal STR undergoes unique developmental changes during adolescence. Male rats exhibit a more prominent rise and decline in striatal dopamine D1 and D2 receptors from adolescence to adulthood than female rats, although adult levels of each receptor subtype are comparable in both sexes (; ; ). Functional reactivity to stimulation of dopamine receptors, at the cyclic AMP level, is also elevated during adolescence compared to adulthood (). DAT density increases in the STR from early adolescence until peaking in late adolescence (), and thereafter declines through adulthood (; but see ). In parallel with DAT, dopamine concentrations in the dorsal STR increase through late adolescence, although they transiently dip at P35 in rats (), and then rise into adulthood (). The dorsal STR also shows increased firing during reward anticipation in adolescents, an effect not observed in adults (). Together, these data suggest that ongoing development in the dorsal STR may underlie an vulnerability to habit formation in adolescence and the development of addiction in adulthood, if drugs are sampled early.

5.3.3. Prevention measures: promoting healthy habits in adolescence

An individual propensity to form automatic habit-guided behaviors may represent an additional risk factor of SUD, and can be assessed in both humans and animal models using paradigms such as reward devaluation, as described earlier (; ; ). The risk of drug-related habits can be combated by the earlier formation of physically beneficial habits, particularly exercise. In individuals with SUD, exercise is effective in promoting abstinence and reducing relapse (; ). High-school aged male and female athletes are less likely to use illicit drugs such as marijuana and cocaine (; ). Moreover, eighth grade to high school-aged students participating in fitness consultations are less likely to abuse alcohol or cigarettes, even at 12-month follow-up (, ). Aerobically fit children have enhanced cognitive control and greater dorsal STR volumes (), suggesting physical exercise has important effects on the “habit” region of the brain.

Similar to humans, in male and female rodents access to running wheels reduces cocaine and heroin seeking (; ; ; ; ). Wheel running during adolescence also reduces concurrent nicotine consumption in male rats (females were not examined; ), and concurrent cocaine consumption in female rats (males were not examined; ). In adult rodents, aerobic exercise increases brain-derived neurotrophic factor (BDNF) levels in the STR (; ), as well as phosphorylated TrkB (the BDNF receptor) and D2 receptor mRNA (). However, the protective effects of pre-pubertal exercise (prior to the sensitive adolescent window) in the brain require further study.

5.4. Stress reactivity and negative reinforcement

Recent evidence suggests that stress facilitates the attribution of incentive salience and the recruitment of habit-related circuitry during learning, which further augment vulnerability to addiction (; ; , ; ). A fourth theory on the etiology SUD proposes that compulsive substance use critically involves negative reinforcement, or the removal of an aversive (physically or psychologically uncomfortable) affective state, such as stress. Over time, the hedonic effects caused by drug activation of the brain’s reward system are increasingly countered by an up-regulation of an anti-reward system (opponent-process counter-adaptation; ). The process drives formation of a new allo-static state in the reward set point (i.e., an increase in what is perceived as rewarding) such that increasingly greater amounts of reinforcement are needed to maintain functioning, leading to further substance abuse and the development of SUD. Higher allo-static reward set points can additionally be driven by prenatal or early life stress (). Exposure to stressors may therefore represent important risk factors for the transition from early substance use to dependence in young individuals.

5.4.1. Evidence from humans

Stress is one of the most commonly recognized triggers for early substance use and dependence (; ; , ). Poverty, low socioeconomic status (SES), and a family history of SUD and other psychiatric disorders are associated with addiction (; ; ). While the stress associated with a low SES household predicts neuropathology in adolescence and adulthood (), high SES is also linked to SUD. For example, low childhood SES is associated with smoking in late adolescence and young adulthood, but high childhood SES is associated with alcohol use, binge drinking, and marijuana use (). Adolescents and young adults from high SES may even be more likely to binge drink and to use marijuana or cocaine (), due in part to more expendable income (spending money; ).

One contributing factor to SUD that is independent of SES is early life stress, often in the form of abuse, loss of a caregiver, or exposure to a natural disaster. Early life stress is associated with early onset substance use as well as SUD in young adulthood (). Adolescents with alcohol abuse or dependence are up to 21 times more likely to have a history of physical or sexual abuse (; ), and drug-dependent adolescents report significantly higher life stress than non-dependent teens (). Exposure to early life stress also accelerates the onset of puberty (), which may in itself be a risk factor for the transition to substance dependence (see Section 2).

Functional MRI studies in human adolescents show that early life stress alters activity in the PFC and STR, resulting in impaired cognitive control (). Correspondingly, individuals experiencing severe early deprivation show blunted ventral STR (NAc) activity during a reward anticipation task (). In addition to PFC → STR changes, the amygdala shows increased activity in human fMRI studies and in animals exposed to early life stress (recently reviewed by ). Pharmacologically, positron emission tomography (PET) studies suggest acute stress induces dopamine release in the ventral STR, particularly in individuals reporting low parental care (). Early life stress thus impacts cognitive and reward-processing circuitry, and by extension may alter an individual’s response to drugs of abuse and risk for addiction.

5.4.2. Evidence from animals

Consistent with the allostasis model, early life stress increases feelings of dysphoria, anhedonia, and anxiety by dampening the reward system (; ), suggesting an increase in the reward set point. In rodent models, stress in the form of maternal separation reduces responding for reward in an intracranial self-stimulation (ICSS) procedure (), and decreases sensitivity to the reinforcing value of cocaine (; ; ). As a consequence, maternally separated or neonatally isolated rats show enhanced cocaine and ethanol intake in adulthood (; ; , , ; , ; ), although these effects of separation are dependent upon the duration and precise ages at which pups are separated, as well as sex. For example, females show greater enhancement of cocaine self-administration, but no change in ethanol consumption, than males following early separation (; , ; ; ).

In addition to increasing reward set point, early life stress may facilitate the transition from experimental substance use to SUD by increasing the salience of reward-related stimuli. Early life stress (deprivation of maternal care) enhances the salience of a rewarding food cues in adulthood (), which may be mediated by increased PFC D1 receptors on projections to the NAc (). Early life stress may also induce a propensity towards habit formation (; ). Both humans and rodents exposed to chronic stress have increased habit-guided, stimulus-response learning over goal-directed responding (; ; ; ), which may increase the risk of SUD (see Section 5.3).

Adolescence itself may be a sensitive period to the effects of stress. Stress sensitivity and the reactivity of the hypothalamic-pituitary-adrenal (HPA) axis, which initiates and terminates the body’s stress response via a negative feedback loop (; ), ramps up during adolescence (). Adolescent rats, especially females, are hyper-responsive to stressors and take longer to return to baseline after provocation (; ; ). Behaviorally, rats with a maternal separation history show increased impulsive behavior and hyperactivity in a novel environment (; ). provide a more detailed review of the effects of early childhood stress and abuse as it relates to the sensitive adolescent period.

The long-term impact of stress during development may be different from that of stress in adults (; ). The effects of stress depend upon the brain’s maturational state at different developmental periods and often do not fully manifest until adolescence or later (, ; ). Subcortical structures, with their earlier maturation, are often dysfunctional before later-developing cortical structures (). Neither the NAc nor the hippocampus, which consolidate the process of reward “liking” (), develop normally following exposure to early life stress (; ; ). Furthermore, a reduction in D1 receptor expression on mPFC → NAc projections in adolescence is observed following maternal separation (, ), and may represent a depressive affect state (). Chronic stress also reduces dendritic branching and/or spine density in mPFC and dorsomedial STR (including the NAc; ; ; ; ; ; but see ). In contrast, chronic stress increases dendritic branching in OFC and dorsolateral STR, the latter of which is involved in habit-driven behaviors (; ).

Taken together, these above findings indicate that chronic or early life stress alter the trajectory of neural development and can increase the risk of SUD (Fig. 3), potentially by increasing reward set points, the incentive salience of drug-related cues, and the propensity to form drug abuse habits. The combination of these elevated risk factors with an immature PFC during the sensitive adolescent period may dramatically increase an individual’s vulnerability to the transition to substance dependence, once drugs are sampled.

5.4.3. Prevention measures: promoting emotional regulation in adolescence

Exposure to early life stress augments the risk of initiating drug use in early adolescence and later transitioning to substance dependence. The National Child Traumatic Stress Network (2008) notes that one in four children and adolescents experience a traumatic event before age 16 years (), making it imperative to identify and intervene in at-risk subjects. Individual stress reactivity can be quantified as a risk factor for SUD by assessing emotional dysregulation, startle and other physiological responses, and in open-field and elevated plus maze tests (; ; ; ). Practices that reduce arousal and promote emotional regulation, such as yoga, meditation, exercise and social support can help counteract the effects of early life stress in pre-teens and adolescents (; ; ; ; ). In rodents, environmental enrichment during the pre-pubertal or adolescent periods (in the form of toys, elaborate habitats, and social housing) reverses the effects of pre-natal and post-natal early life stress on HPA axis function, spatial memory, social play and fear responses (; ; ). Most importantly, it is critical that preventative interventions are implemented early in life, before the sensitive adolescent manifests, in order to be maximally effective.

6. Conclusions

Substance use is a substantial public health issue that is estimated to cost the U.S. over $600 billion each year (). Given that early substance use increases the risk of SUD four-fold, it is imperative to identify and intervene with high-risk individuals before dependence develops. Adolescence represents an evolved sensitive period when the circuitry underlying incentive salience, habit formation and stress are uniquely vulnerable to hijacking by drugs of abuse, in part due to reduced cortical control and elevated drive of subcortical systems. Current theories on the etiology of substance dependence lend insight into the risk factors that render a young person vulnerable to transitioning from experimental substance use to substance dependence. By identifying at-risk individuals early, preventative interventions can be used to promote resilience to substance dependence. Additional research that focuses on the juvenile and adolescent period is needed to understand sex differences in the risk for substance dependence and to determine the most efficacious early preventative interventions for SUD.


This work was supported by the National Institutes of Drug Abuse DA-10543 and DA-026485 (to SLA) and by the John A. Kaneb Young Investigator Award (to CJJ). We thank Dr. Heather Brenhouse for the data presented in Fig. 3A.


ACCAnterior Cingulate Cortex
ACTHAdrenocorticotropic Hormone
ADHDAttention-Deficit/Hyperactivity Disorder
BLABasolateral Amygdala
BNSTBed Nucleus of the Stria Terminalis
cAMPCyclic AMP
CKCam-Kinase II
CRFCorticotropin Releasing Factor
DATDopamine Transporter
fMRIFunctional Magnetic Resonance Imaging
mPFCMedial Prefrontal Cortex
MRIMagnetic Resonance Imaging
NAcNucleus Accumbens
OFCOrbitofrontal Cortex
PETPositron Emission Tomography
PFCPrefrontal Cortex
P(#)Post-Natal Day
SERTSerotonin Transporter
SESSocioeconomic Status
STNSubthalamic Nucleus
SUDSubstance Use Disorder
VTAVentral Tegmental Area



Declaration of interest

The authors do not have any competing interests with the present review.



  1. Adriani W, et al. Peculiar vulnerability to nicotine oral self-administration in mice during early adolescence. Neuropsychopharmacology. 2002;27:212–224. [PubMed]
  2. Adriani W, et al. Behavioral and neurochemical vulnerability during adolescence in mice: studies with nicotine. Neuropsychopharmacology. 2004;29:869–878. [PubMed]
  3. Adriani W, et al. Methylphenidate administration to adolescent rats determines plastic changes on reward-related behavior and striatal gene expression. Neuropsychopharmacology. 2006a;31:1946–1956. [PubMed]
  4. Adriani W, et al. Short-term effects of adolescent methylphenidate exposure on brain striatal gene expression and sexual/endocrine parameters in male rats. Ann. N. Y. Acad. Sci. 2006b;1074:52–73. [PubMed]
  5. Aguiar AS, Jr., et al. Downhill training upregulates mice hippocampal and striatal brain-derived neurotrophic factor levels. J. Neural Transm. (Vienna) 2008;115:1251–1255. [PubMed]
  6. Alarcon G, et al. Developmental sex differences in resting state functional connectivity of amygdala sub-regions. Neuroimage. 2015;115:235–244. [PMC free article] [PubMed]
  7. American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Association; Washington, DC.: 2013.
  8. Andersen SL, Gazzara RA. The ontogeny of apomorphine-induced alterations of neostriatal dopamine release: effects on spontaneous release. J. Neurochem. 1993;61:2247–2255. [PubMed]
  9. Andersen SL, Navalta CP. Annual Research Review: new frontiers in developmental neuropharmacology: can long-term therapeutic effects of drugs be optimized through carefully timed early intervention? J. Child Psychol. Psychiatry. 2011;52:476–503. [PMC free article] [PubMed]
  10. Andersen SL, Teicher MH. Sex differences in dopamine receptors and their relevance to ADHD. Neurosci. Biobehav. Rev. 2000;24:137–141. [PubMed]
  11. Andersen SL, Teicher MH. Delayed effects of early stress on hippocampal development. Neuropsychopharmacology. 2004;29:1988–1993. [PubMed]
  12. Andersen SL, Teicher MH. Stress, sensitive periods and maturational events in adolescent depression. Trends Neurosci. 2008;31:183–191. [PubMed]
  13. Andersen SL, Teicher MH. Desperately driven and no brakes: developmental stress exposure and subsequent risk for substance abuse. Neurosci. Biobehav. Rev. 2009;33:516–524. [PMC free article] [PubMed]
  14. Andersen SL, Dumont NL, Teicher MH. Developmental differences in dopamine synthesis inhibition by (+/−)-7-OH-DPAT. Naunyn. Schmiedebergs Arch. Pharmacol. 1997a;356:173–181. [PubMed]
  15. Andersen SL, et al. Sex differences in dopamine receptor overproduction and elimination. Neuroreport. 1997b;8:1495–1498. [PubMed]
  16. Andersen SL, et al. Altered responsiveness to cocaine in rats exposed to methylphenidate during development. Nat. Neurosci. 2002a;5:13–14. [PubMed]
  17. Andersen SL, et al. Pubertal changes in gonadal hormones do not underlie adolescent dopamine receptor overproduction. Psychoneuroendocrinology. 2002b;27:683–691. [PubMed]
  18. Andersen SL, et al. Juvenile methylphenidate modulates reward-related behaviors and cerebral blood flow by decreasing cortical D3 receptors. Eur. J. Neurosci. 2008a;27:2962–2972. [PubMed]
  19. Andersen SL, et al. Preliminary evidence for sensitive periods in the effect of childhood sexual abuse on regional brain development. J. Neuropsychiatry Clin. Neurosci. 2008b;20:292–301. [PMC free article] [PubMed]
  20. Andersen SL. Changes in the second messenger cyclic AMP during development may underlie motoric symptoms in attention deficit/hyperactivity disorder (ADHD). Behav. Brain Res. 2002;130:197–201. [PubMed]
  21. Andersen SL. Trajectories of brain development: point of vulnerability or window of opportunity? Neurosci. Biobehav. Rev. 2003;27:3–18. [PubMed]
  22. Andersen SL. Stimulants and the developing brain. Trends Pharmacol. Sci. 2005;26:237–243. [PubMed]
  23. Andersen SL. Exposure to early adversity: points of cross-species translation that can lead to improved understanding of depression. Dev. Psychopathol. 2015;27:477–491. [PMC free article] [PubMed]
  24. Andrzejewski ME, et al. A comparison of adult and adolescent rat behavior in operant learning extinction, and behavioral inhibition paradigms. Behav. Neurosci. 2011;125:93–105. [PubMed]
  25. Anker JJ, Carroll ME. Reinstatement of cocaine seeking induced by drugs, cues, and stress in adolescent and adult rats. Psychopharmacology (Berl.) 2010;208:211–222. [PMC free article] [PubMed]
  26. Arain M, et al. Maturation of the adolescent brain. Neuropsychiatr. Dis. Treat. 2013;9:449–461. [PMC free article] [PubMed]
  27. Arnsten AF, Rubia K. Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: disruptions in neurodevelopmental psychiatric disorders. J. Am. Acad. Child Adolesc. Psychiatry. 2012;51:356–367. [PubMed]
  28. Averbeck BB, et al. Estimates of projection overlap and zones of convergence within frontal-striatal circuits. J. Neurosci. 2014;34:9497–9505. [PMC free article] [PubMed]
  29. Badanich KA, Adler KJ, Kirstein CL. Adolescents differ from adults in cocaine conditioned place preference and cocaine-induced dopamine in the nucleus accumbens septi. Eur. J. Pharmacol. 2006;550:95–106. [PubMed]
  30. Bailey J, Penhune VB. A sensitive period for musical training: contributions of age of onset and cognitive abilities. Ann. N. Y. Acad. Sci. 2012;1252:163–170. [PubMed]
  31. Bardo MT, Compton WM. Does physical activity protect against drug abuse vulnerability? Drug Alcohol Depend. 2015;153:3–13. [PubMed]
  32. Bardo MT, Donohew RL, Harrington NG. Psychobiology of novelty seeking and drug seeking behavior. Behav. Brain Res. 1996;77:23–43. [PubMed]
  33. Barnea-Goraly N, et al. White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cereb. Cortex. 2005;15:1848–1854. [PubMed]
  34. Baskin BM, Dwoskin LP, Kantak KM. Methylphenidate treatment beyond adolescence maintains increased cocaine self-administration in the spontaneously hypertensive rat model of attention deficit/hyperactivity disorder. Pharmacol. Biochem. Behav. 2015;131:51–56. [PMC free article] [PubMed]
  35. Beckmann JS, Bardo MT. Environmental enrichment reduces attribution of incentive salience to a food-associated stimulus. Behav. Brain Res. 2012;226:331–334. [PMC free article] [PubMed]
  36. Belin D, Everitt BJ. Cocaine seeking habits depend upon dopamine-dependent serial connectivity linking the ventral with the dorsal striatum. Neuron. 2008;57:432–441. [PubMed]
  37. Belin D, et al. High impulsivity predicts the switch to compulsive cocaine-taking. Science. 2008;320:1352–1355. [PMC free article] [PubMed]
  38. Bellis MA, et al. Predictors of risky alcohol consumption in schoolchildren and their implications for preventing alcohol-related harm. Subst. Abuse Treat. Prev. Policy. 2007;2:15. [PMC free article] [PubMed]
  39. Belluzzi JD, et al. Age-dependent effects of nicotine on locomotor activity and conditioned place preference in rats. Psychopharmacology (Berl.) 2004;174:389–395. [PubMed]
  40. Bereczkei T, Csanaky A. Evolutionary pathway of child development: lifestyles of adolescents and adults from father-absent families. Hum. Nat. 1996;7:257–280. [PubMed]
  41. Berridge CW, Arnsten AF. Psychostimulants and motivated behavior: arousal and cognition. Neurosci. Biobehav. Rev. 2013;37:1976–1984. [PubMed]
  42. Berridge KC. The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology (Berl.) 2007;191:391–431. [PubMed]
  43. Berridge KC. Wanting and liking: observations from the neuroscience and psychology laboratory. Inquiry (Oslo) 2009a;52:378. [PMC free article] [PubMed]
  44. Berridge KC. ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol. Behav. 2009b;97:537–550. [PMC free article] [PubMed]
  45. Biederman J, et al. Pharmacotherapy of attention-deficit/hyperactivity disorder reduces risk for substance use disorder. Pediatrics. 1999;104:e20. [PubMed]
  46. Biegel GM, et al. Mindfulness-based stress reduction for the treatment of adolescent psychiatric outpatients: a randomized clinical trial. J. Consult. Clin. Psychol. 2009;77:855–866. [PubMed]
  47. Bjork JM, et al. Incentive-elicited brain activation in adolescents: similarities and differences from young adults. J. Neurosci. 2004;24:1793–1802. [PubMed]
  48. Bjorklund DF, Pellegrini AD. Child development and evolutionary psychology. Child Dev. 2000;71:1687–1708. [PubMed]
  49. Bogin B, Smith BH. Evolution of the human life cycle. Am. J. Hum. Biol. 1996;8:703–716. [PubMed]
  50. Bolanos CA, et al. Methylphenidate treatment during pre- and periadolescence alters behavioral responses to emotional stimuli at adulthood. Biol. Psychiatry. 2003;54:1317–1329. [PubMed]
  51. Bowen S, et al. Mindfulness-based relapse prevention for substance use disorders: a pilot efficacy trial. Subst. Abuse. 2009;30:295–305. [PMC free article] [PubMed]
  52. Brandon CL, et al. Enhanced reactivity and vulnerability to cocaine following methylphenidate treatment in adolescent rats. Neuropsychopharmacology. 2001;25:651–661. [PubMed]
  53. Brandon CL, Marinelli M, White FJ. Adolescent exposure to methylphenidate alters the activity of rat midbrain dopamine neurons. Biol. Psychiatry. 2003;54:1338–1344. [PubMed]
  54. Brenhouse HC, Andersen SL. Delayed extinction and stronger reinstatement of cocaine conditioned place preference in adolescent rats compared to adults. Behav. Neurosci. 2008;122:460–465. [PMC free article] [PubMed]
  55. Brenhouse HC, Andersen SL. Developmental trajectories during adolescence in males and females: a cross-species understanding of underlying brain changes. Neurosci. Biobehav. Rev. 2011;35:1687–1703. [PMC free article] [PubMed]
  56. Brenhouse HC, Sonntag KC, Andersen SL. Transient D1 dopamine receptor expression on prefrontal cortex projection neurons: relationship to enhanced motivational salience of drug cues in adolescence. J. Neurosci. 2008;28:2375–2382. [PMC free article] [PubMed]
  57. Brenhouse HC, et al. Juvenile methylphenidate exposure and factors that influence incentive processing. Dev. Neurosci. 2009;31:95–106. [PMC free article] [PubMed]
  58. Brenhouse HC, Dumais K, Andersen SL. Enhancing the salience of dullness: behavioral and pharmacological strategies to facilitate extinction of drug-cue associations in adolescent rats. Neuroscience. 2010;169:628–636. [PMC free article] [PubMed]
  59. Brenhouse HC, Lukkes JL, Andersen SL. Early life adversity alters the developmental profiles of addiction-related prefrontal cortex circuitry. Brain Sci. 2013;3:143–158. [PMC free article] [PubMed]
  60. Brown JD, Siegel JM. Exercise as a buffer of life stress: a prospective study of adolescent health. Health Psychol. 1988;7:341–353. [PubMed]
  61. Burton AC, Nakamura K, Roesch MR. From ventral-medial to dorsal-lateral striatum: neural correlates of reward-guided decision-making. Neurobiol. Learn. Mem. 2015;117:51–59. [PMC free article] [PubMed]
  62. Cain ME, Green TA, Bardo MT. Environmental enrichment decreases responding for visual novelty. Behav. Processes. 2006;73:360–366. [PMC free article] [PubMed]
  63. Callaghan BL, et al. The international society for developmental psychobiology Sackler symposium: early adversity and the maturation of emotion circuits—a cross-species analysis. Dev. Psychobiol. 2014;56:1635–1650. [PMC free article] [PubMed]
  64. Carlezon WA, Jr., Mague SD, Andersen SL. Enduring behavioral effects of early exposure to methylphenidate in rats. Biol. Psychiatry. 2003;54:1330–1337. [PubMed]
  65. Casey BJ, Jones RM. Neurobiology of the adolescent brain and behavior: implications for substance use disorders. J. Am. Acad. Child Adolesc. Psychiatry. 2010;49:1189–1201. [PMC free article] [PubMed]
  66. Casey BJ, Getz S, Galvan A. The adolescent brain. Dev. Rev. 2008;28:62–77. [PMC free article] [PubMed]
  67. Casey B, Jones RM, Somerville LH. Braking and accelerating of the adolescent brain. J. Res. Adolesc. 2011;21:21–33. [PMC free article] [PubMed]
  68. Cass DK, et al. Developmental disruption of gamma-aminobutyric acid function in the medial prefrontal cortex by noncontingent cocaine exposure during early adolescence. Biol. Psychiatry. 2013;74:490–501. [PMC free article] [PubMed]
  69. Chaddock L, et al. Basal ganglia volume is associated with aerobic fitness in preadolescent children. Dev. Neurosci. 2010;32:249–256. [PMC free article] [PubMed]
  70. Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am. J. Psychiatry. 2003;160:1041–1052. [PMC free article] [PubMed]
  71. Chang SE, Wheeler DS, Holland PC. Roles of nucleus accumbens and basolateral amygdala in autoshaped lever pressing. Neurobiol. Learn. Mem. 2012;97:441–451. [PMC free article] [PubMed]
  72. Clark DB, Lesnick L, Hegedus AM. Traumas and other adverse life events in adolescents with alcohol abuse and dependence. J. Am. Acad. Child Adolesc. Psychiatry. 1997;36:1744–1751. [PubMed]
  73. Cobb S. Presidential Address-1976: social support as a moderator of life stress. Psychosom. Med. 1976;38:300–314. [PubMed]
  74. Colorado RA, et al. Effects of maternal separation, early handling, and standard facility rearing on orienting and impulsive behavior of adolescent rats. Behav. Processes. 2006;71:51–58. [PubMed]
  75. Congdon E, et al. Measurement and reliability of response inhibition. Front. Psychol. 2012;3:37. [PMC free article] [PubMed]
  76. Connor-Smith JK, et al. Responses to stress in adolescence: measurement of coping and involuntary stress responses. J. Consult. Clin. Psychol. 2000;68:976–992. [PubMed]
  77. Cook SC, Wellman CL. Chronic stress alters dendritic morphology in rat medial prefrontal cortex. J. Neurobiol. 2004;60:236–248. [PubMed]
  78. Crawford CA, et al. Early methylphenidate exposure enhances cocaine self-administration but not cocaine-induced conditioned place preference in young adult rats. Psychopharmacology (Berl.) 2011;213:43–52. [PMC free article] [PubMed]
  79. Cressman VL, et al. Prefrontal cortical inputs to the basal amygdala undergo pruning during late adolescence in the rat. J. Comp. Neurol. 2010;518:2693–2709. [PMC free article] [PubMed]
  80. Cruz FC, et al. Maternal separation stress in male mice: long-term increases in alcohol intake. Psychopharmacology (Berl.) 2008;201:459–468. [PMC free article] [PubMed]
  81. Cui M, et al. Enriched environment experience overcomes the memory deficits and depressive-like behavior induced by early life stress. Neurosci. Lett. 2006;404:208–212. [PubMed]
  82. Cunningham MG, Bhattacharyya S, Benes FM. Amygdalo-cortical sprouting continues into early adulthood: implications for the development of normal and abnormal function during adolescence. J. Comp. Neurol. 2002;453:116–130. [PubMed]
  83. Cunningham MG, Bhattacharyya S, Benes FM. Increasing Interaction of amygdalar afferents with GABAergic interneurons between birth and adulthood. Cereb. Cortex. 2008;18:1529–1535. [PubMed]
  84. Curtis CE, D’Esposito M. Persistent activity in the prefrontal cortex during working memory. Trends Cognit. Sci. 2003;7:415–423. [PubMed]
  85. Darwin CR. The descent of man, and selection in relation to sex. 1 st Edition John Murray; London: 1871.
  86. Diamond A, Lee K. Interventions shown to aid executive function development in children 4 to 12 years old. Science. 2011;333:959–964. [PMC free article] [PubMed]
  87. Diamond A. Executive functions. Annu. Rev. Psychol. 2013;64:135–168. [PMC free article] [PubMed]
  88. Dias-Ferreira E, et al. Chronic stress causes frontostriatal reorganization and affects decision-making. Science. 2009;325:621–625. [PubMed]
  89. Dickinson A. Actions and habits: the development of behavioural autonomy. Philos. Trans. R Soc. Lond. B Biol. Sci. 1985;308:67–78.
  90. Doherty JM, Frantz KJ. Heroin self-administration and reinstatement of heroin-seeking in adolescent vs: adult male rats. Psychopharmacology (Berl.) 2012;219:763–773. [PubMed]
  91. Dow-Edwards D. Sex differences in the effects of cocaine abuse across the life span. Physiol. Behav. 2010;100:208–215. [PMC free article] [PubMed]
  92. Duncan DF. Life stress as a precursor to adolescent drug dependence. Int. J. Addict. 1977;12:1047–1056. [PubMed]
  93. Durston S, et al. Anatomical MRI of the developing human brain: what have we learned? J. Am. Acad. Child Adolesc. Psychiatry. 2001;40:1012–1020. [PubMed]
  94. de Bruijn GJ, van den Putte B. Adolescent soft drink consumption, television viewing and habit strength: investigating clustering effects in the Theory of Planned Behaviour. Appetite. 2009;53:66–75. [PubMed]
  95. de Wit H. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict. Biol. 2009;14:22–31. [PMC free article] [PubMed]
  96. Eagle DM, Baunez C. Is there an inhibitory-response-control system in the rat? Evidence from anatomical and pharmacological studies of behavioral inhibition. Neurosci. Biobehav. Rev. 2010;34:50–72. [PMC free article] [PubMed]
  97. El Rawas R, et al. Environmental enrichment decreases the rewarding but not the activating effects of heroin. Psychopharmacology (Berl.) 2009;203:561–570. [PubMed]
  98. Enoch MA. The role of early life stress as a predictor for alcohol and drug dependence. Psychopharmacology (Berl.) 2011;214:17–31. [PMC free article] [PubMed]
  99. Ernst M, et al. Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. Neuroimage. 2005;25:1279–1291. [PubMed]
  100. Ernst M, Pine DS, Hardin M. Triadic model of the neurobiology of motivated behavior in adolescence. Psychol. Med. 2006;36:299–312. [PMC free article] [PubMed]
  101. Ernst M. The triadic model perspective for the study of adolescent motivated behavior. Brain Cognit. 2014;89:104–111. [PMC free article] [PubMed]
  102. Eshel N, et al. Neural substrates of choice selection in adults and adolescents: development of the ventrolateral prefrontal and anterior cingulate cortices. Neuropsychologia. 2007;45:1270–1279. [PMC free article] [PubMed]
  103. Everitt BJ, Robbins TW. From the ventral to the dorsal striatum: devolving views of their roles in drug addiction. Neurosci. Biobehav. Rev. 2013;37:1946–1954. [PubMed]
  104. Everitt BJ, Robbins TW. Drug addiction: updating actions to habits to compulsions ten years on. Annu. Rev. Psychol. 2016;67:23–50. [PubMed]
  105. Everitt BJ, et al. Review: neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Philos. Trans. R Soc. Lond. B Biol. Sci. 2008;363:3125–3135. [PMC free article] [PubMed]
  106. Fareri DS, Tottenham N. Effects of early life stress on amygdala and striatal development. Dev. Cognit. Neurosci. 2016;19:233–247. [PMC free article] [PubMed]
  107. Farrell MR, et al. Sex-specific effects of early life stress on social interaction and prefrontal cortex dendritic morphology in young rats. Behav. Brain Res. 2016;310:119–125. [PubMed]
  108. Ferron C, et al. Sport activity in adolescence: associations with health perceptions and experimental behaviours. Health Educ. Res. 1999;14:225–233. [PubMed]
  109. Francis DD, et al. Environmental enrichment reverses the effects of maternal separation on stress reactivity. J. Neurosci. 2002;22:7840–7843. [PubMed]
  110. Freund N, et al. When the party is over: depressive-like states in rats following termination of cortical D1 receptor overexpression. Psychopharmacology (Berl.) 2016;233:1191–1201. [PMC free article] [PubMed]
  111. Fuchs RA, Branham RK, See RE. Different neural substrates mediate cocaine seeking after abstinence versus extinction training: a critical role for the dorsolateral caudate-putamen. J. Neurosci. 2006;26:3584–3588. [PMC free article] [PubMed]
  112. Gabard-Durnam LJ, et al. The development of human amygdala functional connectivity at rest from 4 to 23 years: a cross-sectional study. Neuroimage. 2014;95:193–207. [PMC free article] [PubMed]
  113. Galvan A, et al. Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. J. Neurosci. 2006;26:6885–6892. [PubMed]
  114. Galvan A. Adolescent development of the reward system. Front. Hum. Neurosci. 2010;4:6. [PMC free article] [PubMed]
  115. Ganella DE, Kim JH. Developmental rodent models of fear and anxiety: from neurobiology to pharmacology. Br. J. Pharmacol. 2014;171:4556–4574. [PMC free article] [PubMed]
  116. Garavan H, et al. Cue-induced cocaine craving: neuroanatomical specificity for drug users and drug stimuli. Am. J. Psychiatry. 2000;157:1789–1798. [PubMed]
  117. Gardner B, Lally P, Wardle J. Making health habitual: the psychology of ‘habit-formation’ and general practice. Br. J. Gen. Pract. 2012;62:664–666. [PMC free article] [PubMed]
  118. Gass JT, et al. Adolescent alcohol exposure reduces behavioral flexibility, promotes disinhibition, and increases resistance to extinction of ethanol self-administration in adulthood. Neuropsychopharmacology. 2014;39:2570–2583. [PMC free article] [PubMed]
  119. Giedd JN, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 1999;2:861–863. [PubMed]
  120. Gluckman PD, Hanson MA. Evolution, development and timing of puberty. Trends Endocrinol. Metab. 2006;17:7–12. [PubMed]
  121. Goff B, et al. Reduced nucleus accumbens reactivity and adolescent depression following early-life stress. Neuroscience. 2013;249:129–138. [PMC free article] [PubMed]
  122. Goldman PS, Alexander GE. Maturation of prefrontal cortex in the monkey revealed by local reversible cryogenic depression. Nature. 1977;267:613–615. [PubMed]
  123. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 2011;12:652–669. [PMC free article] [PubMed]
  124. Grace AA, et al. Regulation of firing of dopaminergic neurons and control of goal-directed behaviors. Trends Neurosci. 2007;30:220–227. [PubMed]
  125. Grant BF, Dawson DA. Age of onset of drug use and its association with DSM-IV drug abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. J. Subst. Abuse. 1998;10:163–173. [PubMed]
  126. Grant BF. Age at smoking onset and its association with alcohol consumption and DSM-IV alcohol abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. J. Subst. Abuse. 1998;10:59–73. [PubMed]
  127. Gremel CM, Cunningham CL. Roles of the nucleus accumbens and amygdala in the acquisition and expression of ethanol-conditioned behavior in mice. J. Neurosci. 2008;28:1076–1084. [PubMed]
  128. Gruber SA, et al. Worth the wait: effects of age of onset of marijuana use on white matter and impulsivity. Psychopharmacology (Berl.) 2014;231:1455–1465. [PMC free article] [PubMed]
  129. Grusser SM, et al. Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology (Berl.) 2004;175:296–302. [PubMed]
  130. Gulley JM, Juraska JM. The effects of abused drugs on adolescent development of corticolimbic circuitry and behavior. Neuroscience. 2013;249:3–20. [PMC free article] [PubMed]
  131. Gustafsson L, Ploj K, Nylander I. Effects of maternal separation on voluntary ethanol intake and brain peptide systems in female Wistar rats. Pharmacol. Biochem. Behav. 2005;81:506–516. [PubMed]
  132. Guyer AE, et al. Striatal functional alteration in adolescents characterized by early childhood behavioral inhibition. J. Neurosci. 2006;26:6399–6405. [PubMed]
  133. Guyer AE, et al. A developmental examination of amygdala response to facial expressions. J. Cognit. Neurosci. 2008;20:1565–1582. [PMC free article] [PubMed]
  134. Haber SN, Fudge JL, McFarland NR. Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. J. Neurosci. 2000;20:2369–2382. [PubMed]
  135. Haber SN, et al. Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning. J. Neurosci. 2006;26:8368–8376. [PubMed]
  136. Hammerslag LR, Gulley JM. Age and sex differences in reward behavior in adolescent and adult rats. Dev. Psychobiol. 2014;56:611–621. [PMC free article] [PubMed]
  137. Hanson JL, et al. Cumulative stress in childhood is associated with blunted reward-related brain activity in adulthood. Soc. Cognit. Affect. Neurosci. 2016;11:405–412. [PMC free article] [PubMed]
  138. Harrell JS, et al. Smoking initiation in youth: the roles of gender, race, socioeconomics, and developmental status. J. Adolesc. Health. 1998;23:271–279. [PubMed]
  139. Harvey RC, et al. Methylphenidate treatment in adolescent rats with an attention deficit/hyperactivity disorder phenotype: cocaine addiction vulnerability and dopamine transporter function. Neuropsychopharmacology. 2011;36:837–847. [PMC free article] [PubMed]
  140. Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: implications for substance abuse prevention. Psychol. Bull. 1992;112:64–105. [PubMed]
  141. Hawley P. The evolution of adolescence and the adolescence of evolution: the coming of age of humans and the theory about the forces that made them. J. Res. Adolesc. 2011;21:307–316.
  142. Hester R, Garavan H. Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J. Neurosci. 2004;24:11017–11022. [PubMed]
  143. Hester R, Lubman DI, Yucel M. The role of executive control in human drug addiction. Curr. Top. Behav. Neurosci. 2010;3:301–318. [PubMed]
  144. Hogarth L, Chase HW. Parallel goal-directed and habitual control of human drug-seeking: implications for dependence vulnerability. J. Exp. Psychol. Anim. Behav. Process. 2011;37:261–276. [PubMed]
  145. Holzel BK, et al. Investigation of mindfulness meditation practitioners with voxel-based morphometry. Soc. Cognit. Affect. Neurosci. 2008;3:55–61. [PMC free article] [PubMed]
  146. Holzel BK, et al. Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Res. 2011;191:36–43. [PMC free article] [PubMed]
  147. Hostinar CE. The Impacts of Social Support and Early Life Stress on Stress Reactivity in Children and Adolescents. University of Minnesota Digital Conservancy. 2013
  148. Houben K, Wiers RW, Jansen A. Getting a grip on drinking behavior: training working memory to reduce alcohol abuse. Psychol. Sci. 2011;22:968–975. [PubMed]
  149. Humensky JL. Are adolescents with high socioeconomic status more likely to engage in alcohol and illicit drug use in early adulthood? Subst. Abuse Treat. Prev. Policy. 2010;5:19. [PMC free article] [PubMed]
  150. Huot RL, et al. Development of adult ethanol preference and anxiety as a consequence of neonatal maternal separation in Long Evans rats and reversal with antidepressant treatment. Psychopharmacology (Berl.) 2001;158:366–373. [PubMed]
  151. Ito R, et al. Dopamine release in the dorsal striatum during cocaine-seeking behavior under the control of a drug-associated cue. J. Neurosci. 2002;22:6247–6253. [PubMed]
  152. Jasinska AJ, et al. Factors modulating neural reactivity to drug cues in addiction: a survey of human neuroimaging studies. Neurosci. Biobehav. Rev. 2014;38:1–16. [PMC free article] [PubMed]
  153. Johnson JS, Newport EL. Critical period effects in second language learning: the influence of maturational state on the acquisition of English as a second language. Cognit. Psychol. 1989;21:60–99. [PubMed]
  154. Johnson TR, et al. Neural processing of a cocaine-associated odor cue revealed by functional MRI in awake rats. Neurosci. Lett. 2013;534:160–165. [PMC free article] [PubMed]
  155. Johnson CM, et al. Long-range orbitofrontal and amygdala axons show divergent patterns of maturation in the frontal cortex across adolescence. Dev. Cognit. Neurosci. 2016;18:113–120. [PMC free article] [PubMed]
  156. Jonkman S, Pelloux Y, Everitt BJ. Differential roles of the dorsolateral and midlateral striatum in punished cocaine seeking. J. Neurosci. 2012;32:4645–4650. [PubMed]
  157. Jordan CJ, et al. Cocaine-seeking behavior in a genetic model of attention-deficit/hyperactivity disorder following adolescent methylphenidate or atomoxetine treatments. Drug Alcohol Depend. 2014;140:25–32. [PMC free article] [PubMed]
  158. Jordan CJ, et al. Adolescent D-amphetamine treatment in a rodent model of ADHD: Pro-cognitive effects in adolescence without an impact on cocaine cue reactivity in adulthood. Behav. Brain Res. 2016;297:165–179. [PMC free article] [PubMed]
  159. Judah G, Gardner B, Aunger R. Forming a flossing habit: an exploratory study of the psychological determinants of habit formation. Br. J. Health Psychol. 2013;18:338–353. [PubMed]
  160. Kalinichev M, et al. Long-lasting changes in stress-induced corticosterone response and anxiety-like behaviors as a consequence of neonatal maternal separation in Long-Evans rats. Pharmacol. Biochem. Behav. 2002;73:131–140. [PubMed]
  161. Kalivas PW, Volkow N, Seamans J. Unmanageable motivation in addiction: a pathology in prefrontal-accumbens glutamate transmission. Neuron. 2005;45:647–650. [PubMed]
  162. Kendig MD, et al. Chronic restricted access to 10% sucrose solution in adolescent and young adult rats impairs spatial memory and alters sensitivity to outcome devaluation. Physiol. Behav. 2013;120:164–172. [PubMed]
  163. Khurana A, et al. Working memory ability predicts trajectories of early alcohol use in adolescents: the mediational role of impulsivity. Addiction. 2013;108:506–515. [PMC free article] [PubMed]
  164. Kilpatrick DG, et al. Risk factors for adolescent substance abuse and dependence: data from a national sample. J. Consult. Clin. Psychol. 2000;68:19–30. [PubMed]
  165. Kilpatrick DG, et al. Violence and risk of PTSD, major depression, substance abuse/dependence, and comorbidity: results from the National Survey of Adolescents. J. Consult. Clin. Psychol. 2003;71:692–700. [PubMed]
  166. Knudsen EI. Sensitive periods in the development of the brain and behavior. J. Cognit. Neurosci. 2004;16:1412–1425. [PubMed]
  167. Koob GF, Le Moal M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology. 2001;24:97–129. [PubMed]
  168. Koss WA, et al. Dendritic remodeling in the adolescent medial prefrontal cortex and the basolateral amygdala of male and female rats. Synapse. 2014;68:61–72. [PubMed]
  169. Kosten TA, Miserendino MJ, Kehoe P. Enhanced acquisition of cocaine self-administration in adult rats with neonatal isolation stress experience. Brain Res. 2000;875:44–50. [PubMed]
  170. Kosten TA, et al. Neonatal isolation enhances acquisition of cocaine self-administration and food responding in female rats. Behav. Brain Res. 2004;151:137–149. [PubMed]
  171. Kosten TA, Zhang XY, Kehoe P. Heightened cocaine and food self-administration in female rats with neonatal isolation experience. Neuropsychopharmacology. 2006;31:70–76. [PubMed]
  172. Kreek MJ, et al. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat. Neurosci. 2005;8:1450–1457. [PubMed]
  173. Kremers SP, van der Horst K, Brug J. Adolescent screen-viewing behaviour is associated with consumption of sugar-sweetened beverages: the role of habit strength and perceived parental norms. Appetite. 2007;48:345–350. [PubMed]
  174. Kuhn C. Emergence of sex differences in the development of substance use and abuse during adolescence. Pharmacol. Ther. 2015;153:55–78. [PMC free article] [PubMed]
  175. Lacy RT, et al. Exercise decreases speedball self-administration. Life Sci. 2014;114:86–92. [PMC free article] [PubMed]
  176. Lakes KD, Hoyt WT. Promoting self-regulation through school-based martial arts training. Appl. Dev. Psychol. 2004;25:283–302.
  177. Lambert NM, Hartsough CS. Prospective study of tobacco smoking and substance dependencies among samples of ADHD and non-ADHD participants. J. Learn. Disabil. 1998;31:533–544. [PubMed]
  178. Lau H, Rogers RD, Passingham RE. Dissociating response selection and conflict in the medial frontal surface. Neuroimage. 2006;29:446–451. [PubMed]
  179. Laviola G, et al. Beneficial effects of enriched environment on adolescent rats from stressed pregnancies. Eur. J. Neurosci. 2004;20:1655–1664. [PubMed]
  180. Lazar SW, et al. Meditation experience is associated with increased cortical thickness. Neuroreport. 2005;16:1893–1897. [PMC free article] [PubMed]
  181. Letchworth SR, et al. Progression of changes in dopamine transporter binding site density as a result of cocaine self-administration in rhesus monkeys. J. Neurosci. 2001;21:2799–2807. [PubMed]
  182. Levin ED, et al. Adolescent-onset nicotine self-administration modeled in female rats. Psychopharmacology (Berl.) 2003;169:141–149. [PubMed]
  183. Levin ED, et al. Adolescent vs: adult-onset nicotine self-administration in male rats: duration of effect and differential nicotinic receptor correlates. Neurotoxicol. Teratol. 2007;29:458–465. [PMC free article] [PubMed]
  184. Lidow MS, Goldman-Rakic PS, Rakic P. Synchronized overproduction of neurotransmitter receptors in diverse regions of the primate cerebral cortex. Proc. Natl. Acad. Sci. U. S. A. 1991;88:10218–10221. [PMC free article] [PubMed]
  185. Liston C, et al. Stress-induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set-shifting. J. Neurosci. 2006;26:7870–7874. [PubMed]
  186. Liu HS, et al. Dorsolateral caudate nucleus differentiates cocaine from natural reward-associated contextual cues. Proc. Natl. Acad. Sci. U. S. A. 2013;110:4093–4098. [PMC free article] [PubMed]
  187. Lomanowska AM, et al. Inadequate early social experience increases the incentive salience of reward-related cues in adulthood. Behav. Brain Res. 2011;220:91–99. [PubMed]
  188. Lopez-Larson MP, et al. Altered prefrontal and insular cortical thickness in adolescent marijuana users. Behav. Brain Res. 2011;220:164–172. [PMC free article] [PubMed]
  189. Lowen SB, et al. Cocaine-conditioned odor cues without chronic exposure: implications for the development of addiction vulnerability. Neuroimage Clin. 2015;8:652–659. [PMC free article] [PubMed]
  190. Lukas SE, et al. Extended-release naltrexone (XR-NTX) attenuates brain responses to alcohol cues in alcohol-dependent volunteers: a bold FMRI study. Neuroimage. 2013;78:176–185. [PubMed]
  191. Lupien SJ, et al. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat. Rev. Neurosci. 2009;10:434–445. [PubMed]
  192. Lyss PJ, et al. Degree of neuronal activation following FG-7142 changes across regions during development. Brain Res. Dev. Brain Res. 1999;116:201–203. [PubMed]
  193. Maas LC, et al. Functional magnetic resonance imaging of human brain activation during cue-induced cocaine craving. Am. J. Psychiatry. 1998;155:124–126. [PubMed]
  194. Manjunath NK, Telles S. Improved performance in the Tower of London test following yoga. Indian J. Physiol. Pharmacol. 2001;45:351–354. [PubMed]
  195. Mannuzza S, et al. Age of methylphenidate treatment initiation in children with ADHD and later substance abuse: prospective follow-up into adulthood. Am. J. Psychiatry. 2008;165:604–609. [PMC free article] [PubMed]
  196. Marais L, Stein DJ, Daniels WM. Exercise increases BDNF levels in the striatum and decreases depressive-like behavior in chronically stressed rats. Metab. Brain Dis. 2009;24:587–597. [PubMed]
  197. Marek S, et al. The contribution of network organization and integration to the development of cognitive control. PLoS Biol. 2015;13:e1002328. [PMC free article] [PubMed]
  198. Marin MT, Planeta CS. Maternal separation affects cocaine-induced locomotion and response to novelty in adolescent but not in adult rats. Brain Res. 2004;1013:83–90. [PubMed]
  199. Mason M, et al. Time-varying effects of a text-based smoking cessation intervention for urban adolescents. Drug Alcohol Depend. 2015;157:99–105. [PMC free article] [PubMed]
  200. Matthews K, et al. Repeated neonatal maternal separation alters intravenous cocaine self-administration in adult rats. Psychopharmacology (Berl.) 1999;141:123–134. [PubMed]
  201. Matthews M, et al. Reduced presynaptic dopamine activity in adolescent dorsal striatum. Neuropsychopharmacology. 2013;38:1344–1351. [PMC free article] [PubMed]
  202. McEwen BS, Gianaros PJ. Central role of the brain in stress and adaptation: links to socioeconomic status health, and disease. Ann. N. Y. Acad. Sci. 2010;1186:190–222. [PMC free article] [PubMed]
  203. Mechelli A, et al. Neurolinguistics: structural plasticity in the bilingual brain. Nature. 2004;431:757. [PubMed]
  204. Mehta MA, et al. Hyporesponsive reward anticipation in the basal ganglia following severe institutional deprivation early in life. J. Cognit. Neurosci. 2010;22:2316–2325. [PubMed]
  205. Meil WM, See RE. Lesions of the basolateral amygdala abolish the ability of drug associated cues to reinstate responding during withdrawal from self-administered cocaine. Behav. Brain Res. 1997;87:139–148. [PubMed]
  206. Mendle J, et al. Associations between early life stress, child maltreatment, and pubertal development among girls in foster care. J. Res. Adolesc. 2011;21:871–880. [PMC free article] [PubMed]
  207. Michaels CC, Easterling KW, Holtzman SG. Maternal separation alters ICSS responding in adult male and female rats: but morphine and naltrexone have little affect on that behavior. Brain Res. Bull. 2007;73:310–318. [PubMed]
  208. Mitchell MR, et al. Adolescent risk taking, cocaine self-administration, and striatal dopamine signaling. Neuropsychopharmacology. 2014;39:955–962. [PMC free article] [PubMed]
  209. Moffett MC, et al. Maternal separation and handling affects cocaine self-administration in both the treated pups as adults and the dams. J. Pharmacol. Exp. Ther. 2006;317:1210–1218. [PubMed]
  210. Moffett MC, et al. Maternal separation alters drug intake patterns in adulthood in rats. Biochem. Pharmacol. 2007;73:321–330. [PMC free article] [PubMed]
  211. Molina BS, et al. Adolescent substance use in the multimodal treatment study of attention-deficit/hyperactivity disorder (ADHD) (MTA) as a function of childhood ADHD, random assignment to childhood treatments, and subsequent medication. J. Am. Acad. Child Adolesc. Psychiatry. 2013;52:250–263. [PMC free article] [PubMed]
  212. Moll GH, et al. Age-associated changes in the densities of presynaptic monoamine transporters in different regions of the rat brain from early juvenile life to late adulthood. Brain Res. Dev. Brain Res. 2000;119:251–257. [PubMed]
  213. Mueller SC, et al. Early-life stress is associated with impairment in cognitive control in adolescence: an fMRI study. Neuropsychologia. 2010;48:3037–3044. [PMC free article] [PubMed]
  214. Munakata Y, et al. A unified framework for inhibitory control. Trends Cognit. Sci. 2011;15:453–459. [PMC free article] [PubMed]
  215. Myers B, et al. Central stress-integrative circuits: forebrain glutamatergic and GABAergic projections to the dorsomedial hypothalamus medial preoptic area, and bed nucleus of the stria terminalis. Brain Struct. Funct. 2014;219:1287–1303. [PMC free article] [PubMed]
  216. Nair SG, et al. Role of dorsal medial prefrontal cortex dopamine D1-family receptors in relapse to high-fat food seeking induced by the anxiogenic drug yohimbine. Neuropsychopharmacology. 2011;36:497–510. [PMC free article] [PubMed]
  217. Naneix F, et al. Parallel maturation of goal-directed behavior and dopaminergic systems during adolescence. J. Neurosci. 2012;32:16223–16232. [PubMed]
  218. National, Institute of Drug Abuse [18 Sept. 2016];Principles of Adolescent Substance Use Disorder Treatment: A Research-Based Guide. 2014 Available at.
  219. National Institute of Drug Abuse [9 Nov. 2016];Trends and Statistics. 2015 Available at.
  220. Nees F, et al. Determinants of early alcohol use in healthy adolescents: the differential contribution of neuroimaging and psychological factors. Neuropsychopharmacology. 2012;37:986–995. [PMC free article] [PubMed]
  221. Newcomb MD, Harlow LL. Life events and substance use among adolescents: mediating effects of perceived loss of control and meaninglessness in life. J. Pers. Soc. Psychol. 1986;51:564–577. [PubMed]
  222. Newman LA, McGaughy J. Adolescent rats show cognitive rigidity in a test of attentional set shifting. Dev. Psychobiol. 2011;53:391–401. [PubMed]
  223. Ogbonmwan YE, et al. The effects of post-extinction exercise on cocaine-primed and stress-induced reinstatement of cocaine seeking in rats. Psychopharmacology (Berl.) 2015;232:1395–1403. [PMC free article] [PubMed]
  224. Ongur D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats monkeys and humans. Cereb. Cortex. 2000;10:206–219. [PubMed]
  225. Parent AS, et al. Early developmental actions of endocrine disruptors on the hypothalamus, hippocampus, and cerebral cortex. J. Toxicol. Environ. Health B Crit. Rev. 2011;14:328–345. [PMC free article] [PubMed]
  226. Patrick ME, et al. Socioeconomic status and substance use among young adults: a comparison across constructs and drugs. J. Stud. Alcohol Drugs. 2012;73:772–782. [PMC free article] [PubMed]
  227. Patton GC, et al. Puberty and the onset of substance use and abuse. Pediatrics. 2004;114:e300–6. [PMC free article] [PubMed]
  228. Peeters M, et al. Weaknesses in executive functioning predict the initiating of adolescents’ alcohol use. Dev. Cognit. Neurosci. 2015;16:139–146. [PubMed]
  229. Perry JL, et al. Acquisition of i: v. cocaine self-administration in adolescent and adult male rats selectively bred for high and low saccharin intake. Physiol. Behav. 2007;91:126–133. [PMC free article] [PubMed]
  230. Peterson AB, Abel JM, Lynch WJ. Dose-dependent effects of wheel running on cocaine-seeking and prefrontal cortex Bdnf exon IV expression in rats. Psychopharmacology (Berl.) 2014;231:1305–1314. [PubMed]
  231. Phillips GD, et al. Isolation rearing enhances the locomotor response to cocaine and a novel environment: but impairs the intravenous self-administration of cocaine. Psychopharmacology (Berl.) 1994;115:407–418. [PubMed]
  232. Ploj K, Roman E, Nylander I. Long-term effects of maternal separation on ethanol intake and brain opioid and dopamine receptors in male Wistar rats. Neuroscience. 2003;121:787–799. [PubMed]
  233. Pool E, et al. Measuring wanting and liking from animals to humans: a systematic review. Neurosci. Biobehav. Rev. 2016;63:124–142. [PubMed]
  234. Porrino LJ, et al. Cocaine self-administration produces a progressive involvement of limbic, association, and sensorimotor striatal domains. J. Neurosci. 2004;24:3554–3562. [PubMed]
  235. Potvin S, et al. Cocaine and cognition: a systematic quantitative review. J. Addict. Med. 2014;8:368–376. [PubMed]
  236. Pruessner JC, et al. Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care: a positron emission tomography study using [11C]raclopride. J. Neurosci. 2004;24:2825–2831. [PubMed]
  237. Quas JA, et al. The symphonic structure of childhood stress reactivity: patterns of sympathetic, parasympathetic, and adrenocortical responses to psychological challenge. Dev. Psychopathol. 2014;26:963–982. [PMC free article] [PubMed]
  238. Radley JJ, et al. Chronic behavioral stress induces apical dendritic reorganization in pyramidal neurons of the medial prefrontal cortex. Neuroscience. 2004;125:1–6. [PubMed]
  239. Rapoport JL, et al. Dextroamphetamine: cognitive and behavioral effects in normal prepubertal boys. Science. 1978;199:560–563. [PubMed]
  240. Ridderinkhof KR, et al. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cognit. 2004;56:129–140. [PubMed]
  241. Robins LN. The natural history of adolescent drug use. Am. J. Public Health. 1984;74:656–657. [PMC free article] [PubMed]
  242. Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Brain Res. Rev. 1993a;18:247–291. [PubMed]
  243. Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Brain Res. Rev. 1993b;18:247–291. [PubMed]
  244. Roman E, Ploj K, Nylander I. Maternal separation has no effect on voluntary ethanol intake in female Wistar rats. Alcohol. 2004;33:31–39. [PubMed]
  245. Romeo RD, McEwen BS. Stress and the adolescent brain. Ann. N. Y. Acad. Sci. 2006;1094:202–214. [PubMed]
  246. Romeo RD. The teenage brain: the stress response and the adolescent brain. Curr. Dir. Psychol. Sci. 2013;22:140–145. [PMC free article] [PubMed]
  247. Rothman AJ, Sheeran P, Wood W. Reflective and automatic processes in the initiation and maintenance of dietary change. Ann. Behav. Med. 2009;1(38 Suppl):S4–17. [PubMed]
  248. Romeo RD, et al. Adolescence and the ontogeny of the hormonal stress response in male and female rats and mice. Neurosci. Biobehav. Rev. 2016;70:206–216. [PMC free article] [PubMed]
  249. Ruedi-Bettschen D, et al. Early deprivation leads to altered behavioural, autonomic and endocrine responses to environmental challenge in adult Fischer rats. Eur. J. Neurosci. 2006;24:2879–2893. [PubMed]
  250. SAMHSA . Results from the 2008 National Survey on Drug Use and Health: National Findings. Office of Applied Studies; Rockville, MD: 2008.
  251. SAMHSA . Results from the 2011 National Survey on Drug Use and Health: Summary of National Findings. Substance Abuse and Mental Health Services Administration; Rockville, MD: 2012.
  252. SAMHSA . Results from the 2013 National Survey on Drug Use and Health: Detailed Tables. Center for Behavioral Health Statistics and Quality; Rockville, MD: 2014.
  253. SAMHSA Behavioral health trends in the United States: Results from the 2014 National Survey on Drug Use and Health (HHS Publication No. SMA 15–4927, NSDUHSeries H-50) 2015.
  254. SAMHSA . 2014 National Survey on Drug Use and Health: Detailed Tables. Center for Behavioral Health Statistics and Quality; Rockville, MD: 2015b.
  255. Sadowski RN, et al. Effects of stress, corticosterone, and epinephrine administration on learning in place and response tasks. Behav. Brain Res. 2009;205:19–25. [PMC free article] [PubMed]
  256. Sanchez CJ, et al. Manipulation of dopamine d1-like receptor activation in the rat medial prefrontal cortex alters stress- and cocaine-induced reinstatement of conditioned place preference behavior. Neuroscience. 2003;119:497–505. [PubMed]
  257. Sanchez V, et al. Wheel running exercise attenuates vulnerability to self-administer nicotine in rats. Drug Alcohol Depend. 2015;156:193–198. [PMC free article] [PubMed]
  258. Schneider S, et al. Risk taking and the adolescent reward system: a potential common link to substance abuse. Am. J. Psychiatry. 2012;169:39–46. [PubMed]
  259. Schramm-Sapyta NL, et al. Are adolescents more vulnerable to drug addiction than adults?: Evidence from animal models. Psychopharmacology (Berl.) 2009;206:1–21. [PMC free article] [PubMed]
  260. Schrantee A, et al. Age-dependent effects of methylphenidate on the human dopaminergic system in young vs adult patients with attention-deficit/hyperactivity disorder: a randomized clinical trial. JAMA Psychiatry. 2016;73:955–962. [PMC free article] [PubMed]
  261. Schwabe L, Wolf OT. Stress prompts habit behavior in humans. J. Neurosci. 2009;29:7191–7198. [PubMed]
  262. Schwabe L, Wolf OT. Stress-induced modulation of instrumental behavior: from goal-directed to habitual control of action. Behav. Brain Res. 2011;219:321–328. [PubMed]
  263. Schwabe L, et al. Chronic stress modulates the use of spatial and stimulus-response learning strategies in mice and man. Neurobiol. Learn. Mem. 2008;90:495–503. [PubMed]
  264. Schwabe L, Dickinson A, Wolf OT. Stress, habits, and drug addiction: a psychoneuroendocrinological perspective. Exp. Clin. Psychopharmacol. 2011;19:53–63. [PubMed]
  265. Schwartz JA, Beaver KM, Barnes JC. The association between mental health and violence among a nationally representative sample of college students from the United States. PLoS One. 2015;10:e0138914. [PMC free article] [PubMed]
  266. See RE, Elliott JC, Feltenstein MW. The role of dorsal vs ventral striatal pathways in cocaine-seeking behavior after prolonged abstinence in rats. Psychopharmacology (Berl.) 2007;194:321–331. [PubMed]
  267. Seeley WW, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 2007;27:2349–2356. [PMC free article] [PubMed]
  268. Seger CA, Spiering BJ. A critical review of habit learning and the Basal Ganglia. Front. Syst. Neurosci. 2011;5:66. [PMC free article] [PubMed]
  269. Shaw P, et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc. Natl. Acad. Sci. U. S. A. 2007;104:19649–19654. [PMC free article] [PubMed]
  270. Shaw P, et al. Psychostimulant treatment and the developing cortex in attention deficit hyperactivity disorder. Am. J. Psychiatry. 2009;166:58–63. [PMC free article] [PubMed]
  271. Sinha R. Chronic stress, drug use, and vulnerability to addiction. Ann. N. Y. Acad. Sci. 2008;1141:105–130. [PMC free article] [PubMed]
  272. Sisk CL, Schulz KM, Zehr JL. Puberty: a finishing school for male social behavior. Ann. N. Y. Acad. Sci. 2003;1007:189–198. [PubMed]
  273. Smith JL, et al. Deficits in behavioural inhibition in substance abuse and addiction: a meta-analysis. Drug Alcohol Depend. 2014;145:1–33. [PubMed]
  274. Smith RF. Animal models of periadolescent substance abuse. Neurotoxicol. Teratol. 2003;25:291–301. [PubMed]
  275. Solinas M, et al. Environmental enrichment during early stages of life reduces the behavioral, neurochemical, and molecular effects of cocaine. Neuropsychopharmacology. 2009;34:1102–1111. [PubMed]
  276. Somerville LH, Jones RM, Casey BJ. A time of change: behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain Cognit. 2010;72:124–133. [PMC free article] [PubMed]
  277. Somerville LH, Hare T, Casey BJ. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents. J. Cognit. Neurosci. 2011;23:2123–2134. [PMC free article] [PubMed]
  278. Sonntag KC, et al. Viral over-expression of D1 dopamine receptors in the prefrontal cortex increase high-risk behaviors in adults: comparison with adolescents. Psychopharmacology (Berl.) 2014;231:1615–1626. [PMC free article] [PubMed]
  279. Sowell ER, et al. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat. Neurosci. 1999;2:859–861. [PubMed]
  280. Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci. Biobehav. Rev. 2000;24:417–463. [PubMed]
  281. Squeglia LM, Jacobus J, Tapert SF. The influence of substance use on adolescent brain development. Clin. EEG Neurosci. 2009;40:31–38. [PMC free article] [PubMed]
  282. Stanger C, et al. Neuroeconomics and adolescent substance abuse: individual differences in neural networks and delay discounting. J. Am. Acad. Child Adolesc. Psychiatry. 2013;52:747–755. e6. [PMC free article] [PubMed]
  283. Stanis JJ, Andersen SL. Reducing substance use during adolescence: a translational framework for prevention. Psychopharmacology (Berl.) 2014;231:1437–1453. [PMC free article] [PubMed]
  284. Steele CJ, et al. Early musical training and white-matter plasticity in the corpus callosum: evidence for a sensitive period. J. Neurosci. 2013;33:1282–1290. [PubMed]
  285. Steinhausen HC, Bisgaard C. Substance use disorders in association with attention-deficit/hyperactivity disorder, co-morbid mental disorders, and medication in a nationwide sample. Eur. Neuropsychopharmacol. 2014;24:232–241. [PubMed]
  286. Sturman DA, Moghaddam B. Reduced neuronal inhibition and coordination of adolescent prefrontal cortex during motivated behavior. J. Neurosci. 2011;31:1471–1478. [PMC free article] [PubMed]
  287. Sturman DA, Moghaddam B. Striatum processes reward differently in adolescents versus adults. Proc. Natl. Acad. Sci. U. S. A. 2012;109:1719–1724. [PMC free article] [PubMed]
  288. Sturman DA, Mandell DR, Moghaddam B. Adolescents exhibit behavioral differences from adults during instrumental learning and extinction. Behav. Neurosci. 2010;124:16–25. [PMC free article] [PubMed]
  289. Surbey MK. Parent and offspring strategies in the transition at adolescence. Hum. Nat. 1998;9:67–94. [PubMed]
  290. Szalay JJ, Jordan CJ, Kantak KM. Neural regulation of the time course for cocaine-cue extinction consolidation in rats. Eur. J. Neurosci. 2013;37:269–277. [PMC free article] [PubMed]
  291. Taliaferro LA, Rienzo BA, Donovan KA. Relationships between youth sport participation and selected health risk behaviors from 1999 to 2007. J. Sch. Health. 2010;80:399–410. [PubMed]
  292. Tang YY, et al. Central and autonomic nervous system interaction is altered by short-term meditation. Proc. Natl. Acad. Sci. U. S. A. 2009;106:8865–8870. [PMC free article] [PubMed]
  293. Tang YY, et al. Improving executive function and its neurobiological mechanisms through a mindfulness-based intervention: advances within the field of developmental neuroscience. Child Dev. Perspect. 2012;6:361–366. [PMC free article] [PubMed]
  294. Tanner JM. Growth at Adolescence. With a General Consideration of the Effects of Hereditary and Environmental Factors upon Growth and Maturation from Birth to Maturity. Blackwell Scientific Oxford; 1962.
  295. Tarazi FI, Tomasini EC, Baldessarini RJ. Postnatal development of dopamine D4-like receptors in rat forebrain regions: comparison with D2-like receptors. Brain Res. Dev. Brain Res. 1998;110:227–233. [PubMed]
  296. Taylor SB, et al. Chronic stress may facilitate the recruitment of habit- and addiction-related neurocircuitries through neuronal restructuring of the striatum. Neuroscience. 2014;280:231–242. [PMC free article] [PubMed]
  297. Teicher MH, Andersen SL, Hostetter JC., Jr. Evidence for dopamine receptor pruning between adolescence and adulthood in striatum but not nucleus accumbens. Brain Res. Dev. Brain Res. 1995;89:167–172. [PubMed]
  298. Teicher MH, Dumont NL, Andersen SL. The developing prefrontal cortex: is there a transient interneuron that stimulates catecholamine terminals? Synapse. 1998;29:89–91. [PubMed]
  299. Teicher MH, Tomoda A, Andersen SL. Neurobiological consequences of early stress and childhood maltreatment: are results from human and animal studies comparable? Ann. N. Y. Acad. Sci. 2006;1071:313–323. [PubMed]
  300. Tekin S, Cummings JL. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. J. Psychosom. Res. 2002;53:647–654. [PubMed]
  301. Thanos PK, et al. Effects of chronic oral methylphenidate on cocaine self-administration and striatal dopamine D2 receptors in rodents. Pharmacol. Biochem. Behav. 2007;87:426–433. [PubMed]
  302. Thompson AB, et al. Methamphetamine blocks exercise effects on Bdnf and Drd2 gene expression in frontal cortex and striatum. Neuropharmacology. 2015;99:658–664. [PMC free article] [PubMed]
  303. Tseng KY, O’Donnell P. Dopamine modulation of prefrontal cortical interneurons changes during adolescence. Cereb. Cortex. 2007;17:1235–1240. [PMC free article] [PubMed]
  304. USAA USAA Training Systems: Critical Periods for Optimal Development. 2011.
  305. Uhl GR. Molecular genetics of substance abuse vulnerability: remarkable recent convergence of genome scan results. Ann. N. Y. Acad. Sci. 2004;1025:1–13. [PubMed]
  306. van der Marel K, et al. Long-term oral methylphenidate treatment in adolescent and adult rats: differential effects on brain morphology and function. Neuropsychopharmacology. 2014;39:263–273. [PMC free article] [PubMed]
  307. Vanderschuren LJ, Di Ciano P, Everitt BJ. Involvement of the dorsal striatum in cue-controlled cocaine seeking. J. Neurosci. 2005;25:8665–8670. [PubMed]
  308. Vastola BJ, et al. Nicotine-induced conditioned place preference in adolescent and adult rats. Physiol. Behav. 2002;77:107–114. [PubMed]
  309. Verdejo-Garcia A, Lawrence AJ, Clark L. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci. Biobehav. Rev. 2008;32:777–810. [PubMed]
  310. Volkow ND, Fowler JS. Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb. Cortex. 2000;10:318–325. [PubMed]
  311. Volkow ND, Swanson JM. Does childhood treatment of ADHD with stimulant medication affect substance abuse in adulthood? Am. J. Psychiatry. 2008;165:553–555. [PMC free article] [PubMed]
  312. Volkow ND, et al. Prediction of reinforcing responses to psychostimulants in humans by brain dopamine D2 receptor levels. Am. J. Psychiatry. 1999;156:1440–1443. [PubMed]
  313. Volkow ND, et al. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J. Neurosci. 2006;26:6583–6588. [PubMed]
  314. Vonmoos M, et al. Cognitive dysfunctions in recreational and dependent cocaine users: role of attention-deficit hyperactivity disorder, craving and early age at onset. Br. J. Psychiatry. 2013;203:35–43. [PubMed]
  315. Voon V, et al. Disorders of compulsivity: a common bias towards learning habits. Mol. Psychiatry. 2015;20:345–352. [PMC free article] [PubMed]
  316. Wagner FA, Anthony JC. From first drug use to drug dependence; developmental periods of risk for dependence upon marijuana, cocaine, and alcohol. Neuropsychopharmacology. 2002;26:479–488. [PubMed]
  317. Weinstock J, Barry D, Petry NM. Exercise-related activities are associated with positive outcome in contingency management treatment for substance use disorders. Addict. Behav. 2008;33:1072–1075. [PMC free article] [PubMed]
  318. Werch C, et al. A sport-based intervention for preventing alcohol use and promoting physical activity among adolescents. J. Sch. Health. 2003;73:380–388. [PubMed]
  319. Werch CC, et al. A multihealth behavior intervention integrating physical activity and substance use prevention for adolescents. Prev. Sci. 2005;6:213–226. [PubMed]
  320. Whelan R, et al. Adolescent impulsivity phenotypes characterized by distinct brain networks. Nat. Neurosci. 2012;15:920–925. [PubMed]
  321. White LS. Reducing stress in school-age girls through mindful yoga. J. Pediatr. Health Care. 2012;26:45–56. [PubMed]
  322. Wilens TE, et al. Does stimulant therapy of attention-deficit/hyperactivity disorder beget later substance abuse? A meta-analytic review of the literature. Pediatrics. 2003;111:179–185. [PubMed]
  323. Wills TA, Vaccaro D, McNamara G. The role of life events, family support, and competence in adolescent substance use: a test of vulnerability and protective factors. Am. J. Community Psychol. 1992;20:349–374. [PubMed]
  324. Wills TA, et al. Coping dimensions, life stress, and adolescent substance use: a latent growth analysis. J. Abnorm. Psychol. 2001;110:309–323. [PubMed]
  325. Wills TA. Stress and coping in early adolescence: relationships to substance use in urban school samples. Health Psychol. 1986;5:503–529. [PubMed]
  326. Willuhn I, et al. Dopamine signaling in the nucleus accumbens of animals self-administering drugs of abuse. Curr. Top. Behav. Neurosci. 2010;3:29–71. [PMC free article] [PubMed]
  327. Wilson DM, et al. Timing and rate of sexual maturation and the onset of cigarette and alcohol use among teenage girls. Arch. Pediatr. Adolesc. Med. 1994;148:789–795. [PubMed]
  328. Witkiewitz K, Marlatt GA, Walker D. Mindfulness-based relapse prevention for alcohol and substance use disorders. journal of cognitive psychotherapy. Int. Q. 2005;19:212–228.
  329. Wong WC, Marinelli M. Adolescent-onset of cocaine use is associated with heightened stress-induced reinstatement of cocaine seeking. Addict. Biol. 2016;21:634–645. [PMC free article] [PubMed]
  330. Wong WC, et al. Adolescents are more vulnerable to cocaine addiction: behavioral and electrophysiological evidence. J. Neurosci. 2013;33:4913–4922. [PMC free article] [PubMed]
  331. Yurgelun-Todd DA, Killgore WD. Fear-related activity in the prefrontal cortex increases with age during adolescence: a preliminary fMRI study. Neurosci. Lett. 2006;406:194–199. [PubMed]
  332. Zakharova E, et al. Social and physical environment alter cocaine conditioned place preference and dopaminergic markers in adolescent male rats. Neuroscience. 2009a;163:890–897. [PMC free article] [PubMed]
  333. Zakharova E, Wade D, Izenwasser S. Sensitivity to cocaine conditioned reward depends on sex and age. Pharmacol. Biochem. Behav. 2009b;92:131–134. [PMC free article] [PubMed]
  334. Zehr JL, et al. Dendritic pruning of the medial amygdala during pubertal development of the male Syrian hamster. J. Neurobiol. 2006;66:578–590. [PubMed]
  335. Zgierska A, et al. Mindfulness meditation for substance use disorders: a systematic review. Subst. Abus. 2009;30:266–294. [PMC free article] [PubMed]
  336. Zlebnik NE, Carroll ME. Prevention of the incubation of cocaine seeking by aerobic exercise in female rats. Psychopharmacology (Berl.) 2015;232:3507–3513. [PMC free article] [PubMed]
  337. Zlebnik NE, Anker JJ, Carroll ME. Exercise to reduce the escalation of cocaine self-administration in adolescent and adult rats. Psychopharmacology (Berl.) 2012;224:387–400. [PMC free article] [PubMed]
  338. Zlebnik NE, Saykao AT, Carroll ME. Effects of combined exercise and progesterone treatments on cocaine seeking in male and female rats. Psychopharmacology (Berl.) 2014;231:3787–3798. [PMC free article] [PubMed]