The Adolescent Brain

B.J. Caseya, Rebecca M. Jonesa, and Todd A. Hareb

a Sackler Institute, Weill Medical College of Cornell University, New York, New York, USA

b California Institute of Technology, Pasadena, California, USA

Abstract

Adolescence is a developmental period characterized by suboptimal decisions and actions that are

associated with an increased incidence of unintentional injuries, violence, substance abuse,

unintended pregnancy, and sexually transmitted diseases. Traditional neurobiological and cognitive

explanations for adolescent behavior have failed to account for the nonlinear changes in behavior

observed during adolescence, relative to both childhood and adulthood. This review provides a

biologically plausible model of the neural mechanisms underlying these nonlinear changes in

behavior. We provide evidence from recent human brain imaging and animal studies that there is a

heightened responsiveness to incentives and socioemotional contexts during this time, when impulse

control is still relatively immature. These findings suggest differential development of bottom-up

limbic systems, implicated in incentive and emotional processing, to top-down control systems

during adolescence as compared to childhood and adulthood. This developmental pattern may be

exacerbated in those adolescents prone to emotional reactivity, increasing the likelihood of poor

outcomes.

Keywords

adolescence; prefrontal cortex; nucleus accumbens; amygdala; limbic; impulsivity; reward;

development; risk taking; emotion

Introduction

Adolescence is the period between childhood and adulthood encompassed by changes in

physical, psychological, and social development (Ernst et al. 2006). These alterations make

this period a time of vulnerability and adjustment (Steinberg 2005). According to the National

Center for Health Statistics, there are over 13,000 adolescent deaths in the United States each

year. Approximately 70% of these deaths result from motor vehicle crashes, unintentional

injuries, homicide, and suicide (Eaton et al. 2006). Results from the 2005 National Youth Risk

Behavior Survey (YRBS) show that adolescents engage in behaviors that increase their

likelihood of death or illness by driving a vehicle after drinking or without a seat belt, carrying

weapons, using illegal substances, and engaging in unprotected sex resulting in unintended

pregnancies and STDs, including HIV infection (Eaton et al. 2006). These statistics underscore

the importance of understanding risky choices and behavior in adolescents.

Address for correspondence: BJ Casey, Sackler Institute, Weill Cornell Medical College, 1300 York Avenue, Box 140, New York, NY

10021, Voice: +212-746-5832 fax: 212-746-5755 USA. bjc2002@med.cornell.edu.

Conflict of Interests

The authors declare no conflicts of interest.

NIH Public Access

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Published in final edited form as:

Ann N Y Acad Sci. 2008 March ; 1124: 111–126.

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Adolescence is also a time of increased emotional reactivity. During this period, the social

environment is changing such that more time is spent with peers versus adults, and more

conflicts arise between the adolescent and his/her parents (Csikszentmihalyi et al. 1977;

Steinberg 1989). These changes in social interactions may influence the rise of emotional

reactivity. In addition, given the increase in risky choices and behavior during adolescence, it

appears the value of positive and negative information may be exaggerated. Greater emotional

reactivity and sensitivity during adolescence may play a role in the higher incidence of affective

disorder onset and addiction during this developmental period (Pine et al. 2001; Silveri et al.

2004; Steinberg 2005).

A number of cognitive and neurobiological hypotheses have been postulated to explain why

adolescents engage in suboptimal choice behavior. In a recent review of the literature on human

adolescent brain development, Yurgelun-Todd (2007) suggests that cognitive development

during adolescence is associated with progressively greater efficiency of cognitive control and

affective modulation. An increase in activity in the prefrontal regions as an indication of

maturation (Rubia et al. 2000; Rubia et al. 2006; Tamm et al. 2002) and diminished activity in

irrelevant brain regions (Brown et al. 2005; Durston et al. 2006; Monk et al. 2003) are described

as the neurobiological explanation for the behavioral changes associated with adolescence.

This general pattern, of improved cognitive control and emotion regulation with maturation of

the prefrontal cortex, suggests a linear increase in development from childhood to adulthood.

As evidenced by the National Center for Health Statistics on adolescent behavior and mortality,

suboptimal choices and actions observed during adolescence represent a nonlinear change in

behavior, distinct from childhood and adulthood. If immaturity of prefrontal cortex were the

basis for suboptimal choice behavior and heightened emotional reactivity in adolescence, then

children who have less developed prefrontal cortex and cognitive abilities should look

remarkably similar or even worse than adolescents. Thus, immature prefrontal function alone

cannot account for adolescent behavior.

This review will provide evidence from developmental animal and human neuroimaging

studies that may account for nonlinear changes in behavior and development during

adolescence. A model of adolescent brain development is presented in the context of risk factors

including suboptimal decision making and heightened emotional reactivity.

Development of Goal-directed Behavior: Risk versus Impulse

An accurate conceptualization of cognitive and neurobiological changes during adolescence

must treat adolescence as a transitional developmental period (Spear 2000), rather than a single

snapshot in time (Casey et al. 2005). In other words, to understand this developmental period,

transitions into and out of adolescence are necessary for distinguishing distinct attributes of

this stage of development. Adolescent behavior has been described as impulsive and risky,

almost synonymously, yet these behaviors rely on different cognitive and neural processes

(Casey et al. in press), which suggest distinct constructs with different developmental

trajectories.

A cornerstone of cognitive development is the ability to suppress inappropriate thoughts and

actions in favor of goal-directed ones, especially in the presence of compelling incentives

(Casey et al. 2005; Casey et al. 2000; Casey et al. 2002). A number of classic developmental

studies have shown that this ability develops throughout childhood and adolescence (Case

1972; Flavell et al. 1966; Keating & Bobbitt 1978; Pascual-Leone 1970). Several theorists

(e.g., Bjorkland 1985, 1987; Case 1985) have argued that cognitive development is due to

increases in processing speed and efficiency and not due to an increase in mental capacity.

Other theorists have included the construct of “inhibitory” processes in their account of

cognitive development (Harnishfeger & Bjorkland 1993). According to this account, immature

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cognition is characterized by susceptibility to interference from competing sources that must

be suppressed (e.g., Brainerd & Reyna 1993; Dempster 1993) (Casey et al. 2002; Diamond

1985; Munakata & Yerys 2001). Thus goal-directed behavior requires the control of impulses

or delay of gratification for optimization of outcomes, and this ability appears to mature across

childhood and adolescence.

On a cognitive or behavioral level, the immature cognition of adolescence is characterized as

impulsive (i.e., lacking cognitive control) and risk taking, with these constructs used

synonymously and without appreciation for distinct developmental trajectories for each.

Human imaging and animal studies suggest distinct neurobiological and developmental

trajectories for the neural systems that underlie these separate constructs of impulse control

and risky decisions. Specifically, a review of the literature suggests that impulsivity diminishes

with age across childhood and adolescence (Casey et al. 2005; Casey et al. 2002; Galvan et al.

2007) and is associated with protracted development of the prefrontal cortex (Casey et al.

2005; Casey et al. 2002; Galvan et al. 2007) and is associated with protracted development of

the prefrontal cortex (Casey et al. 2005). However, there are individual differences in the degree

of impulsivity, regardless of age.

In contrast to the linear increase with age associated with impulse control, risk taking appears

greater during adolescence relative to childhood and adulthood and is associated with

subcortical systems known to be involved in evaluation of incentives and affective information.

Human imaging studies that are reviewed here suggest an increase in subcortical activation

(accumbens and amygdala) when making risky choices and processing emotional information

(Ernst et al. 2005; Monk et al. 2003; Montague & Berns 2002) (Kuhnen & Knutson 2005;

Matthews et al. 2004) that is exaggerated in adolescents, relative to children and adults (Ernst

et al. 2005; Galvan et al. 2006).

These findings suggest distinct neurobiological trajectories for impulse versus risk taking

behavior. The limbic subcortical systems appear to be developed by adolescence in contrast to

control systems that show a protracted and linear developmental course into young adulthood.

The prefrontal cortical control systems are necessary for overriding inappropriate choices and

actions in favor of goal-directed ones.

Animal Studies of Adolescent Brain Development

Until recently, much of our understanding of the adolescent brain has come from animal studies.

These experiments have been critical for obtaining information about the neurochemical and

cellular changes that occur as a function of age. The validity of animal models to study

adolescence has been questioned, since it is argued that only humans undergo the psychological

stress of adolescence (e.g., Bogin 1994). However, animals including rodents and nonhuman

primates exhibit increased social interactions during adolescence (Primus & Kellogg 1989) as

well as novelty-seeking and risk-taking behaviors (Adriani et al. 1998; Spear 2000). These

behavioral findings suggest that animal models are appropriate for studying neurobiological

changes during adolescence.

Studies in rodents have shown at the cellular level that there are distinct changes in limbic and

prefrontal regions during adolescence. During early puberty, there is an overproduction of

axons and synapses, followed by rapid pruning in later adolescence (Crews et al. 2007).

Specifically, there is dendritic pruning in the amygdala (Zehr et al. 2006), nucleus accumbens

(Teicher et al. 1995), and prefrontal cortex (Andersen & Teicher 2004; Andersen et al. 2000)

and continual growth in the density of the fibers connecting the amygdala and prefrontal cortex

into early adulthood (Cunningham et al. 2002). There is more prolonged pruning throughout

adolescence in the prefrontal cortex versus the accumbens (Andersen et al. 2000; Teicher et

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al. 1995). These differences in pruning in rodents are consistent with our model suggesting

that the accumbens matures earlier than the prefrontal cortex.

Consistent with the cellular changes in animals, there are alterations in neurotransmission in

these subcortical and cortical areas. Animal studies have shown that dopamine is crucial for

communication between the accumbens, amygdala, and prefrontal cortex and that signaling

between these regions relies upon the fine balance between excitatory and inhibitory dopamine

transmission (Floresco & Tse 2007; Grace et al. 2007; Jackson et al. 2001). There are significant

peaks in dopamine expression during adolescence. Dopamine projections to the prefrontal

cortex continue to develop into early adulthood, with dopamine levels peaking in the prefrontal

cortex during adolescence versus earlier or later in life in nonhuman primates (Rosenberg &

Lewis 1994, 1995) and in rats (Kalsbeek et al. 1988). Dopamine receptor expression is highest

in the accumbens during early adolescence (Tarazi et al. 1998). These findings in rodents

suggest that there are specific regions undergoing structural changes, and therefore,

connections and communication between subcortical and cortical regions are in transition and

in flux during adolescence. Significant evidence suggests that the neuroanatomical changes

described above are also occurring during adolescence in humans, but our methods for studying

humans only provide an approximate index of such changes.

Neuroimaging Studies of Human Brain Development

Our current understanding of the human adolescent brain has come from advances in

neuroimaging methodologies that can be used with developing human populations. These

methods depend on magnetic resonance imaging (MRI) methods (see Fig. 1) and include

structural MRI, which is used to measure the size and shape of structures; diffusion tensor

imaging (DTI), which is used to index connectivity of white matter fiber tracts; and functional

MRI which is used to measure patterns of brain activity. These methods have furthered our

understanding of the neurobiological basis and development of reward or incentive behavior

relative to goal-directed behavior.

MRI Studies of Human Brain Development

Several studies have used structural MRI to map the developmental course of the normal brain

(for review, see Durston et al. 2001). Although the brain reaches approximately 90% of its

adult size by age six, the gray and white matter subcomponents of the brain continue to undergo

dynamic changes throughout adolescence. Data from recent longitudinal MRI studies indicate

that the change in gray matter volume over time has an inverted U-shape pattern and has greater

regional variation than white matter (Giedd 2004; Gogtay et al. 2004, Sowell et al. 2003,

2004). In general, regions that involve primary functions, such as motor and sensory systems,

mature earliest compared to the higher-order association areas that integrate these primary

functions (Gogtay et al. 2004; Sowell et al. 2004). MRI studies show loss of cortical gray matter

first in primary sensorimotor areas, followed by that in the dorsolateral prefrontal and lateral

temporal cortices (Gogtay et al. 2004) (see Fig. 2). This pattern of change is consistent with

nonhuman primate (Bourgeois et al. 1994) and human postmortem studies (Huttenlocher

1979) indicating that the prefrontal cortex is one of the last brain regions to mature. In contrast

to gray matter, white matter volume increases in a roughly linear pattern throughout

development and into adulthood (Gogtay et al. 2004). These changes most likely reflect

ongoing myelination of axons by oligodendrocytes enhancing neuronal conduction and

communication.

When examining neuroanatomical changes across development, the subcortical regions are

often overlooked, however, it is important to note that these areas have some of the largest

changes during development in the brain, particularly in the basal ganglia (Sowell et al.

1999) and specifically in males (Caviness et al. 1996; Giedd et al. 1996; Reiss et al. 1996).

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Developmental changes in structural volume within basal ganglia and prefrontal regions are

interesting in light of the previously mentioned animal work showing pruning in these regions

during adolescence. These processes allow for the fine tuning and strengthening of connections

between prefrontal and subcortical regions during development and learning that may

correspond to greater cognitive control.

How do these changes in structure relate to differences in cognition? A number of studies have

related frontal lobe structural maturation and cognitive function using neuropsychological and

cognitive measures (e.g., Sowell et al. 2003). Specifically, these studies showed associations

between MRI-based regional volumes of the prefrontal cortex and basal ganglia with measures

of cognitive control (i.e., ability to override an inappropriate response in favor of another or to

suppress attention toward irrelevant stimulus attribute in favor of relevant stimulus attribute)

(Casey et al. 1997, 1997). These findings suggest that cognitive changes are reflected in

structural brain changes and underscore the importance of subcortical (basal ganglia) as well

as cortical (e.g., prefrontal cortex) development. While these findings showed associations

between structure and function, a more in-depth discussion of functional imaging evidence for

changes in activity that more directly coincide with behavior across development is presented

in the fMRI section.

DTI Studies of Human Brain Development

The MRI-based morphometry studies previously reviewed suggest that during development,

cortical connections are fine tuned via elimination of an over-abundance of synapses and by

strengthening of relevant connections, although these measures do not have the resolution to

visualize or measure synapses. Recent advances in MRI technology, like DTI, provide a

potential tool for examining the role of specific white matter tracts in the development of the

brain and behavior (for review, see Cascio et al. 2007). Examining white matter tracts can

provide knowledge about pathways of connectivity in the brain, and presumably it is via these

pathways that information is able to travel from one region of the brain to another (Cascio et

al. 2007). Relevant to this paper are the neuroimaging studies that have linked the development

of white matter fiber tracts with improvements in cognitive ability with age.

Recently, associations have been shown between DTI-based measures of prefrontal white

matter development and cognition in children. Nagy and colleagues showed a positive

correlation between maturation of prefrontal–parietal fiber tracts and working memory in

children (Nagy et al. 2004), which is consistent with functional neuroimaging studies showing

differential recruitment of these regions in children relative to adults. Using a similar approach,

Liston and colleagues (2006) have shown that white matter tracts between prefrontal–basal

ganglia and posterior fiber tracts continue to develop across childhood into adulthood, but only

tracts between the prefrontal cortex and basal ganglia are correlated with impulse control, as

measured by performance on a go/no-go task. The prefrontal fiber tracts were defined by

regions of interests, which were identified in an fMRI study using the go/no-go task. In

developmental DTI studies, fiber tract measures were correlated with age, but specificity of

particular fiber tracts with cognitive performance were shown by dissociating the particular

tract (Liston et al. 2006) or cognitive ability (Nagy et al. 2004). These findings highlight the

importance of examining not only regional, but also related circuitry changes, when making

inferences about neural changes in cognition across development.

Functional MRI Studies of Human Brain Development

Compared to MRI and DTI, fMRI is a more direct approach for examining behavior changes

during development and for establishing structure–function relationships. Using fMRI to

measure functional changes in the developing brain has significant potential for the field of

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developmental science and provides a means for constraining interpretations of adolescent

behavior.

As stated previously, the development of the prefrontal cortex is believed to play an important

role in the maturation of higher cognitive abilities such as decision making and cognitive

control (Casey et al. 2002; Casey et al. 1997; Hare & Casey 2005). Many behavioral paradigms,

together with fMRI, have assessed the neurobiological basis of these abilities, including

flanker, Stroop, and go/no-go tasks (Casey et al. 1997; Casey et al. 2000; Durston et al.

2003). Collectively, these studies show that children recruit distinct but often larger and more

diffuse prefrontal regions when performing these tasks than do adults. The patterns of brain

activity that are important for task performance, such as those regions that correlate with

cognitive performance, become more fine tuned with age. Regions that are not correlated with

task performance diminish in activity with age. This pattern has been observed across both

cross-sectional (Brown et al. 2005) and longitudinal studies (Durston et al. 2006) and across a

variety of paradigms. Neuroimaging studies cannot definitively characterize the mechanism

of such developmental changes as dendritic arborization or synaptic pruning. However, these

studies suggest that change over a period of time results in both refinement within brain regions

as well as fine tuning of projections from these regions (Brown et al. 2005; Bunge et al.

2002; Casey et al. 1997; Casey et al. 2002; Luna et al. 2001; Moses et al. 2002; Schlaggar et

al. 2002; Tamm et al. 2002; Thomas et al. 2004; Turkeltaub et al. 2003).

Functional MRI Studies of Behavior during Adolescence

The question remains how can fMRI studies help explain whether adolescents, compared to

children or adults, are 1) lacking sufficient cognitive control (impulsive), 2) risky in their

choices and actions, and 3) more sensitive to affective information when required to exert

cognitive control than children or adults.

Impulse control, as measured by cognitive control tasks like the go/no-go task, shows a linear

pattern of development across childhood and adolescence, as described above. However, we

were interested in understanding changes across development in top-down control regions and

subcortical reward-seeking regions. It is only recently that risk taking in adolescents has been

examined with neuroimaging techniques (Ernst et al. 2005; May et al. 2004). These studies

have focused primarily on the region of the accumbens, a portion of the basal ganglia involved

in predicting reward outcomes. Although two recent reports showed less ventral prefrontal

activity (Eshel et al. 2007) and posterior mesofrontal activity (Bjork et al. 2007) in adolescents

versus adults on risk-taking behavior, the goal of our studies was to characterize the

development of limbic subcortical regions involved in motivation and emotional reactivity in

conjunction with top-down control regions (prefrontal cortex). Many studies have examined

the neural response in children and adolescents to affective information (e.g., emotional faces)

(Baird et al. 1999; Killgore et al. 2001; Monk et al. 2003; Thomas et al. 2001b; Yurgelun-Todd

& Killgore 2006) but typically have used passive viewing or attention tasks (Monk et al.

2003) unrelated to processing of the affective information. Our studies examine how affect

influences cognitive control across development and characterizes the activation of the

subcortical systems (amygdala) involved in affect regulation relative to the cortical (prefrontal)

regions associated with cognitive control.

A Neurobiological Model of Adolescence

How do neural changes in subcortical regions (e.g., accumbens and amygdala) associated with

reward-seeking and emotion coincide with development of the prefrontal regions and do they

relate to impulsivity and risk-taking behaviors? We have developed a neurobiological model

of adolescent development within this framework that builds on rodent models (Laviola et al.

1999; Spear 2000) and recent imaging studies of children, adolescents, and adults (Ernst et al.

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2005; Galvan et al. 2007; Galvan et al. 2006; Hare & Casey, in press). Figure 3 depicts this

model illustrating how bottom-up limbic and prefrontal top-down control regions should be

considered together. The graph shows different developmental trajectories for these systems,

with limbic systems developing earlier than prefrontal control regions. According to this model,

the individual is biased more by functionally mature limbic regions during adolescence (i.e.,

imbalance of limbic relative to prefrontal control), compared to children, for whom these

systems are both still developing, and compared to adults, for whom these systems are fully

mature. This perspective provides a basis for nonlinear shifts in behavior across development,

due to earlier maturation of this limbic system relative to the less mature top-down prefrontal

control region. Furthermore, with development and experience, the functional connectivity

between these regions provides a mechanism for top-down control of these regions (Hare &

Casey, in press). Our model reconciles the contradiction between health statistics of risky

behavior during adolescence and the astute observation by Reyna and Farley (2006) that

adolescents are able to reason and understand risks of behaviors in which they engage.

According to our model, in emotionally salient situations, the more mature limbic system will

win over the prefrontal control system. In other words, when a poor decision is made in an

emotional context, the adolescent may know better, but the salience of the emotional context

biases his or her behavior in opposite direction of the optimal action.

Our neurobiological model proposes that the combination of heightened responsiveness to

rewards and immaturity in behavioral control areas may bias adolescents to seek immediate

rather than long-term gains, perhaps explaining their increase in risky decision making and

emotional reactivity. Tracking subcortical (e.g., accumbens and amygdala) and cortical (e.g.,

prefrontal) development of decision making and emotional reactivity across childhood and

through adulthood provides additional clarification on whether changes reported in

adolescence are specific to this period of development or, rather, reflect maturation that is

steadily occurring in a somewhat linear pattern from childhood to adulthood.

Two recent fMRI studies spanning from childhood to adulthood provide empirical evidence

consistent with our neurobiological model. In the first study (Galvan et al. 2006), we examined

behavioral and neural responses to reward manipulations across development, focusing on

brain regions implicated in reward-related learning and behavior in animal (Hikosaka &

Watanabe 2000; Pecina et al. 2003; Schultz 2006) and adult imaging studies (e.g., Knutson et

al. 2001; O’Doherty et al. 2001; Zald et al. 2004) and in studies of addiction (Hyman & Malenka

2001; Volkow & Li 2004). Based on rodent models (Laviola et al. 1999; Spear 2000) and

previous imaging work (Ernst et al. 2005), we hypothesized that relative to children and adults,

adolescents would show exaggerated responses to reward as indexed by elevated accumbens

activity in concert with less mature recruitment of top-down prefrontal control regions.

Our findings were consistent with rodent models (Laviola et al. 2003) and previous imaging

studies during adolescence (Ernst et al. 2005), which show enhanced accumbens activity to

rewards. Adolescents, as compared to children and adults, showed an exaggerated accumbens

response in anticipation of reward. However, both children and adolescents showed a less

mature response in prefrontal control regions than adults. These findings suggest that there are

different developmental trajectories for these regions. The enhancement in accumbens activity

during adolescence may relate to the increase in impulsive and risky behaviors observed during

this period of development (see Fig. 4).

In the second study, we examined the development of behavioral and neural responses in

performance of an emotional go/no-go paradigm (Hare & Casey, in press; Hare et al. 2005).

During the experiment, participants were presented with two emotional facial expressions

(fearful, neutral, or happy) and were asked to respond to one of the emotions (e.g., fear) and

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suppress their response to the other emotion (e.g., neutral). In the context of negative emotional

information (fearful faces), reaction times improved with age but were longer when detecting

fearful faces relative to a neutral or happy face. This slowing in reaction time was correlated

with greater amygdala activity (Hare & Casey, in press). Activity in the orbital frontal cortex

increased with age, and greater orbital frontal activity relative to amygdala was associated with

more efficiency in suppressing emotional reactivity (longer reaction times and greater

amygdala activity). These findings are in accordance with animal studies (Baxter et al. 2000)

which show connectivity between the amgydala and orbital frontal cortex are important for

assessing changes in emotional value of an object and adapting behavior accordingly.

Differential recruitment of prefrontal and subcortical regions has been reported across a number

of developmental fMRI studies (Casey et al. 2002; Monk et al. 2003; Thomas et al. 2004).

These findings were typically interpreted in terms of immature prefrontal regions rather than

as an imbalance between prefrontal and subcortical regional development. Given evidence of

prefrontal regions in guiding appropriate actions in different contexts (Miller & Cohen 2001),

immature prefrontal activity might hinder appropriate estimation of future outcomes, especially

when making a decision within an emotional context (i.e., heat of the moment). This

interpretation is consistent with previous research showing elevated subcortical, relative to

cortical, activity when decisions are biased by immediate versus long-term gains (McClure et

al. 2004). Further, fMRI studies have shown limbic subcortical activity positively correlates

with suboptimal choice behaviors (Kuhnen & Knutson 2005).

In sum, during adolescence, relative to childhood or adulthood, an immature ventral prefrontal

cortex may not provide sufficient top-down control of robustly activated reward and affect

processing regions (e.g., accumbens and amygdala). This imbalance in development of these

regions and relative top-down control results in less influence of prefrontal systems

(orbitofrontal cortex) relative to the accumbens and amygdala in reward valuation and

emotional reactivity.

Why Would the Adolescent Brain Be Programmed This Way?

Adolescence can be described as a progressive transition from childhood into adulthood with

an indefinable ontogenetic time course (Spear 2000) yet often co-occurring with puberty, which

is defined by specific biological markers. The significant neuroendocrinological changes

associated with puberty, such as increases in adrenal and gonadal hormones, are correlated

with the development of secondary sexual characteristics and can influence brain function (for

a review see Spear 2000). The onset and hormone fluctuations of puberty may provide an

explanation for the observed functional differences in subcortical activity between children

and adolescents, versus activity in the prefrontal region, which reflects a linear change with

age.

From an evolutionary perspective, adolescence is the period in which independence skills are

acquired in order to increase the success of separating from the protective influence of the

family. It is also a period when there is an increase in the likelihood of harm such as injury,

depression, anxiety, drug use, and addiction (Kelley et al. 2004). However, our neurobiological

model suggests that risky behavior and emotional reactivity are the products of a biologically

driven imbalance between increased novelty and positive sensation seeking in conjunction with

immature “self-regulatory competence” (Steinberg 2004).

As previously mentioned, during adolescence, independence-seeking behaviors are prevalent

across species, such as increases in peer-directed social interactions and intensifications in

novelty seeking and risk-taking behaviors. In other species such as rodents, nonhuman

primates, and birds, behaviors like seeking out same-age peers and fighting with parents are

also observed and may be important adaptive skills to remove the adolescent from the home

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territory in order to mate (Spear 2000). Relative to adults, periadolescent rats show increased

novelty-seeking behaviors in a free-choice novelty paradigm (Laviola et al. 1999).

Neurochemical evidence indicates that the balance in the adolescent brain between cortical and

subcortical dopamine systems begins to shift toward greater cortical dopamine levels during

adolescence (Spear 2000). And as previously described, in the nonhuman primate there is an

increase in dopamine enervation of the prefrontal cortex into early adulthood (Rosenberg &

Lewis 1995). Thus this elevated risk-taking behavior appears to occur across species and have

important adaptive functions.

It is possible that this developmental pattern is an evolutionary feature. One needs to engage

in high-risk behavior in order to leave the family and village to find a mate. This risk behavior

occurs simultaneously with an increase in sexual hormones, resulting in adolescents seeking

sexual partners and is seen in other species. In conjunction with this novelty-seeking behavior,

there would need to be some mechanism for detecting cues of safety or danger. The increase

in emotional reactivity during this period may allow adolescents to be more vigilant and aware

of threat, to ensure their survival as they move from a safe environment to a novel one. In

today’s society when adolescence may extend indefinitely—with individuals well into their

20s living with their parents, remaining financially dependent, and choosing mates later in life

—these behaviors may be deemed inappropriate.

Biological Predispositions, Development, and Risky Behavior

The recognition of individual differences in impulse control and taking risks is not new in the

field of psychology (Benthin et al. 1993). Perhaps one of the classic examples of individual

differences in the social, cognitive, and developmental psychology literatures is delay of

gratification (Mischel et al. 1989). Delay of gratification is typically assessed in 3- to 4-yearold toddlers. A toddler is seated in a room with two cookies and a bell. The child is then told

that the experimenter will leave the room in order to prepare for upcoming activities and

explains to the child that if she remains in her seat and does not eat a cookie, she will receive

the large reward (2 cookies). If the child cannot wait, she should ring a bell to summon the

experimenter and thereby receive the smaller reward (1 cookie). Distractions in the room are

minimized, with no toys, books, or pictures. The experimenter returns after 15 minutes or after

the child has rung the bell, eaten the rewards, or shown any signs of distress. Mischel (1989)

showed that children typically behave in one of two ways: 1) either they ring the bell almost

immediately in order to have the cookie, which means they only get one; or 2) they wait and

optimize their gains to receive both cookies. This observation suggests that some individuals

are better than others in their ability to control impulses in the presence of highly salient

incentives, and this bias can be detected in early childhood (Mischel et al. 1989). This

differential in impulse control appears to remain throughout adolescence and young adulthood

(Eigsti et al. 2006).

What might explain individual differences in decision making and behavior? Some theorists

have postulated that the dopaminergic mesolimbic circuitry, implicated in reward processing,

underlies risky behavior (Blum et al. 2000), and that individual differences in this circuitry

might relate to the propensity to engage in risky behavior (O’Doherty 2004). A number of

studies have shown increases in activity in the nucleus accumbens immediately prior to making

risky choices on monetary-risk paradigms (Kuhnen & Knutson 2005; Matthews et al. 2004;

Montague & Berns 2002), and as described previously, adolescents show exaggerated

accumbens activity to rewarding outcomes relative to children or adults (Ernst et al. 2005;

Galvan et al. 2006). Collectively, these data suggest that as a group adolescents may be more

likely to engage in risky choices (Gardener & Steinberg 2005). However, some adolescents

will be more prone than others to engage in risky behaviors, putting them at potentially greater

risk for negative outcomes. Therefore it is important to consider individual variability when

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examining complex brain–behavior relationships related to risk taking and reward processing

in developmental populations.

To explore individual differences in risk-taking behavior, Galvan and colleagues (2007)

recently examined the association between activity in reward-related neural circuitry in

anticipation of a large monetary reward with behavioral measures of risk taking and impulsivity

in adolescence. Specifically, Galvan and colleagues used functional magnetic resonance

imaging and anonymous self-report rating scales of risky behavior, risk perception, and

impulsivity in individuals between the ages of 7 and 29 years (see Fig. 6). There was a positive

association between accumbens activity and the likelihood of engaging in risky behavior across

development. In other words, those individuals, who perceived risky behaviors as leading to

dire consequences, activated the accumbens less to reward. Impulsivity ratings were not

associated with accumbens activity, but rather with age, further dissociating impulse control

from incentive-based risky behaviors. These findings suggest that during adolescence, some

individuals have a predisposition to engage in risky behaviors due to developmental neural

changes.

Adolescent behavior is repeatedly characterized as impulsive and risky, yet this review of the

imaging literature suggests different neurobiological substrates and developmental trajectories

for these two types of behavior. Specifically, impulsivity is associated with immature ventral

prefrontal development and gradually diminishes from childhood to adulthood (Casey et al.

2005). The negative correlation between impulsivity ratings and age in the study by Galvan

and colleagues (2007) further supports this notion. In contrast, risk taking is associated with

an increase in accumbens activity (Kuhnen & Knutson 2005; Matthews et al. 2004; Montague

& Berns 2002) that is exaggerated in adolescents, relative to both children and adults (Ernst et

al. 2005; Galvan et al. 2006). Thus adolescent choices and behavior cannot be explained by

impulsivity or protracted development of the prefrontal cortex alone, as children would then

be predicted to be greater risk takers. The findings provide a neural basis for why some

adolescents are at greater risk than others, but also demonstrate a basis for why adolescent risktaking behavior in general is different from risk taking in children and adults.

Adolescence, Individual Differences, and Affective Disorders

Adolescence is a time of greater emotional reactivity and a period when symptoms of many

psychiatric disorders (e.g., schizophrenia, depression, anxiety) manifest. Normal adolescent

development can be interpreted as the coordination of emotions and behavior in the social and

intellectual environment, and the development of psychopathology during adolescence can be

seen as resulting from a difficulty in balancing these factors (Steinberg 2005). We have

previously described enhanced bottom-up emotional processing in subcortical regions relative

to less effective top-down modulation in prefrontal regions to affective information during

adolescence. It is possible that this imbalance may play a role in the increased risk for affective

disorders during adolescence (Steinberg 2005). Clearly, not all adolescents develop

psychopathology; there must be individual variability in emotional reactivity and the ability to

modulate these behaviors. Individual differences may predispose a person to be at greater risk

for poorer outcomes.

The amygdala has been implicated as a key neural region in emotional dysregulation in

psychiatric disorders. This region is essential to learning the emotional significance of cues in

the environment (see Maren & Quirk 2004 for review). In animal studies, amygdala lesions

result in a reduction of fear behavior (Anglada-Figueroa & Quirk 2005; Davis & Whalen

2001; Kalin et al. 2004), and human neuroimaging studies have shown increases in activity in

the amygdala to fearful stimuli in adults (Breiter et al. 1996; Morris et al. 1998) and in children

(Thomas et al. 2001b). There is evidence for dysregulation of amygdala activity in anxious and

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depressed children (Thomas et al. 2001a) and adults (Leppanen 2006; Rauch et al. 2003;

Thomas et al. 2001a).

In a recent study, we examined individual differences in anxiety levels as measured by the

Spielberger State Trait Anxiety Index and neural responses to affective information in

adolescents and adults during an emotional go/no-go task (Hare et al. in press). Adolescents

showed greater initial amygdala activity than adults, and sustained amygdala activity was

correlated with trait anxiety. Increased activity in orbitofrontal regions correlated with a

decrease in amygdala activity over time (i.e., repeated presentation of a fearful face, see Fig.

7), suggesting dampening of emotional reactivity due to top-down control from prefrontal

regions. These findings are consistent with animal studies showing the importance of the

orbitofrontal cortex (OFC) in extinction of fear conditioning in animals with repeated exposure

to empty threat (Gallagher et al. 1999).

Our results are consistent with previous fMRI studies in clinically anxious adults and children

that have shown unregulated amygdala activity to negative emotional information and less

activity in the prefrontal cortex (McClure et al. 2007; Shin et al. 2004; Thomas et al. 2001b)

in adolescents at risk for anxiety disorders (Perez-Edgar et al. 2007). Together these findings

suggest that prefrontal regions serve to regulate emotional reactivity and that individual

differences in emotion regulation may be due to an imbalance in activity between these regions

that is exacerbated during adolescence.

Conclusions

Human imaging studies show structural and functional changes in frontolimbic regions

(Jernigan et al. 1991; Giedd et al. 1999; Giedd et al. 1996; Sowell et al. 1999; for review, Casey

et al. 2005; Jernigan et al. 1991; Giedd et al. 1999; Giedd et al. 1996; Sowell et al. 1999) for

review, (Casey et al. 2005) that seem to parallel increases in cognitive control and selfregulation (Casey et al. 1997; Luna & Sweeney 2004; Luna et al. 2001; Rubia et al. 2000;

Steinberg 2004). These changes appear to show a shift in activation of prefrontal regions from

diffuse to more focal recruitment over time (Brown et al. 2005; Bunge et al. 2002; Casey et al.

1997; Durston et al. 2006; Moses et al. 2002) and elevated recruitment of subcortical regions

during adolescence (Casey et al. 2002; Durston et al. 2006; Luna et al. 2001). Although

neuroimaging studies cannot definitively characterize the mechanism of such developmental

changes, these changes in volume and structure may reflect development within, and

refinement of, projections to and from these brain regions during maturation suggestive of fine

tuning of the system with development.

We have discussed the importance of considering individual variability when examining

complex brain–behavior relationships related to risk taking, reward processing, and emotional

reactivity in developmental populations. Using an approach that looks at developmental

trajectories, rather than snapshots in time, allows one to comprehensively study these behaviors

during development and examine individual differences. It is not possible to fully explain the

emergence of affective disorders or atypical development by simply examining one time point.

Longitudinal studies across development would be the best methodology to address these

issues.

Taken together, the findings synthesized here indicate that increased risk-taking behavior and

greater emotional reactivity in adolescence are associated with different developmental

trajectories of subcortical limbic regions relative cortical control regions. These developmental

changes can be exacerbated by individual differences (e.g., genetic risk) in baseline activity of

limbic systems.

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This model of development reconciles a number of contradictions and myths about

adolescence. First, there have been many reports that suggest that adolescent behavior is due

to protracted development of prefrontal cortex. However, if this were the case, then children

would engage in similar or worse behavior than adolescents. The National Center for Health

Statistics on adolescent behavior and mortality shows that suboptimal choices and actions

observed during adolescence represent a nonlinear change in behavior, distinct from childhood

and adulthood. Adolescents, unlike children, may be in situations (e.g., driving a car) that may

put them at greater risk for mortality, but even when taking these conditions into account, there

is still a significant elevation of risky behavior in adolescents in comparison to children.

Furthermore, experimental studies have shown that when risk is held constant, such as in the

appraisal of risky vignettes, children perceive greater risk in hypothetical scenarios than do

adolescents (reviewed in Furby & Beyth-Marom 1992). Our neurodevelopmental model

provides an explanation for these nonlinear changes in behavior.

Reyna and Farley (2006) have reconciled the second myth by showing that adolescents are

able to reason and understand risks of behaviors in which they engage and do not consider

themselves invincible. Prior research has also shown that adolescents knowingly engage in

risky behavior, and this is often due to influences of feelings, emotions, and peers (Gardener

& Steinberg 2005; Steinberg 2004, 2005). The observation that adolescents know that they are

engaging in risky behavior is not supported by the sole explanation of a less developed

prefrontal cortex. In this context, our model suggests that the adolescent is capable of making

rational decisions, but in emotionally charged situations the more mature limbic system will

win over the prefrontal control system.

When faced with an immediate personal decision, adolescents will rely less on intellectual

capabilities and more on feelings. Nevertheless, when reasoning about a hypothetical, moral

dilemma, the adolescent will rely more on logical information (Steinberg 2005). In other words,

when a poor decision is made in the heat of the moment, the adolescent may know better, but

the salience of the emotional context biases his or her behavior in opposite direction of the

optimal action. This work coincides with studies of social cognition showing that adolescents

make more rational decisions about hypothetical scenarios versus real-life situations (Sobesky

1983). The environmental context and emotional significance of the decision greatly influence

the adolescent (Steinberg 2005).

Our findings and model have significant implications for heated debates on public policy and

the treatment of minors in our judicial system. Adolescents show adult levels of intellectual

capability earlier than they show evidence of adult levels of impulse control (Reyna & Farley

2006). As such, adolescents may be capable of making informed choices about their future

(e.g., terminating a pregnancy) but do not yet have full capacity to override impulses in

emotionally charged situations that require decisions in the heat of the moment. Unfortunately,

judges, politicians, advocates, and journalists are biased toward drawing a single line between

adolescence and adulthood for different purposes under the law that is at odds with

developmental cognitive neuroscience (Steinberg et al. in press). Our neurodevelopmental

model of adolescence will hopefully help to make strides in moving this single line to multiple

lines that consider developmental changes across both context (emotionally charged or not)

and time (in the moment or in the future).

Acknowledgements

This work was supported in part by grants from the National Institute of Drug Abuse R01 DA18879 and the National

Institute of Mental Health R01 MH73175 and P50 MH62196 to BJC.

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FIGURE 1.

Illustrations of the most common magnetic resonance methods used in the study of human

development. (A) Structural magnetic resonance imaging (MRI) to produce structural images

of the brain useful for anatomical and morphometric studies, (B) diffusion tensor imaging (DTI)

measures myelination and directionality of fiber tracts between anatomical structures, and

(C) functional MRI (fMRI) measures patterns of brain activity within those structures (from

Casey et al. 2005).

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FIGURE 2.

Illustration of gray matter volume maturation over the cortical surface from 5 to 20 years of

age (from Lenroot & Giedd 2006).

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FIGURE 3.

The traditional explanation of adolescent behavior has been that it is due to the protracted

development of the prefrontal cortex. Our model takes into consideration the development of

the prefrontal cortex together with subcortical limbic regions (e.g., nucleus accumbens and

amygdala) that have been implicated in risky choices and emotional reactivity.

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FIGURE 4.

Magnitude and extent of accumbens and OFC activity to reward. Adolescents (13–17 years)

showed greater percent signal change to large rewards than either children (aged 7–11 years)

or adults (23–29 years) in the accumbens (A). Children had the greatest percent signal change

in the OFC compared to adolescents and adults (B). Children had the greatest volume of activity

in the accumbens relative to adolescents and adults (C) Children and adolescents showed

greater volume of activity in the OFC than adults (D). Adapted from Galvan et al. (2006).

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FIGURE 5.

Bilateral amygdala activation (left). Graph depicts amygdala activity in adults, teenagers, and

children. Adapted from Hare et al. (in press).

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FIGURE 6.

Activity in the nucleus accumbens in anticipation of reward (A). Percent change in fMRI signal

in the accumbens in anticipation of reward as a function of age (B). The association between

accumbens activity to reward and the likelihood of engaging in risky behavior in three age

groups (C) (Adapted from Galvan et al. 2007).

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FIGURE 7.

Picture depicts left orbitofrontal activity. Graph illustrates correlation of activity in the OFC

and in the amygdala in both adults and adolescents (adapted from Hare et al. in press).

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