Developmental trajectories during adolescence in males and females: a cross-species understanding of underlying brain changes (2011)

Adolescence is a transitional period between childhood and adulthood that encompasses vast changes within brain systems that parallel some, but not all, behavioral changes. Elevations in emotional reactivity and reward processing follow an inverted U shape in terms of onset and remission, with the peak occurring during adolescence. However, cognitive processing follows a more linear course of development. This review will focus on changes within key structures and will highlight the relationships between brain changes and behavior, with evidence spanning from functional magnetic resonance imaging (fMRI) in humans to molecular studies of receptor and signaling factors in animals. Adolescent changes in neuronal substrates will be used to understand how typical and atypical behaviors arise during adolescence. We draw upon clinical and preclinical studies to provide a neural framework for defining adolescence and its role in the transition to adulthood.

1.1 Defining adolescence

Adolescence can be defined as the period between 10–19 years of age in humans (WHO, 2010s), between two-four years in primates (Schwandt et al., 2007), and between 35–60 days of age in rodents (Andersen et al., 2000; McCutcheon and Marinelli, 2009). Spear (2000) begins her discussion of this period with typical adolescence defined as a behavioral transitional period. Such behavioral transitions are consistently observed across diverse mammalian species by an increased sensitivity to peers and social cues (Blakemore, this journal; Forbes and Dahl, 2005; Steinberg, 2010; Panksepp, 1981), risk taking (Laviola et al., 2003), and maturing cognitive control (Casey et al., 2008). Definitions of adolescence can also be reasonably based on gonadal changes as they are relevant to sexual maturation (Sisk and Foster, 2004). The arguments laid out here are by no means exhaustive and should not be used decisively, rather as a point of reference.

A new developmental stage, emerging adulthood, occurs between 18–29 years of age in humans (Arnett, 2000). Defined culturally, emerging adulthood in humans describes observations that while a majority of neurobiological changes associated with adolescence are over, the organism is not yet `mature’ as evidenced by delays in attaining a job or marriage. Historically, G. Stanley Hall (1904) described a `new’ maturational period that described adolescence from a socioeconomic view points that ultimately led to increased recognition of a distinct stage. As a result, we have identified unique and important neurobiological changes that characterize adolescence. While this review focuses primarily on these neurobiological indices of adolescence, it is important to recognize that in rodent species that a period exists that may capture emerging adulthood (less information is available on non-human primates). As discussed below, rats show marked changes between 40–60 days, but the period between 60–100 days is associated with a slower, steady change that gradually stabilizes. Might this be a new “emerging adult” period that deserves research attention, rather than a media phenomenon to explain a new cultural shift in developed nations? The importance of defining stages is to arrive at a consensus of the maturational state of the organism that is described to facilitate cross-species and sex comparisons.

1.2 Why have such a transitional period?

From an evolutionary perspective, behavior has been shaped by natural selection to prepare an individual to succeed in the social and physical world as an adult, including successfully finding a mate and reproducing. This process culminates during adolescence. Behaviorally, mammals spanning from rodents to humans all experience a tumultuous transitional period where navigation through puberty and decreased parental influence is coupled with increased peer influence, sexual competition, and new decision-making challenges (reviewed by Spear, 2000). Neuroplasticity allows for appropriate responding to emerging environments and this is evident in the development of reward and affect-related systems (Galvan, 2010). However, other developmental processes exhibit steady increases in cognitive control during adolescence that facilitate decision making (Geier and Luna, 2009; Somerville and Casey, 2010). Together, this yin and yang underlie typical development, where the majority of adolescents struggle with the transition to individuate from peers and parents and emerge as independent, self-regulating adults as these processes achieve balance. When these transitions develop normally, individual adaptations are made to unique environmental and social forces. However, errors in this process result in maladaptive behavior. The emergence of psychopathology can be partially attributed to deviance from the normal trajectory of maturation, resulting in life-long issues with reward- and emotion-related processing. Aside from genetically-driven abnormality, errors in overproduction and pruning of neurons or receptors, poor refinement of fiber conductivity or the unmasking of early life insults are all likely contributors. This review will focus on these developmental processes in the mammalian brain, with an overall emphasis on typical rather than atypical (e.g., Andersen and Teicher, 2008; 2009; Marco et al., 2011).

1.3 Nature of the change

A neural systems approach provides insight to the complexity of the nature of adolescent development. As discussed by Paus (Paus et al., 2008), trajectories of different aspects of brain function clearly illustrate how regional and functional diversity contribute to the multifaceted nature of the adolescent brain. In this review, we examine what is known about changes in developmental trajectories with a focus on adolescent processes as described across mammalian species and between the sexes. Our framework is partially based on the triadic model, described by Ernst and colleagues (Ernst and Fudge, 2009; Ernst and Korelitz, 2009). The triadic model roots behavioral changes in three primary systems, or nodes, namely the affective system, the reward system, and cognition/response inhibition. These three different nodes work together to produce behaviors that typify adolescent maturation. Each node has its own developmental trajectory, which creates an adolescent system in a state of flux. Final behavioral outcomes are likely to depend on the dominant node of a given stage or could result from a weakened node that fails to perform regulatory functions. The triadic model in its simplified form offers to explain adolescent exaggerated reactivity to a number of emotional stimuli, changes in reward sensitivity, and the marked transition in cortical control and cognitive development. Here, we will use this framework to describe detailed changes in adolescent development across species and sex with a focus on cortical and limbic brain regions.

2.1. Characteristics of Overproduction and Pruning

2.2. Overproduction and pruning of receptor systems

2.2.1 Overproduction of monoamine receptors

The overproduction and pruning of receptor systems is more complex in comparison to synaptic changes, and two waves of age-related changes in density occur. A number of neurotransmitter systems, including dopamine (Gelbard et al., 1990; Kalsbeek et al., 1988; Lankford et al., 1988; Todd, 1992), norepinephrine (Feeney and Westerberg, 1990; Kline et al., 1994) and serotonin (Kuppermann and Kasamatsu, 1984; Lauder and Krebs, 1978; Whitaker-Azmitia and Azmitia, 1986) have age-limited trophic roles in the brain. Ectopic expression of various receptor subtypes during the course of early postnatal development are associated with increased synaptic sprouting, axonal growth, and synapse formation. For example, ectopic expression of serotonin 5-HT7 receptors within the hippocampus occurs briefly during the first two weeks of life in rats (Louiset et al., 2006; Vizuete et al., 1997). Similarly, the serotonin transporter (5-HTT) is found on non-serotonergic neurons embryonically in cortical and striatal neuroepithelia and sensory thalamic pathways postnatally at P0–P10 (Zhou et al., 2000). Transient expression of the 5-HTT and the vesicular monoamine transporter (VMAT) was also observed in sensory cranial nerves, in the hippocampus, cerebral cortex, septum, and amygdala (Lebrand et al., 1998). These transporters and/or receptors are believed to guide neuronal innervation. The effects of trophic neurotransmitters are concentration-dependent (Mazer et al., 1997), suggesting that baseline levels are integrally important for the nature of effect. Similar ectopic receptor expression is also observed in white matter. For example, noradrenergic receptor α2 is observed in immature white matter in the rat (Happe et al., 2004). However, not all receptor expression plays a trophic role.

A second wave of receptor over-expression occurs during adolescence, during which receptors and signaling mechanisms show an inverted U-shape curve of development that results in expression levels that endure into adulthood. In contrast to ectopic, transient expression that is virtually absent by adulthood, these populations of receptors gradually rise, peak, and decline during maturation. A review of adolescent receptor changes is found in Table 1, with an emphasis on receptors within limbic and cortical regions. The timecourse of overproduction and pruning is regionally-dependent (Andersen et al., 2000), and is observed in a vast array of markers. Different receptor systems include: dopamine, serotonin, norepinephrine, glutamate, GABA, neurotensin, endocannabinoid, and cholinergic (Andersen et al., 2000; Eggan et al., 2010; Lidow et al., 1991). In rhesus monkey, Lidow et al (Lidow et al., 1991) have shown that the density of receptors develops in concert with synaptogenesis.

 

Table 1

If we focus further on microcircuits to examine age-related distributions of receptors, recent results suggest even more complex changes during adolescence. Receptor distribution itself changes between different neuronal phenotypes. For example, D1 dopamine receptors do not seem to change their expression level significantly between post-weaning ages to adulthood on GABAergic neurons (Brenhouse et al., 2008; Vincent et al., 1995). In contrast, the overproduction and pruning of D1 receptors occurs significantly on glutamatergic output neurons (Brenhouse et al., 2008). Specifically, only 2% of these glutamatergic projections are D1 immunoreactive in juvenile rats, rising to 44% at P40, and falling down to 6% with maturity at P100. Whether other receptors show differential expression on other neuronal subtypes during adolescence needs to be examined. Table 1 provides information on other receptor classes changes, but identification on specific neuronal types is typically not known. In contrast, D2 receptors inhibit the activity of fast-spiking GABA interneurons after puberty (O’Donnell, 2010; Tseng and O’Donnell, 2007). These neurons are important for efficiently integrating multiple inputs in real-time. Thus, receptor distribution within microcircuits and their functional capacities change dramatically during adolescence.

3.1. Specific innervation of neurotransmitter systems

In this section, we discuss how specific neurotransmitter systems innervate a given brain region. Innervation begins prenatally, but actively continues into the adolescent period and adulthood. However, most studies bypass characterizing adolescence and assume that innervation proceeds in a linear fashion. Human post-mortem studies of connectivity are nearly impossible to conduct, as brain tissue resource centers typically dissect brain tissues into smaller areas that prevent tract tracing. The resolution of MRI does not permit tract tracing of specific neuronal populations communicating with each other (other than via tractography, which assess both myelin and axon caliber simultaneously). Transporter density is often used as an indicator of innervation patterns (e.g., (Moll et al., 2000)). However, transporter densities may vary independently of innervation and thus may not be ideally suited for such purposes.

Based on the few animal studies that use standard tracing methods to characterize adolescence, some show a linear progression of innervation across maturation (e.g., (Brenhouse et al., 2008; Brummelte and Teuchert-Noodt, 2006; Cunningham et al., 2002; Erickson et al., 2000), whereas others (Cressman et al., 2010; Rios and Villalobos, 2004) demonstrate an inverted U-shape pattern. We have observed a linear progression of innervation of layer V glutamate neurons of the medial PFC into the nucleus accumbens core between 25, 44, and 100 days in the rat (Brenhouse et al., 2008). In a study by Cunningham and colleagues (Cunningham et al., 2002), a linear innervation pattern was also found in the glutamatergic connections between the amygdala and PFC, which continue from birth into late adolescence/young adulthood (60 days of age) in the rat. Age differences in synaptic connections are qualitative as well. For example, glutamate neurons formed axo-dendendritic (36.5%), axo-spinous (7.7%), and axo-somatic synapses (5.8%) on GABAergic neurons, but 17.3%, 30.8% and 1.9% on non-GABAergic neurons. The formation of these contacts generally followed a curvilinear pattern across age.

In contrast, some patterns of innervation show non-linear courses in their trajectory. For instance, the medial PFC (both prelimbic and infralimbic regions) projections to the basolateral amygdala remain stable between 25 and 45 days of age in the rat, but decrease by about 50% between 45 and 90 (Cressman et al., 2010). Similar findings are observed in mice. Afferents from the dorsalmedial thalamus to the frontal cortex increase until 13 days of age, followed by a 67% decrease in the third week of life, when they progressively increase until adolescence and stabliize (Rios and Villalobos, 2004). The first over-production phase of innervation has been linked to the functional organization of layer III neurons, suggesting that glutamate input drive synaptogenesis. Dopamine neurons follow a comparable pattern of innervation in the primate cortex (areas 4, 9, 46): dopaminergic axons in layer III increased three-fold before 5–7 months of age, with no appreciable change in layers 1 and V (Erickson et al., 1998). Labeled varicosities continued to increase, reaching a peak (six-fold greater than in the youngest monkeys) in animals 2–3 years of age (adolescence) before declining to stable adult levels (Rosenberg and Lewis, 1995; Woo et al., 1997). Gerbils demonstrate a similar pattern. Dopamine innervation into the amygdala increases the first three weeks in life in gerbils, before a slight decline in density during early adolescence that stabilizes into late adulthood (Brummelte and Teuchert-Noodt, 2006). Thus, it is likely (and notably not adequately covered in this review) that other neurotransmitter systems show similar changes in innervation patterns.

At this stage, it is unclear why different patterns of innervation (e.g., linear versus inverted U-shaped) occur in different cortical layers (Figure 2). The first possibility lies in the sampling of ages, where critical discontinuities may exist that were not adequately characterized. The second possibility lies in the nature/function of the region being innervated. We have raised this issue previously in the context of dopamine receptors (Teicher et al., 1995) and others for innervation (Erickson et al., 1998). Specifically, different regions that are involved in functions that require constant updating may benefit from linear increases that occur relatively early in life (prior to adolescence). In contrast, regions involved in the learning of a life-long function, such as a habit, benefit from streamlining that is associated with pruning. The third possibility is that innervation shows age-specific patterns in the laminar organization, with layer III in the cortex demonstrating an inverted U-shape, and the deep and superficial layers demonstrating a more progressive pattern. Taken together, the unique connectivity in intrinsic and extrinsic afferents critically aids in sculpting neuronal circuitry during adolescence (Benes, 2009).

 

Figure 2

a) Drawings of cortical lamination in vertical cross-section by Santiago Ramon y Cajal following Nissl (left, middle) in an adult and Golgi staining (right) in a 1½ month old infant. B) patterns of synaptic changes that occur during the transitions …

4.1. Energy utilization

The morphological changes described above are typically preceded by functional changes within the brain. The original studies on functional changes used PET imaging of glucose to map energy usage in a cross-sectional design (Chugani, 1998; Feinberg, 1988). Glucose utilization in humans reaches adult levels by two years of life (Chugani et al., 1987) but then rises at 4–5 years of age and maintains this plateau until 10 years of age before pruning by ~50% by 16–18 years of age (Chugani, 1998). Genes related to glucose metabolism, e.g., gene acyl coA dehydrogenase (ACADSB), are expressed in high levels during adolescence, although their functional significance is not known at this time (Harris et al., 2009).

Other markers of brain activity that examine brain metabolism, such as n-acetylaspartate (NAA; a marker of neurons and processes), phosphocreatine (PCr; energy dynamics), and membrane phospholipid metabolism (with makers sPME and sPDE) have been examined with magnetic resonance spectroscopic imaging (MRSI) to provide a non-invasive index of development. Changes in these markers were characterized in axial slices of the brain across males and females 6–9.5, 9.5–12, and 12–18 years old in n=106 subjects (Goldstein et al., 2009). Comparisons between 6–9.5 year olds to the 12–18 year olds show no difference in NAA, which suggests no marked neuronal changes. This observation is in direct contrast to well-characterized neuronal loss determined by direct measurement in post-mortem tissue (e.g., Huttenlocher, 1979). However, NAA provides acetate for olidgodendrocytes that are responsible for myelin production. Thus, no net change in NAA across adolescent development could reflect a balance between neuronal loss and increased myelination. PCr was reduced in the younger age group, but elevations in percent gray matter and sPME/sPDE ratios, which reflect membrane phospholipid turnover, were higher. PCr and percent gray matter were highly correlated with age, but NAA, sPME, sPDE, and sPME/sPDE were not. While some potential changes may have been missed by combining males and females, these data suggest that MRSI does not show decisive age-related metabolic changes.

4.2 Functional connectivity as defined with MRI

Functional connectivity is another approach used to show temporal inter-relationships between areas of activation during resting state or during an fMRI task (Fair et al., 2008; Supekar et al., 2009; Thomason et al., 2009; Zuo et al., 2010). Maps of functional connectivity are also referred to as connectomes (Biswal et al., 2010), with applications to fMRI representing a recent application of this field (Lichtman and Sanes, 2008). This approach provides some insight into adolescent brain development, although it is limited by some observations that `functional connectivity’ is observed in areas absent of true anatomical connections (Honey et al., 2009; Koch et al., 2002). Resting state fMRI is based on observations that large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations occur (Biswal et al., 2010). Approaches to understanding functional connectivity include seed-based (where a starting point is manually identified to identify a starting point), independent component analysis (ICA), and frequency-domain analyses. Functional development of different brain systems includes the combination of decreasing short-range connections (i.e., segregation) and increasing long-range connections (i.e., integration) (Fair et al., 2007; Stevens et al., 2009). In other words, development proceeds from a local to a more distributed network as different regions become more interconnected (Fair et al., 2009). This interconnectivity is not synchronous, but rather individual regions become connected and then interconnected (Supekar et al., 2010).

Functional connectivity studies of resting fMRI show that a “default network” exists in the brain when it is not actively processing information. The default network is comprised of the posterior cingulate cortex, mPFC, medial temporal lobes, and angular gyrus. These structures demonstrate coherent, low frequency oscillations (0.1 Hz) when the individual is in a quiet, resting state. As the brain becomes more integrated inter-regionally between childhood and adolescence (Fair et al., 2008), increased connectivity within the default network occurs during this transition (between 9–12 years of age; Broyd et al., 2009). The default network has been hypothesized to play a role in creativity, whereas a reduction within the default network has been associated with schizophrenia and autism.

Other functional networks, however, certainly exist in the brain. In a study that compares young adolescents (mean age 12.5 ± 0.51 [SD] years) to young adults (22.2 ± 1.67 [SD] years) in mixed sex groups, 13 main functional networks were identified (Jolles et al., 2010). Of these networks, eight showed increased activity between cortical regions during adolescence, two showed no difference in activity, and three were associated with basic visual or sensorimotor functions (i.e., sensorimotor, visual system, and ventral stream networks) and showed less activity during adolescence than young adulthood. Identification of these networks will now facilitate future investigations into why they demonstrate age-related changes.

5.1. Functional development of affective circuits

The functional development of circuits and systems in the brain is complex, with many moving pieces to put together. As a way to approach developmental circuits, we provide the following overviews as they relate to both affect and reward during the adolescent period. These approaches do not include the countless and important studies that examine behavioral and pharmacological transitions that occur during adolescence, but are focused on studies that have neuroanatomical relationships at their root.

Much of human behavior and motivation arises from previously-acquired associations between rewarding or aversive stimuli and the contexts in which they occur (Cardinal et al., 2002). These powerful, learned associations drive our present and future behavior (Cardinal et al., 2002) and occur through Pavlovian conditioning mechanisms (Rosenkranz et al., 2003). Information about the environment and emotions is processed within the basolateral amygdala (BLA) (Grace and Rosenkranz, 2002), which forms powerful associations between stimuli that predict the occurrence of an appetitive or aversive outcome, and produces “affect” within the BLA (Cardinal et al., 2002; Laviolette et al., 2005; Schoenbaum, 2004; See et al., 2003). However, responding to a given stimuli needs to be specific and appropriate in terms of mood, emotional significance, or attention as it relates to choice (Paus et al., 1996). This process occurs in the PFC (Cardinal et al., 2003; Rebec and Sun, 2005; Schoenbaum, 2004; Ventura et al., 2007). Noradrenergic and dopaminergic receptors in the PFC mediate the regulation of attention, behavior and emotion by strengthening network connections between neurons with shared inputs (Arnsten, 2009). Within the mPFC, the salience of information is processed to regulate selected attention.

Thus, information from the BLA is relayed to the mPFC by glutamatergic projections (Bechara et al., 1999; Laviolette et al., 2005; McDonald and Pearson, 1989), where it is processed for salience (Schultz, 1998) and errors that are relevant for predicting future outcomes (Falkenstein et al., 2000; Price, 1999). As a result, stimuli that predict an aversive outcome can be responded to in an appropriately adaptive manner (Pezze et al., 2003). This function is performed by dopaminergic signals in the mPFC (Jackson and Moghaddam, 2004), which encode additional information of salience and novelty with emotional information (Cardinal et al., 2002; Milad and Quirk, 2002) to influence goal-directed, motivated behavior. The mPFC sends this information to the nucleus accumbens directly (Goto and Grace, 2005; Voorn et al., 2004), or indirectly via the amygdala. Subsequently, the resultant activity within the mPFC, directly or indirectly, influences the motivated behavior in the nucleus accumbens.

Immature processing between the amygdala and the PFC has been proposed to underlie the delayed emergence of affective illness until adolescence (Ernst et al., 2006). Within the triadic model proposed by Ernst and colleagues (Ernst et al., 2006), the avoidance system associated with the amygdala drives behavior relatively unchecked by an immature PFC. According to this model, the nucleus accumbens adjusts the strength of the link between appetitive and aversive conditioning (Horvitz, 2002). This theory is one of a rare few that incorporates what is known about the neurobiology of depression within a developmental framework. However, the theory implies that children and adolescents would grow out of their depression with emerging cortical maturity and connectivity, which is not the case (Andersen and Teicher, 2004; 2008).

We have recently reviewed developmental changes during the adolescent period that may increase vulnerability to depression (Andersen and Teicher, 2008). Briefly, children have more activity than adults in the amygdala in response to emotional stimuli (Killgore et al., 2001), which is further exacerbated in children and adolescents with social anxiety disorder (Beesdo et al., 2009). However, the nucleus accumbens is more involved in the processing of appetitive and aversive stimuli in adolescence instead to the amygala (Ernst et al., 2005). Recruitment of the PFC in response to emotionally-laden stimuli does not occur until adulthood (Killgore et al., 2001). Preclinically, this is consistent with the tract tracing experiments that show both continued development of BLA to PFC innervation during adolescence (Cunningham et al., 2002), but more importantly, a peak in innervation of PFC to BLA inputs during adolescence (Cressman et al., 2010). Together, increased anatomical connections may provide a basis for the delayed (adolescent) emergence of depressive symptoms and emotional lability that epitomizes this maturational state as regulatory control over affect develops (or fails to develop).

5.2. Functional development of reward circuits

Sophisticated MRI and electrophysiologic studies demonstrate the unique roles of subdivisions within the frontal cortex in reward processing. The mPFC (Broadman areas [BA] 10/12/32 and including the anterior cingulate cortex; BA 24) responds to the outcome of the reward: it is activated if an anticipated reward is received and deactivated when not received (Knutson et al., 2003; Schulz et al., 2004). The orbital frontal cortex (OFC) encodes expected outcomes and estimates motivational value based on potential reward. The OFC plays an important role in reversal learning and delayed reinforcement (Dalley et al., 2004) through its connections to sensory, limbic, frontal, and subcortical regions. The OFC is functionally divided with medial portions responding selectively to reward value, while the lateral portions suppress previous reward-associated processes (Elliott et al., 2000; Elliott et al., 2003; London et al., 2000).

The accumbens (ventral striatal region) responds to the saliency (Ernst et al., 2004), valence (appetitive or aversive) (Jensen et al., 2003) and the predictability of the reward (unpredicted reward activates greater than predicted reward (Berns et al., 2001; Elliott et al., 2000)), but not the motor component (Zink et al., 2004). During adolescence, the accumbens responds greater than the OFC to reward (Galvan et al., 2005). Taken together, these data suggest that the adolescent accumbens drives change in reward processing (Galvan, 2010).

However, evidence of how the cortical and subcortical systems respond to reward stimuli suggests that the cortex plays an even larger role in adolescent transitions in reward processing. Animal studies have shown that reward processing transitions during adolescence through the pruning and potential re-focusing of cortical networks as the networks mature and become adult-like (Brenhouse et al., 2008; Crews et al., 2007). Clinical fMRI studies suggest that both ventral striatum and the mPFC activate to reward stimuli during adolescence (Bjork et al., 2004). Prior to this transition, reward-related BOLD tasks produce more diffuse and less intense activation of frontal regions in children than in adults (Durston et al., 2003). However, children show greater activation in the ventral striatum (accumbens) (Ernst et al., 2005; Galvan et al., 2006). As we know little mechanistically about reward development in humans, we will draw upon preclinical research for a greater understanding.

The maturation of the mPFC is delayed relative to most other brain regions (Andersen et al., 2000; Huttenlocher, 1979) and reaches peak synaptic density closer to adulthood (Benes et al., 2000). Increased sprouting of dopamine neurons (Benes et al., 1996; Kalsbeek et al., 1988; Verney et al., 1982), receptor density (Andersen et al., 2000; Leslie et al., 1991), and second messenger system activity (Andersen, 2002) culminate in an enhanced dopaminergic drive to the mPFC during adolescence. Recent findings also demonstrate an age-related increase in D1 activation of non-fast spiking cells in the mPFC, which occurs after puberty (Tseng et al., 2006), and a peak in the firing rate of the VTA dopaminergic neurons at this same age (McCutcheon and Marinelli, 2009). The over-expression of D1 receptors on glutamatergic outputs to the accumbens also peaks during adolescence in parallel with drug-seeking behavior (Badanich et al., 2006; Brenhouse et al., 2008). This receptor population has been implicated in drug-relapse, and thus its overexpression during adolescence is noteworthy (Kalivas, 2005). These changes in cortical reward processing are also likely to influence subcortical responses to psychostimulants.

In contrast, basal levels of extracellular dopamine and dopaminergic responses to stimulants do not change appreciably between adolescents and adults in the accumbens (Frantz et al., 2007) or mPFC (Jezierski et al., 2007). However, the ratio between cortical:accumbens expression of the immediate early gene c-fos in response to stimulants increases between adolescence and adulthood (Andersen et al., 2001). Additionally, amphetamine produces subcortical > cortical activation patterns of c-fos in juveniles (Andersen et al., 2001), but cortical > subcortical activation in adolescents (Cao et al., 2007). Taken together, these data suggest that juveniles differ markedly from adolescents, who are more adult-like, in their responses to stimulants subcortically. In other words, the likelihood that substance use rises substantially during adolescence follows from either direct or indirect effects of cortical processes on subcortical activity.

5.3. Functional development of cognition

Experimental paradigms such as the Stroop, Simon, Flanker, Go/No-Go, and Stop-Signal tasks require suppression of a more automatic behavior to perform a less automatic one. Attentional regulation, response inhibition, and conflict and error monitoring are cognitive processes that are engaged in the service of cognitive control and successful task performance. Performance on all of these tasks improves steadily throughout development, but does not approach adult levels until at least late childhood or early adolescence (Bunge et al., 2002; Casey et al., 1997; Davidson et al., 2006; Luna and Sweeney, 2004; Rubia et al., 2000). As with working memory, the self-regulatory capacity of children can be overwhelmed easily by increasing task demands. In adults, self-regulation relies on broad cortical areas such as supplementary motor area, frontal eye fields, anterior cingulate cortex, dorsolateral PFC, ventralPFC/lateral orbitofrontal cortex, as well as temporal, and parietal regions all of which have connections with striatum in the subcortex (Leung et al., 2000; Marsh et al., 2007).

Effective responding to environmental stimuli requires selective attention and motivational direction, coupled with suppression of actions that are no longer required or that are inappropriate. This suppression is measured experimentally via response inhibition, which involves three interrelated processes, as proposed by Barkely (Barkley, 1997): 1) inhibition of an initial pre-potent response, 2) stopping of an ongoing response or delayed responding, and 3) limiting interference or distractibility during delay periods. The basal ganglia and PFC are both implicated in these processes (Casey et al., 2008). In general, while the basal ganglia control the inhibition of inappropriate behaviors (Mink, 1996), the PFC acts to prevent interference with relevant information by competing information (Miller and Cohen, 2001).

In contrast to approach-avoidance, which requires incentive salience attribution and is largely mediated through a triadic cooperation of the PFC, striatum, and amygdala (reviewed (Ernst and Fudge, 2009)), response inhibition recruits circuitries that regulate motor planning and timing (Deiber et al., 1999). The primary role of fronto-striatal networks lends itself to a different developmental profile than that of motivation and selective attention systems.

5.4. Development of Response inhibition

While adolescents can perform sophisticated cognitive tasks, the ability to do so consistently continues to improve during adolescence and into adulthood, This linear improvement throughout development suggests that the neurobiological underpinnings of cognition follow similarly linear progression. Children show significantly higher intensity of activation than adults in frontal lobe regions (Bunge et al., 2002) including bilateral medial frontal gyrus and medial aspects of bilateral superior frontal gyrus (Booth et al., 2003). This is consistent with age-related differences in accuracy and reaction time on go/no-go tasks across childhood. Interestingly, a joint DTI and fMRI study performed by Stevens and colleagues (Stevens et al., 2009) reported a direct relationship between age-related changes in functional connectivity between the bilateral frontopolar, right parietal cortex and right caudate, increased myelination, and improved performance on the Go/No Go task. In another DTI study, response inhibition in 7–13 year olds was significantly associated with higher FA and lower MD in both the right inferior frontal gyrus and the right pre-supplementary motor cortex (Madsen et al., 2010). The linear developmental trajectory of myelination discussed above is therefore consistent with an apparent linear development of cognitive control, relative to the inverted U-shaped trajectory of affect and reward processing. Children also display greater intensity of activation than adults in the left caudate nucleus during go/no-go (Booth et al., 2003) and stop (Rubia et al., 1999) tasks. The basal ganglia has been proposed to be involved in the inhibition of inappropriate behaviors (Casey et al., 2001), and the basal ganglia appears to mature linearly from childhood through adulthood.

The basic neurobiology of these circuits have either been previously discussed above or have yet to be studied within a developmental context. While there is a wealth of neuroimaging data surrounding response inhibition tasks, there has been less investigation of neurochemistry behind these systems (for a comprehensive review, see Eagle et al., 2008). One of the main problems associated with preclinical modeling of these behaviors lies in the weeks that are required to train animals to perform these tasks, which precludes their study during development. Given the importance that cognitive control and impulse regulation during adolescent maturation into adulthood, this field requires more attention than it has received.

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