“Everybody's youth is a dream, a form of chemical madness.” - F. Scott Fitzgerald

1. Introduction

Understanding the neural underpinnings of adolescent behavior is of increasing interest, and is enabled by functional magnetic resonance imaging (fMRI) technology for non-invasive probes of human brain function. This research has led to an influential theory that attributes behavior-related mortality and morbidity in adolescents to overactive incentive-motivational circuitry relative to underactive frontocortical behavior control neurocircuitry. In this review, we present a case that in light of epidemiological and longitudinal data, this brain functioning imbalance is likely specific to a subset of youth with disruptive behavior disorders (DBD), and is not especially pronounced or significant in neurotypical youth. We first briefly describe the neuroanatomy of reward-related decision-making, and the fMRI studies of these brain regions that give rise to these opponent-process theories. We then discuss how longitudinal studies, laboratory behavioral studies, and fMRI studies of youth with DBD indicate that these individuals, who are at extreme risk of substance use disorder (SUD) are likely the youth who would show an aberrant opponent-process. We conclude with some directions for future research.

2. Neurodevelopmental models of adolescent risk-taking

Adolescents are renowned for risky behavior, from skateboard stunts to binge drinking and unprotected sex. Reports of adolescents committing violent crime grab headlines. Empirical assessments with psychometric questionnaires and laboratory tasks have also supported a peak in venturesomeness or risk-seeking in mid-adolescence (reviewed in (Steinberg, 2004)). While age-comparison findings with laboratory decision tasks are somewhat inconsistent, the results generally support either a linear decline in risky-choice from adolescence to adulthood (Deakin et al., 2004, Overman et al., 2004), or a developmental peak in pursuit of risky choices in mid-adolescence relative to younger children and adults (Steinberg, 2005, Figner et al., 2009, Burnett et al., 2010). The advent of fMRI has sparked intense interest in whether trajectories of brain maturation contribute to adolescent risk-taking, where developmental differences in structure and function of brain regions involved in incentive processing and behavioral control are touted (and funded) as having a potential public health impact.

Where in the brain do we look? Portions of ventral striatum(VS); including nucleus accumbens (NAcc) have been extensively linked with motivational processing (reviewed in (Knutson et al., 2009)). Notably, adolescents show greater ambiguity tolerance (willingness to take risks when odds are not known) compared to adults, but not greater explicit risk tolerance (Tymula et al., 2012). When the probability of reward in a goal-directed task is uncertain, a wide variety of rewarding stimuli activate cortico-basal ganglia system that includes the oribitofrontal cortex (OFC), anterior cingulate cortex (ACC), insula, thalamus, and dorsal and ventral striatum (Delgado, 2007, Dolan, 2007, Seymour et al., 2007). In these tasks, punishment (i.e., the loss of money) often recruits a similar set of neural circuits, albeit areas in the VS often show less pronounced or even negative activation relative to baseline (e.g. (Delgado et al., 2000, Tom et al., 2007)). Inhibiting approach to potential rewards that may also result in a penalty involves frontal cortex structures, which have been extensively linked to cognitive control in both lesion studies (Bechara et al., 2001, Bechara and Van Der Linden, 2005) and in functional imaging studies (Durston et al., 2002, Ridderinkhof et al., 2004b). For example, in healthy adolescents and adults, cognitive control tasks activate a neural network that includes the dorsolateral and inferior prefrontal regions, ACC, and inferior parietal cortex (Rubia et al., 2001, Aron et al., 2004, Luna and Sweeney, 2004, Buchsbaum et al., 2005).

Initial developmental surveys using magnetic resonance imaging (MRI) documented morphological brain differences from childhood to adulthood in several brain regions. For example, frontocortical gray matter volume follows an inverted-U pattern, peaking around age 12, while temporal lobe gray matter volume increases nearly linearly throughout adolescence (Giedd et al., 1999, Sowell et al., 1999, Sowell et al., 2001). Meanwhile, frontocortical white matter volume as a proportion of total frontocortical volume increases from childhood to adulthood (reviewed in (Marsh et al., 2008)). Finally, developmental diffusion tensor imaging (DTI) studies indicate that organization of this increased frontocortical white matter is composed of increasingly orderly fiber tracts, in that fractional anisotropy of white matter water flow increases from childhood to adulthood (Barnea-Goraly et al., 2005, Imperati et al., 2011, Jernigan et al., 2011).

Two cross-sectional surveys of resting-state functional connectivity (RSFC) (Fox and Raichle, 2007) during fMRI indicated that from childhood to mid-adulthood, the strength of long-range connections between brain regions tends to increase with age while the strength of short-range connections tends to get weaker with age (Supekar et al., 2009, Dosenbach et al., 2010), and these relative connection strengths can predict an omnibus developmental “age” of the brain (Dosenbach et al., 2010). Whether inter-regional brain connectivity is directly assessed (structurally) by DTI measures of white matter or whether connectivity is inferred from synchronized brain activity between regions during a resting-state, indices of frontocortical network maturation may have clinical or behavioral significance in that decision-making requires extensive cortical integration for the representations of incentive values, potential penalties, future self with the respective outcomes, as well as for formulation of action-plans.

Particularly compelling, however, are findings of developmental (age-group) differences in brain responsiveness to risk and rewards when children, adolescents, and adults perform incentive-laden tasks during functional fMRI. Most experiments indicate that adolescents show greater responsiveness of the VS to rewards than younger children or adults. First, adolescents showed greater left VS activation by notification of money won in a “Wheel of Fortune” gambling task compared to adults (Ernst et al., 2005). Later Galvan et al. (2006) reported that once associations between cartoon cues and rewarding outcomes had become learned, adolescents showed greater VS activation during delivery of unspecified monetary reward compared to activation in adults or younger children. Similarly, Van Leijenhorst et al. (2010a) found that mid-adolescents showed greater VS activation by risky gains than younger children or young adults. A decision-making task also indicated that the adolescent striatum is more sensitive to the delivery of unexpected rewards during cue-reward association learning (Cohen et al., 2010). Moreover, in a slot machine simulation, mid-adolescents also showed more VS activation by reward-predictive slot results than younger children and young adults (Van Leijenhorst et al., 2010b). If one assumes that visual stimuli of happy faces is rewarding, Somerville et al. (2011) reported that compared to younger children and young adults, adolescents emitted more commission errors to (and had greater VS recruitment by) photographs of happy faces assigned as non-target stimuli in a go–nogo task. Finally, in a seminal investigation on the effects of social context on reward processing, Chein et al. (2011) demonstrated that running virtual yellow lights in a simulated driving game resulted in increased VS recruitment in adolescents compared to adults, but only when adolescent peer observers were present in the scanning control room.

Ironically, the first fMRI comparison between adolescents and adults in reward processing, which used a monetary incentive delay (MID) reaction-time task with simple cues (Knutson et al., 2001), indicated that adolescents showed reduced right VS recruitment by reward cues compared to adults, with no age group differences in VS or anterior mesofrontal cortex (mFC) recruitment by reward notifications (Bjork et al., 2004b). This right-lateralized striatal activation decrement in adolescents was replicated in a larger sample, using a jittered variant of the MID task that temporally disentangled reward anticipation activation from reward notification activation (Bjork et al., 2010b). An adolescent decrement in reward-anticipatory striatal activation by the MID task (Cho et al., 2012) and a MID-like task (Hoogendam et al., 2013) was also replicated in other labs. Finally, a longitudinal study showed that reward-anticipatory striatal recruitment by MID task reward cues increases across adolescence (Lamm et al., 2014). Developmental differences may also depend on the component of instrumental behavior. Adolescents showed relatively lower reward-anticipatory activation compared to young adults, but greater consummatory/notification activation by rewards in both an antisaccade task (Geier et al., 2010) and a MID-like reaction-time task (Hoogendam et al., 2013).

These opposing findings may actually provide a useful conceptual foil, and suggest that provided the incentive task for fMRI features either: (a) very engaging visual stimuli (such as casino iconography, social cartoons or driving simulations), (b) decisions between rewarding options, or (c) peers present in the lab to potentially stoke the social reward of taking a risk, normative functional development of human mesolimbic incentive neurocircuitry features a non-linear trajectory, with peak in striatal responsiveness to rewards occurring around age 14–15. Conversely, blunted reward anticipation activation in adolescents tends to be found in more “work-like” reaction-time tasks that feature minimal visual stimuli, require intense vigilance and rapid responses, and/or have no decision-making component. It could be argued that the former class of tasks may be more naturalistically-relevant to real-world risk-taking scenarios for adolescents. Irrespective of developmental directionality, it is important to consider, however, that age-group typically accounts for only a modest portion of variance in VS responses to rewards due to substantial inter-subject variability (c.f. Fig. 2B of Somerville et al., 2010). For example, in the Bjork et al. (2010b) study, age group accounted for only 12% of the variance in right VS responses to high reward cues, despite a significant group-wise difference.

This mid-adolescent peak in reward processing may be occurring against a backdrop of relatively immature or underactive frontocortical behavior control circuitry. A well-established literature indicates that with aging from adolescence to adulthood, cognitive control in rapid stimulus evaluation tasks improves (Bunge and Wright, 2007), in tandem with more focal (potentially more efficient) frontocortical activation during inhibition (Durston et al., 2006). Developmental differences in frontocortical recruitment by potential penalties have also been detected. First, using a wheel-of-fortune (WoF) task, where probabilities for winning and magnitudes of potential wins were explicitly indicated by pie-chart displays, adolescents (compared to adults) showed reduced activation of posterior mFC when choosing lower-probability but more potentially rewarding (i.e. riskier) pie-slices (Eshel et al., 2007). This adolescent activation decrement occurred in a region of mFC consistently recruited by pre-decision conflict (Ridderinkhof et al., 2004a). Second, while performing a monetary game of “chicken” akin to the Balloon Analogue Risk-Taking Task (BART; Lejuez et al., 2002), accrual of risky reward (as a contrast with accruing guaranteed reward) activated posterior mFC in adults, but not in adolescents (Bjork et al., 2007). In both experiments, greater engagement in risk-taking behavior was associated with decrements in posterior mFC recruitment.

This combined developmental pattern of a mid-adolescent peak in brain responsiveness to rewards, coupled with immature behavior control neurocircuitry has given rise to an influential dual-process model that ostensibly accounts in part for a proneness for engaging in illegal and risky behaviors beginning in adolescence (Somerville et al., 2010), such as experimentation with drugs and alcohol (Casey and Jones, 2010). Specifically, this opponent-process model posits a functional imbalance resulting from relatively rapid development of motivational circuits of the VS relative to more protracted development of behavior control circuits of frontal cortex, where this imbalance is most pronounced in mid-adolescence, and essentially normalizes or remits by young adulthood. This model seems very plausible in light of differing maturational trajectories among human brain structures (Brown et al., 2012) if morphometric differences with development (such as cortical thickness) reflect underlying functional differences. Moreover, preclinical research indicates greater DA responsiveness in younger relative to older animals (Luciana et al., 2012). Increased fMRI-measured activity in the ventral striatum is generally interpreted as reflecting greater phasic dopamine (DA) activity (Knutson and Gibbs, 2007), based on single unit primate studies of instrumental behavior (Schultz, 2007). A related “triadic” model of neurodevelopment (Ernst, 2014) features a more prominent role of developmental differences in fear/aversion neurocircuitry, and subdivides motivated behavior as the net output of approach and avoidance circuits, where each system of this opponent process is in turn governed by elements of a third, regulatory circuit.

3. Attributing public health burden of youth behavior to neurodevelopmental patterns

Previous reviews have addressed the possibility that the maturation of cognitive control neurocircuitry in adolescence is already sufficient for rational decision-making (Epstein, 2007), at least in explicit, deliberative contexts (Steinberg et al., 2009), where adolescent behaviors that adults consider ill-advised result from rational benefit-cost trade-off calculations (Furby and Beyth-Marom, 1992, Reyna and Farley, 2006). We raise a different concern. Recently, Willoughby and colleagues noted that the mid-adolescent peak in reward processing (and by extension the mid-adolescent functional imbalance between motivation and control neurocircuitry) occurs several years before the real-world peak in mortality and morbidity in youth from behavioral causes such as violence or substance intoxication(Willoughby et al., 2013). The functional imbalance in the prevailing dual-process model occurs at around age 14–16. The actual peak ages of unintentional injury (and especially death) from behavioral causes, however, occurs between ages 19 and 23 (U.S. Centers for Disease Control; c.f. http://www.cdc.gov/injury/wisqars/index.html ), as does the age-peak in binge drinking (National Survey on Drug Use and Health; c.f. www.samhsa.gov/data/NSDUH.aspx ) as well as delinquency and risky sex (Piquero et al., 2012). Notably, this is the same age range as “adult” subject groups in which striatal responsiveness to rewards reverts to pre-adolescent levels in some fMRI reports (e.g. Van Leijenhorst et al., 2010b).

We note that some degree of drug and alcohol experimentation is arguably normative in adolescence (Spear, 2000), and it could also be argued that the relatively low rates of binge drinking and behavior-related death and injury among younger adolescents are artificially compressed by societal structures and laws that limit their access to alcohol, guns, and motor vehicles. A subset of youth are relatively undaunted by those strictures, however, and are characterized by persistent, clinically-significant levels of antisocial or risky behavior. Decades ago, the “problem behavior theory” (Jessor and Jessor, 1977) first explained how deviant behaviors tend to co-occur within the same subject (e.g. drug use and theft) as a function of environmental factors, social learning, and personality. Subsequent longitudinal studies of youth consistently indicate that subjects who already abuse substances or who have engaged in other dangerous risky behaviors by mid-adolescence (who commonly meet criteria for a disruptive behavior disorder (DBD)) frequently show a longstanding history of behavioral disinhibition since early childhood (reviewed below), which endures for decades in a subset of individuals as a neurobehavioral trait (Casey et al., 2011).

We argue that these youth may in fact be the adolescents who truly show an aberrant opponent-process in mid-adolescence envisioned by these neuromaturational models. We contend that despite how neuromaturational models of adolescent risky behavior are derived almost exclusively from “neurotypical” research subjects (selected for scanning by virtue of absence of psychiatric symptomatology), these models actually describe youth with DBD or other constructs of antisocial behavior, whose excessive risky reward-seeking might result from delayed and/or permanently stunted maturation of frontocortical control circuitry (Shaw et al., 2007), and where atypical maturation likely results in significant individual differences between adolescents in the opponent processing.

4. Behavioral disinhibition in childhood correlates with reward-sensitivity and predicts risky behavior in adolescence

DBD are comprised largely of Attention Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant Disorder (ODD) and the more severe Conduct disorder (CD), which is characterized by a persistent pattern of behaviors that involve violating the rights of others and major societal norms (e.g., fighting, stealing, and truancy), but the disorder is not explicitly defined by early substance use (Pardini et al., 2010). Longitudinal studies indicate that behaviors consistent with CD in childhood and adolescence are one of the robust predictors of the development of substance use disorders, even after controlling for early-onset substance use and other forms of psychopathology (Fergusson et al., 1996, Fergusson et al., 2007, Grant et al., 2001, Elkins et al., 2007, Pardini et al., 2007). For example, children with CD are twice as likely to initiate alcohol use at age 15 than children without the disorder and several times more likely to initiate illicit drug use (Hopfer et al., 2013). CD that precedes the onset of regular substance use is also a predictor of poor substance use disorder (SUD) treatment outcomes in adolescents (Myers et al., 1995, Brown et al., 1996b). One study reported that 95% of adolescents (predominantly age 15–16) in substance use treatment met criteria for CD, and this proportion remained at 47% when CD symptoms related to obtaining alcohol or drugs (e.g., robbing drug dealers) were not counted (Brown et al., 1996a). CD and ODD are extensively comorbid with ADHD (Connor et al., 2010), such that a case could be made for a more dimensional approach to poor behavioral inhibition and robust reward processing as cross-cutting brain circuit abnormalities underlying multiple syndromes (Cuthbert and Insel, 2013).

The preponderance of laboratory behavioral evidence suggests that hypersensitivity to rewards may serve as a characteristic or liability factor for DBD (reviewed in Byrd et al., 2014), especially in DBD youth with low levels of anxiety and those exhibiting callous-unemotional (e.g., lack of empathy/guilt) traits (O’Brien et al., 1994). For example, hospitalized adolescents with DBD emit more frequent free-operant responses for reward in a task that rewards each press as a function of time since last press (Dougherty et al., 2003). Moreover, when their lower IQ was controlled for, these patients also showed increased choices of smaller immediate rewards over larger-but delayed rewards in an “experiential” variant of the discrete-choice delay-discounting task (where subjects had to sit and acutely endure the delays). In adulthood, individuals with SUD tend to value smaller immediate rewards over larger delayed rewards on discounting tasks (Kirby et al., 1999), and this response style is particularly pronounced in individuals with SUD and features of CD (Petry, 2002, Bjork et al., 2004a, Bobova et al., 2009). These findings parallel animal literature, in that rodents with a reward-dominant style prior to drug exposure more rapidly self-administer cocaine (Dalley et al., 2007, Belin et al., 2008) and exhibit punishment resistant drug-seeking behavior (Belin et al., 2008, Economidou et al., 2009a, Economidou et al., 2009b) relative to control rodents.

Although reversal-learning decision tasks do not directly probe reward-sensitivity per se, the deficits in reversal learning performance characteristic of drug abusers or subjects at risk for drug use (Izquierdo and Jentsch, 2012), may exaggerate the effects of reward sensitivity. For example, children and adolescents with externalizing disorders (CD in particular) and those exhibiting callous-unemotional traits continue responding to previously rewarded cues even when contingencies change and the response results in escalating punishments (Fonseca and Yule, 1995, Ernst et al., 2003, Matthys et al., 2004, Byrd et al., 2014), similar to substance abusing adolescents and adults (Damasio et al., 2000, Lane et al., 2007). Decrements in reversal learning are thought to contribute to addiction if the subject is insensitive to increasing negative consequences of substance use

An important counterpoint to this narrative, however, is the reward deficiency hypothesis (RDH) of addiction (Blum et al., 2000). The RDH attributes substance use initiation, and especially chronic use of substances, to a hypofunctioning reward system. Drugs of abuse, by virtue of their ability to trigger a dopamine surge, are uniquely able to stimulate deficient mesolimbic incentive neurocircuitry compared to natural rewards, leading to a bias toward drugs. This bias may be especially critical when substances of abuse themselves degrade mesolimbic function with repeated exposure (Koob and Le Moal, 2008). Evidence for the RDS in fMRI findings is mixed, however, with studies showing both greater as well as lesser mesolimbic responses to reward prospects or deliveries in abusers of alcohol and cannabis (Hommer et al., 2011). Studies of tobacco smokers, however, consistently show reduced VS activation by rewards in accord with the RDH including in teens (Peters et al., 2011). This may be a specific effect of nicotinic acetylcholine desensitization with chronic exposure (or perhaps acute nicotine withdrawal) to reduce phasic stimulus-elicited dopamine responses (Faure et al., 2014).

In addition to evidence for greater reward-sensitivity in at-risk youth, problems with cognitive control have also been implicated in the development of both DBD and SUD. Cognitive control refers to a set of abilities necessary to engage in adaptive decision-making in response to changing environmental demands, including sustained attention, response inhibition, and performance monitoring. Children and adolescents with CD exhibit performance deficits on cognitive control tasks compared to normally developing youth (Oosterlaan et al., 1998, Toupin et al., 2000, Loeber et al., 2007), as do adolescents with a history of chronic and problematic substance use (Tapert and Brown, 2000, Medina et al., 2007). In addition, problems with cognitive control appear to be particularly pronounced in individuals with SUD and a history of early CD (Finn et al., 2002). While chronic substance use may cause cognitive control deficits over time (Tapert et al., 2002b), poor performance on cognitive control tasks has been shown to prospectively predict the development of heavy and problematic substance use (Aytaclar et al., 1999, Tapert et al., 2002a). In a longitudinal study, Castellanos-Ryan et al. (2011) administered neurocognition measures to mid-adolescents and found that the link between sensation-seeking personality at 14 and binge drinking at 16 was mediated by reward sensitivity measured in an incentivized go–nogo task, and the link between self-reported impulsivity at 14 and actual CD symptoms at 16 was mediated by inhibitory task performance. To reiterate, whereas an aberrant opponent-process between reward-motivation and inhibition neurocircuity has been postulated to underlie normative developmental differences between adolescents and other age groups in risky behavior, an equally compelling case could be made that an aberrant opponent process is what underlies individual differences between adolescents in neurocircuit function- to promote greater risk of addiction, criminal offending, and violence in some individuals. This is illustrated in Fig. 1.

5. Brain signatures of increased reward sensitivity and reduced cognitive control in youth with behavior problems

As mentioned previously, developmental differences in VS recruitment by rewards during fMRI have been interpreted as reflecting underlying development of DA activity in the mesolimbic system (Luciana et al., 2012). Might individual differences in DA functioning also apply to individual differences in risk traits? In Positron Emission Tomography(PET) studies of adults, displacement of radiolabeled DA ligands in VS by rewards have correlated within-subject with fMRI-measured VS recruitment by rewards (Schott et al., 2008, Buckholtz et al., 2010), and dynamic DA synthesis in VS correlated with sensation-seeking personality (Lawrence and Brooks, 2014). Third, antisocial and psychopathic personality features in adults have been associated with increased amphetamine-induced dopamine release in nucleus accumbens (Buckholtz et al., 2010). Therefore, it stands to reason that individual differences between adolescents in VS responsiveness to fMRI rewards could also be DA-mediated.

By extension, greater VS activation by rewards in fMRI might be expected in youth with greater impulsivity or sensation-seeking personality. For example, in adolescents with no psychiatric disorder, individual differences in sensation-seeking (Bjork et al., 2008), and individual differences in a tally of self-reported risky behaviors and substance use (Bjork et al., 2011) correlated positively with mesolimbic recruitment by cues for operant rewards. Moreover, striatal sensitivity to rewards correlated positively with self-reported likelihood to engage in future risky behavior in both adolescents and adults (Galvan et al., 2007). Researchers have also begun directly examining brain function abnormalities in youth with significant antisocial behavior (AB) using cognitive control tasks as well as various monetary reward tasks (recently reviewed in Hyde et al., 2013). These studies collectively implicate (correlate) altered brain activity in limbic circuits (such as insula and VS) in syndromes of antisocial behavior, with differences between findings likely resulting from differing definitions of antisocial behavior such as the presence versus absence psychopathic features, including callous-unemotional traits. For example, adolescents with DBD also showed significantly greater VS recruitment by reward notifications in the MID task (Bjork et al., 2010a). Gatzke-Kopp et al. (2009) reported that when an operant response was no longer rewarded, AB subjects persisted in striatal activation by reward-linked cues, while controls instead recruited error-monitoring circuitry of the ACC when response contingencies changed.

In large-scale twin studies of psychiatric diagnoses, a latent neurobiological trait has been suggested that underlies both addiction and DBD (Kendler et al., 2003). Recent functional neuroimaging studies suggest that abnormalities in the neural network subserving cognitive control may represent a mechanism for this latent trait or common risk factor for the development of both CD and SUD. In comparison to controls, adolescents with CD have been shown to exhibit reduced dorsomedial prefrontal activity during attention allocation (Rubia et al., 2009) and reduced posterior ACC and inferior parietal reactivity when committing response inhibition errors (Rubia et al., 2008). Reduced dorsal pre-frontal activation during response inhibition has also been found in adolescents with high levels of neurobehavioral disinhibition, which includes features of CD (McNamee et al., 2008). Individuals with SUD exhibit similar abnormalities, including reduced brain activation in the dorsolateral/dorsomedial prefrontal cortex (Tapert et al., 2001, Eldreth et al., 2004, Chang et al., 2006, Schweinsburg et al., 2008), ACC (Kaufman et al., 2003, Bolla et al., 2004, Eldreth et al., 2004, Forman et al., 2004, Lee et al., 2005, Li et al., 2008), and posterior parietal cortex (Tapert et al., 2001) during cognitive control tasks. However, it remains unclear whether these deficits are a consequence of chronic use, a risk factor for the development SUD, or a combination of the two.

6. Conclusions and future directions

Functional neuroimaging has opened a window into the development of human motivation and behavior control, to reveal divergent trajectories across different brain systems critical for risk-taking decision-making (e.g. Somerville et al., 2011). Innovative naturalistic tasks during fMRI also hold promise to reveal mechanisms of many behavioral proclivities of adolescents, such as elements of social behavior (Jones et al., 2014). We caution, however, that due to the preponderance of youth with childhood histories of behavior problems among adolescents with significant risky behavior by mid-adolescence, coupled with how the peak in real-world severe risky behavior actually occurs later in young adulthood, it seems doubtful that severe risk-taking behavior in adolescence is significantly accounted for by individual differences in normative neurodevelopment. We suggest instead that severe risky behavior in adolescence can result from problematic individual differences in opponent process function between motivational versus inhibitory brain circuits that are present prior to adolescence, where these individual differences may interact with or be derived from altered neurodevelopment.

Future research on brain mechanisms of significant adolescent risk-taking would therefore benefit from a greater emphasis on individual differences between adolescents, such as histories of behavior disorders. Divergent findings in this emerging literature could in turn be resolved by careful phenotyping of emotionality components, such as callous-unemotional traits, or reactive versus instrumental aggression (Hyde et al., 2013). Moreover, it remains largely unexplored how changes in either persistence, escalation or remittance of drug use, delinquency, or risky behaviors track with brain changes from childhood to adulthood. Large-scale longitudinal neuroimaging projects that follow subjects past their peak crime- and risk-prone young adult ages will be critical in uncovering the functional and structural brain underpinnings of behavior change with development. In order to more convincingly demonstrate that changes in brain structure and function are causally associated with the initial escalation and subsequent decline in risky behaviors, linkages between these dynamic processes must be examined within-individuals from adolescence into early adulthood.

Finally, we caution that attributing real-world behavior to brain activation differences remains speculative, particularly since there is considerable heterogeneity in the factors underlying substance misuse and antisocial behavior in youth. Longitudinal studies are needed to address the functional significance of individual differences (whether developmental differences or differences between adolescents) in brain activation with respect to a causal explanation of real-world risky behaviors, akin to the decades of rigorous longitudinal research that has characterized the influence of socio-contextual factors on adolescent behavioral outcomes.

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Neurodevelopmental Models of Adolescent Risk-Taking: Reconsidering the Role of Behavioral Disinhibition

1. Introduction

Functional magnetic resonance imaging (fMRI) research investigating the neural correlates of adolescent behavior has given rise to a prominent theory: an imbalance between hyperactive incentive-motivational circuitry and underdeveloped frontocortical control circuitry underlies heightened risk-taking in adolescence. This review challenges the universality of this theory, proposing instead that this imbalance is more characteristic of youth with disruptive behavior disorders (DBD) than neurotypical adolescents. We first outline the neuroanatomy of reward-based decision-making and the fMRI findings that support opponent-process theories. We then discuss longitudinal, behavioral, and fMRI studies of DBD youth, arguing that this population, at elevated risk for substance use disorder (SUD), likely exhibits the proposed aberrant opponent-process. Finally, we offer suggestions for future research directions.

2. Neurodevelopmental Models of Adolescent Risk-Taking

Adolescence is often associated with increased risk-taking, supported by both anecdotal evidence and empirical research using psychometric measures and laboratory tasks. fMRI research has sought to elucidate the neural underpinnings of this phenomenon, focusing on developmental trajectories of brain regions implicated in incentive processing and behavioral control.

The ventral striatum (VS), particularly the nucleus accumbens (NAcc), plays a central role in motivational processing. fMRI studies have shown that rewarding stimuli activate a cortico-basal ganglia network encompassing the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), insula, thalamus, and dorsal and ventral striatum. Conversely, punishment typically elicits a similar but less pronounced response in the VS. Inhibitory control over potentially rewarding but risky actions engages frontal cortex structures, as evidenced by lesion and functional imaging studies. These regions, including dorsolateral and inferior prefrontal regions, ACC, and inferior parietal cortex, form a neural network crucial for cognitive control.

MRI studies have documented age-related changes in brain morphology from childhood to adulthood. Frontocortical gray matter volume follows an inverted-U shape, peaking around age 12, while temporal lobe gray matter volume increases linearly. Frontocortical white matter volume, as a proportion of total volume, increases throughout adolescence, becoming increasingly organized as indicated by diffusion tensor imaging (DTI) studies.

Developmental changes in resting-state functional connectivity (RSFC) reveal strengthening long-range and weakening short-range connections with age. This maturation of frontocortical networks, evidenced by both DTI and RSFC, likely impacts decision-making by facilitating the integration of information regarding incentive values, potential consequences, future outcomes, and action planning.

fMRI studies investigating age-related differences in brain responses to risk and reward have yielded mixed results. Some studies indicate heightened VS responsiveness to rewards in adolescents compared to children and adults, particularly when tasks involve engaging stimuli, decision-making between rewarding options, or peer presence. In contrast, other studies utilizing less engaging tasks with minimal stimuli and high cognitive demands report blunted reward anticipation activation in adolescents. This discrepancy highlights the importance of task design and ecological validity in interpreting developmental differences.

Despite these inconsistencies, a dominant theory proposes that a mid-adolescent peak in reward processing, coupled with immature frontocortical control circuitry, contributes to increased risk-taking during this period. This "dual-process" model suggests a functional imbalance stemming from the rapid development of motivational circuits relative to the slower maturation of control circuits, normalizing in adulthood. While supported by findings of differing developmental trajectories across brain regions and preclinical evidence of age-related DA responsiveness, it's crucial to acknowledge the substantial inter-individual variability in VS responses to rewards.

3. Attributing Public Health Burden of Youth Behavior to Neurodevelopmental Patterns

Critically, the proposed mid-adolescent peak in reward processing and associated functional imbalance precedes the peak age range (19-23 years) for mortality and morbidity related to risky behaviors like substance use, accidents, and violence. While some experimentation with risk-taking may be normative, a subset of youth exhibit persistent, clinically-significant antisocial or risky behaviors, often meeting criteria for DBD.

Longitudinal studies demonstrate that individuals displaying DBD symptoms in childhood are at increased risk for developing SUD and other risky behaviors later in life, exceeding the predictive power of early substance use or other psychopathology. This suggests that the aberrant opponent-process proposed by neuromaturational models might be more accurately attributed to this specific population. We argue that DBD youth, rather than representing a deviation from typical development, may actually exemplify the true manifestation of this imbalance. This perspective is further supported by evidence of persistent behavioral disinhibition from early childhood in this group, potentially reflecting delayed or disrupted frontocortical maturation.

4. Behavioral Disinhibition in Childhood Correlates with Reward-Sensitivity and Predicts Risky Behavior in Adolescence

DBD, encompassing ADHD, ODD, and the more severe CD, is characterized by persistent violation of societal norms and the rights of others. Importantly, this diagnostic category is not defined by early substance use. Longitudinal studies consistently identify CD in childhood and adolescence as a strong predictor of future SUD, even after accounting for early substance use and other psychopathologies.

Laboratory studies suggest that hypersensitivity to rewards, particularly in DBD youth exhibiting low anxiety and callous-unemotional traits, may be a predisposing factor for DBD. DBD adolescents demonstrate increased reward-seeking behavior and a preference for smaller, immediate rewards over larger, delayed rewards, mirroring findings in adults with SUD and CD traits. This pattern aligns with animal models, where reward-dominant rodents are more prone to drug self-administration and exhibit punishment-resistant drug-seeking behavior.

Additionally, while not directly measuring reward sensitivity, deficits in reversal learning, observed in individuals with SUD or at risk for SUD, may exacerbate the effects of reward sensitivity. Both DBD youth and individuals with SUD demonstrate difficulty adapting their behavior when previously rewarded cues are no longer associated with positive outcomes, potentially contributing to continued substance use despite negative consequences.

However, it is important to acknowledge the alternative perspective offered by the reward deficiency hypothesis (RDH). RDH posits that substance use, particularly chronic use, arises from a hypofunctioning reward system, with drugs of abuse compensating for this deficiency. This theory is supported by findings of blunted mesolimbic responses to rewards in some individuals with SUD, particularly tobacco smokers.

In addition to heightened reward sensitivity, compromised cognitive control has also been implicated in the development of both DBD and SUD. DBD and SUD individuals, particularly those with comorbid CD, exhibit deficits in cognitive control tasks compared to typically developing youth. Importantly, these deficits can predate and predict future substance use, suggesting a role as a risk factor rather than solely a consequence of substance use.

These findings collectively suggest that, rather than a universal phenomenon of adolescence, the aberrant opponent-process between reward motivation and inhibition circuitry might better explain individual differences in neurocircuit function that increase risk for addiction, criminal behavior, and violence in specific individuals.

5. Brain Signatures of Increased Reward Sensitivity and Reduced Cognitive Control in Youth with Behavior Problems

Given the link between VS activity and DA signaling, individual differences in VS responsiveness to rewards during fMRI might reflect underlying differences in DA functioning. This is supported by PET studies showing correlations between VS activation, DA displacement by rewards, and personality traits like sensation-seeking.

Indeed, studies have shown positive correlations between VS recruitment by reward cues and measures of impulsivity, sensation-seeking, self-reported risky behaviors, and likelihood of engaging in future risky behaviors.

Neuroimaging studies focusing on youth with antisocial behavior (AB) have implicated altered activity in limbic circuits, including the VS and insula. These studies suggest that AB youth exhibit heightened VS reactivity to reward notifications and persistent striatal activation to reward-linked cues even when contingencies change, unlike controls who engage error-monitoring circuitry.

Functional neuroimaging research also points towards abnormalities in cognitive control networks as a potential mechanism underlying the shared risk for both CD and SUD. Adolescents with CD demonstrate reduced activation in prefrontal regions, ACC, and parietal cortex during cognitive control tasks, mirroring findings in individuals with SUD. However, it remains unclear whether these deficits are a cause or consequence of chronic substance use.

6. Conclusions and Future Directions

Functional neuroimaging has advanced our understanding of the developing brain and its role in adolescent risk-taking. However, attributing real-world behavior solely to observed brain activation differences requires caution, considering the complex interplay of individual, environmental, and neurodevelopmental factors.

We propose that instead of reflecting a universal adolescent phenomenon, the proposed imbalance between motivational and inhibitory brain circuits might better explain individual differences in risk-taking, particularly in youth with DBD. Future research should prioritize:

• Focus on Individual Differences: Investigate brain mechanisms of risk-taking with a greater emphasis on individual differences, particularly DBD history and related factors like callous-unemotional traits.

• Longitudinal Studies: Conduct large-scale longitudinal neuroimaging studies following individuals from adolescence into adulthood to understand how brain changes track with the onset, persistence, and desistance of risky behaviors.

• Within-Individual Analyses: Examine brain-behavior relationships within individuals over time to establish a causal link between neurodevelopmental trajectories and behavioral outcomes.

By integrating knowledge from developmental neuroscience, clinical psychology, and longitudinal research, we can move towards a more nuanced understanding of the complex interplay between brain development, individual differences, and the emergence of risky behavior during adolescence.

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The Trouble with Tempting Teen Brains: Why Typical Development Might Not Explain Risky Behavior

1. Introduction

We're increasingly interested in understanding what's happening in the adolescent brain, especially when it comes to their behavior. fMRI technology, which allows us to safely observe brain activity, has been a game-changer. This research has led to a popular theory: risky behavior in teenagers is due to an overactive reward system in the brain, coupled with an underdeveloped system for controlling impulses. This imbalance, the theory states, is why adolescents experience higher rates of injury and death.

This review argues that this brain imbalance is probably not true for most teenagers. Instead, this imbalance is likely present in a smaller group of youth with disruptive behavior disorders (DBD). We'll start by explaining how the brain makes decisions about rewards, drawing on fMRI studies. Then, we'll look at studies of youth with DBD, a group at high risk for substance use disorders. These studies suggest that these individuals are more likely to exhibit the imbalanced brain activity associated with risky behavior. We'll conclude by suggesting future research directions.

2. How the Teenage Brain Develops and Its Link to Risk-Taking

It's no secret that teenagers are known for risky behavior, whether it's skateboarding stunts or experimenting with drugs and alcohol. Research using questionnaires and lab tasks confirms that risk-taking peaks in mid-adolescence. While studies using decision-making tasks show some mixed results, they generally suggest that risky choices either decline steadily from adolescence to adulthood, or peak in mid-adolescence compared to younger children and adults.

The development of fMRI technology has fueled interest in how the maturing brain contributes to adolescent risk-taking. Researchers are particularly interested in how differences in the structure and function of brain regions involved in processing rewards and controlling behavior might play a role.

So, where in the brain are we looking? A brain area called the ventral striatum (VS), particularly a part called the nucleus accumbens (NAcc), is heavily involved in processing motivation. Interestingly, adolescents are more comfortable with ambiguity when it comes to rewards (meaning they'll take risks even when the odds are unclear) compared to adults, although their tolerance for clearly defined risks is not higher.

When the chance of a reward is uncertain, a network of brain regions is activated, including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), insula, thalamus, and both dorsal and ventral striatum. This happens in response to a variety of rewarding stimuli. On the other hand, punishments (like losing money) often activate a similar network, but the VS might show a less intense response or even decreased activity.

The frontal cortex plays a crucial role in resisting potentially rewarding but risky choices. Studies where parts of the frontal cortex are damaged show impaired decision-making, and brain imaging studies confirm its importance in controlling behavior. In teenagers and adults, tasks requiring cognitive control activate a network that includes parts of the prefrontal cortex, ACC, and inferior parietal cortex.

Early MRI studies found differences in brain structure between children, adolescents, and adults. For example, gray matter volume in the frontal cortex peaks around age 12, while gray matter volume in the temporal lobe increases throughout adolescence. Also, the proportion of white matter in the frontal cortex increases from childhood to adulthood. DTI, a specialized imaging technique, shows that the organization of this white matter improves with age, reflecting more efficient connections between brain cells.

Studies on resting-state functional connectivity (RSFC) – essentially, how different brain regions communicate when someone is at rest – suggest that long-range connections strengthen with age, while short-range connections weaken. This pattern can even predict a brain's developmental "age." Because decision-making relies heavily on communication between different brain regions, the maturation of the frontal cortex network may be clinically significant.

However, the most interesting findings come from fMRI studies that look at how the brains of children, adolescents, and adults respond differently to risks and rewards. Most research indicates that the VS in adolescents shows a stronger response to rewards than in children or adults. For example, teenagers showed greater left VS activation when they won money in a gambling task compared to adults. Another study found that once adolescents learned the association between cues and rewards, their VS showed greater activation when receiving a reward compared to adults or children. Similarly, studies using slot machine simulations and "go/no-go" tasks with happy faces as stimuli showed heightened VS activity in adolescents compared to other age groups.

Interestingly, one of the first fMRI studies comparing adolescents and adults in reward processing found that adolescents actually showed reduced right VS activity when anticipating rewards in a simple, timed task with minimal visual stimulation. This finding has since been replicated in other studies using similar tasks.

These seemingly contradictory findings might actually help us understand what's going on. When fMRI tasks are more engaging (like those involving casino games, social interactions, or driving simulations), require choosing between rewards, or include the presence of peers, the VS appears to be most responsive to rewards around age 14-15. Conversely, tasks that are more tedious, require intense focus and quick responses, or don't involve decision-making tend to show blunted reward anticipation in adolescents. It's possible that the more engaging tasks are more relevant to real-world risk-taking scenarios for teenagers.

It's crucial to remember that age alone doesn't explain everything. Individual differences in VS responses to rewards are substantial.

The peak in reward processing during mid-adolescence may be happening while the brain's frontal cortex, responsible for controlling behavior, is still developing. Studies show that cognitive control improves from adolescence to adulthood alongside more focused (and possibly more efficient) frontal cortex activation during tasks involving inhibition.

Developmental differences in how the frontal cortex responds to potential punishments have also been observed. For instance, in a task where participants chose between different levels of risk and reward, adolescents showed less activation in a specific part of the mFC (a part of the frontal cortex) compared to adults when choosing riskier options. This area of the mFC is known to be involved in handling conflicting choices. Similarly, in a risk-taking game, adults showed increased activation in this same mFC area when accumulating risky rewards compared to safe rewards, while adolescents did not. In both studies, engaging in riskier behavior was linked to less mFC activity.

This pattern of heightened reward sensitivity in mid-adolescence alongside an immature impulse control system has led to a popular theory. This theory, called the dual-process model, suggests that this functional imbalance in the brain partially explains why adolescents are prone to risky and illegal behaviors, including drug and alcohol use. This model proposes that the brain's reward system develops faster than its control system, creating an imbalance that peaks in mid-adolescence and evens out in adulthood. This model makes sense considering the different developmental timelines of various brain regions and preclinical research showing greater dopamine responsiveness in younger animals.

3. Does This Brain Imbalance Really Explain Risky Behavior in Youth?

While the dual-process model is compelling, some experts argue that the development of the brain's control system in adolescence is sufficient for rational decision-making, at least in situations where individuals carefully consider their options. In these cases, adolescents may engage in risky behaviors after weighing the potential benefits and costs.

This review raises a different concern. Recent research points out that the peak in reward sensitivity (and the related imbalance between motivation and control circuits) occurs years before the actual peak in youth mortality and illness due to risky behaviors like accidents or substance abuse. This peak in real-world consequences typically occurs between ages 19 and 23, coinciding with the peak ages for binge drinking, delinquency, and risky sexual activity. Notably, this is the same age range when striatal responsiveness to rewards returns to pre-adolescent levels in some fMRI studies.

While some experimentation with drugs and alcohol during adolescence might be considered normal, the lower rates of binge drinking and risky behavior in younger teens could be due to societal restrictions and laws limiting their access to alcohol, firearms, and vehicles. However, some young people are less deterred by these restrictions and display persistent patterns of risky and antisocial behavior.

The "problem behavior theory," developed decades ago, proposed that deviant behaviors tend to occur together in the same individual (e.g., drug use and theft) as a result of environmental factors, social learning, and personality. Longitudinal studies consistently demonstrate that individuals who engage in substance abuse or other risky behaviors by mid-adolescence (and often meet the criteria for DBD) often have a history of impulsive behavior since early childhood. This pattern of behavior can persist for decades in some individuals as a stable characteristic.

This review argues that these are the individuals who truly exhibit the imbalanced brain activity proposed by the dual-process model. While this model is based on research with "neurotypical" individuals (those without psychiatric symptoms), it might better describe youth with DBD or other antisocial behaviors. Their heightened risk-taking could be due to delayed or disrupted development in the frontal cortex, leading to greater variability in the balance between reward and control systems.

4. Impulsive Behavior, Reward Sensitivity, and Future Risks

Disruptive behavior disorders (DBDs), encompassing conditions like ADHD, ODD, and the more severe conduct disorder (CD), are characterized by a persistent pattern of violating the rights of others and societal norms. While CD doesn't explicitly involve substance use, longitudinal studies show that it's a strong predictor of developing substance use disorders later, even when controlling for early substance use and other mental health issues. Children with CD are more likely to start using alcohol and illicit drugs at a younger age. CD preceding regular substance use also predicts poorer treatment outcomes for substance use disorders in teenagers.

Research suggests that hypersensitivity to rewards might be a key factor contributing to DBD, especially in individuals with low anxiety and callous-unemotional traits (lacking empathy or guilt). For example, adolescents with DBD tend to work harder for rewards and prefer smaller, immediate rewards over larger, delayed rewards. This mirrors findings in adults with substance use disorders, particularly those with a history of CD. Similar patterns are observed in animal studies, where rodents showing a preference for immediate rewards are quicker to self-administer cocaine and display persistent drug-seeking behavior even when punished.

Although not directly measuring reward sensitivity, deficits in reversal learning – the ability to adapt to changing reward contingencies – might worsen the impact of heightened reward sensitivity. Individuals with substance use disorders or at risk for developing them often struggle with reversal learning. For example, children and adolescents with externalizing disorders like CD, especially those with callous-unemotional traits, continue to pursue previously rewarded actions even when those actions are no longer rewarded and instead result in punishment. This behavior resembles that of adolescents and adults with substance use disorders. Deficits in reversal learning are thought to contribute to addiction by making individuals less sensitive to the negative consequences of their substance use.

However, the reward deficiency hypothesis (RDH) offers an alternative perspective on addiction. RDH proposes that substance abuse, particularly chronic use, stems from an underactive reward system. According to this theory, drugs of abuse can uniquely stimulate this deficient reward system compared to natural rewards, leading to a preference for drugs. This preference might become even stronger as substance abuse further weakens the reward system. Evidence for RDH in fMRI studies is mixed, with some showing greater and others showing lesser reward system activity in individuals with alcohol and cannabis use disorders. However, studies on tobacco smokers consistently show reduced reward system activation, aligning with RDH. This might be a specific effect of chronic nicotine exposure.

In addition to increased reward sensitivity, problems with cognitive control have also been linked to the development of both DBD and substance use disorders. Cognitive control encompasses abilities like sustained attention, inhibiting impulsive responses, and monitoring one's performance. Children and adolescents with CD often struggle with cognitive control tasks compared to their peers, as do those with a history of substance use. These difficulties seem particularly pronounced in individuals with both substance use disorders and a history of CD. While chronic substance use can impair cognitive control over time, poor performance on cognitive control tasks can actually predict the development of problematic substance use.

To reiterate, while the dual-process model suggests that an imbalance between reward and control systems contributes to typical adolescent risk-taking, a equally strong argument can be made that it's actually individual differences in this balance that drive the increased risk of addiction, criminal behavior, and violence observed in some individuals.

5. Brain Activity Patterns in Youth with Behavioral Problems

As mentioned earlier, developmental differences in reward system activity observed in fMRI studies are often interpreted as reflecting changes in dopamine activity, a neurotransmitter crucial for motivation and reward. This raises the question: could individual differences in dopamine function also contribute to variations in risk-taking tendencies? Studies in adults using PET scans show a correlation between dopamine release in the VS in response to rewards and VS activation observed in fMRI during reward processing. Furthermore, antisocial and psychopathic traits in adults have been linked to increased dopamine release in the NAcc when given amphetamine. Therefore, it's plausible that differences in how teenagers' VS responds to rewards in fMRI could also be related to dopamine activity.

This suggests that teenagers with higher levels of impulsivity or sensation-seeking might exhibit greater VS activation in response to rewards during fMRI tasks. Studies support this notion, showing positive correlations between sensation-seeking, self-reported risky behavior, and reward system activation in adolescents. Researchers have started to directly investigate brain function in youth with significant antisocial behavior using cognitive control and reward tasks. These studies point to altered brain activity in brain circuits associated with emotions and motivation (including the insula and VS). Differences in findings across studies might stem from varying definitions of antisocial behavior and the presence or absence of callous-unemotional traits.

For instance, adolescents with DBD displayed greater VS activation when receiving rewards in a timed task. Another study found that when a previously rewarded action was no longer rewarded, individuals with antisocial behavior continued to show reward system activation, while control participants activated brain regions associated with error monitoring.

Large-scale studies on twins with psychiatric diagnoses suggest that a shared underlying biological factor might contribute to both addiction and DBD. Recent neuroimaging research suggests that abnormalities in the brain network responsible for cognitive control could be a mechanism for this shared risk factor. Compared to typically developing youth, adolescents with CD demonstrate reduced activity in specific areas of the prefrontal cortex during tasks involving attention and response inhibition. These findings are similar to those observed in individuals with substance use disorders. However, whether these brain activity patterns are a consequence of chronic substance use, a pre-existing risk factor, or a combination of both remains unclear.

6. Conclusion and Future Directions

Functional neuroimaging has provided valuable insights into how motivation and behavior control develop in the human brain, highlighting the distinct developmental paths of brain systems critical for risk-taking. New and creative fMRI tasks that simulate real-world situations hold promise for unraveling the mechanisms behind various adolescent behaviors, such as social interactions.

However, it's important to remember that a significant portion of adolescents engaging in risky behaviors have a history of behavioral problems starting in childhood, and the peak in severe risky behavior occurs in young adulthood. Therefore, it's unlikely that typical developmental differences in the brain can fully account for risky behavior in adolescents.

Instead, this review proposes that severe risk-taking in adolescence may stem from individual differences in the balance between the brain's motivation and control systems, present even before adolescence. These differences might interact with or result from atypical brain development.

Future research should prioritize understanding individual differences among adolescents, such as examining their history of behavioral disorders. Inconsistent findings in this field could be addressed by carefully categorizing emotional components of behavior, such as callous-unemotional traits or different types of aggression. Furthermore, we need to understand how changes in risky behaviors (whether they persist, escalate, or diminish) relate to brain changes from childhood to adulthood. Large-scale longitudinal studies that follow individuals past their peak risk-taking years will be crucial for uncovering how brain structure and function contribute to these changes. To solidify the link between brain changes and behavior, these studies must examine how these dynamic processes unfold within individuals over time.

Finally, it's important to acknowledge that directly linking real-world behavior to brain activation differences is still speculative, especially considering the complex interplay of factors contributing to substance abuse and antisocial behavior in youth. Longitudinal studies are needed to determine the true significance of individual differences in brain activation (whether related to development or other factors) in explaining risky behaviors. This approach would mirror the rigor of decades-long longitudinal research that has illuminated the influence of social and environmental factors on adolescent behavioral outcomes.

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The Teenage Brain and Risky Behavior: Is It Really Just a Phase?

1. Introduction

Scientists are really interested in figuring out why teenagers act the way they do, especially when it comes to risky stuff. Using brain imaging technology like fMRI, we can see what's happening inside a person's brain while they think and make decisions. There's a popular theory that teenagers are more likely to do risky things because the parts of their brain that get excited about rewards are stronger than the parts that control their impulses. This review explores whether this theory is true for all teenagers or just a specific group who struggle with behavioral problems.

2. How the Brain Develops and Influences Teen Risk-Taking

We all know teenagers take risks, from skateboarding to partying too hard. But why? Some studies show that teenagers are more open to taking risks when they don't know the odds, while others show they're more interested in risky choices overall. Brain imaging research suggests that this might be because of how their brains are developing.

Scientists have found that specific parts of the brain, like the ventral striatum (VS) and nucleus accumbens (NAcc), are really important for motivation and feeling rewarded. These areas get activated when we encounter rewarding things like winning money or seeing a happy face. Interestingly, studies have shown that the teenage brain responds more strongly to these rewards compared to children or adults, particularly when peers are around or the situation is exciting. On the other hand, when the task is boring or requires a lot of focus, teens show less activation in these areas compared to adults. This suggests that the type of reward and the situation can influence how the brain responds.

At the same time, the parts of the brain responsible for controlling impulses and thinking ahead, like the prefrontal cortex, are still maturing during adolescence. This means that teenagers might be more driven by immediate rewards and less able to consider the potential consequences of their actions. This combination of a strong reward system and a developing control system has led scientists to propose the "dual-process model," suggesting that this imbalance in brain activity contributes to risky behavior in teenagers.

3. Is This Imbalance to Blame for All Risky Behavior in Youth?

While the dual-process model seems to make sense, there's a catch: the peak time for this brain imbalance happens a few years before the peak age for serious risky behaviors like car accidents and substance abuse, which usually occur in late adolescence and early adulthood. This suggests that something else is going on.

One possibility is that this brain imbalance is not a universal teenage experience, but rather a characteristic of a specific group of youth with disruptive behavior disorders (DBD). These disorders, including ADHD, ODD, and CD, involve persistent patterns of impulsive, defiant, and sometimes aggressive behavior, and are often linked to substance abuse later in life. Research suggests that these youth may have a heightened sensitivity to rewards and struggle with impulse control more than their peers.

4. The Link Between Early Behavioral Problems, Reward Sensitivity, and Later Risks

Kids with DBD often show signs of impulsivity and reward-seeking from a young age. They're more likely to choose smaller, immediate rewards over larger, delayed rewards and struggle to learn from mistakes when punishments are involved. This suggests that their brains might be wired differently, leading them to be more drawn to rewards and less influenced by negative consequences.

While some theories, like the reward deficiency hypothesis (RDH), suggest that addiction is linked to a less active reward system, research on DBD points towards a hyperactive reward system in combination with weak impulse control as a potential driver of risky behavior.

5. Brain Imaging Shows Differences in Reward and Control Centers in Youth with Behavior Problems

Brain imaging studies have provided further evidence for these differences. Youth with DBD show greater activation in reward-related brain regions when they receive rewards, and less activation in control-related brain areas when they need to inhibit their impulses. This supports the idea that an imbalance between these systems might be a key factor contributing to their increased risk for engaging in dangerous activities.

6. Conclusion and Future Directions

While brain imaging research has provided valuable insights into teenage brain development, it's important to remember that not all teenagers are the same. While some level of risk-taking is normal, serious risky behaviors are more common among youth with early signs of behavioral problems. These individuals may have brains that are wired to be more sensitive to rewards and less able to control their impulses.

Future research should focus on understanding the individual differences in brain function among teenagers, particularly those with DBD and related disorders. This will help us develop more effective interventions and prevention programs to help these youth avoid the potentially devastating consequences of risky behavior. While it's tempting to dismiss risky behavior as "just a phase," understanding the complex interplay between brain development, individual differences, and environmental factors is crucial for promoting the well-being of all teenagers.

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The Teen Brain and Risky Behavior: Is It Really Just Hormones?

1. Introduction

People often say that teenagers are more likely to do risky things? Scientists have been trying to understand why that is by looking at how the teenage brain works. They use a special technique called fMRI, which is like a giant magnet that takes pictures of your brain while you're thinking! Some scientists believe that teenagers take more risks because the part of their brain that gets excited about rewards is stronger than the part that helps them make safe choices. But in this article, we're going to talk about how this brain imbalance might only be true for some teenagers, especially those who have trouble behaving well. We'll explore how studying these teenagers can help us understand why some people are more likely to make risky choices.

2. How Our Brains Develop and Why Teens Take Risks

Teenagers are known for doing risky things, whether it's skateboarding without a helmet or trying dangerous drugs. But why? Scientists have been studying different parts of the brain to understand this.

One important part is the "reward center," called the ventral striatum (VS). It's like a little part of your brain that gives a person a good feeling when they do something enjoyable, like winning a game or eating their favorite food. Scientists use fMRI scans to see how active this reward center is when teenagers play games or make choices about rewards.

Studies show that the reward center in teenagers can be more active than in adults when they see exciting things, like pictures of money or games. This might be because the teenage brain is still growing and changing.

Another important part of the brain is the "control center," located in the front part of your brain called the prefrontal cortex. Think of it like the brakes on a car – it helps a person stop and think before acting. This part of the brain also keeps developing throughout the teenage years.

Some scientists believe that teenagers take more risks because their reward center is developing faster than their control center. It's like having a powerful engine in a car with weak brakes – it can be hard to control!

3. Is the "Teen Brain" Theory Enough to Explain Risky Behavior?

While it's true that teenagers are more likely to engage in risky behaviors, like drinking alcohol or using drugs, those activities actually peak when they're a bit older, around 19-23 years old. This is interesting because by that time, the brain has mostly finished developing!

This means there might be more to the story than just the "teen brain" idea. It turns out that teenagers who start engaging in risky behaviors early on, like getting into fights or breaking rules (which are signs of a disruptive behavior disorder or DBD), are more likely to continue these behaviors as they get older.

So, maybe it's not just about the brain developing differently in all teenagers, but rather that some teenagers' brains develop in a way that makes them more sensitive to rewards and less able to control their impulses, leading them towards riskier choices.

4. When Reward Trumps Control: Understanding Disruptive Behavior

DBD is a group of behavioral problems that some children and teenagers experience. These problems include things like having trouble paying attention (ADHD), being defiant and argumentative (ODD), and engaging in more serious rule-breaking behaviors (CD), like stealing or harming others.

Research shows that children who struggle with these behaviors often have a hard time resisting tempting rewards and may be less sensitive to punishment. It's like their "reward center" is super strong, while their "control center" needs more practice.

Interestingly, these children are also more likely to develop problems with drugs and alcohol later on. This suggests that having difficulty controlling impulses and seeking out rewards might be early warning signs for future risky behavior.

5. Looking Inside the Brain: Reward and Control in Teenagers with Behavior Problems

Remember fMRI scans? Scientists use them to study how the brain works in teenagers with DBD. They've found that when these teenagers see something rewarding, like the possibility of winning money in a game, their reward center lights up more strongly compared to teenagers without DBD.

Additionally, the control center in these teenagers doesn't seem to activate as strongly when they need to stop themselves from making a mistake. This further supports the idea that a strong reward system and a weaker control system might be contributing to risky behaviors.

6. What Does This All Mean?

Studying the brain helps us understand why teenagers might take more risks. While it's true that the teenage brain is still developing, we also need to consider individual differences. Some teenagers might be more likely to engage in risky behaviors due to how their brains process rewards and control impulses.

More research is needed to understand the complex relationship between brain development, individual differences, and risky behavior. By studying these factors, we can develop better ways to help teenagers make safer and healthier choices.

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