Journal of Adolescent Health 42 (2008) 335–343

Review article

The Teen Brain: Insights from Neuroimaging
Jay N. Giedd, M.D.*
Brain Imaging Unit, Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland
Manuscript received October 4, 2007; manuscript accepted January 16, 2008

See Editorial p. 321
Abstract:

Few parents of a teenager are surprised to hear that the brain of a 16-year-old is different from the brainof an 8-year-old. Yet to pin down these differences in a rigorous scientific way has been elusive.
Magnetic resonance imaging, with the capacity to provide exquisitely accurate quantifications of brain
anatomy and physiology without the use of ionizing radiation, has launched a new era of adolescentneuroscience. Longitudinal studies of subjects from ages 3–30 years demonstrate a general pattern ofchildhood peaks of gray matter followed by adolescent declines, functional and structural increases inconnectivity and integrative processing, and a changing balance between limbic/subcortical and frontal
lobe functions, extending well into young adulthood. Although overinterpretation and premature application of neuroimaging findings for diagnostic purposes remains a risk, converging data from multiple
imaging modalities is beginning to elucidate the implications of these brain changes on cognition,
emotion, and behavior. © 2008 Society for Adolescent Medicine. All rights reserved.

Keywords:

Child; Adolescent; Development; MRI; DTI; MT; fMRI; Gray matter; White matter“A science of the mind must reduce . . . complexities (ofbehavior) to their elements. A science of the brain must pointout the functions of its elements. A science of the relations ofmind and brain must show how the elementary ingredients ofthe former correspond to the elementary functions of the latter.” — William James, The Principles of Psychology, 1890
For most of the 117 years since William James’s formulation of the quest to link biology with behavior, the study of theadolescent brain remained inaccessible. Wrapped in a toughleathery membrane, surrounded by a protective moat of fluid,and completely encased in bone, the brain is well protectedfrom falls, attacks from predators, and the curiosity of neuroscientists. The invention of imaging technologies such as x-rays,
computed tomography, and positron emission tomography offered some progress, but the reliance on ionizing radiation precluded the ethical application to studies of healthy subjects.
The advent of magnetic resonance imaging (MRI) finallybroke through the formidable barrier thwarting the pursuit of*Address correspondence to: Jay N. Giedd, M.D., Chief, Brain Imaging
Unit, Child Psychiatry Branch, NIMH, Building 10, Room 4C110, 10Center Drive, Bethesda, MD 20892.
E-mail address: jg@nih.gov

James’s vision. MRI combines radio waves, strong magnetic
fields, and sophisticated computer technology to provide detailed information about the anatomy and physiology of the brain
without the use of ionizing radiation. The lack of ionizing radiation allows not only scanning in healthy children but also repeatedscans in the same individual over the course of development.
This manuscript summarizes results of on ongoing, longitudinal, structural MRI project looking at typical andatypical brain development. Because adolescence does nothave a precise biologic definition and the onset of puberty
can vary by as much as 6 years in typical development, data
are presented across ages 3–27 years, and readers can examine specific ages of interest in the figures accompanying
the text. In addition, an Addendum after the main text
provides further discussion of technical aspects of image
acquisition and analysis, as well as a brief overview of someother imaging modalities used in adolescent research.
NIMH Child Psychiatry Branch Longitudinal Brain
Imaging ProjectBegun in 1989 under the direction of Markus Krusei,
M.D., the Child Psychiatry Branch (CPB) of the National

1054-139X/08/$ – see front matter © 2008 Society for Adolescent Medicine. All rights reserved.
doi:10.1016/j.jadohealth.2008.01.007

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Institute of Mental Health has been conducting a longitudinal study of brain development in health and illness. The
study design is for participants to come to the National
Institutes of Health at approximately 2-year intervals forbrain imaging, neuropsychologic and behavioral assessment, and collection of DNA. As of September 2007, wehave acquired approximately 5000 scans from 2000 subjects.
From the outset the study has included typically developing people, both to provide a comparison from which toassess pathology and to explore mechanisms and timing of
brain development as a guide to interventions. The sampleof participants who have remained free of psychopathology(and constrained to only one subject per family for statistical independence), consists of 829 scans from 387 subjects
aged 3–27 years. The data for the following sections regarding quantification of brain structure sizes are largely fromthis cohort. The emphasis on this single source is not todevalue the many excellent contributions of other investigators, but to provide an integrated account from theworld’s largest collection of child and adolescent brain MRI
scans with data acquired using uniform screening and assessment batteries, the same scanner, and the same methodsof image analyses.
Data regarding brain physiology, such as that from functional MRI (fMRI) or other imaging modalities, are drawn
from the literature reported by other investigators or fromcollaborative work with other neuroimaging teams. Although there is high optimism for the novel and complementary information potentially provided by the newer imaging methods currently the number of subjects for whom
structural MRI (sMRI) is available dwarfs that of the othermodalities. As opposed to the data set of more than 1000 for
sMRI, no pediatric study using fMRI, diffusion tensor imaging (DTI), or magnetization transfer (MT) has been reported with a sample of more than 100.
Developmental Anatomic Trajectories During TypicalChildhood and Adolescence
Total Cerebral Volume
In the CPB cohort, total cerebral volume peaks at 10.5
years in girls and 14.5 years in boys [1]. By age 6 years, thebrain is at approximately 95% of this peak (Figure 1a). Total
cerebral volume decreases during adolescence were notpreviously detected with postmortem data [2,3] or crosssectional MRI studies [4,5]. Consistent with the adult neuroimaging literature [6], mean total cerebral volume is approximately 10% larger in boys. Total brain size differences
should not be interpreted as imparting any sort of functional
advantage or disadvantage. Gross structural measures may
not reflect sexually dimorphic differences in functionally
relevant factors such as neuronal connectivity and receptor
density. Of note is the high variability of brain size even inthis group of rigorously screened healthy children and adolescents. Healthy children at the same age may have as
much as a 50% differences in total brain volume, further
highlighting the need to be cautious regarding functional
implications of absolute brain sizes.
Cerebellum
Cerebellum volume peaks about 2 years later than cerebral volume and is the only structure we have quantified thatremains significantly larger in males after covarying for
total cerebral volume [7].
The cerebellum has traditionally been associated withbalance and motor control. However a converging body ofevidence from electroencephalography (EEG) studies [8],fMRI studies [9], studies in subjects with vascular anddegenerative cerebellar disease [10,11], and histologic studies demonstrating cerebellar connections to dorsolateral prefrontal cortex, the medial frontal cortex, and the parietal andsuperior temporal areas [12,13] clearly establish the cerebellum’s role in many higher cognitive functions. Consistent with the extended maturation of the cerebellum, these
cerebellar-subserved higher cognitive functions continue toimprove during childhood and adolescence.
Ventricles
Lateral ventricular volume increased robustly with age in
the CPB sample of healthy children and adolescents (Figure1d). This is in agreement with previous reports of greater
ventricular volume in adults versus children [4], and isnoteworthy because increased ventricular volumes are associated with a broad range of neuropsychiatric conditions.
That ventricular volume is highly variable [14] and increases in healthy pediatric development informs interpretation of ventricular volume changes in patient populations.
White Matter
Whether a voxel is classified as gray matter (GM) or
white matter (WM) depends largely on the amount of myelinated axons. The MRI signal intensities of nonfluid brain
matter voxels generally fall into two bell-shaped distributions; however there is overlap between the distributions, so
the exact amount of myelin necessary for classification as
WM is somewhat arbitrary and varies slightly depending on
different algorithms. Myelination is the wrapping of oligodendrocytes around axons, which acts as an electrical insulator and increases the speed of neuronal signal transmission. An important feature of myelination that has only
recently been appreciated is that it does not simply maximize speed of transmission but modulates the timing andsynchrony of neuronal firing patterns that convey meaning
in the brain [15].
Consistent with previous reports [1,16 –21], WM volumes increased throughout childhood and adolescence in
the CPB sample (Figure 1c). The rate of increase is age

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337

Figure 1. Mean volume by age in years for males (N ! 475 scans) and females (N ! 354 scans). Middle lines in each set of three lines represent mean values;
upper and lower lines represent upper and lower limits of 95% confidence intervals. Arrows indicate point of peak volume. All curves differed significantly
between males and females in height and shape with the exception of lateral ventricles, in which only height was different, and mid-sagittal area of the corpus
callosum, in which neither height nor shape was different. (a) Total brain volume, (b) gray matter volume, (c) white matter volume, (d) lateral ventricle
volume, (e) mid-sagittal area of the corpus callosum, and (f) caudate volume.

dependent [18] and can increase by as much as 50% in a
2-year period in small regions of interest [22]; but at thelobar level (frontal, temporal, and parietal lobes), developmental WM trajectories are similar.
The corpus callosum (CC) is the most prominent WM
structure, and consists of approximately 200 million axons
connecting homologous areas of the left and right cerebral
hemispheres. The functions of the CC can generally bethought of as integrating the activities of the left and rightcerebral hemispheres, including functions related to theunification of sensory fields [23,24], memory storage andretrieval [25], attention and arousal [26], and enhancementof language and auditory functions [27]. In agreement withseveral studies that have indicated increasing CC size during adolescence [22,28 –31], total midsagittal CC area increased robustly from ages 4 –20 years in the CPB sample(Figure 1e).
The growing interest in exploring neural circuitry has
encouraged the development of newer MR techniques, suchas DTI and MT, which allow characterization of the micro-

structure of WM and the direction of axons. DTI studies
show decreases of overall diffusion and increases in anisotropy (a measure of the directionality or nonrandomness ofthe diffusion) during typical child and adolescent development [32]. High anisotropy reflects coherently bundled myelinated axons and axonal pruning, which allow greater
efficiency of neuronal communication [33]. A growing
body of literature has shown positive correlations between
anisotropy and cognitive performance. Specifically, highanisotropy in the temporal lobe correlates with memory
capacity [34], in the frontal lobe with language ability [34],in frontal and occipitoparietal association areas with IQ[35], in temporal and parietal areas with reading ability[36 –38], and in frontostriatal areas with the ability to inhibit
responses to a visual stimulus [39].
Studies using MT imaging have reported increasing
magnetization transfer ratio (MTR) values (which increase with myelination) during childhood and adolescence [40 – 42], although only an adult study has linked
MTR values to cognitive performance [43].

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posed to WM), the temporally linked EEG power and GM
changes suggests that the GM volume reductions are accompanied by reductions in the number of synapses. Another consideration is that myelination may change classification of voxels along the interior cortical border from GM
to WM, resulting in cortical thinning as assessed by MR
volumetrics, but that it would would not necessarily entailchanges in synaptic density [20]. Knowledge of the degree
to which these and other phenomema may be driving the
MR changes has profound implications for interpreting theimaging results. Imaging of nonhuman primates with postmortem validation may help in this regard.
Subcortical GM

Figure 2. Gray matter subdivisions: (a) frontal lobe, (b) parietal lobe, (c)temporal lobe, and (d) occipital lobe.

Gray Matter
Unlike WM increases during childhood and adolescence,
GM trajectories follow an inverted U-shaped path (Figure1b). This decoupling of GM and WM developmental curves
belies the inseparable connection among neurons, glial
cells, and myelin, which are fellow components in neuralcircuits and are bound by lifelong reciprocal relationships[15].
Cortical GM
The GM volumes peak in the frontal lobes at age 9.5
years in girls and 10.5 years in boys; in the temporal lobes
at age 10.0 years in girls and 11.0 years in boys; and in theparietal lobes at 7.5 years in girls and 9 years in boys
(Figure 2).
At the voxel level, GM densities are not uniform withina given lobe [44]. (An animation of cortical GM changes
from ages 4 –20 years at the voxel level derived from scansof 13 subjects who had each undergone scanning four times
at approximately 2-year intervals is available at http://www.
nimh.nih.gov/videos/press/prbrainmaturing.mpeg.) The ageof peak GM density is earliest in primary sensorimotor areas
and latest in higher order association areas that integrate
those primary functions such as the dorsolateral prefrontal
cortex, inferior parietal, and superior temporal gyrus.
Postmortem studies suggest that part of the GM changes
may be related to synaptic proliferation and pruning [45].
The connection between GM volume reductions, EEGchanges, and synaptic pruning is also supported by an MRI
and quantified EEG study of 138 healthy subjects aged
10 –30 years; this study that found curvelinear reductions infrontal and parietal GM were matched by similar curvilinear
reductions in the EEG power of the corresponding regions[46]. Because EEG power reflects synaptic activity (as op-

Basal Ganglia
The basal ganglia are a collection of subcortical nuclei(caudate, putamen, globus pallidus, subthalamic nucleus,and substantia nigra) that are involved in circuits mediating
movement, higher cognitive functions, attention, and affective states. Basal ganglia anomalies have been reported foralmost all neuropsychiatric disorders that have been investigated by neuroimaging [47]. Because of the small size andthe ambiguity of MR signal contrast of the borders defining
the structures, only the caudate, putamen, and globus pallidus are readily quantifiable by MRI, and reliable automatedtechniques have been established only for the caudate. Likecortical GM, the caudate follows an inverted U-shaped
developmental trajectory, peaking at age 10.5 years in girls
and 14.0 years in boys (Figure 1f). The shape of the caudate
developmental trajectory is more similar to that of frontal
and parietal GM than temporal, supporting the notion thatbrain regions that share extensive connections also sharesimilar developmental courses.
Amygdala and Hippocampus
The temporal lobes, amygdala, and hippocampus areintegral players in the arenas of emotion, language, andmemory [48]. Human capacity for these functions changes
markedly between the ages of 4 and 18 years [49 –51],
although the relationship between the development of thesecapacities and morphologic changes in the structures subserving these functions is poorly understood. The amygdala
and hippocampus are adjacent brain structures and part ofsome of the same neural circuits, but they also subservedistinct functions. The amygdala is a key component ofcircuitry involved in assessing salience, or the importance ofenvironmental stimuli to survival. The hippocampus is involved in memory storage and retrieval. Connections between the amygdala and hippocampus result in enhanced
memory for stimuli with high salience [52,53].
Valid quantification of amygdala and hippocampus volumes still requires manual tracing by expert raters and havenot been completed for the longitudinal sample. In a previous report of a cross-sectional sample subset of the CPB
sample, amygdala volume increased significantly during

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adolescence only in males and hippocampal volume increased significantly only in females [54]. This pattern of
sex-specific maturational volumetric changes is consistent
with nonhuman primate study findings, indicating a relatively high number of androgen receptors in the amygdala[55] and a relatively higher number of estrogen receptors in
the hippocampus [56].
Summary of sMRI Changes Occurring in the Second
DecadeIn the typically developing CPB cohort, total cerebral
and GM volumes peak during the ages from 10 –20 years,
whereas WM and ventricular volumes increase. Age of peaksize for GM volumes differs, varies by region, and is generally earlier in females than in males.
Influences on Developmental Trajectories of Brain
Anatomy During Childhood and Adolescence
Genes and Environment
To discern the relative contributions of genetic and nongenetic influences on trajectories of brain development, weare conducting a longitudinal neuroimaging study ofmonozygotic (MZ) and dizygotic (DZ) twins. To date wehave acquired approximately 600 scans from 90 MZ and 60
DZ twin pairs. Correlation differences between MZ and DZ
twins are analyzed with structural equation modeling toestimate the relative contributions to phenotypic variance ofadditive genetic (A), shared environmental (C), or uniqueenvironmental (E) factors [57]. Structural equation modeling is also useful to assess gene– environment interactions
and other epistatic phenomena that challenge conventional
interpretation of twin data.
For most brain morphometric measures, additive genetic
effects (i.e., “heritability”) are high and shared environmental effects are low [58]. Additive genetic effects for total
cerebral and lobar volumes (including GM and WM subcompartments) ranged from 0.77– 0.88; for the caudate,
0.80; and for the corpus callosum, 0.85. The cerebellum has
a distinctive heritability profile with an additive genetic
effect of only 0.49, although wide confidence intervals merit
cautious interpretation. Highly heritable brain morphometric measures provide biologic markers for inherited traits,
and may serve as targets for genetic linkage and association
studies [59].
Multivariate analyses allow assessment of the degree towhich the same genetic or environmental factors contributeto multiple neuroanatomic structures. Like the univariate
variables, these interstructure correlations can be parceled
into relationships of either genetic or environmental origin.
This knowledge is vitally important for interpretation ofmost of the twin data, including understanding the impact ofgenes that may affect distributed neural networks, as well as

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interventions that may have global brain impacts. Sharedeffects account for more of the variance than structure
specific effects, with a single genetic factor accounting for
60% of variability in cortical thickness [60]. Six factors
account for 58% of the remaining variance, with five groupsof structures strongly influenced by the same underlying
genetic factors. These findings are consistent with the radialunit hypothesis of neocortical expansion proposed by Rakic[61] and with hypotheses that global, genetically mediateddifferences in cell division were the driving force behindinterspecies differences in total brain volume [62– 64]. Expanding the entire brain when only specific functions might
be selected for is metabolically costly, but the number ofmutations required to affect cell division would be far lessthan that required to completely change cerebral organization.
Age-related changes in heritability may be linked to thetiming of gene expression and related to the age of onset of
disorders. In general, heritability increases with age for WM
and decreases for GM volumes [58], whereas heritability
increases for cortical thickness in regions within the frontal
cortex, parietal, and temporal lobes [65]. Knowledge ofwhen certain brain structures are particularly sensitive togenetic or environmental influences during development
could have important educational and/or therapeutic implications.
Male/Female Differences
Given that nearly all neuropsychiatric disorders havedifferent prevalence, age of onset, and symptomatology
between males and females, sex differences in typical developmental brain trajectories are highly relevant for studiesof pathology. Robust sex differences in developmental trajectories were noted for nearly all structures, with GM
volume peaks generally occurring 1–3 years earlier in females [1]. In our pediatric sample, brain size differences are
not accounted for by differences in height or body size. To
assess the relative contributions of sex chromosomes andhormones, our group is studying subjects with anomalous
sex chromosome variations (e.g., XXY, XXX, XXXY,
XYY) [66], as well as subjects with anomalous hormone
levels (e.g., Congenital Adrenal Hyperplasia, Familial Male
Precocious Puberty, Cushing syndrome) [67,68].
Specific Genes
As with any quantifiable behavioral or physical parameter, individuals can be categorized into groups based on
genotype. Brain images of individuals in the different genotype groups can then be averaged and compared statistically. In adult populations, one of the most frequently studied genes has been apolipoprotein E (apoE), which
modulates risk for Alzheimer’s disease. Carriers of the 4allele of apoE have increased risk, whereas carriers of the 2allele are possibly at decreased risk. To explore whether

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apoE alleles have distinct neuroanatomic signatures identifiable in childhood and adolescence, we examined 529 scansfrom 239 healthy subjects aged 4 –20 years [69]. Although
there were no significant IQ– genotype interactions, there
was a stepwise effect on cortical thickness in the entorhinaland right hippocamapal regions, with the 4 group exhibiting the thinnest, the 3 homozygotes in the middle range,
and the 2 group the thickest. These data suggest that
pediatric assessments might one day be informative for
adult-onset disorders.
Discussion
Three themes emerge from the cumulative neuroimaging
research of adolescents, each buttressed by behavioral,
EEG, and postmortem studies.
The first is an increase in associative cognitive activity as
distributed brain modules become more and more integrated[70]. This increased connectivity is reflected by the WM
changes, with fMRI studies suggesting more extensive neural networks, and by increased EEG coherence (reviewed in[71]). If we consider a literary/linguistic metaphor, maturation would not be the addition of new letters but of combining earlier formed letters into words, and then words into
sentences, and then sentences into paragraphs.
The second is a general pattern of childhood peaks followed by adolescent declines. This pattern is found not only
for GM volumes but for the number of synapses [72–74],
glucose use [75], EEG power [76], and neurotransmitter
receptor densities [77]. The powerful process of overproduction followed by selective/competitive elimination thatshapes the developing nervous system in utero seems tocontinue to refine the brain throughout adolescent development.
The third theme is a changing balance between competing neuronal networks as different cognitive and emotional
systems mature at different rates. Many of the cognitive andbehavioral changes taking place during adolescence may beunderstood from the perspective of increased “executivefunctioning,” a term encompassing a broad array of abilities, including attention, response inhibition, regulation ofemotion, organization, and long-range planning. These abilities are thought to rely heavily on frontal lobe circuitry that,as indicated above, is relatively late maturing. In addition to
the sMRI studies, fMRI consistently shows an increasing
proportion of frontal versus striatal or limbic activity fromchildhood to adulthood for a variety of cognitive tasks [78].
Some changes in limbic reward and motivational systems
seem to be associated with the onset puberty, whereas otherchanges occur earlier or well after the advent of puberty. For
example, in an fMRI study of 37 subjects aged 7–29 years
that assessed response to rewards, adolescent nucleus accumbens response was equivalent to that in adults, butadolescent orbitofrontal activity was similar to that in children [79].

Elucidating the relationship between neuroimaging findings
and behavior is an area of active investigation. Because behaviors emanate from the integrated activity of distributed networks, demonstrating straight-forward relationships between
the size of a given brain structure and a particular behavior or
ability has been elusive. An important consideration in linking
form and function in the brain is that differences in the trajectories of development may in some cases be more informative
than the final adult differences. For instance, in our longitudinal study looking at the relationship between cortical thickness
and IQ differences in age by cortical thickness, developmental
curves were more predictive of IQ than differences in cortical
thickness at age 20 years [80].
A target for future investigations is puberty-specific versus puberty-independent changes in brain development. In
the CPB sample, we assessed Tanner stage by self-report
but did not quantify hormone levels. Studies specificallydesigned to address this issue, including more precise measures of puberty and comparison of performance in pre- andpostpubertal individuals of the same age may help to address this question.
The diagnostic utility of neuroimaging in psychiatry has
been the subject of much debate. Although group neuroimaging differences have been reported for nearly all neuropsychiatric disorders, the large overlap of values between
clinical and control populations precludes routine application for individuals, except to rule out possible central
nervous system insults such as tumors, intracranial bleeds,
or congenital anomalies as etiologies for the symptoms.
There is no identified “lesion” common to all, or even most,children with the most frequently studied disorders of autism, attention-deficit/hyperactivity disorder, childhood-onset schizophrenia, dyslexia, fragile X, juvenile onset bipolar
disorder, post-traumatic stress disorder, Sydenham’s chorea, or Tourette’s syndrome. The more immediate utility ofneuroimaging may be to provide endophenotypes, biologic
markers that are intermediate between genes and behavior.
Neuroimaging endophenotypes have the potential to define
biologically meaningful subtypes of disorders that may respond to different interventions.
Future neuroimaging studies are likely to increasingly combine multiple imaging modalities in the same individuals, suchas structural MRI, fMRI, diffusion tensor imaging, magnetization transfer imaging, EEG, and MEG, which will synergistically enhance our ability to interpret the signals for each of themodalities. Being able to simultaneously examine interindividual variation from cellular to macroscopic levels will be
instrumental in bridging gaps among genes, the brain, andbehavior. A related future direction may be an increase inpostmortem studies of animals that have undergone neuroimaging. This would help to clarify the nature of changes driving
the MRI findings, such as discerning the degree to which
cortical GM changes, as detected with MRI, are related toarborization or pruning of neurons, or to encroachment of WM
on the inner cortical border. Another important direction forJ.N. Giedd / Journal of Adolescent Health 42 (2008) 335–343

future neuroimaging studies will be increased integration withsocial and educational science, which have remained relatively
separate despite the shared goal of guiding individuals throughthe adolescent years safely and optimally prepared for the adultworld.
Adolescence is a time of substantial neurobiologic andbehavioral change, but the teen brain is not a broken or
defective adult brain. The adaptive potential of the overproduction/selective elimination process, increased connectivity and integration of disparate brain functions, changing
reward systems and frontal/limbic balance, and the accompanying behaviors of separation from family of origin, increased risk taking, and increased sensation seeking havebeen highly adaptive in our past and may be so in our future.
These changes and the enormous plasticity of the teen brain
make adolescence a time of great risk and great opportunity.
Addendum: Technical Aspects, Analysis, andModalities of Imaging
The term magnetic resonance imaging (MRI), if not specifically qualified as a different type, usually refers to thetechnique that yields different signal intensities for different
tissue types (i.e., white matter [WM], gray matter [GM], or
cerebrospinal fluid [CSF]). It is sometimes referred to as structural MRI (sMRI) or anatomic MRI to distinguish it from themore recent variants, such as diffusion tensor imaging (DTI),
magnetization transfer (MT), or functional MRI (fMRI).
The DTI technique assesses how free water is to diffuse
in any direction and provides information about the directionality of WM tracts [32]. The MT imaging techniqueassesses the ratio of the number of protons bound to macromolecules to the number of unbound protons [81]. Thisratio provides a characterization of the microstructure ofbrain tissue that is different from that provided by sMRI or
DTI. Functional MRI (fMRI) capitalizes on the different
magnetic properties of oxygenated versus deoxygenated hemoglobin to localize areas of the brain that have increased
blood flow during a given task, presumably as a result ofneuronal activity triggering greater metabolic need. All ofthese types of MRI can be performed on the same machineusing different software.
An overarching goal of image analysis is to characterize
the tissue properties of discrete brain units and to discern aone-to-one correspondence between the unit in one brain
image to the unit in another brain image, either from adifferent person or from the same person at a different time.
Discerning one-to-one correspondence between brains ischallenging because of the high variation in structural andfunctional localization. Striving to optimize valid correspondence remains one of the most active areas of image
analysis research. The smallest units of MRI pictures arecalled pixels (or picture elements) and their three-dimensional counterparts voxels (volume elements). Each voxel isassigned a single value based on the average magnetic

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properties of the tissue in that box. Computer algorithms
that combine information about the intensity of the voxel
with atlases that inform the probability of tissue type basedon the voxel’s location in the brain to classify tissue as GM,
WM, or CSF are commonly used in sMRI analyses [82].
The size of the voxel can vary depending on magnet
strength, and reductions in voxel size can usually be purchasedwith the currency of time. Most of the literature is from scans
producing voxels of 1–3 ml. It is worth noting that even a 1-ml
voxel may contain millions of neurons and trillions of synaptic
connections, which confers substantial—but often unheeded—
implications for interpretation. Also, the single value for agiven voxel arises from the average of its more microscopic
constituents, and two voxels with the same value may not haveidentical constituents. In general, voxels classified as WM arethought to consist mostly of myelinated axons, and voxels ofsolid brain tissue without enough myelin are classified as GM.
An electron microscope analysis of a single GM voxel from anadult mouse comprised 29.3% axons, 30.2% dendrites, 12.06%
dendritic spines, 9.5% glia, 13.8% cell bodies and blood vessels, and 5.2% extracellular space [83]. However the specificcomposition may be slightly different in human beings andmay vary by age and region. In some voxels, a modest increase
in myelin may switch the voxel designation from GM to WM.
Despite these interpretation challenges, MRI’s combination of safety, diversity of output parameters (e.g., anatomy,
physiology, tissue composition, directionality of WM), andwidespread accessibility has unleashed unprecedented insight into the living, growing brain.
A limitation of fMRI is that it relies on changes in bloodflow that take place on the scale of several seconds. Modalities
such as electroencephalography (EEG) and magnetoencephalography (MEG) have less spatial resolution but provide better
temporal resolution by capturing electrical changes at a scale ofmilliseconds, and provide important complementary information to our understanding of brain development.
The EEG technique measures brain electrical activity
thought to be generated largely by ion flow during synaptic
activity. A large body of EEG literature has documented stepwise changes in electrical activity throughout the lifespan,
including adolescence. Correlations between changes in thecoherence of EEG signals from different parts of the brain and
the Piagetian capacity for formal operations have been reported[84]. EEG changes in response to various stimuli (i.e., eventrelated potentials [ERP]) have demonstrated child–adolescent–adult differences that correspond to behavioral changes incapacities [85]. One recent study reports early versus lateadolescent ERP differences in the anterior cingulate during
detection of error-related conflict [86].
The MEG technique is related to EEG by Maxwell’sequations, which eloquently reveal that an electrical current
will produce an orthogonally oriented magnetic field. Although both EEG and MEG presumably capture the same
phenomena of electromagnetic changes stemming from ionsflowing during neural activity, magnetic fields tend to be342

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less distorted by the skull, affording MEG potentially better
spatial resolution [87]. Thus far, MEG studies in adolescents have primarily addressed epilepsy; however a growing
number of projects are underway to assess language, impulse control, and other cognitive phenomena.
The drawback to these high temporal resolution techniques is that they have poorer spatial resolution than MRI.
Currently no single technique offers excellent spatial andtemporal resolution of physiologic activity, and comprehensive characterization therefore relies on the integration of
information from multiple modalities.
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