Neurosci Biobehav Rev. 2011 Aug;35(8):1704-12. doi: 10.1016/j.neubiorev.2011.04.003. Epub 2011 Apr 15.
Adolescence is a period of increased behavioral and psychiatric vulnerabilities. It is also a time of dramatic structural and functional neurodevelopment. In recent years studies have examined the precise nature of these brain and behavioral changes, and several hypotheses link them together. In this review we discuss this research and recent electrophysiological data from behaving rats that demonstrate reduced neuronal coordination and processing efficiency in adolescents. A more comprehensive understanding of these processes will further our knowledge of adolescent behavioral vulnerabilities and the pathophysiology of mental illnesses that manifest during this period.
Adolescence is a period in which individuals observe physical changes to their bodies, experience new interests and desires, and find themselves with greater freedom, independence, and responsibility. Although variably defined, adolescence is generally considered to begin with the onset of puberty and ends as one takes on adult social roles (Dahl, 2004; Spear, 2000). The span of puberty—which involves increased growth, changes in body composition, the development of gonads and secondary sexual organs and characteristics, and cardiovascular and respiratory changes—typically occurs from age 10 to 17 in girls and 12 to 18 in boys (Falkner and Tanner, 1986). As this occurs the adolescent undergoes a variety of cognitive, behavioral, and psychosocial transitions. The various changes of adolescence do not all start and end together, and thus the puzzle of relating adolescent brain changes with behavior is challenging. Studying adolescence is like shooting at a moving target, with researchers designating “adolescent” groups of different ages and levels of development. Furthermore, from the mid-19th through the 20th century, an earlier average age of menarche has been observed in the western world (Falkner and Tanner, 1986; Tanner, 1990). The educational process is more prolonged and individuals are tending to wait longer before starting their careers, getting married, and having children (Dahl, 2004). Thus, the length of adolescence is not fixed (and has been lengthening) and while the period correlates with many biological developmental processes, it is partially defined according to psychosocial and behavioral criteria. With these caveats in mind, the literature reviewed here has primarily defined adolescence in humans as the second decade of life, in monkeys as age two to four years, and in rodents as week four to week six or seven.
Despite the definitional ambiguities, it is well recognized that during this period major transitions do occur, including a variety of characteristic behavioral changes seen across species. There is increased social behavior (Csikszentmihalyi et al., 1977), novelty and sensation seeking (Adriani et al., 1998; Stansfield and Kirstein, 2006; Stansfield et al., 2004), tendencies toward risk taking (Spear, 2000; Steinberg, 2008), emotional instability (Steinberg, 2005), and impulsivity (Adriani and Laviola, 2003; Chambers et al., 2003; Fairbanks et al., 2001; Vaidya et al., 2004). Peer relationships become dominant, and there are greater inclinations to seek out fun and exciting experiences (Nelson et al., 2005). Increased novelty and sensation seeking may be evolutionarily adaptive, as these behaviors could improve the increasingly independent adolescent’s chances of finding food and a mate (Spear, 2010). In modern society, however, these features can be associated with taking unnecessary risks. Therefore, adolescence is considered a period of behavioral vulnerability: teens are more likely to experiment with tobacco and illicit drugs and alcohol; drive recklessly; engage in unprotected sex; and have interpersonal conflicts (Arnett, 1992; Arnett, 1999; Chambers et al., 2003; Spear, 2000). Adolescent risk taking is more likely to occur in groups (e.g. vehicular accidents), when certain behaviors are perceived to be acceptable by one’s peers (e.g. unprotected sex, drug use) (Steinberg, 2008), and in emotionally charged situations (Figner et al., 2009). Thus, while adolescents have survived the potential health problems of early childhood their morbidity and mortality rates are twice that of pre-pubescent children (Dahl, 2004).
In addition to the added risks of normal adolescent development, it is also the time when symptoms of a variety of mental illnesses often manifest, including mood disorders, eating disorders, and psychotic disorders such as schizophrenia (Paus et al., 2008; Pine, 2002; Sisk and Zehr, 2005; Volkmar, 1996). During this period there is a vast array of neurobiological changes that drive everything from a cascade of hormonal signals that initiate puberty (Sisk and Zehr, 2005), to increased cognitive ability and motivational changes (Doremus-Fitzwater et al., 2009; Luna et al., 2004). Understanding precisely how the brain develops through adolescence, and relating such changes to both normal behavioral tendencies and pathological conditions, is critically important to public health. Here we review some of the behavioral, and neurodevelopmental changes of adolescence and discuss several models that connect them, including our own hypothesis of reduced processing efficiency.
2. Adolescent behavior
Studies in rodents and humans have shown that adolescents exhibit greater “impulsive choice,” defined as the preference for smaller rewards that occur sooner over larger delayed rewards, as measured with delay-discounting tasks (Adriani and Laviola, 2003; Steinberg et al., 2009). It is notable that in human studies only younger adolescents exhibit this difference; with delay discounting reaching adult levels by age 16–17 (Steinberg et al., 2009). Adolescent humans also score higher on the Sensation-Seeking Scale than adults, with males exhibiting higher levels than females (Zuckerman et al., 1978). Sensation seeking is “the need for varied, novel, and complex sensations and experiences…” (Zuckerman et al., 1979, p. 10), which may occur independently, or together with impulsivity. Sensation seeking is greatest during early- to mid-adolescence and lower thereafter, while impulse control appears to steadily improve through the teenage years, suggesting that they are subserved by different biological processes (Steinberg et al., 2008). Consistent with human evidence of heightened adolescent sensation seeking, adolescent rodents prefer novelty (Adriani et al., 1998; Douglas et al., 2003; Stansfield et al., 2004), exhibit greater novelty-induced locomotion (Stansfield and Kirstein, 2006; Sturman et al., 2010), and spend more time exploring open arms in an elevated plus maze than adults (Adriani et al., 2004; Macrì et al., 2002).
Adolescents’ tendencies to seek novel experiences, even at the risk of physical or social harm, might be expected if their capacity to assess risk or compute outcome probability is underdeveloped. Cognitive abilities do continue to develop at this time (Luna et al., 2004; Spear, 2000). According to Piaget, the formal operation period, which is associated with more abstract reasoning, reaches full maturity during adolescence (Schuster and Ashburn, 1992), and may be less well developed in some individuals. Also, the persistence of egocentrism, in which teenagers experience an ‘imaginary audience’ along with the ‘personal fable’ of unique feelings, may cause them to believe they are exceptional and give them a sense of invulnerability (Arnett, 1992; Elkind, 1967). However, only modest cognitive improvements appear from mid-adolescence onward (Luna et al., 2004; Spear, 2000), and even young children exhibit an accurate implicit understanding of probability (Acredolo et al., 1989). Furthermore, there is little evidence that adolescents actually perceive themselves as invulnerable or underestimate risk; in fact, they often overestimate risk, such as the chance they will become pregnant within a year, go to jail, or die young (de Bruin et al., 2007). Finally, any cognitive explanation for adolescent risk-taking must account for the fact that children take fewer risks and yet are less cognitively developed than adolescents.
Alternatively, adolescent behavioral disparities could relate to differences in cognitive strategies. One hypothesis, called “fuzzy trace theory,” states that far from lacking in cognitive ability, adolescents process the risk/benefit details of choices more explicitly than adults. Paradoxically, adolescents may behave more rationally than adults by more explicitly computing the expected values of different options, but this could lead to greater risk taking (Rivers et al., 2008). According to Rivers and colleagues (2008), through development we progress from doing more literal “verbatim” to a “fuzzy” gist-level heuristic that captures the essence or bottom line without details. This presumably improves the efficiency of decision making and tends to bias us away from risky choices as we tend to avoid potential adverse outcomes without assessing the actual probabilities involved. For example, unlike adolescents, adults favor choices that attach certainty to increased gains or reduced losses over probabilistic alternatives with identical expected values (Rivers et al., 2008). Overall, the idea that adolescent choices could reflect differences in cognitive strategy—but not deficiencies in outcome prediction—is intriguing. Future neuroimaging and physiology studies of adolescent decision making might benefit from considering the possibility that differences in the precise pattern of neural activity, even within the same brain regions, along with the level of integration between different regions, could facilitate alternative styles of cognitive deliberation.
Adolescents’ greater recklessness could be due to differences in how they experience risk and reward. One explanation is that human adolescents experience more negative affect and depressed mood, and may feel less pleasure from stimuli of low or moderate incentive value. Adolescents would therefore seek stimuli of greater hedonic intensity to satisfy a deficiency in their experience of reward (see Spear, 2000). This is supported by studies showing differences in the hedonic value of sucrose solutions to adults versus adolescents. Once sucrose concentrations exceed a critical point, the hedonic value sharply decreases; however such decreases are less pronounced or non-existent in children and adolescents (De Graaf and Zandstra, 1999; Vaidya et al., 2004). An alternative explanation is that adolescents have greater sensitivity to the reinforcing properties of pleasurable stimuli. Either possibility is consistent with animal models in which adolescents consume more sucrose solution (Vaidya et al., 2004), prefer chambers previously associated with social interaction (Douglas et al., 2004), and exhibit evidence of higher incentive value for drugs such as nicotine, alcohol, amphetamine, and cocaine than adults (Badanich et al., 2006; Brenhouse and Andersen, 2008; Shram et al., 2006; Spear and Varlinskaya, 2010; Vastola et al., 2002). This is not always seen, however, (Frantz et al., 2007; Mathews and McCormick, 2007; Shram et al., 2008), and increased adolescent drug preference could also be related to reduced sensitivity to aversive side-effects and withdrawal (Little et al., 1996; Moy et al., 1998; Schramm-Sapyta et al., 2007; Schramm-Sapyta et al., 2009). Similarly, adolescents might perform more risky behaviors if their assessment of possible aversive consequences is less motivating or salient (or if the excitement of risk-taking itself makes such behavior more likely).
Another factor that could account for some adolescent behavioral differences is the impact of emotions (valence, feelings, arousal, and specific emotional states) on behavior. Behavioral disparities may arise if adolescents experience emotions differently, or if emotions differently influence decision making during this period of heightened emotional intensity and volatility (Arnett, 1999; Buchanan et al., 1992). Emotion is often thought to cloud rational decision making. While this may be true in some cases (especially when emotional content is unrelated or irrelevant to a decision context), recent work has examined how emotions may improve certain decisions. For example, the somatic marker hypothesis states that in ambiguous situations, emotional processes can advantageously guide behavior (Damasio, 1994). The Iowa Gambling Task was designed to test decision making under conditions of uncertainty (Bechara et al., 1994). Individuals with lesions of the ventromedial PFC or amygdala have difficulty favoring the advantageous risk-avoiding strategy, suggesting that deficiencies in integrating emotional information can lead to poor decisions (Bechara et al., 1999; Bechara et al., 1996). Adolescents and adults may differ in the way they integrate emotional information in decisions: adolescents may be less adept at interpreting or integrating relevant emotional content, or less effective at forming such associations. Cauffman et al. (2010) recently tested children, adolescents, and adults on a modified version of the Iowa Gambling Task; they observed that while both adolescents and adults improved their decision-making over time, adults did this more rapidly. Another study demonstrated that only by mid- to late- adolescence did subjects improve their gambling task performance, and that this improvement coincided with the appearance of physiological correlates of arousal (Crone and van der Molen, 2007). These results suggest that adolescents may be less effective at forming or interpreting the sort of relevant affective information necessary to avoid risky decisions.
According to Rivers and colleagues (2008) differences in effective gist processing make adolescents more susceptible to potentially deleterious effects of arousal on decision making. In conditions of heightened arousal, a reduction in behavioral inhibition may cause one to switch from a “reasoned” to a “reactive” or impulsive mode. They further argue that the adolescent tendency to perform more verbatim-analytical processing makes this more likely, while the values and biases of the simpler adult “gist” processing is more impervious to arousal state (Rivers et al., 2008). Others have also argued that adolescent behavior may be particularly sensitive to conditions of high emotional arousal (Dahl, 2001; Spear, 2010). A recent study by Figner and colleagues (2009) directly tested this hypothesis using a task that measured risk taking under different affective conditions. Adolescents and adults performed the Columbia Card Task, in which the level of tolerated risk was examined under conditions of greater/lesser arousal and while varying factors that could be used to make more informed decisions (such as the magnitude of gains/losses and their probability). Adolescents took more risks than adults only in the high-arousal condition, and in this context, adolescents were less affected by gain/loss magnitude and probability, suggesting simplified information usage by adolescents under conditions of heightened arousal (Figner et al., 2009).
Collectively these studies indicate that although adolescents often reason and behave like adults, in certain contexts there are differences in their cognitive strategy and/or in their response to risk and reward, especially under conditions of heightened emotional arousal. These behavioral changes likely reflect the substantial development of brain networks—including structures in the PFC, basal ganglia, and neuromodulatory systems (e.g. dopamine) — that are critical to motivated behavior (Table 1).
3. Adolescent structural neurodevelopment
The adolescent brain undergoes dramatic changes in gross morphology. Human structural imaging studies have demonstrated that throughout the cerebral cortex there is a loss of gray matter during adolescence, with gray-matter reductions in portions of the temporal lobe and dorsolateral PFC occurring in late adolescence (Gogtay et al., 2004; Sowell et al., 2003; Sowell et al., 2001; Sowell et al., 2002). Gray matter reductions are also apparent in the striatum and other subcortical structures (Sowell et al., 1999; Sowell et al., 2002). These changes may be related to a massive pruning of synapses observed during this period from animal studies (Rakic et al., 1986; Rakic et al., 1994), although some question this connection as synaptic boutons make up only a small proportion of cortical volume (Paus et al., 2008). Human imaging has also revealed that white matter increase through adolescence in cortical and subcortical fiber tracts (Asato et al., 2010; Benes et al., 1994; Paus et al., 2001; Paus et al., 1999), resulting from increased myelination, axon caliber, or both (Paus, 2010). Changes in the patterns of connectivity also occur during adolescence. For example, axonal sprouting and growth have been observed in circuits connecting the amygdala to cortical targets (Cunningham et al., 2002), and increasing measures of white matter are observed between the PFC and striatum and other areas (Asato et al., 2010; Giedd, 2004; Gogtay et al., 2004; Liston et al., 2006; Paus et al., 2001; Sowell et al., 1999).
At a finer scale, rat and primate studies have demonstrated numerous differences in adolescent neurotransmitter systems. Adolescents tend to over-express dopaminergic, adrenergic, serotonergic and endocannabinoid receptors across many regions followed by pruning to adult levels (Lidow and Rakic, 1992; Rodriguez de Fonseca et al., 1993). They express D1 and D2 dopamine receptors at higher levels in subcortical targets such as the dorsal striatum and nucleus accumbens, although some have not found reduced adult expression in this latter region (Gelbard et al., 1989; Tarazi and Baldessarini, 2000; Tarazi et al., 1999; Teicher et al., 1995). During adolescence, there are also changes in dopamine production and turnover, as well as evidence for changes in downstream effects of receptor-ligand binding (Badanich et al., 2006; Cao et al., 2007; Coulter et al., 1996; Laviola et al., 2001; Tarazi et al., 1998). Functionally, there is evidence from anesthetized rats that the spontaneous activity of midbrain dopamine neurons peaks during adolescence and then decreases (McCutcheon and Marinelli, 2009). Developmental changes in mesocorticolimbic dopamine circuitry and activity may underlie some differences in motivated behavior generally, as well as risk taking and addiction vulnerability in particular. Several studies have observed reduced psychomotor effects of stimulant drugs in adolescent animals but enhanced or similar reinforcing effects (Adriani et al., 1998; Adriani and Laviola, 2000; Badanich et al., 2006; Bolanos et al., 1998; Frantz et al., 2007; Laviola et al., 1999; Mathews and McCormick, 2007; Spear and Brake, 1983). In contrast, adolescents are more sensitive to the cataleptic effects of neuroleptics (e.g., haloperidol), which are antagonists for dopamine receptors (Spear and Brake, 1983; Spear et al., 1980; Teicher et al., 1993). Some have proposed that this pattern, along with the increased exploration and novelty-seeking, indicates that the adolescent dopamine system is near a “functional ceiling” at baseline (Chambers et al., 2003).
Several lines of evidence suggest that the balance of large-scale excitatory and inhibitory neurotransmission is vastly different in adolescents compared to adults. Levels of GABA, the main inhibitory neurotransmitter in the brain, increases linearly through adolescence in rat forebrain (Hedner et al., 1984). The expression of the activating glutamate NMDA receptors on fast-spiking neurons (thought to be inhibitory interneurons) changes dramatically in the PFC of adolescents. At this time the vast majority of fast-spiking interneurons exhibit no synaptic NMDA receptor-mediated currents (Wang and Gao, 2009). Additionally the modulatory impact of dopamine-receptor binding shifts during adolescence (O’Donnell and Tseng, 2010). It is only by this time that the activation of dopamine D2 receptors increases interneuron activity (Tseng and O’Donnell, 2007). Furthermore, the synergistic interaction between dopamine D1 receptor activation and the NMDA receptor changes during adolescence, allowing for plateau depolarizations which may facilitate context-dependent synaptic plasticity (O’Donnell and Tseng, 2010; Wang and O’Donnell, 2001). These adolescent dopamine, glutamate, and GABA signaling changes suggest fundamental neural activity differences in the adolescent brain. All of these systems are essential to cognitive and emotional processes. Their dysfunction is implicated in numerous psychiatric illnesses ranging from mood disorders and addiction to schizophrenia.
4. Adolescent functional neurodevelopment
Neuroimaging studies have shown differences in human adolescent functional activity in several forebrain regions. These differences are primarily observed in brain regions that encode emotional significance (e.g. the amygdala) integrate sensory and emotional information for the computation of value expectations (e.g. the orbitofrontal cortex), and play various roles in motivation, action selection, and association learning (e.g. striatum). Compared to adults, adolescents have a reduced hemodynamic response in lateral orbitofrontal cortex and increased activity in ventral striatum to rewards (Ernst et al., 2005; Galvan et al., 2006). Others have found reduced activity in right ventral striatum and right extended amygdala during reward anticipation, with no observed age-related activity differences after gain outcome (Bjork et al., 2004). In a decision-making task, adolescents had reduced right anterior cingulate and left orbitofrontal/ventrolateral PFC activation compared to adults during risky choices (Eshel et al., 2007). Adolescents also activated their ventral striatum and orbitofrontal cortex more strongly than did adults as they took greater risks during a Stoplight driving game —an effect driven by implicit peer pressure (Chein et al., 2011).
Several studies have observed immaturity of adolescent cognitive control systems, along with poorer behavioral performance (Luna et al., 2010). For example, during tasks that require the inhibition of a prepotent response (the performance of which improves with age), adolescents have increased PFC activity in some subregions and decreased activity in others (Bunge et al., 2002; Rubia et al., 2000; Tamm et al., 2002). During an antisaccade cognitive control task, adolescent (but not adult) ventral striatum activity was reduced while viewing a cue that indicated if reward was available during a given trial, but it was more activated than its adult counterpart during reward anticipation (Geier et al., 2009). Thus adolescents generally activate similar cognitive and affective structures as adults, although often with different magnitudes or spatial and temporal patterns, or levels functional interconnectivity (Hwang et al., 2010).
Maturation of intra- and inter-regional connectivity and neuronal coordination may play a central role in adolescent behavioral development. There is a direct relationship between measures of frontostriatal white matter, which increases through adolescence, and inhibitory control performance (Liston et al., 2006). White-matter development is also directly related to improved functional integration of gray matter regions, suggesting more-distributed network activity through development (Stevens et al., 2009). This is corroborated by a study that, using resting state functional connectivity MRI along with graph analyses, observed a shift from greater connectivity with anatomically proximal nodes to networks that were more extensively integrated across all nodes in adulthood regardless of distance (Fair et al., 2009). Similarly, age-related increases in the functional integration of frontal and parietal regions support improved top-down inhibitory control performance in an antisaccade task (Hwang et al., 2010). White matter development, the rapid pruning of synapses (which are largely local excitatory connections), and developmental shifts in local interneuron activity may together facilitate more extensive functional coordination between brain regions through development. Less widely distributed activity in adolescents has also been demonstrated in another cognitive control task (Velanova et al., 2008). At the same time, diffuse functional signal uncorrelated with task-performance decreases through development (Durston et al., 2006). Thus, the adult pattern of utilizing more-distributed networks is coincident with reduced task-irrelevant activity, indicating greater efficiency in the pattern and extent of cortical processing.
Electrophysiological studies have also found evidence of further development of neuronal responses and greater local and long-range coordinated activity through adolescence. For example, the Contingent Negative Variation, which is a negative voltage event-related potential during response preparation, only develops in late childhood and continues to become larger through adolescence (Bender et al., 2005; Segalowitz and Davies, 2004). This is thought to reflect age-related differences in the distribution of PFC processing of attention and executive motor control (Segalowitz et al., 2010). Another age-related electrophysiological change is the development of strong positive peak (P300) approximately 300 ms after attending to a stimulus. A mature P300 pattern does not appear until approximately age 13 (Segalowitz and Davies, 2004). Finally, the Error-Related Negativity is a negative voltage centered over the anterior cingulate cortex during error trials of different tasks. Although there is some variability in the age of its appearance, it seems to arrive around mid-adolescence (Segalowitz and Davies, 2004). These findings provide additional evidence for the continued maturation of prefrontal cortical processing during adolescence. Segalowitz and colleagues also found that the signal-to-noise ratio of the electrical signals of children and adolescents were often lower than that of adults. This could be due to functional immaturity or intra-individual instability of brain regions producing these signals (Segalowitz et al., 2010). It might also reflect reduced adolescent neural coordination within and between brain regions. This interpretation is consistent with work performed by Uhlhaas and colleagues (2009b), in which electroencephalograms (EEGs) were recorded in children, adolescents, and adults during a facial recognition task. They observed reduced theta (4–7 Hz) and gamma band (30–50 Hz) oscillatory power in adolescents compared to adults. Additionally there was greater long-range phase-synchrony in theta, beta (13–30 Hz), and gamma bands, along with improved task performance in adults. EEG oscillations are due to fluctuations in neuronal excitability and are thought to fine-tune the timing of spike output (Fries, 2005). Measures of synchrony in specific frequency bands facilitate communication between neuronal groups, and may be critical to numerous perceptual and cognitive processes (Uhlhaas et al., 2009a). Thus, these findings are evidence of enhanced coordinated local processing and improved inter-regional communication from adolescence to adulthood (Uhlhaas et al., 2009b).
Another useful approach for examining neural activity changes through adolescence is with in vivo electrophysiological recording from implanted electrode arrays in awake behaving animals. This technique enables one to record the activity of individual neurons as well as larger-scale field potentials. We recently carried out such a study, in which adolescent and adult rats performed a simple goal-directed behavior (Figure 1a) as recordings were taken from orbitofrontal cortex. While adolescents and adults performed the same behavior, striking age-related neural encoding differences were observed, especially to reward (Sturman and Moghaddam, 2011). This indicates that even when behavior may appear similar, the adolescent prefrontal cortex is in a different state than that of adults. Specifically, adolescent orbitofrontal cortex neurons became far more excited to the reward, while the proportion of adolescent inhibited neurons was substantially smaller at that time and at other points in the task (Figure 1b). As neural inhibition is critical for the controlling the precise timing of spikes and entraining synchronized oscillatory activity (Cardin et al., 2009; Fries et al., 2007; Sohal et al., 2009), reduced task-related adolescent orbitofrontal cortex neuronal inhibition may be directly related to larger-scale neural encoding differences observed in this study and described by others. Finally, throughout much of the task adolescents exhibited greater cross-trial spike-timing variability, which could indicate lower signal-to-noise in adolescent prefrontal cortex. Therefore, as the prefrontal cortex develops, increased phasic inhibition at the single-unit level could support greater intra- and inter-regional neural coordination and processing efficiency.
5. Neurobehavioral Hypotheses
With all of the neurodevelopmental changes of adolescence, what accounts for the particular behavioral differences and vulnerabilities of this period? The previous sections outline evidence for a variety of adolescent neurodevelopmental changes and age-related behavioral differences and vulnerabilities. Here we present several hypotheses or models that explicitly connect adolescent differences in motivated behavior, social development, and behavioral inhibition with the maturity of specific neural circuits (Table 2).
Adolescent refinement of a social information processing network is one model connecting adolescent social development with brain changes (Nelson et al., 2005). This framework describes three interconnected functional nodes with distinct neural structural underpinnings: the detection node (inferior occipital cortex, inferior and anterior temporal cortex, intraparietal sulcus, fusiform gyrus, and superior temporal sulcus), the affective node (amygdala, ventral striatum, septum, bed nucleus of the stria terminalis, hypothalamus, and orbitofrontal cortex in some conditions), and the cognitive-regulatory node (portions of the prefrontal cortex). The detection node determines whether stimuli contain social information, which is further processed by the affective node which imbues such stimuli with emotional significance. The cognitive-regulatory node further processes this information, performing more complex operations related to perceiving the mental states of others, inhibiting prepotent responses, and generating goal-directed behavior (Nelson et al., 2005). Adolescent changes in the sensitivity and interaction of these nodes is hypothesized to intensify social and emotional experiences, strongly influence adolescent decision making, and contribute to the emergence of psychopathologies during this period (Nelson et al., 2005).
The triadic node model (Ernst et al., 2006) posits that the specific developmental trajectory of brain regions subserving affective processing and cognitive control, and the balance between them, may underlie the risk-taking propensity of adolescents. This model is also based on the activity of three nodes corresponding to specific brain regions. In this case a node responsible for reward approach (ventral striatum) is in balance with a punishment-avoidance node (amygdala). A modulation node (prefrontal cortex) affects the relative influence of these countervailing forces, and risky behavior will result from a final calculus favoring approach. According to this model, in situations involving some probabilistic trade-off between appetitive and aversive stimuli, the approach node is more dominant in adolescents. Hyperactivity or hypersensitivity of a reward-approach system might otherwise be adjusted by activity in portions of the prefrontal cortex, however its underdevelopment in adolescents does not permit adequate self monitoring and inhibitory control (Ernst and Fudge, 2009).
Casey and colleagues hypothesize that differences in the developmental trajectory of adolescent prefrontal cortex versus subcortical structures (e.g. ventral striatum and amygdala), along with the connections between them, might account for adolescent behavioral propensities (Casey et al., 2008; Somerville and Casey, 2010; Somerville et al., 2010). During a task involving the receipt of different reward values, the extent of adolescent activity in the nucleus accumbens was similar to that of adults (although with greater magnitudes) whereas the pattern of orbitofrontal cortical activity looked more like that of children than adults (Galvan et al., 2006). The relative maturity of subcortical systems and the immaturity of the prefrontal cortex, which is critical to cognitive control, may lead to a greater adolescent propensity toward sensation seeking and risk taking. The key here, as in the triadic node model, is the concept of a relative inter-regional imbalance during adolescence, in contrast to childhood when these regions are all relatively immature and adulthood when they are all mature (Somerville et al., 2010). This model is also similar to Steinberg’s framework, in which the relative decrease in risk taking from adolescence to adulthood is due to the development of cognitive control systems, connections facilitating the integration of cognition and affect among cortical and subcortical regions, and differences in reward salience or sensitivity (Steinberg, 2008).
The central theme of these models is that in adolescents, there are differences in the sensitivity, level, or effect of activity in cortical and subcortical regions within networks that subserve emotional processing and cognitive control. Based on our data and other evidence, we hypothesize that such differences may be the result of reduced neuronal coordination and processing efficiency in adolescents which manifests as a result of less-effective information transfer between regions and imbalances in neuronal excitation and inhibition within critical brain regions, such as the orbitofrontal cortex and portions of the basal ganglia. As described earlier, in vitro work has demonstrated dramatic changes in the expression patterns of various receptors, and the effects of receptor activation, including the response of inhibitory fast-spiking interneurons to dopamine and NMDA receptor stimulation. Such changes would be expected to affect both the balance of excitation and inhibition and the coordination of neuronal groups. As fast-spiking interneuron activity is critical to controlling the precise timing of neural activity and the entrainment of oscillations, the developmental shifts in adolescent interneuron activity and their response to neuromodulators like dopamine may be central to some of these age-related processing differences. As a result of this, adolescent neural activity may be less well-coordinated, noisier, and more local, and also perhaps more sensitive to the behaviorally activating effects of rewards, novelty, or other salient stimuli. Reduced inter-regional oscillatory coordination, further hampered by incomplete myelination, could together account for the less-distributed functional activity observed in imaging studies. The previously mentioned tendency for adolescents to favor risky choices in emotionally charged contexts could also be related to a combination of reduced inter-regional communication (e.g. failure of the prefrontal cortex to effectively dampen subcortical “go” signals in the basal ganglia), and exaggerated activation and/or reduced inhibition to salient cues in the context of motivated behavior, as we observed during reward anticipation in the orbitofrontal cortex.
As we have learned more about the specific brain and behavioral changes of adolescence several neurobehavioral models have been proposed. Central to most of these is the notion that immature neuronal processing in the prefrontal cortex and other cortical and subcortical regions, along with their interaction, leads to behavior that is biased towards risk, reward, and emotional reactivity during the adolescent period. Recent work on the development of inhibitory interneuron circuits and their changing interaction with neuromodulatory systems during adolescence may also shed light on why illnesses like schizophrenia typically manifest at this time. Using techniques like fMRI in humans and electrophysiological recordings in laboratory animals, we are beginning to identify more precisely how adolescents process reward and other aspects of motivated behavior differently from adults. Doing so is a critical step toward ascertaining the brain-based vulnerabilities of normal adolescent behavior and in understanding the pathophysiology of the psychiatric illnesses that develop during this period.
We review adolescent behavioral and neurodevelopmental changes.
The adolescent brain processes salient events differently from that of adults.
Several models link specific brain immaturities with age-related vulnerabilities.
We present evidence of reduced adolescent neural processing efficiency.
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- Acredolo C, O’Connor J, Banks L, Horobin K. Children’s ability to make probability estimates: skills revealed through application of Anderson’s functional measurement methodology. Child development. 1989;60:933–945. [PubMed]
- Adriani W, Chiarotti F, Laviola G. Elevated novelty seeking and peculiar d-amphetamine sensitization in periadolescent mice compared with adult mice. Behavioral neuroscience. 1998;112:1152–1166. [PubMed]
- Adriani W, Granstrem O, Macri S, Izykenova G, Dambinova S, Laviola G. Behavioral and neurochemical vulnerability during adolescence in mice: studies with nicotine. Neuropsychopharmacology. 2004;29:869–878. [PubMed]
- Adriani W, Laviola G. A unique hormonal and behavioral hyporesponsivity to both forced novelty and d-amphetamine in periadolescent mice. Neuropharmacology. 2000;39:334–346. [PubMed]
- Adriani W, Laviola G. Elevated levels of impulsivity and reduced place conditioning with d-amphetamine: two behavioral features of adolescence in mice. Behavioral neuroscience. 2003;117:695–703. [PubMed]
- Arnett J. Reckless behavior in adolescence: A developmental perspective. Developmental Review. 1992;12:339–373.
- Arnett JJ. Adolescent storm and stress, reconsidered. The American psychologist. 1999;54:317–326. [PubMed]
- Asato MR, Terwilliger R, Woo J, Luna B. White matter development in adolescence: a DTI study. Cereb Cortex. 2010;20:2122–2131. [PMC free article] [PubMed]
- Badanich KA, Adler KJ, Kirstein CL. Adolescents differ from adults in cocaine conditioned place preference and cocaine-induced dopamine in the nucleus accumbens septi. European journal of pharmacology. 2006;550:95–106. [PubMed]
- Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50:7–15. [PubMed]
- Bechara A, Damasio H, Damasio AR, Lee GP. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J Neurosci. 1999;19:5473–5481. [PubMed]
- Bechara A, Tranel D, Damasio H, Damasio AR. Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cereb Cortex. 1996;6:215–225. [PubMed]
- Bender S, Weisbrod M, Bornfleth H, Resch F, Oelkers-Ax R. How do children prepare to react? Imaging maturation of motor preparation and stimulus anticipation by late contingent negative variation. NeuroImage. 2005;27:737–752. [PubMed]
- Benes FM, Turtle M, Khan Y, Farol P. Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Archives of general psychiatry. 1994;51:477–484. [PubMed]
- Bjork JM, Knutson B, Fong GW, Caggiano DM, Bennett SM, Hommer DW. Incentive-elicited brain activation in adolescents: similarities and differences from young adults. J Neurosci. 2004;24:1793–1802. [PubMed]
- Bolanos CA, Glatt SJ, Jackson D. Subsensitivity to dopaminergic drugs in periadolescent rats: a behavioral and neurochemical analysis. Brain research. 1998;111:25–33. [PubMed]
- Brenhouse HC, Andersen SL. Delayed extinction and stronger reinstatement of cocaine conditioned place preference in adolescent rats, compared to adults. Behavioral neuroscience. 2008;122:460–465. [PubMed]
- Buchanan CM, Eccles JS, Becker JB. Are adolescents the victims of raging hormones: evidence for activational effects of hormones on moods and behavior at adolescence. Psychological bulletin. 1992;111:62–107. [PubMed]
- Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron. 2002;33:301–311. [PubMed]
- Cao J, Lotfipour S, Loughlin SE, Leslie FM. Adolescent maturation of cocaine-sensitive neural mechanisms. Neuropsychopharmacology. 2007;32:2279–2289. [PubMed]
- Cardin JA, Carlen M, Meletis K, Knoblich U, Zhang F, Deisseroth K, Tsai LH, Moore CI. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459:663–667. [PMC free article] [PubMed]
- Casey BJ, Getz S, Galvan A. The adolescent brain. Dev Rev. 2008;28:62–77. [PMC free article] [PubMed]
- Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. The American journal of psychiatry. 2003;160:1041–1052. [PMC free article] [PubMed]
- Chein J, Albert D, O’Brien L, Uckert K, Steinberg L. Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental science. 2011;14:F1–F10. [PMC free article] [PubMed]
- Coulter CL, Happe HK, Murrin LC. Postnatal development of the dopamine transporter: a quantitative autoradiographic study. Brain research. 1996;92:172–181. [PubMed]
- Crone EA, van der Molen MW. Development of decision making in school-aged children and adolescents: evidence from heart rate and skin conductance analysis. Child development. 2007;78:1288–1301. [PubMed]
- Csikszentmihalyi M, Larson R, Prescott S. The ecology of adolescent activity and experience. Journal of Youth and Adolescence. 1977;6:281–294.
- Cunningham MG, Bhattacharyya S, Benes FM. Amygdalo-cortical sprouting continues into early adulthood: implications for the development of normal and abnormal function during adolescence. The Journal of comparative neurology. 2002;453:116–130. [PubMed]
- Dahl RE. Affect regulation, brain development, and behavioral/emotional health in adolescence. CNS spectrums. 2001;6:60–72. [PubMed]
- Dahl RE. Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address. Annals of the New York Academy of Sciences. 2004;1021:1–22. [PubMed]
- Damasio AR. Descartes’ error: emotion, reason, and the human brain. New York: Putnam; 1994.
- de Bruin WB, Parker AM, Fischhoff B. Can adolescents predict significant life events? J Adolesc Health. 2007;41:208–210. [PubMed]
- De Graaf C, Zandstra EH. Sweetness intensity and pleasantness in children, adolescents, and adults. Physiology & behavior. 1999;67:513–520. [PubMed]
- Doremus-Fitzwater TL, Varlinskaya EI, Spear LP. Motivational systems in adolescence: Possible implications for age differences in substance abuse and other risk-taking behaviors. Brain and cognition. 2009 [PMC free article] [PubMed]
- Douglas LA, Varlinskaya EI, Spear LP. Novel-object place conditioning in adolescent and adult male and female rats: effects of social isolation. Physiology & behavior. 2003;80:317–325. [PubMed]
- Douglas LA, Varlinskaya EI, Spear LP. Rewarding properties of social interactions in adolescent and adult male and female rats: impact of social versus isolate housing of subjects and partners. Developmental psychobiology. 2004;45:153–162. [PubMed]
- Durston S, Davidson MC, Tottenham N, Galvan A, Spicer J, Fossella JA, Casey BJ. A shift from diffuse to focal cortical activity with development. Developmental science. 2006;9:1–8. [PubMed]
- Elkind D. Egocentrism in adolescence. Child development. 1967;38:1025–1034. [PubMed]
- Ernst M, Fudge JL. A developmental neurobiological model of motivated behavior: anatomy, connectivity and ontogeny of the triadic nodes. Neuroscience and biobehavioral reviews. 2009;33:367–382. [PMC free article] [PubMed]
- Ernst M, Nelson EE, Jazbec S, McClure EB, Monk CS, Leibenluft E, Blair J, Pine DS. Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. NeuroImage. 2005;25:1279–1291. [PubMed]
- Ernst M, Pine DS, Hardin M. Triadic model of the neurobiology of motivated behavior in adolescence. Psychological medicine. 2006;36:299–312. [PMC free article] [PubMed]
- Eshel N, Nelson EE, Blair RJ, Pine DS, Ernst M. Neural substrates of choice selection in adults and adolescents: development of the ventrolateral prefrontal and anterior cingulate cortices. Neuropsychologia. 2007;45:1270–1279. [PMC free article] [PubMed]
- Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, Schlaggar BL, Petersen SE. Functional brain networks develop from a “local to distributed” organization. PLoS computational biology. 2009;5:e1000381. [PMC free article] [PubMed]
- Fairbanks LA, Melega WP, Jorgensen MJ, Kaplan JR, McGuire MT. Social impulsivity inversely associated with CSF 5-HIAA and fluoxetine exposure in vervet monkeys. Neuropsychopharmacology. 2001;24:370–378. [PubMed]
- Falkner FT, Tanner JM. Human growth : a comprehensive treatise. 2nd ed. New York: Plenum Press; 1986.
- Figner B, Mackinlay RJ, Wilkening F, Weber EU. Affective and deliberative processes in risky choice: age differences in risk taking in the Columbia Card Task. Journal of experimental psychology. 2009;35:709–730. [PubMed]
- Frantz KJ, O’Dell LE, Parsons LH. Behavioral and neurochemical responses to cocaine in periadolescent and adult rats. Neuropsychopharmacology. 2007;32:625–637. [PubMed]
- Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences. 2005;9:474–480. [PubMed]
- Fries P, Nikolic D, Singer W. The gamma cycle. Trends in neurosciences. 2007;30:309–316. [PubMed]
- Galvan A, Hare TA, Parra CE, Penn J, Voss H, Glover G, Casey BJ. Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. J Neurosci. 2006;26:6885–6892. [PubMed]
- Geier CF, Terwilliger R, Teslovich T, Velanova K, Luna B. Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence. Cereb Cortex. 2009 [PMC free article] [PubMed]
- Gelbard HA, Teicher MH, Faedda G, Baldessarini RJ. Postnatal development of dopamine D1 and D2 receptor sites in rat striatum. Brain research. 1989;49:123–130. [PubMed]
- Giedd JN. Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences. 2004;1021:77–85. [PubMed]
- Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF, 3rd, Herman DH, Clasen LS, Toga AW, Rapoport JL, Thompson PM. Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:8174–8179. [PMC free article] [PubMed]
- Hedner T, Iversen K, Lundborg P. Central GABA mechanisms during postnatal development in the rat: neurochemical characteristics. Journal of neural transmission. 1984;59:105–118. [PubMed]
- Hwang K, Velanova K, Luna B. Strengthening of top-down frontal cognitive control networks underlying the development of inhibitory control: a functional magnetic resonance imaging effective connectivity study. J Neurosci. 2010;30:15535–15545. [PMC free article] [PubMed]
- Laviola G, Adriani W, Terranova ML, Gerra G. Psychobiological risk factors for vulnerability to psychostimulants in human adolescents and animal models. Neuroscience and biobehavioral reviews. 1999;23:993–1010. [PubMed]
- Laviola G, Pascucci T, Pieretti S. Striatal dopamine sensitization to D-amphetamine in periadolescent but not in adult rats. Pharmacology, biochemistry, and behavior. 2001;68:115–124. [PubMed]
- Lidow MS, Rakic P. Scheduling of monoaminergic neurotransmitter receptor expression in the primate neocortex during postnatal development. Cereb Cortex. 1992;2:401–416. [PubMed]
- Liston C, Watts R, Tottenham N, Davidson MC, Niogi S, Ulug AM, Casey BJ. Frontostriatal microstructure modulates efficient recruitment of cognitive control. Cereb Cortex. 2006;16:553–560. [PubMed]
- Little PJ, Kuhn CM, Wilson WA, Swartzwelder HS. Differential effects of ethanol in adolescent and adult rats. Alcoholism, clinical and experimental research. 1996;20:1346–1351. [PubMed]
- Luna B, Garver KE, Urban TA, Lazar NA, Sweeney JA. Maturation of cognitive processes from late childhood to adulthood. Child development. 2004;75:1357–1372. [PubMed]
- Luna B, Padmanabhan A, O’Hearn K. What has fMRI told us about the development of cognitive control through adolescence? Brain and cognition. 2010;72:101–113. [PMC free article] [PubMed]
- Macrì S, Adriani W, Chiarotti F, Laviola G. Risk taking during exploration of a plus-maze is greater in adolescent than in juvenile or adult mice. Animal Behaviour. 2002;64:541–546.
- Mathews IZ, McCormick CM. Female and male rats in late adolescence differ from adults in amphetamine-induced locomotor activity, but not in conditioned place preference for amphetamine. Behavioural pharmacology. 2007;18:641–650. [PubMed]
- McCutcheon JE, Marinelli M. Age matters. The European journal of neuroscience. 2009;29:997–1014. [PMC free article] [PubMed]
- Moy SS, Duncan GE, Knapp DJ, Breese GR. Sensitivity to ethanol across development in rats: comparison to [3H]zolpidem binding. Alcoholism, clinical and experimental research. 1998;22:1485–1492. [PubMed]
- Nelson EE, Leibenluft E, McClure EB, Pine DS. The social re-orientation of adolescence: a neuroscience perspective on the process and its relation to psychopathology. Psychological medicine. 2005;35:163–174. [PubMed]
- O’Donnell P, Tseng KY. Postnatal maturation of dopamine actions in the prefrontal cortex. In: Iversen LL, Iversen SD, editors. Dopamine Handbook. New York: Oxford University Press; 2010. pp. 177–186.
- Paus T. Growth of white matter in the adolescent brain: myelin or axon? Brain and cognition. 2010;72:26–35. [PubMed]
- Paus T, Collins DL, Evans AC, Leonard G, Pike B, Zijdenbos A. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain research bulletin. 2001;54:255–266. [PubMed]
- Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nature reviews. 2008;9:947–957. [PMC free article] [PubMed]
- Paus T, Zijdenbos A, Worsley K, Collins DL, Blumenthal J, Giedd JN, Rapoport JL, Evans AC. Structural maturation of neural pathways in children and adolescents: in vivo study. Science (New York, N.Y. 1999;283:1908–1911. [PubMed]
- Pine DS. Brain development and the onset of mood disorders. Semin Clin Neuropsychiatry. 2002;7:223–233. [PubMed]
- Rakic P, Bourgeois JP, Eckenhoff MF, Zecevic N, Goldman-Rakic PS. Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science (New York, N.Y. 1986;232:232–235. [PubMed]
- Rakic P, Bourgeois JP, Goldman-Rakic PS. Synaptic development of the cerebral cortex: implications for learning, memory, and mental illness. Progress in brain research. 1994;102:227–243. [PubMed]
- Rivers SE, Reyna VF, Mills B. Risk Taking Under the Influence: A Fuzzy-Trace Theory of Emotion in Adolescence. Dev Rev. 2008;28:107–144. [PMC free article] [PubMed]
- Rodriguez de Fonseca F, Ramos JA, Bonnin A, Fernandez-Ruiz JJ. Presence of cannabinoid binding sites in the brain from early postnatal ages. Neuroreport. 1993;4:135–138. [PubMed]
- Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC, Simmons A, Andrew C, Bullmore ET. Functional frontalisation with age: mapping neurodevelopmental trajectories with fMRI. Neuroscience and biobehavioral reviews. 2000;24:13–19. [PubMed]
- Schramm-Sapyta NL, Cha YM, Chaudhry S, Wilson WA, Swartzwelder HS, Kuhn CM. Differential anxiogenic, aversive, and locomotor effects of THC in adolescent and adult rats. Psychopharmacology. 2007;191:867–877. [PubMed]
- Schramm-Sapyta NL, Walker QD, Caster JM, Levin ED, Kuhn CM. Are adolescents more vulnerable to drug addiction than adults? Evidence from animal models. Psychopharmacology. 2009;206:1–21. [PMC free article] [PubMed]
- Schuster CS, Ashburn SS. The process of human development : a holistic life-span approach. 3rd ed. New York: Lippincott; 1992.
- Segalowitz SJ, Davies PL. Charting the maturation of the frontal lobe: an electrophysiological strategy. Brain and cognition. 2004;55:116–133. [PubMed]
- Segalowitz SJ, Santesso DL, Jetha MK. Electrophysiological changes during adolescence: a review. Brain and cognition. 2010;72:86–100. [PubMed]
- Shram MJ, Funk D, Li Z, Le AD. Periadolescent and adult rats respond differently in tests measuring the rewarding and aversive effects of nicotine. Psychopharmacology. 2006;186:201–208. [PubMed]
- Shram MJ, Funk D, Li Z, Le AD. Nicotine self-administration, extinction responding and reinstatement in adolescent and adult male rats: evidence against a biological vulnerability to nicotine addiction during adolescence. Neuropsychopharmacology. 2008;33:739–748. [PubMed]
- Sisk CL, Zehr JL. Pubertal hormones organize the adolescent brain and behavior. Frontiers in neuroendocrinology. 2005;26:163–174. [PubMed]
- Sohal VS, Zhang F, Yizhar O, Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature. 2009;459:698–702. [PubMed]
- Somerville LH, Casey B. Developmental neurobiology of cognitive control and motivational systems. Current opinion in neurobiology. 2010 [PMC free article] [PubMed]
- Somerville LH, Jones RM, Casey BJ. A time of change: behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain and cognition. 2010;72:124–133. [PMC free article] [PubMed]
- Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nature neuroscience. 2003;6:309–315. [PubMed]
- Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature neuroscience. 1999;2:859–861. [PubMed]
- Sowell ER, Thompson PM, Tessner KD, Toga AW. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. J Neurosci. 2001;21:8819–8829. [PubMed]
- Sowell ER, Trauner DA, Gamst A, Jernigan TL. Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Developmental medicine and child neurology. 2002;44:4–16. [PubMed]
- Spear LP. The adolescent brain and age-related behavioral manifestations. Neuroscience and biobehavioral reviews. 2000;24:417–463. [PubMed]
- Spear LP. The behavioral neuroscience of adolescence. 1st ed. New York: W. W. Norton; 2010.
- Spear LP, Brake SC. Periadolescence: age-dependent behavior and psychopharmacological responsivity in rats. Developmental psychobiology. 1983;16:83–109. [PubMed]
- Spear LP, Shalaby IA, Brick J. Chronic administration of haloperidol during development: behavioral and psychopharmacological effects. Psychopharmacology. 1980;70:47–58. [PubMed]
- Spear LP, Varlinskaya EI. Sensitivity to ethanol and other hedonic stimuli in an animal model of adolescence: implications for prevention science? Developmental psychobiology. 2010;52:236–243. [PMC free article] [PubMed]
- Stansfield KH, Kirstein CL. Effects of novelty on behavior in the adolescent and adult rat. Developmental psychobiology. 2006;48:10–15. [PubMed]
- Stansfield KH, Philpot RM, Kirstein CL. An animal model of sensation seeking: the adolescent rat. Annals of the New York Academy of Sciences. 2004;1021:453–458. [PubMed]
- Steinberg L. Cognitive and affective development in adolescence. Trends in cognitive sciences. 2005;9:69–74. [PubMed]
- Steinberg L. A social neuroscience perspective on adolescent risk-taking. Developmental Review. 2008;28:78–106. [PMC free article] [PubMed]
- Steinberg L, Albert D, Cauffman E, Banich M, Graham S, Woolard J. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model. Developmental psychology. 2008;44:1764–1778. [PubMed]
- Steinberg L, Graham S, O’Brien L, Woolard J, Cauffman E, Banich M. Age differences in future orientation and delay discounting. Child development. 2009;80:28–44. [PubMed]
- Stevens MC, Skudlarski P, Pearlson GD, Calhoun VD. Age-related cognitive gains are mediated by the effects of white matter development on brain network integration. NeuroImage. 2009;48:738–746. [PMC free article] [PubMed]
- Sturman DA, Mandell DR, Moghaddam B. Adolescents exhibit behavioral differences from adults during instrumental learning and extinction. Behavioral neuroscience. 2010;124:16–25. [PMC free article] [PubMed]
- Sturman DA, Moghaddam B. Reduced Neuronal Inhibition and Coordination of Adolescent Prefrontal Cortex during Motivated Behavior. J Neurosci. 2011;31:1471–1478. [PMC free article] [PubMed]
- Tamm L, Menon V, Reiss AL. Maturation of brain function associated with response inhibition. Journal of the American Academy of Child and Adolescent Psychiatry. 2002;41:1231–1238. [PubMed]
- Tanner JM. Foetus into man : physical growth from conception to maturity, Rev. and enl. ed. Cambridge, Mass.: Harvard University Press; 1990.
- Tarazi FI, Baldessarini RJ. Comparative postnatal development of dopamine D(1), D(2) and D(4) receptors in rat forebrain. Int J Dev Neurosci. 2000;18:29–37. [PubMed]
- Tarazi FI, Tomasini EC, Baldessarini RJ. Postnatal development of dopamine and serotonin transporters in rat caudate-putamen and nucleus accumbens septi. Neuroscience letters. 1998;254:21–24. [PubMed]
- Tarazi FI, Tomasini EC, Baldessarini RJ. Postnatal development of dopamine D1-like receptors in rat cortical and striatolimbic brain regions: An autoradiographic study. Developmental neuroscience. 1999;21:43–49. [PubMed]
- Teicher MH, Andersen SL, Hostetter JC., Jr. Evidence for dopamine receptor pruning between adolescence and adulthood in striatum but not nucleus accumbens. Brain research. 1995;89:167–172. [PubMed]
- Teicher MH, Barber NI, Gelbard HA, Gallitano AL, Campbell A, Marsh E, Baldessarini RJ. Developmental differences in acute nigrostriatal and mesocorticolimbic system response to haloperidol. Neuropsychopharmacology. 1993;9:147–156. [PubMed]
- Tseng KY, O’Donnell P. Dopamine modulation of prefrontal cortical interneurons changes during adolescence. Cereb Cortex. 2007;17:1235–1240. [PMC free article] [PubMed]
- Uhlhaas PJ, Pipa G, Lima B, Melloni L, Neuenschwander S, Nikolic D, Singer W. Neural synchrony in cortical networks: history, concept and current status. Frontiers in integrative neuroscience. 2009a;3:17. [PMC free article] [PubMed]
- Uhlhaas PJ, Roux F, Rodriguez E, Rotarska-Jagiela A, Singer W. Neural synchrony and the development of cortical networks. Trends in cognitive sciences. 2009b;14:72–80. [PubMed]
- Vaidya JG, Grippo AJ, Johnson AK, Watson D. A comparative developmental study of impulsivity in rats and humans: the role of reward sensitivity. Annals of the New York Academy of Sciences. 2004;1021:395–398. [PubMed]
- Vastola BJ, Douglas LA, Varlinskaya EI, Spear LP. Nicotine-induced conditioned place preference in adolescent and adult rats. Physiology & behavior. 2002;77:107–114. [PubMed]
- Velanova K, Wheeler ME, Luna B. Maturational changes in anterior cingulate and frontoparietal recruitment support the development of error processing and inhibitory control. Cereb Cortex. 2008;18:2505–2522. [PMC free article] [PubMed]
- Volkmar FR. Childhood and adolescent psychosis: a review of the past 10 years. Journal of the American Academy of Child and Adolescent Psychiatry. 1996;35:843–851. [PubMed]
- Wang HX, Gao WJ. Cell type-specific development of NMDA receptors in the interneurons of rat prefrontal cortex. Neuropsychopharmacology. 2009;34:2028–2040. [PMC free article] [PubMed]
- Wang J, O’Donnell P. D(1) dopamine receptors potentiate nmda-mediated excitability increase in layer V prefrontal cortical pyramidal neurons. Cereb Cortex. 2001;11:452–462. [PubMed]
- Zuckerman M, Eysenck S, Eysenck HJ. Sensation seeking in England and America: cross-cultural, age, and sex comparisons. Journal of consulting and clinical psychology. 1978;46:139–149. [PubMed]