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Nat Rev Neurosci. Author manuscript; available in PMC Oct 16, 2009.
Published in final edited form as:
PMCID: PMC2762785
NIHMSID: NIHMS67026

Why do many psychiatric disorders emerge during adolescence?

Abstract

What do we know about the maturation of the human brain during adolescence? Do structural changes in cerebral cortex reflect synaptic pruning? Are increases in white-matter volume driven by myelination? Is the adolescent brain more or less sensitive to reward? These are but a few questions we ask in this review while attempting to indicate how findings obtained in the healthy brain help in furthering our understanding of mental health during adolescence.

Introduction

Across cultures and centuries, adolescence has been noted as a time of dramatic changes in body and behavior. Although most teenagers successfully navigate the transition from dependence upon a caregiver to becoming a self-sufficient adult member of the society, adolescence is also a time of increasing incidence of several classes of psychiatric illnesses, including anxiety and mood disorders, psychosis, eating disorders, personality disorders and substance abuse. The pathophysiology of these disorders is being increasingly understood as arising from aberrations of maturational changes that normally occur in the adolescent brain.

In this review we will address the neurobiological changes that occur during adolescence and discuss their possible relationship to the emergence of psychopathology. We will focus on three major disorders, namely schizophrenia, substance use disorders and affective/anxiety disorder, because our understanding of their developmental neurobiological basis has increased considerably in the recent years.

Typical development: findings and interpretations

The last 15 years have seen an impressive accumulation of knowledge about the development of structure and function of the human brain. Studies carried out with magnetic resonance imaging (MRI) in children and adolescents have allowed investigators to chart trajectories of grey and white-matter volumes, cortical thickness and, more recently, other structural properties of white matter such as fractional anisotropy and magnetization-transfer ratio, as well age-related changes in brain activity (Box 1)1-4.

Box 1: Neuroimaging

Magnetic resonance imaging has revolutionized the way we can study structure and function of the human brain in living human beings throughout the entire life span1. The principles of MRI are relatively straightforward; in most applications, MR signal is based on magnetic properties of the hydrogen atoms, which constitute the most abundant substance in the human body, water. By placing the human body in a strong static magnetic field (B0; 0.5 to 7.0 T) and applying a brief pulse of electromagnetic energy, we can make the little dipoles formed by the hydrogen nuclei rotate away from their axes and, in turn, measure the time it takes for the nuclei to “relax” back to their original position. By changing slightly the static magnetic field at different positions along/across the B0, we can establish the spatial origin of the signal and, eventually, create a 3-dimensional (3D) image of the measurement. What is measured depends on the combination of various imaging parameters or, in the terminology of the MR physicists, on the acquisition sequence.

For imaging brain structure, the most common acquisition sequences include T1-weighted (T1W) and T2-weighted (T2W) images, diffusion-tensor images (DTI) and magnetization-transfer images (MT). The T1W and T2W images are typically used for quantifying the volume of grey and white matter (global and regional), and estimating the cortical thickness or other morphological properties of the cerebral cortex, such as its folding. Using DTI and MT imaging, one can assess different properties of white matter, again in both a global and regional manner. The various features of brain structure that can be extracted from these four types of images are described below. In addition to the above sequences, less common but often even more informative acquisitions include T1 and T2 relaxometry (i.e. measurement of the actual relaxation times 2) and magnetic resonance spectroscopy 3.

For imaging brain function, the most common MR parameter to measure is so-called blood oxygenation-level dependent (BOLD) signal. The BOLD signal reflects the proportion of oxygenated and de-oxygenated blood in a given brain region at a given moment. A strong correlation between the amount of synaptic activity and regional cerebral blood flow is the reason why the BOLD signal is a good, albeit indirect, measure of brain “function” 4. In the majority of functional MRI (fMRI) studies, one measures changes in BOLD signal in response to various sensory, motor or cognitive stimuli. Therefore, only brain regions that are likely to respond to such stimuli can be interrogated using a given paradigm.

Brain structure

Most of the existing literature on age-related changes in brain structure has been reviewed in detail elsewhere 5, 6. Here we note only the most salient findings.

Volumes of cortical grey-matter appear to increase during childhood, reaching peak levels around the time of the puberty onset, after which they gradually decline; this is the case mostly for the frontal and parietal but not temporal lobes 7. Local volume of cortical grey-matter declines during childhood and adolescence in most regions, with the slope of the decline varying from flat (e.g. anterior portion of the superior temporal gyrus [STG]) to steep (posterior portion of the STG) and, in some cases, displaying a non-linear relationship with age, for example inverted “U” in post-central gyrus and “U” shaped [between 10 and 20 years] in the mid-dorsolateral frontal cortex 8, 9 (Fig. 1)8, 10-13.

Figure 1
Schematic representations of developmental trajectories in local volume of cortical grey-matter (A), glucose metabolism (B) and synaptic density (C). Plots of grey matter are based on data by Gogtay et al8 and illustrate local grey-matter volume in the ...

Volumes of white matter show a rather clear linear increase throughout childhood and adolescence, with the maximum volumes reached often as late as in the third decade of life 14. It appears that the slope of the age-related increase is steeper in males compared with females 7, 15. More recently, diffusion tensor imaging (DTI) has been employed to assess age-related changes in the human brain during childhood and adolescence. Overall, DTI studies reveal age-related decreases in magnitude and increases in directionality of water diffusion in a number of white matter regions, many of which are identical to those revealed by the above MRI studies 16-18, such as the arcuate fasciculus. Such changes in DTI-derived measures may indicate ongoing maturation of the axon and/or its myelin sheath.

Brain activity

The overall picture to be gleaned from the existing descriptive studies of age-related changes in brain activity is less coherent. This is due to the fact that a given functional MRI (fMRI) study focuses on a particular brain function, which is assessed with behavioural paradigms that often differ across laboratories. Interpretation of fMRI is also more challenging than that of structural findings, owing to the indirect nature of the fMRI signal (Box 1) and the large number of potential confounders, such as levels of anxiety/arousal during scanning, varying performance across participants or different cognitive strategies used by different participants in the same task - all of which may interact with the effects of age. We will touch here on two sets of fMRI studies that focused respectively, on cognitive control (or executive functions) and on experiencing gains and losses of various rewards during adolescence.

A number of the initial studies that investigated how task-related brain activity changes during development focused on executive functions such as working memory and response inhibition. But as we reviewed previously6, many of such “executive” abilities are fully developed by the time a child enters adolescence (Fig. 4 in 6). On the other hand, certain aspects of executive function, such as planning time19 and delayed gratification, improve significantly from mid-adolescence (~16 years of age) onward, as indicated by behavioural studies. Using fMRI, Kwon and colleagues20 found age-related (age 7 to 22 yr) increases in the BOLD signal in the prefrontal and parietal cortices during the performance of a working-memory task even after factoring out inter-individual differences in performance. Similar BOLD increases were observed in these regions during the performance of a variety of tasks involving some form of response inhibition, including the Stroop task21, anti-saccade task22, the Stop task23 and, to a certain extent, during the performance of a go/no-go task24 and the Eriksen flanker task25.

Adolescence has been traditionally associated with risk-taking and sensation-seeking behaviour26. In this context, several investigators used functional MRI to examine possible differences between children, adolescents and young adults in brain activity during the experience of gains or losses of various rewards. Given its role in reward and motivation27, the nucleus accumbens (or ventral striatum) has been the focus of the majority of these studies. If adolescents were “driven” by reward seeking, one would expect heightened engagement of this structure during tasks that involve reward seeking. This appeared to be the case in participants in some28, 29 but not other30 studies. For example, Bjork and colleagues30 described an increase from early adolescence to young adulthood (12 to 28 years) in the BOLD signal in the nucleus accumbens during the anticipation of monetary gains; this was the case even when self-reported level of excitement, when seeing anticipatory cues, was taken into account. It is worthwhile to point out that, in the same study, excitement correlated positively with the BOLD signal in the nucleus accumbens even when age was taken into account. This observation highlights the importance of considering various aspects of behaviour when interpreting fMRI findings.

Although functional imaging studies are beginning to illuminate functional maturation of neural circuits involved, for example, in executive functions and reward processing, future studies need to substantially increase sample size and to enhance the behavioural characterization of the performance in the scanner in order to learn more about brain-behaviour relationships during adolescence.

The age-related changes in brain structure and function during adolescence described above have been interpreted using various conceptual frameworks. Changes in synaptic pruning and myelination have been the most popular explanations for the structural findings in adolescence, whereas age-related alterations in neural connectivity and neurotransmission might underlie the functional changes associated with adolescence. We will now address, in a critical manner, such mechanistic interpretations.

Adolescence = pruning + myelination?

It is the case that MRI-based estimates of the volume of cortical grey-matter and cortical thickness appear to decrease during adolescence. This has been often interpreted as an indication of “synaptic pruning”, a process by which “redundant” synapses overproduced in the early years of life are being eliminated (see Purves and colleagues31 for a critical appraisal of “neural Darwinism”).

The initial evidence for accelerated synaptic pruning during development came from post mortem studies by Peter Huttenlocher and colleagues who described a decrease in the number of dendritic spines in the human cerebral cortex during childhood and adolescence 10, 32, 33. It should be noted, however, that these studies were limited by the low number of specimens available for the different stages of human development, especially the adolescent period. Furthermore, most of the data do not actually indicate accelerated pruning of synapses during adolescence but a rather gradual decrease in their numbers, beginning (in several cortical regions) in childhood. A more definite evidence of synapse elimination during adolescence was provided by studies carried out by Pasko Rakic and colleagues in non-human primates34, 35. Using electron microscopy, they did observe a dramatic decrease in the number of synapses in the visual cortex (and other cortical areas) during puberty (between the age of 2.5 and 5 years), whether expressed as a number of synapses per neuron or per 1 mm3 of neuropil (~45% loss). But it is unlikely that this decrease in synaptic density translates into a decrease in cortical volume: Bourgeois and Rakic commented that “changes in the density of synapses affect very little either the volume or surface of the cortex because the total volume of synaptic boutons ... is only a very small fraction of the cortical volume” and concluded that “... a decline of synaptic number during puberty should have a rather small effect on the overall volume of the cortex”35. Finally, it is often assumed that age-related changes in cortical grey-matter, glucose metabolism and synaptic density follow similar developmental trajectories from birth to adulthood and, hence, may reflect the same cellular events; this is clearly not the case - again especially not during adolescence (Fig. 1).

If the number of synapses per se is unlikely to change the cortical volume/thickness, then what other cellular elements could affect it? About 10% of the (mouse) cortex is occupied by glial cells and about 60% by neuropil, the latter consisting of dendritic and axonal processes 36. It is conceivable that a reduced number of synapses, and a corresponding decrease in metabolic requirements, would be accompanied by a reduction in the number of glial cells, leading to a decrease in the regional volume or thickness of cortical grey-matter. But it is perhaps even more likely that the apparent loss of grey matter reflects an increase in the degree of myelination of intra-cortical axons. Myelination of intra-cortical fibres progresses gradually from birth to adulthood 37, 38. The more myelinated the fibres are, the less “grey” the cortex would appear on regular T1-weighted images. Such a “partial-volume” effect could result in an apparent loss of cortical grey-matter6.

Given the well-documented histology-based increase in the degree of myelination of white-matter pathways during the first two decades of human life39, it is perhaps not surprising that any changes in the volume or “density” of white matter, as revealed by computational analyses of T1-weighted images, are attributed to changes in myelination. Again, assumptions based on previous knowledge influence the interpretation of new data. Quite often we read articles that report age-related changes in myelination only to realize that what had been actually measured were volumes of white matter. Is it only a matter of semantics or could other, myelination-independent processes affect volume and/or other features of white matter? In one of our large studies of human adolescence, we have observed a dissociation between age-related changes in the volume of white matter and those in magnetization transfer ratio (MTR), the latter being an indirect index of the amount of myelin in white matter. Although white-matter volume increased with age during male adolescence, MTR values decreased, thus indicating a decrease in the amount of myelin per unit of volume (Fig. 2)40

Figure 2
Sexual dimorphism in the maturation of white matter during adolescence. Top panel (A) illustrates age-related changes in the relative (brain-size corrected) volume of white matter summed across the frontal, parietal, temporal and occipital lobes. Bottom ...

If myelin does not increase, what could be driving the observed increase in white matter volume during adolescence in males? Our tentative answer is a change in axonal caliber: the larger the calibre, the fewer axons fit in the same unit of the imaged volume, which would result in a relative decrease in the myelination index40. Although more work is needed to confirm this initial observation, it serves as a reminder that most of the MRI sequences are not specific enough to interpret MRI-based findings as reflecting a single neurobiological process, such as myelination.

Overall, as tempting as it might be to interpret descriptive findings obtained with structural MRI using mechanistic neurobiological processes such as synaptic pruning or myelination, the evidence that supports such interpretations is limited. There is a pressing need to acquire direct evidence using experimental models in which investigators can combine in vivo and ex vivo methods to bring together descriptive and mechanistic levels of analysis. Until this happens, we suggest that a more cautious and open-minded approach is warranted,

Neural connectivity

Two key features characterize functional organization of the mammalian brain: specialization and integration. Clearly, structural and functional maturation of neural pathways connecting a set of specialized brain regions is therefore a condition sine qua non for the successful development of cognitive, motor and sensory functions from infancy, through childhood and adolescence, and into adulthood. There are many different “connectivities”. Anatomic connectivity allows one to determine, using injection of radioactive tracers into the brain of experimental animals, efferent and afferent projections of small populations of neurons. This is not the same as anatomic “connectivity” assessed with DTI-based tractography, which does not allow one to identify point-to-point (or cell-to-cell) connections between distinct neural populations. Functional connectivity captures the correlational relationship across a set of brain regions “engaged” during a particular task or measured at rest. However, such correlations do not provide information regarding the causality and/or directionality of inter-regional interactions. Finally, effective connectivity attempts to address the latter either by manipulation of brain activity in one region and evaluating the effect of such manipulation elsewhere, or by employing mathematic models 41.

In an example of studies investigating functional connectivity during childhood and adolescence, one study researched memory encoding in subjects between 11 and 19 years of age 42. The study revealed an age-related decrease in the fMRI signal in the left medial temporal-lobe while subjects viewed a series of novel photographs of natural outdoor scenes, as compared with viewing the same scene over and over (control condition). The authors used voxel-wise regression analysis to identify the brain regions in which the fMRI signal correlated with the fMRI signal measured in two subregions of the left medial temporal-lobe, namely the hippocampus and the entorhinal cortex, structures known to participate in the encoding of novel information. This analysis revealed an age-related increase in the correlation between the left entorhinal cortex and the left dorsolateral prefrontal cortex. This work nicely illustrates the importance of including analyses of functional connectivity in developmental studies: although the fMRI signal decreased with age in one of the memory-relevant structures (entorhinal cortex), the hypothesized interaction between this structure and other brain regions (prefrontal cortex) actually increased with age.

A second study investigated functional connectivity in the context of possible neural substrates of resistance to peer influences (RPI) in early adolescence (10-year old children)43. This study aimed to determine whether the probability with which an adolescent follows the goals set by peers or those set by himself/herself might depend on the interplay between the following three neural systems. First, the action-observation network, which is considered by many to represent the neural substrate of imitation; it consists of frontal and parietal regions involved in the preparation and execution of actions. So-called ‘mirror neurons’ within the inferior premotor cortex and/or inferior frontal gyrus, as well as in the anterior inferior parietal lobe, are active both when subjects perform a specific action themselves and when they observe another individual performing the same action. Second, the biological-motion processing network, which plays an important role in extracting socially relevant cues, such as those imparted by the movements of eyes or hands. Neurons within the superior temporal sulcus (STS) respond selectively to the presentation of dynamic bodies, body parts or faces. Third, the executive network, which supports a number of cognitive processes underlying decision making, working memory and the suppression of alternative programs interfering with planned actions; it consists of a set of regions in the lateral and medial prefrontal-cortex (PFC). In the scanner, we asked the subjects to watch brief video clips containing face or hand/arm actions that were executed in neutral or angry ways, and measured changes in fMRI signals. Outside the scanner, we administered an RPI questionnaire44. We found that the children with high RPI scores showed stronger inter-regional correlations in brain activity across the three networks while watching angry hand-actions, as compared with children who had low RPI scores (Fig. 3). The pattern of inter-regional correlations identified by this method included both regions involved in action observation (the fronto-parietal as well as temporo-occipital systems) and regions in the prefrontal cortex. Thus, what distinguished subjects with high and low resistance to peer influences was not the magnitude of the BOLD response in the individual brain regions but the degree of functional connectivity43.

Figure 3
Functional connectivity, indexed by inter-regional correlations in fMRI signal, during the observation of angry hand movements in children differing in their resistance to peer influences.

Neurochemistry

The efficacy of communication across neuronal networks depends critically on the state of the various neurotransmitter systems (Box 2)45-49..

Box 2: Basics of neurotransmission

Transmission of information from one neuron to the next involves several steps. Local excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs) are continuously being summed at the axonal hillock and, once a threshold value is reached, an action potential is generated. The action potential then travels along the axon and, at the synapse, causes a release of neurotransmitters. The so-called conduction velocity is higher in myelinated vs. non-myelinated axons and in axons with larger vs. smaller diameter45-47. Neurotransmitters are chemicals that either relay action potentials or modulate (e.g. amplify) this process. Neurotransmitters include amino acids (e.g. glutamate and gamma amino-butyric acid [GABA]), monoamines (e.g. dopamine, serotonin, norepinephrine), acetylcholine, and many neuropeptides (e.g. oxytocin). Glutamate and GABA are the main excitatory and inhibitory neurotransmitters, respectively, and dopamine is one of the most studied neuromodulators. The action of a particular neurotransmitter is mediated by a receptor; a given neurotransmitter can bind to a number of receptor subtypes that are found in different brain regions, or different layers of the cerebral cortex, with varied densities48, 49. The very complex interaction between different neurotransmitters released at any given time at the synapse is going to determine the number of EPSPs and IPSPs generated on the post-synaptic membrane and, in turn, firing of the neuron.

In adults, positron emission tomography (PET) is one of the in vivo techniques used to assess the state of neurotransmitter systems, such as the activity of enzymes involved in the synthesis or metabolism of a given neurotransmitter or the number of the receptors. Owing to radiation concerns, however, PET cannot be used in healthy children and adolescents. Therefore, we derive most of the knowledge of developmental changes in neurotransmitters from post mortem studies in human and non-human primates.

We now consider developmental changes in the dopaminergic system, which has often been conceptualized as underlying adolescent-specific changes in motivational behaviour50. The existing data are not entirely consistent with this view, however. In the monkey, levels of the catecholamine-synthesizing enzyme tyrosine hydroxylase (TH) do not change during postnatal development in cortical layers I and VI. In layer III, TH levels are the highest during infancy (5-7 months) in the entorhinal cortex 51 and during puberty (2-3 years) in the prefrontal cortex 52.

In humans, two recent post mortem studies evaluated age-related changes in TH, COMT, and a number of dopamine receptors in the human prefrontal cortex; COMT is a dopamine-metabolizing enzyme that is particularly important for dopaminergic transmission in the prefrontal cortex. No differences in COMT activity were found between infants (5-11 months), adolescents (14-18 yr) and young adults (20-24 yr) 53; COMT activity increased only in adulthood (31 to 43 yr). A different study showed that TH levels in the human prefrontal cortex were the highest in neonates and, by adolescence, declined to the levels observed in adults54. The same was true, in the same region, for expression of the dopamine D2 receptor (DRD2) gene. By contrast, expression of DRD1 was the highest in adolescents (14 to 18 years) and young adults (20 to 24 years) in all layers of the prefrontal cortex. Levels of DRD4 in the PFC did not change with age. These findings illustrate that, contrary to prior assumptions, developmental changes in the different elements of dopaminergic transmission during adolescence are complex, with very if any peaking during adolescence. As such, these age-related variations - in particular in the prefrontal cortex - are not very likely to account for differences between adolescents and adults in motivation-related modulation of cortical activity.

Relationship to psychopathology in adolescence

Results of the National Comorbidity Survey Replication study, which entailed in-person household assessments of over 9,000 people representative of the United States population (conducted from February 2001 to April of 2003), have indicated that the peak age of onset for having any mental health disorder is 14 years 55. Anxiety disorders, bipolar disorder, depression, eating disorder, psychosis including schizophrenia, and substance abuse all most commonly emerge during adolescence 55, 56 (Fig. 4). The emergence of certain psychopathology is likely related to anomalies or exaggerations of typical adolescent maturation processes acting in concert with psychosocial (e.g. school, relationships) and/or biological environmental factors (e.g. pubertal hormonal changes, drugs of abuse), as will be discussed later. In this paper, we focus on schizophrenia, affective and anxiety disorders, and substance-use disorders because they are among the most well studied, common and disabling disorders that emerge during adolescence, and serve to highlight aberrations in the key developmental domains of cognition, affect and motivational behavior56, 57

Figure 4
Ranges of onset age for common psychiatric disorders. Recent data from the National Comorbidity Survey Replication study55, 57, a nationally representative epidemiological survey of mental disorders, suggest that about half of the population fulfill criteria ...

Schizophrenia

Schizophrenia is a common disorder with a life-time prevalence of about 1%. It typically begins in adolescence or early adulthood, and is characterized by unusual beliefs and experiences, namely delusions and hallucinations - collectively termed “positive” symptoms, social withdrawal and flat affect - that is “negative” symptoms, and cognitive impairments, notably in executive functions. Earlier onset of schizophrenia during adolescence or even before is associated with more severe impairments58.The emerging ability to think abstractly during adolescence permits the application of advanced reasoning to social and interpersonal processes. These abilities are critically impaired in patients with schizophrenia, which led Irwin Feinberg to propose a relationship between late- adolescent onset schizophrenia and changes that occur during adolescent brain development 59. For example, the amount of “delta” sleep and duration normally decreases during healthy adolescence 59. In adolescents and young adults with schizophrenia, this reduction in delta sleep is even more pronounced. Delta sleep represents the summed synchronous electrical activities of large assemblies of cortical neurons. Based on these observations, Feinberg speculated that schizophrenia might be a consequence of an exaggeration of the typical synaptic elimination that takes place during adolescence.

Subsequently, several lines of evidence have lent support to this hypothesis that an “exaggeration of typical adolescent changes” has occurred in patients with schizophrenia 60. In addition to the exaggerated reductions in delta sleep in adolescent patients with schizophrenia 61, patients with schizophrenia have also prominent reductions in the level of membrane phospholipid precursors in the prefrontal cortex 62, prefrontal metabolism 63 and volumes of gray matter in frontal cortex 64; all these are consistent with an exaggeration of the changes that occur in typical development. In a rare condition of childhood-onset schizophrenia (onset prior age of 12 years), which is phenomenologically similar to the adolescent or adult-onset schizophrenia, the typical decrease in frontal gray-matter that is seen in healthy subjects during adolescence was exaggerated 4-fold 64. Recent data suggest similar gray-matter losses occurring before illness onset, in persons deemed to be at clinical risk for schizophrenia, i.e. in the prodromal phase before the onset of the characteristic psychotic symptoms of this illness 65

Direct evidence of a decrease in the number of synapses and other neural elements in schizophrenia comes from post mortem studies that have indicated a decreased density of synaptic spines 66, reduction in neuropil 67, and decreased expression of the synaptic marker synaptophysin 68 in the brain of schizophrenia patients. Although the above evidence supports a neurodevelopmental pathophysiology of schizophrenia, it does not provide indications regarding its aetiology. The cause of schizophrenia likely lies in the interplay between genetic and the environmental factors, perhaps involving pre- and peri-natal adverse events, suboptimal post-natal environment during infancy and childhood, and biological stressors during adolescence.

Substance Abuse

Adolescents are more likely to experiment with drugs. Substance-abuse disorders in adults typically begin during teenage years; they may be preceded by behavioral disturbances and poor adjustment in childhood as shown by recent results from the National Child Development Study 69. Earlier onset of drug use predicts a greater severity of the addiction problem70 and may serve as a “gateway” to the use of multiple substances later in life 71.

An important risk factor for substance use includes personality traits, including high novelty seeking and low harm avoidance 72, 73. Across a wide array of mammalian species, adolescents exhibit increased risk taking, novelty seeking, and a greater valuation of social factors 74, 75. While these characteristics foster independence from the natal family, they also increase the risk for harmful behaviors including substance use and abuse. Some investigators have speculated that risk-taking and reward-seeking behaviors in adolescents might be related to a heightened sensitivity for reward 28. As reviewed above, this notion has been supported by fMRI studies that found greater feedback-related activity using a monetary reward task in reward circuitry, namely the nucleus accumbens 29. However, other studies found the opposite pattern, namely lower accumbens activity in response to monetary gains in adolescents as compared with young adults 30. On the other hand, activity of medial-frontal circuitry, which is implicated in conflict monitoring and decision-making, increases from adolescence to adulthood during fMRI tasks in which participants assume some risk of penalty in pursuit of an explicit reward. However, this developmental difference is reduced when potential penalties in the task are severe76.

Compounding these social and behavioral risks is the possibility that adolescents may have less aversive biological responses to substances of abuse. In adolescent rats, nicotine, amphetamine, and alcohol produce less pronounced acute effects and milder withdrawal responses77, 78. Under the influence of alcohol, for instance, adolescent rats are less sensitive to developing motor impairment79, getting a “hangover”80, or becoming sedated. These developmental differences might be related to immaturity of the developing GABA-a receptor systems81.

By contrast to their possibly more rewarding and less aversive responses, adolescents may be more prone to the deleterious effects of substance abuse. Thus, the hippocampus of adolescent rats is unusually susceptible to ethanol-induced inhibition of long-term potentiation, making the rats more sensitive to the memory-impairing effect of alcohol82. The mechanism for this effect, which occurs at alcohol concentrations as low as 5 mM, equivalent to a single drink, appears to be largely mediated via alcohol’s effect on NMDA receptors, occurs at the single-cell level and is not confined to the hippocampus 83.

Clearly, some neural alterations that take place during adolescence predispose to risk whereas others, such as memory impairments, may be actually the result of the abuse. Morphometric studies of humans are in support of this notion. For instance, in youths with a family history of alcohol abuse the right amygdala is smaller even prior to the onset of problem drinking, whereas hippocampal volumes are reduced only after a history of alcohol use 84, 85.

Exposure to substances of abuse in adolescence may also increase the likelihood of addictive disorders emerging later in life. Thus, exposure to nicotine during adolescence, but not in the post-adolescent period, increases the reinforcing effects of nicotine in a self-administration paradigm in adult rats 86.

Affective and Anxiety Disorders

Affective disorders, such as major depression, are common and serious disorders of adolescence; adolescent onset is associated with more severe and disabling forms of these illnesses87, 88. Anxiety symptoms frequently precede depression in adolescence89 and during childhood 90.

Structural MRI studies of adolescents with anxiety and affective disorders have reported structural anomalies in the superior temporal gyrus, ventral prefrontal cortex and amygdala91-93. An fMRI study of depressed and anxious adolescents reported anomalous amygdala response to social stimuli94. In another fMRI study, adults but not adolescents were able to engage the orbitofrontal cortex when asked to switch from an emotional assessment of a face (i.e. How afraid does it make you feel?) to a non-emotional one (i.e. How wide is the nose?)95. The abnormal engagement of brain regions to emotional facial expressions in adolescents may underlie realistic appraisal of emotions and thereby predispose to anxiety and depression.

Hormonal changes that occur during adolescence are likely to account for at least part of the risk for mood and anxiety disorders. Indeed, an intriguing clue to the biology of depression, anxiety and panic disorders is the change from equal female:male prevalence prepuberty to a 2:1 female:male prevalence after puberty. Epidemiological evidence indicates that it is only after Tanner stage III that the sex differences in the incidence of depression emerge96. The finding that pubertal status predicts the sex difference in prevalence better than chronologic age97, 98 suggests that sex hormones play a part in the pathophysiology of these disorders.

A recent mouse study examining tetra-hydro-progesterone (THP), a steroid derived from progesterone, provides a possible mechanism for this phenomenon99. This hormone is released during stress and has an anxiolytic effect that is mediated by activation of GABA-A receptors, which are also activated by alcohol and benzodiazepines. However, when it binds to a particular subtype of the GABA-A receptor, namely the alpha4-beta2-delta receptor subtype THP has the opposite effect to that of alcohol and benzodiazepines: it increases anxiety. The expression of the alpha4-beta2-delta receptor in the CA1 region of the hippocampus surges after puberty and is accompanied by increased anxiety as measured on an elevated maze paradigm. Moreover, blocking the formation of THP alleviated the increase in anxiety in adolescent mice99. Whether stress-related hormone effects on the brain explain differences in rates of anxiety and depressive disorders in prepubescents versus adult awaits further investigation.

In summary, robust changes in hormones and hormonal receptors, increasingly powerful emotional responses to social stimuli, and rapid alterations in motivation and reward systems may underlie the onset of anxiety and depressive disorders during adolescence.

Conclusions and future directions

The relationship between typical changes in the adolescent brain and the onset of psychopathology is not a unitary phenomenon, but an underlying theme may be conceptualized as “moving parts get broken”. Adolescence is characterized by major changes in the neural systems that subserve higher cognitive functions, reasoning and interpersonal interactions, cognitive control of emotions, risk-vs-reward appraisal and motivation. Not surprisingly, when not adequately surmounted, it is precisely these challenges that increase the risk of cognitive, affective and addictive disorders. Understanding the basis of these disorders therefore requires a comprehensive knowledge of how the brain is put together. Many advances are being made, though a lot remains to be learned.

An emerging theme from pediatric neuroimaging studies is that the journey of brain development is often as important as the destination. For example, IQ is predicted by the developmental trajectory of cortical thickness, not by the adult size100. Large individual variability in brain anatomy and function call for longitudinal study designs that capture the nuances of heterochronous developmental curves. The first phases of longitudinal studies have mapped developmental trajectories for typical development but less so for some psychiatric illnesses. The next phases should go beyond simply mapping brain growth and begin to discern the adverse as well as protective factors that influence those trajectories.

A common initial approach to assessing causal influences on brain development is to discern the relative effects of genetic versus non-genetic factors. This is best addressed through comparisons of monozygotic and dyzygotic twins. Results from an ongoing pediatric longitudinal neuroimaging project at the Child Psychiatry Branch of the National Institute of Mental Health indicate significant age-by-heritability interactions, with gray-matter heritability generally decreasing with age and white-matter heritability generally increasing with age101. Heritability-by-age interactions may be related to the timing of gene expression, which in turn may relate to the timing of the onset of illness. Postmortem human and animal studies indicate that ‘developmental’ genes have diverse effects at various stages of brain development. But differences in heritability in different age groups may also reflect the cumulative effect of experience on brain structure; depending on certain inherent traits (e.g. musical talents or personality), it is only with time that specific experiences start to shape the brain.

Multivariate analyses of twin data indicate that a relatively small number of shared genetic and environmental factors account for a substantial portion of the variance across multiple neuroanatomic structures102. Ongoing studies of specific gene effects on brain maturation may help to sharpen our understanding of brain development mechanisms and provide insight into the etiologies of various pathologies. The Saguenay Youth Study, carried out in a geographically isolated population with the known founder effect, will facilitate our search for genes that influence brain and behaviour during adolescence103. Finally, genetics may also provide biologically relevant subtypes of neuropsychiatric disorders that are obscured in current diagnostic schemes.

The marked sex differences in age of onset, prevalence and symptomatology for nearly every neuropsychiatric disorder may provide important clues as to their pathophysiology. The most obvious outward physical manifestations of puberty are caused by changing levels of hormones11. Perhaps this has contributed to the tendency to attribute all of the cognitive and behavioral changes of adolescence to “raging hormones”104. But the relationship between hormones, brain and behavior is complex, reciprocal and poorly understood. Steroid hormones affect neuronal activity and morphology throughout development. Most neurons have receptors for adrenal and gonadal hormones that, when these receptors are activated they can affect neurotransmitter function. Short-term effects are mediated by membrane-bound receptors, whereas long-term effects alter gene expression via intraneuronal or nuclear receptors. Conversely, the dramatic hormonal changes of puberty are triggered by alterations in excitatory and inhibitory inputs to gonadotropin-releasing hormone neurons in the pituitary. Behaviorally, hormonal effects drive aggression and sexual interest but their impact on impulse control, logical problem solving and other cognitive tasks has not been well established.

Social and cultural factors for boys and girls are profoundly different and the relationship of these differences to manifest pathology should be explored. In the biological realm, sex differences likely stem directly from different genes on the X or Y chromosomes or indirectly through the effects of different hormone levels. Studies of subjects with sex-chromosome variations (e.g. XO, XXY, XXYY, XXX, XXXXY) or anomalous hormone levels (e.g. congenital adrenal hyperplasia, androgen insensitivity syndrome, familial male precocious puberty) will be useful to sort out the relative contributions of gene and hormone effects. For instance, males with an extra X chromosome (XXY or Klinefelter’s syndrome) have a high incidence of language disorders, ADHD, and social skills deficits that are reflected in differences in cortical thickness, consistent with reports in the literature for XY subjects with those disorders105. Girls with Congenital Adrenal Hyperplasia, which is characterized by intrauterine exposure to high levels of testosterone, have an entirely different pattern of structural findings, indicating differential effects of sex chromosomes and hormones on the brain106.

Although neuroimaging is beginning to establish correlations between brain structure/physiology and behavior, the link between typical behavioral changes and psychopathology has not been firmly established. For example, the neural circuitry underlying “moodiness” in an adolescent may not be the same circuitry involved in depression or bipolar disorder. Neuroimaging data can help develop neuroanatomical models of cognitive, affective and social processes based on findings from developmental psychology107. Imaging studies of healthy adolescents are also helping to construct age-appropriate structural and functional brain templates.

Newer imaging approaches are being developed. Magnetic Resonance Spectroscopy studies at high magnetic field can help to quantify neurotransmitter systems, such as glutamate and GABA, as well as markers of neurogenesis108. Combining multiple imaging modalities on the same individuals, such as structural MRI, fMRI, diffusion tensor imaging, magnetization transfer imaging, EEG or MEG, will enhance our ability to interpret the signals for each of the modalities. Being able to examine simultaneously inter-individual variation from cellular to macroscopic levels will be instrumental in bridging gaps between genes, brain, and behavior.

Studies of the neural substrates of adolescent behavior and decision-making will need to be integrated better with social and educational science. Laboratory studies of teenagers using hypothetical situations in calm environments without peer influence may have little relevance for understanding real-world decision making that occurs often in the context of intense physical or emotion arousal, conflicting priorities, and in the presence of peers109.

Many questions about adolescent brain development and its impact on disease can best be investigated in animal models. Modeling the adolescent phase in animals is useful for the investigation of risk for addictive and other early-onset neuropsychiatric disorders86. While animal models that represent the full phenotypic spectrum of a psychiatric disorder, such as schizophrenia or depression, are non-existent, individual phenotypic components of disorders - such as developmental alterations that might be associated with the illness - can be used to construct animal models that are aimed at unraveling disease mechanisms and that allow testing novel interventions110.

Another translational approach involves combined in vivo (e.g. MRI) and postmortem studies in animals; such studies are essential for clarifying the nature of neurobiological changes driving the MRI findings. Of immediate relevance will be studies that attempt to discern the degree to which changes in cortical gray-matter, as detected by MRI, are related to dendritic arborization, intracortical myelination or the encroachment of white matter on the inner cortical border.

Adolescence is a time of substantial neurobiological and behavioral change. These changes are usually beneficial and optimize the brain for the challenges ahead, but may also confer a vulnerability to certain types of psychopathology. The technologies to elucidate the relationship between specific neurobiological maturational processes and specific normative or pathologic changes are already in place. Applying these tools to understand when and how deviations from typical development occur may enhance our ability to prevent or treat disorders affecting a substantial number of people.

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Glossary

Diffusion tensor imaging
Diffusion tensor imaging is a MRI-based technique allowing one to characterize structural properties of white matter.
STS network
STS network consists of a set of regions, located along the superior temporal sulcus, that are involved in processing of biological motion related to the movement of different body parts, such as eyes, face, or the entire body.
Delta sleep
Delta sleep is a stage of non-rapid eye movement (non-REM) sleep characterized by slow, or delta waves [0.5-4Hz]; the more delta waves, the deeper the sleep.
Tanner stage III
Tanner stage III is one of the five stages of puberty. Short of a physical exam, pubertal stages can be assessed, for example, using Puberty Development Scale111, which is an eight-item self-report measure of physical development based on the Tanner stages with separate forms for males and females. For this scale, there are five categories of pubertal status: (1) prepubertal, (2) beginning pubertal, (3) midpubertal, (4) advanced pubertal, (5) postpubertal.

References

1. Bushong S. Magnetic resonance imaging. 3rd ed Mosby Inc.; 2003.
2. Roberts TP, Mikulis D. Neuro MR: principles. J Magn Reson Imaging. 2007 Oct;26(4):823–837. [PubMed]
3. Keshavan MS, Kapur S, Pettegrew JW. Magnetic resonance spectroscopy in psychiatry: Potential, pitfalls and promise. The American journal of psychiatry. 1991;148(8):976–985. [PubMed]
4. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001 Jul 12;412(6843):150–157. [PubMed]
5. Lenroot RK, Giedd JN. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neuroscience and biobehavioral reviews. 2006;30(6):718–729. [PubMed]
6. Paus T. Mapping brain maturation and cognitive development during adolescence. Trends in cognitive sciences. 2005 Feb;9(2):60–68. [PubMed]
7. Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nature neuroscience. 1999;2(10):861–863. [PubMed]
8. Gogtay N, Giedd JN, Lusk L, et al. 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 May 25;101(21):8174–8179. [PMC free article] [PubMed]
9. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nature neuroscience. 2003 Mar;6(3):309–315. [PubMed]
10. Huttenlocher PR, de Courten C. The development of synapses in striate cortex of man. Human neurobiology. 1987;6(1):1–9. [PubMed]
11. Chugani HT, Phelps ME, Mazziotta JC. Positron-emission tomography study of human brain functional development. Annals of neurology. 1987;22:487–497. [PubMed]
12. Huttenlocher PR. Synaptic density in human frontal cortex - developmental changes and effects of aging. Brain research. 1979 Mar 16;163(2):195–205. [PubMed]
13. 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]
14. Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO. A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of neurology. 1994 Sep;51(9):874–887. [PubMed]
15. DeBellis MD, Keshavan MS, Beers SR, Hall J, Frustaci K, Masalehdan A, Noll J, Boring AM. Sex differences in brain maturation during childhood and adolescence. Cerebral Cortex. 2001;11:552–557. [PubMed]
16. Klingberg T, Vaidya CJ, Gabrieli JD, Moseley ME, Hedehus M. Myelination and organization of the frontal white matter in children: a diffusion tensor MRI study. Neuroreport. 1999 Sep 9;10(13):2817–2821. [PubMed]
17. Schmithorst VJ, Wilke M, Dardzinski BJ, Holland SK. Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: a cross-sectional diffusion-tensor MR imaging study. Radiology. 2002 Jan;222(1):212–218. [PMC free article] [PubMed]
18. Snook L, Paulson LA, Roy D, Phillips L, Beaulieu C. Diffusion tensor imaging of neurodevelopment in children and young adults. NeuroImage. 2005 Jul 15;26(4):1164–1173. [PubMed]
19. Steinberg L, Albert D, Cauffman E, Banich MT, Graham S, Woodland J. Age differences in sensation seeking and impusivity as indexed by behaviour and self-report: evidence for a dual systems model. Developmental psychology. in press. [PubMed]
20. Kwon H, Reiss AL, Menon V. Neural basis of protracted developmental changes in visuo-spatial working memory. Proceedings of the National Academy of Sciences of the United States of America. 2002 Oct 1;99(20):13336–13341. [PMC free article] [PubMed]
21. Adleman NE, Menon V, Blasey CM, White CD, Warsofsky IS, Glover GH, Reiss AL. A developmental fMRI study of the Stroop color-word task. NeuroImage. 2002 May;16(1):61–75. [PubMed]
22. Luna B, Thulborn KR, Munoz DP, et al. Maturation of widely distributed brain function subserves cognitive development. NeuroImage. 2001;13(5):786–793. [PubMed]
23. 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 Jan;24(1):13–19. [PubMed]
24. 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 Oct;41(10):1231–1238. [PubMed]
25. Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron. 2002 Jan 17;33(2):301–311. [PubMed]
26. Steinberg L. A Neurobehavioral Perspective on Adolescent Risk-Taking. Dev Rev. 2008 Mar;28(1):78–106. [PMC free article] [PubMed]
27. Robbins TW, Everitt BJ. Neurobehavioural mechanisms of reward and motivation. Current opinion in neurobiology. 1996 Apr;6(2):228–236. [PubMed]
28. 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 Jun 21;26(25):6885–6892. [PubMed]
29. 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 May 1;25(4):1279–1291. [PubMed]
30. 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 Feb 25;24(8):1793–1802. [PubMed]
31. Purves D, White LE, Riddle DR. Is neural development Darwinian? Trends in neurosciences. 1996 Nov;19(11):460–464. [PubMed]
32. Huttenlocher PR. Synapse elimination and plasticity in developing human cerebral cortex. American journal of mental deficiency. 1984 Mar;88(5):488–496. [PubMed]
33. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. The Journal of comparative neurology. 1997 Oct 20;387(2):167–178. [PubMed]
34. 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, NY. 1986;232:232–235. [PubMed]
35. Bourgeois JP, Rakic P. Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J Neurosci. 1993 Jul;13(7):2801–2820. [PubMed]
36. Braitenberg V, Schuz A. Cortex: statistics and geometry of neuronal connectivity. Springer Verlag; Heidelberg, Germany: 1998. 2nd thoroughly revised edition of: Anatomy of the cortex. Statistics and geometry 1991 ed.
37. Kaes T. Die grosshirnrinde des menschen in ihren massen und ihrem fasergehalt. Gustav Disher; Jena: 1907.
38. Conel J. Postnatal development of the human cerebral cortex: the cortex of the seventy-two-month infant. Vol. 8. Harvard Univ. Press; Cambridge, MA: 1967.
39. Yakovlev P, Lecours A, editors. Blackwell Scientific; Oxford: 1967.
40. Perrin J, Leonard G, Perron M, et al. Growth of white matter in the adolescent brain: role of testosterone and androgen receptor. Journal of Neuroscience. in press. [PubMed]
41. Paus T, editor. Maturation of structural and functional connectivity in the human brain. Springer-Verlag; New York: 2007.
42. Menon V, Boyett-Anderson JM, Reiss AL. Maturation of medial temporal lobe response and connectivity during memory encoding. Brain Res Cogn Brain Res. 2005 Sep;25(1):379–385. [PubMed]
43. Grosbras MH, Jansen M, Leonard G, et al. Neural mechanisms of resistance to peer influence in early adolescence. J Neurosci. 2007 Jul 25;27(30):8040–8045. [PubMed]
44. Steinberg L, Monahan KC. Age differences in resistance to peer influence. Developmental psychology. 2007 Nov;43(6):1531–1543. [PMC free article] [PubMed]
45. Hursh J. Conduction velocity and diameter of nerve fibers. American Journal of Physiology. 1939;127:131–139.
46. Rushton WA. A theory of the effects of fibre size in medullated nerve. The Journal of physiology. 1951 Sep;115(1):101–122. [PMC free article] [PubMed]
47. Schmidt-Nielson K. Animal physiology: adaptation and environment. 5th ed. Cambridge University Press; Cambridge: 1997.
48. Eickhoff SB, Schleicher A, Scheperjans F, Palomero-Gallagher N, Zilles K. Analysis of neurotransmitter receptor distribution patterns in the cerebral cortex. NeuroImage. 2007 Feb 15;34(4):1317–1330. [PubMed]
49. Zilles K, Palomero-Gallagher N, Schleicher A. Transmitter receptors and functional anatomy of the cerebral cortex. Journal of anatomy. 2004 Dec;205(6):417–432. [PMC free article] [PubMed]
50. Tseng KY, O’Donnell P. Dopamine modulation of prefrontal cortical interneurons changes during adolescence. Cereb Cortex. 2007 May;17(5):1235–1240. [PMC free article] [PubMed]
51. Erickson SL, Akil M, Levey AI, Lewis DA. Postnatal development of tyrosine hydroxylase- and dopamine transporter-immunoreactive axons in monkey rostral entorhinal cortex. Cereb Cortex. 1998 Jul-Aug;8(5):415–427. [PubMed]
52. Rosenberg DR, Lewis DA. Postnatal maturation of the dopaminergic innervation of monkey prefrontal and motor cortices: a tyrosine hydroxylase immunohistochemical analysis. The Journal of comparative neurology. 1995 Jul 31;358(3):383–400. [PubMed]
53. Tunbridge EM, Weickert CS, Kleinman JE, Herman MM, Chen J, Kolachana BS, Harrison PJ, Weinberger DR. Catechol-o-methyltransferase enzyme activity and protein expression in human prefrontal cortex across the postnatal lifespan. Cereb Cortex. 2007 May;17(5):1206–1212. [PubMed]
54. Weickert CS, Webster MJ, Gondipalli P, Rothmond D, Fatula RJ, Herman MM, Kleinman JE, Akil M. Postnatal alterations in dopaminergic markers in the human prefrontal cortex. Neuroscience. 2007 Feb 9;144(3):1109–1119. [PubMed]
55. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of general psychiatry. 2005 Jun;62(6):593–602. [PubMed]
56. Hafner H, Riecher A, Maurer K, Loffler W, Munk-Jorgensen P, Stromgren E. How does gender influence age at first hospitalization for schizophrenia? A transnational case register study. Psychological medicine. 1989 Nov;19(4):903–918. [PubMed]
57. Kessler RC, Wang PS. The descriptive epidemiology of commonly occurring mental disorders in the United States. Annual review of public health. 2008;29:115–129. [PubMed]
58. Kyriakopoulos M, Frangou S. Pathophysiology of early onset schizophrenia. International review of psychiatry (Abingdon, England) 2007 Aug;19(4):315–324. [PubMed]
59. Feinberg I. Schizophrenia: Caused by a fault in programmed synaptic elimination during adolescence? Journal of psychiatric research. 198283;17:319–334. [PubMed]
60. Keshavan MS, Anderson S, Pettegrew JW. Is schizophrenia due to excessive synaptic pruning in the prefrontal cortex? The Feinberg hypothesis revisited. Journal of psychiatric research. 1994 May-Jun;28(3):239–265. [PubMed]
61. Keshavan MS, Reynolds CF, Miewald JM, Montrose DM, Sweeney JA, Vasko RC, Kupfer DJ. Delta sleep deficits in schizophrenia: Evidence from automated analyses of sleep data. Archives of general psychiatry. 1998;55:443–448. [PubMed]
62. Pettegrew JW, Keshavan MS, Panchalingam K, Strychor S, Kaplan DB, Tretta MG, Allen M. Alterations in brain high-energy phosphate and membrane phospholipid metabolism in first-episode, drug-naive schizophrenics. A pilot study of the dorsal prefrontal cortex by in vivo phosphorus 31 nuclear magnetic resonance spectroscopy. Archives of general psychiatry. 1991 Jun;48(6):563–568. [PubMed]
63. Andreasen NC, Rezai K, Alliger R, Swayze VW, 2nd, Flaum M, Kirchner P, Cohen G, O’Leary DS. Hypofrontality in neuroleptic-naive patients and in patients with chronic schizophrenia. Assessment with xenon 133 single-photon emission computed tomography and the Tower of London. Archives of general psychiatry. 1992 Dec;49(12):943–958. [PubMed]
64. Sporn AL, Greenstein DK, Gogtay N, et al. Progressive brain volume loss during adolescence in childhood-onset schizophrenia. The American journal of psychiatry. 2003 Dec;160(12):2181–2189. [PubMed]
65. Sun D, Phillips L, Velakoulis D, et al. Progressive brain structural changes mapped as psychosis develops in ‘at risk’ individuals. Schizophrenia research. 2008 Feb 8; [PMC free article] [PubMed]
66. Garey LJ, Ong WY, Patel TS, Kanani M, Davis A, Mortimer AM, Barnes TR, Hirsch SR. Reduced dendritic spine density on cerebral cortical pyramidal neurons in schizophrenia. Journal of neurology, neurosurgery, and psychiatry. 1998 Oct;65(4):446–453. [PMC free article] [PubMed]
67. Selemon LD, Rajkowska G, Goldman-Rakic PS. Abnormally high neuronal density in the schizophrenic cortex. A morphometric analysis of prefrontal area 9 and occipital area 17. Archives of general psychiatry. 1995 Oct;52(10):805–818. discussion 819-820. [PubMed]
68. Eastwood SL, Harrison PJ. Decreased synaptophysin in the medical temporal lobe in schizophrenia demonstrated using immunoautoradiography. Neuroscience. 1995;69:339–343. [PubMed]
69. Maggs JL, Patrick ME, Feinstein L. Childhood and adolescent predictors of alcohol use and problems in adolescence and adulthood in the National Child Development Study. Addiction (Abingdon, England) 2008 May;103(Suppl 1):7–22. [PubMed]
70. Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. The American journal of psychiatry. 2003 Jun;160(6):1041–1052. [PMC free article] [PubMed]
71. Kandel DB, Yamaguchi K, Chen K. Stages of progression in drug involvement from adolescence to adulthood: further evidence for the gateway theory. Journal of studies on alcohol. 1992 Sep;53(5):447–457. [PubMed]
72. Cloninger CR, Sigvardsson S, Bohman M. Childhood personality predicts alcohol abuse in young adults. Alcoholism, clinical and experimental research. 1988 Aug;12(4):494–505. [PubMed]
73. Wills TA, Vaccaro D, McNamara G. Novelty seeking, risk taking, and related constructs as predictors of adolescent substance use: an application of Cloninger’s theory. Journal of substance abuse. 1994;6(1):1–20. [PubMed]
74. Adams G, Montemayor R, Gullotta T. Biology of adolescent behavior and development. Sage Publications; Newbury Park, CA: 1989.
75. Savin-Williams R. Adolescence: an ethological perspective. Springer-Verlag; New York: 1987.
76. Bjork JM, Smith AR, Danube CL, Hommer DW. Developmental differences in posterior mesofrontal cortex recruitment by risky rewards. J Neurosci. 2007 May 2;27(18):4839–4849. [PubMed]
77. Levin ED, Rezvani AH, Montoya D, Rose JE, Swartzwelder HS. Adolescent-onset nicotine self-administration modeled in female rats. Psychopharmacology. 2003 Sep;169(2):141–149. [PubMed]
78. Spear LP. Alcohol’s effects on adolescents. Alcohol Res Health. 2002;26(4):287–291. [PubMed]
79. White AM, Truesdale MC, Bae JG, Ahmad S, Wilson WA, Best PJ, Swartzwelder HS. Differential effects of ethanol on motor coordination in adolescent and adult rats. Pharmacology, biochemistry, and behavior. 2002 Oct;73(3):673–677. [PubMed]
80. Doremus TL, Brunell SC, Varlinskaya EI, Spear LP. Anxiogenic effects during withdrawal from acute ethanol in adolescent and adult rats. Pharmacology, biochemistry, and behavior. 2003 May;75(2):411–418. [PubMed]
81. Silveri MM, Spear LP. The effects of NMDA and GABAA pharmacological manipulations on ethanol sensitivity in immature and mature animals. Alcoholism, clinical and experimental research. 2002 Apr;26(4):449–456. [PubMed]
82. White AM, Swartzwelder HS. Hippocampal function during adolescence: a unique target of ethanol effects. Annals of the New York Academy of Sciences. 2004 Jun;1021:206–220. [PubMed]
83. Li Q, Wilson WA, Swartzwelder HS. Differential effect of ethanol on NMDA EPSCs in pyramidal cells in the posterior cingulate cortex of juvenile and adult rats. Journal of neurophysiology. 2002 Feb;87(2):705–711. [PubMed]
84. Brown SA, Tapert SF. Adolescence and the trajectory of alcohol use: basic to clinical studies. Annals of the New York Academy of Sciences. 2004 Jun;1021:234–244. [PubMed]
85. De Bellis MD, Clark DB, Beers SR, Soloff PH, Boring AM, Hall J, Kersh A, Keshavan MS. Hippocampal volume in adolescent-onset alcohol use disorders. The American journal of psychiatry. 2000 May;157(5):737–744. [PubMed]
86. Adriani W, Laviola G. Windows of vulnerability to psychopathology and therapeutic strategy in the adolescent rodent model. Behavioural pharmacology. 2004 Sep;15(56):341–352. [PubMed]
87. Andersen SL, Teicher MH. Stress, sensitive periods and maturational events in adolescent depression. Trends in neurosciences. 2008 Apr;31(4):183–191. [PubMed]
88. Birmaher B, Axelson D. Course and outcome of bipolar spectrum disorder in children and adolescents: a review of the existing literature. Development and psychopathology Fall. 2006;18(4):1023–1035. [PubMed]
89. Beesdo K, Bittner A, Pine DS, Stein MB, Hofler M, Lieb R, Wittchen HU. Incidence of social anxiety disorder and the consistent risk for secondary depression in the first three decades of life. Archives of general psychiatry. 2007 Aug;64(8):903–912. [PubMed]
90. Reinherz HZ, Paradis AD, Giaconia RM, Stashwick CK, Fitzmaurice G. Childhood and adolescent predictors of major depression in the transition to adulthood. The American journal of psychiatry. 2003 Dec;160(12):2141–2147. [PubMed]
91. Blumberg HP, Kaufman J, Martin A, et al. Amygdala and hippocampal volumes in adolescents and adults with bipolar disorder. Archives of general psychiatry. 2003 Dec;60(12):1201–1208. [PubMed]
92. De Bellis MD, Keshavan MS, Shifflett H, Iyengar S, Beers SR, Hall J, Moritz G. Brain structures in pediatric maltreatment-related posttraumatic stress disorder: a sociodemographically matched study. Biological psychiatry. 2002 Dec 1;52(11):1066–1078. [PubMed]
93. DelBello MP, Zimmerman ME, Mills NP, Getz GE, Strakowski SM. Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar disorders. 2004 Feb;6(1):43–52. [PubMed]
94. Thomas KM, Drevets WC, Dahl RE, et al. Amygdala response to fearful faces in anxious and depressed children. Archives of general psychiatry. 2001 Nov;58(11):1057–1063. [PubMed]
95. Monk CS, McClure EB, Nelson EE, et al. Adolescent immaturity in attention-related brain engagement to emotional facial expressions. NeuroImage. 2003 Sep;20(1):420–428. [PubMed]
96. Angold A, Costello EJ. Puberty and depression. Child and adolescent psychiatric clinics of North America. 2006 Oct;15(4):919–937. ix. [PubMed]
97. Hayward C, Sanborn K. Puberty and the emergence of gender differences in psychopathology. J Adolesc Health. 2002 Apr;30(4 Suppl):49–58. [PubMed]
98. Patton GC, Hibbert ME, Carlin J, Shao Q, Rosier M, Caust J, Bowes G. Menarche and the onset of depression and anxiety in Victoria, Australia. Journal of epidemiology and community health. 1996 Dec;50(6):661–666. [PMC free article] [PubMed]
99. Shen H, Gong QH, Aoki C, Yuan M, Ruderman Y, Dattilo M, Williams K, Smith SS. Reversal of neurosteroid effects at alpha4beta2delta GABAA receptors triggers anxiety at puberty. Nature neuroscience. 2007 Apr;10(4):469–477. [PMC free article] [PubMed]
100. Shaw P, Greenstein D, Lerch J, et al. Intellectual ability and cortical development in children and adolescents. Nature. 2006 Mar 30;440(7084):676–679. [PubMed]
101. Wallace GL, Eric Schmitt J, Lenroot R, et al. A pediatric twin study of brain morphometry. Journal of child psychology and psychiatry, and allied disciplines. 2006 Oct;47(10):987–993. [PubMed]
102. Schmitt JE, Wallace GL, Rosenthal MA, et al. A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample. NeuroImage. 2007 Mar;35(1):70–82. [PubMed]
103. Pausova Z, Paus T, Abrahamowicz M, et al. Genes, maternal smoking, and the offspring brain and body during adolescence: design of the Saguenay Youth Study. Human brain mapping. 2007 Jun;28(6):502–518. [PubMed]
104. Sisk CL, Foster DL. The neural basis of puberty and adolescence. Nature neuroscience. 2004 Oct;7(10):1040–1047. [PubMed]
105. Shen D, Liu D, Liu H, Clasen L, Giedd J, Davatzikos C. Automated morphometric study of brain variation in XXY males. NeuroImage. 2004 Oct;23(2):648–653. [PubMed]
106. Giedd JN, Clasen LS, Lenroot R, et al. Puberty-related influences on brain development. Molecular and cellular endocrinology. 2006 Jul 25;254-255:154–162. [PubMed]
107. Ernst M, Mueller SC. The adolescent brain: insights from functional neuroimaging research. Developmental neurobiology. 2008 May;68(6):729–743. [PMC free article] [PubMed]
108. Manganas LN, Zhang X, Li Y, et al. Magnetic resonance spectroscopy identifies neural progenitor cells in the live human brain. Science (New York, NY. 2007 Nov 9;318(5852):980–985. [PMC free article] [PubMed]
109. Steinberg L. Cognitive and affective development in adolescence. Trends in cognitive sciences. 2005 Feb;9(2):69–74. [PubMed]
110. Arguello PA, Gogos JA. Modeling madness in mice: one piece at a time. Neuron. 2006 Oct 5;52(1):179–196. [PubMed]
111. Peterson A, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: reliability, validity, and initial norms. J Youth Adolesc. 1988;17:117–133. [PubMed]
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