Neuron

Review
Structural MRI of Pediatric Brain Development:
What Have We Learned and Where Are We Going?
Jay N. Giedd1,* and Judith L. Rapoport1
1Child Psychiatry Branch/NIMH, Bethesda, MD 20892, USA*Correspondence: jg@nih.gov
DOI 10.1016/j.neuron.2010.08.040

Magnetic resonance imaging (MRI) allows unprecedented access to the anatomy and physiology of thedeveloping brain without the use of ionizing radiation. Over the past two decades, thousands of brain MRI
scans from healthy youth and those with neuropsychiatric illness have been acquired and analyzed withrespect to diagnosis, sex, genetics, and/or psychological variables such as IQ. Initial reports comparing
size differences of various brain components averaged across large age spans have given rise to longitudinalstudies examining trajectories of development over time and evaluations of neural circuitry as opposed tostructures in isolation. Although MRI is still not of routine diagnostic utility for evaluation of pediatric neuropsychiatric disorders, patterns of typical versus atypical development have emerged that may elucidate
pathologic mechanisms and suggest targets for intervention. In this review we summarize general contributions of structural MRI to our understanding of neurodevelopment in health and illness.
MRI of Brain Anatomy in Typical Pediatric Development
The human brain has a particularly protracted maturation, withdifferent tissue types, brain structures, and neural circuits having
distinct developmental trajectories undergoing dynamicchanges throughout life. Longitudinal MR scans of typicallydeveloping children and adolescents demonstrate increasing
white matter (WM) volumes and inverted U shaped trajectories
of gray matter (GM) volumes with peak sizes occurring atdifferent times in different regions. Figure 1 shows age by size
trajectories from a longitudinal study comprising 829 scansfrom 387 subjects, ages 3–27 years (see Figure 1 and Supplemental Experimental Procedures).
Total Cerebral Volume
In the Child Psychiatry Branch cohort noted above, total cerebral
volume follows an inverted U shaped trajectory peaking at age
10.5 in girls and 14.5 in boys (Lenroot et al., 2007). In both males
and females, the brain is already at 95% of its peak size by age 6(Figure 1A). Across these ages, the group average brain size formales is 10% larger than for females. This 10% differences is
consistent with a vast adult neuroimaging and postmortem study
literature but is often explained as being related to the larger bodysize of males. However, in our pediatric subjects the boys’ bodies
are not larger than girls’ until after puberty. Further evidence thatbrain size is not tightly linked to body size is the fundamental
decoupling of brain and body size maturational trajectories,
with body size increasing through approximately age 17.
Differences in brain size should not be interpreted as necessarily imparting any sort of functional advantage or disadvantage.
In the case of male/female differences, gross structural measures
may not reflect sexually dimorphic differences in functionally relevant factors such as neuronal connectivity and receptor density.
Sowell and colleagues measured changes in brain volume ina group of 45 children scanned twice (2 years apart) between
ages 5 and 11 (Sowell et al., 2004). Using a very different method,
728 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

in which the distance was measured between points on the brain
surface and the center of the brain, they found increases in brain
size during this age span, particularly in the frontal and occipitalregions.
Cerebellum
Caviness et al., in a cross-sectional sample of 15 boys and
15 girls aged 7–11, found that the cerebellum was at adultvolume in females but not males at this age range, suggesting
the presence of late development and sexual dimorphism (Caviness et al., 1996). The function of the cerebellum has traditionallybeen described as related to motor control, but it is now
commonly accepted that the cerebellum is also involved inemotional processing and other higher cognitive functions thatmature throughout adolescence (Riva and Giorgi, 2000;
Schmahmann, 2004).
In the Child Psychiatry Branch cohort, developmental curvesof total cerebellum size were similar to that of the cerebrum
following an inverted U shaped developmental trajectory withpeak size occurring at 11.3 in girls and 15.6 in boys. In contrast
to the evolutionarily more recent cerebellar hemispheric lobes
that followed the inverted U shaped developmental trajectory,
cerebellar vermis size did not change across this age span
(Tiemeier et al., 2010).
White Matter
The white color of ‘‘white matter’’ is produced by myelin, fatty
white sheaths formed by oligodendrocytes that wrap aroundaxons and drastically increase the speed of neuronal signals.
The volume of WM generally increases throughout childhood
and adolescence (Lenroot et al., 2007), which may underlie
greater connectivity and integration of disparate neural circuitry.
An important feature that has only recently been appreciated isthat myelin does not simply maximize speed of transmission
but modulates the timing and synchrony of the neuronal firing
patterns that create functional networks in the brain (Fields andNeuron

Review
Figure 1. Developmental Trajectories ofBrain Morphometry: Ages 6–20 Years
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,
and upper and lower lines represent upper andlower 95% confidence intervals. All curves differedsignificantly in height and shape with the exception of lateral ventricles, in which only height wasdifferent, and midsagittal area of the corpus callosum, in which neither height nor shape wasdifferent. (A) Total brain volume, (B) gray mattervolume, (C) white matter volume, (D) lateral
ventricle volume, (E) midsagittal area of the corpus
callosum, and (F) caudate volume. Reprinted from
Lenroot et al. 2007).

reductions are driven by synaptic pruning
versus myelination along the GM/WM
border (Sowell et al., 2001). The volumeof the caudate nucleus, a subcortical
GM structure, also follows an inverted U
shaped developmental trajectory, withpeaks similar to the frontal lobes withwhich they share extensive connections(Lenroot et al., 2007).

Stevens-Graham, 2002). Consistent with this, a study using
a measure of white matter density to map regional white matter
growth found rapid localized increases between childhood andadolescence. Corticospinal tracts showed increases that weresimilar in magnitude on both sides, while tracts connecting thefrontal and temporal regions showed more increase in the leftsided language-associated regions (Paus et al., 1999).
Gray Matter
Whereas WM increases during childhood and adolescence,
the trajectories of GM volumes follow an inverted U shaped
developmental trajectory. The different developmental curvesof WM and GM belie the intimate connections among neurons,
glial cells, and myelin, which are fellow components in neuralcircuits and are linked by lifelong reciprocal relationships.
Cortical GM changes at the voxel level from ages 4 to 20 years
derived from scans of 13 subjects who had each beenscanned four times at 2 year intervals are shown in Figure 2(animation is available at http://www.nimh.nih.gov/videos/press/prbrainmaturing.mpeg) (Gogtay et al., 2004b). The age ofpeak GM density is earliest in primary sensorimotor areas andlatest in higher-order association areas such as the dorsolateralprefrontal cortex, inferior parietal, and superior temporal gyrus.
An unresolved question is the degree to which the cortical GM

Developmental Trajectories: The
Journey as Well as the Destination
A prominent tenet now established in theneuroimaging literature is that the shapeof the age by size trajectories may berelated to functional characteristics evenmore so than the absolute size. For
instance, in a longitudinal study comprising 692 scans from 307 typicallydeveloping subjects, age by cortical thickness developmental
curves were more predictive of IQ than differences in cortical
thickness at age 20 years (Shaw et al., 2006a). Age by size trajectories are also more discriminating than static measures forsexual dimorphism where lobar peak GM volumes occur 1–3
years earlier in females (Lenroot et al., 2007). Trajectories are
increasingly being employed as a discerning phenotype instudies of psychopathology as well (Giedd et al., 2008).
Many psychiatric disorders (both child and adult) have long
been hypothesized to reflect subtle abnormalities in brain development. Anatomic brain developmental studies have revived
and extended our understanding of normal and abnormal developmental patterns as well as plastic response to illness. It isbeyond the scope of this review to discuss any disorder in great
depth, but an overview of a series of studies for attention-deficit/
hyperactivity disorder (ADHD), very early (childhood) onset
schizophrenia (COS), and autism illustrate some key principles.
Attention-Deficit/Hyperactivity Disorder
ADHD is the most common neurodevelopment disorder of childhood, affecting between 5% and 10% of school-age children
and 4.4% of adults (Kessler et al., 2005). There remains controversy over this disorder because of the lack of any biologicaldiagnostic test, the frequency of salient symptoms (inattention,
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Review

Figure 3. Developmental Delay of Cortical Thickness in ADHD

Figure 2. Regional Maturation of Cortical Thickness: Ages 4–21
Years
Right lateral and top views of the dynamic sequence of GM maturation over thecortical surface. The side bar shows a color representation in units of GM
volume. Reprinted from Gogtay et al. (2004a).

restlessness, and impulsivity) in the general population, the goodlong-term outcome for about half of childhood cases, and thepossible overuse of stimulant drug treatment.
Cross-sectional anatomical imaging studies of ADHD consistently point to involvement of the frontal lobes (Castellanos et al.,2002), parietal lobes (Sowell et al., 2003), basal ganglia (Castellanos and Giedd, 1994), corpus callosum (Giedd et al., 1994),
and cerebellum (Berquin et al., 1998). Imaging studies of brain
physiology also support involvement of right frontal-basal
ganglia circuitry with a powerful modulatory influence from thecerebellum (see Giedd et al., 2001, for review).
Because of the wide range of clinical outcomes seen in ADHD,
longitudinal studies have been of particular interest. Suchstudies indicate a developmental delay of cortical thickness
trajectories most markedly for the frontal lobes (Shaw et al.,2007a) (see Figure 3). The general pattern of primary sensory
areas attaining peak cortical thickness before polymodal, highorder association areas occurred in both those with and without
ADHD. However, the median age by which 50% of the corticalpoints attained peak thickness was 10.5 years for ADHD and
7.5 years for controls. The area with the greatest age difference
was the middle prefrontal cortex, reaching peak thickness at
10.9 years in those with ADHD and 5.9 years for controls.
A theme of our ADHD studies is that clinical improvement isoften mirrored by a convergence of developmental trajectories
toward typical development and that persistence of ADHD is
accompanied by a progressive divergence away from typicaldevelopment. We have demonstrated this for the cortex, where
right parietal cortical normalization accompanied clinicalimprovement (Shaw et al., 2006b)—and for the cerebellum,
where progressive volume loss of the inferior posterior lobes
mirrors persistence of ADHD (Mackie et al., 2007). A similar principle may hold for the hippocampus: children with ADHD who
730 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

Regions where the ADHD group had delayed cortical maturation, as indicatedby an older age of attaining peak cortical thickness. Reprinted from Shaw et al.
(2006b).

remit show a trajectory that is similar to that of typical development, whereas persistent ADHD is accompanied by a progressive loss of hippocampal volume (Shaw et al., 2007b). These
highly significant findings occur independently, and thus one
cannot simply see ADHD as ‘‘delayed frontal development.’’ Italso should be stressed that, to date, these measures alone or
combined are not clinically useful for either diagnosis or clinicaloutcome.
Stimulants remain the most effective and widely used treatment of ADHD, improving on task behavior and minimizing
disruptive symptoms. Earlier studies indicating that stimulants
have a normalizing influence on subcortical and white matterdevelopment (Castellanos et al., 2002) have been extended tocortical development (Shaw et al., 2009) and to the thalamus(Logvinov et al., 2009). Whether this normalization represents
activity-driven or treatment-related plastic changes or a more
direct neural effect of medication remains unknown.
There is considerable epidemiological and neuropsychological evidence that ADHD is best considered dimensionally, lying
at the extreme of a continuous distribution of symptoms andunderlying cognitive impairments. We thus asked if cortical brain
development in typically developing children with symptoms ofhyperactivity and impulsivity resembles that found in the
syndrome. Specifically, we found that a slower rate of corticalthinning during late childhood and adolescence, which we previously found in ADHD, is also linked with severity of symptoms ofhyperactivity and impulsivity in typically developing children,
providing neurobiological evidence for the dimensionality ofthe disorder.
Schizophrenia
Schizophrenia is widely considered a neurodevelopmental
disorder (Weinberger, 1987; Rapoport et al., 2005). The studyof COS provides an excellent opportunity to investigate thespecifics of the neurodevelopmental deviations as (1) scans
can be obtained during the most dynamic and relevant periodsof brain development and (2) childhood-onset counterparts ofNeuron

Review
typical adult-onset illnesses usually show a more severe phenotype less likely to be influenced by environmental factors andmore likely to show genetic influences.
A study of COS has been ongoing at the NIMH since 1990. The
diagnosis is made using unmodified DSM-III-R/IV criteria and,in most cases, after a drug-free inpatient observation. Althoughrare, occurring 1/500th as often as adult-onset schizophrenia(AOS), the COS cases (n = 102 to date) clinically resemble
poor-outcome AOS cases, in that all phenomenological, family,
and neurobiological studies in COS show similar findings as in
AOS, suggesting continuity between these two forms of theillness (Gogtay and Rapoport, 2008).
Neuroimaging findings from the COS cohort are consistent
with the AOS literature showing increased lateral ventricularvolume, decreased total and regional cortical GM volumes,
decreased hippocampal and amygdala volumes, and increased
basal ganglia volumes that progressed during adolescence (seeGogtay and Rapoport, 2008, for review). Most strikingly revealed
by longitudinal data is progressive cortical GM loss duringadolescence (Thompson et al., 2001) and delayed white matterdevelopment (Gogtay et al., 2008). The cortical GM reduction
becomes more circumscribed with age (as the healthy group
trajectory of cortical thinning ‘‘catches up’’ with the accelerated
pattern of cortical GM loss seen in COS). The cortical GM loss inschizophrenia has been shown to be due to the loss of ‘‘neuropil,’’ which consists of glia, synaptic and dendritic arbors, andvasculature (Selemon and Goldman-Rakic, 1999). Postmortem
studies show no widespread neuronal loss in schizophrenia ora glial response to a potential neuronal injury. Based on theseand other converging data, developmental models of abnormal
synaptic function or structure have predominated (Weinberger
et al., 1992).
AutismAutism is defined by abnormal behavior in the spheres ofcommunication, social relatedness, and stereotyped behaviors
within the first 3 years of life. In children with autism, there isan early acceleration of brain growth, which overshoots typicaldimensions, leading to transient cerebral enlargement (Courchesne et al., 2007). Brain imaging and genetic studies of COS
provided unexpected links to autism with respect to a ‘‘shift tothe right’’ in early brain development (more rapid brain growthduring the first years of life in autism and premature decreasein cortical thickness during adolescence for COS). A possible
intermediate phenotype of altered timing of brain developmental
events (Rapoport et al., 2009) or alternate ‘‘polar’’ brain pathways have been proposed (Crespi et al., 2010). We predict that
future treatment research will focus on agents that have moregeneral ‘‘normalizing effects’’ on brain development. To date,there is limited evidence that stimulant drugs may have suchan effect as mentioned above (Sobel et al., 2010).
In summary, clinical studies indicate diagnosis-specific group
anatomic brain differences that although not diagnostic are
beginning to elucidate the timing and nature of deviations fromtypical development. Using trajectories (i.e., morphometric measures by age) as an endophenotype may provide discriminating
power where static measures do not (Giedd et al., 2008). It is
increasingly clear that the same molecular genetic risk can beassociated with a range of psychiatric phenotypes, including

autism, bipolar disorder, schizophrenia, mental retardation, andepilepsy. Conversely, the same psychiatric phenotype is likely toreflect numerous individually rare genetic abnormalities suchas copy number variants (Bassett et al., 2010; McClellan andKing, 2010). Exploring the role of genetic variants on timing of
brain development may clarify some of these issues of sensitivity
and specificity.
High Variability of Brain Measures across Individuals
All of the data presented above must be interpreted in light ofthe strikingly high variability of brain size measures acrossindividuals (Lange et al., 1997). This high variability extends tomeasures of brain substructures as well. The high variability
and substantial overlap of most measures for most groups being
compared has profound implications for the diagnostic utility ofpsychiatric neuroimaging and the sensitivity/specificity in using
neuroimaging to make predictions about behavior or ability ina particular individual. For example, although group average
anatomic MRI differences have been reported for all major
psychiatric disorders, MRI is not currently indicated for theroutine diagnosis of any. Likewise, although on group average
there are statistically robust differences between male andfemale brains, there is nothing on an individual MRI brain scan
to confidently discern whether it is of a man or a woman. As ananalogy, height for adult men is significantly greater than height
for adult women. However, there are so many women taller thanso many men that height alone would not be a very useful way todetermine someone’s sex. Male/female differences in height areabout twice the effect size of most neuroimaging or neuropsychological measures.
Going from group average differences to individual use is oneof the preeminent challenges of neuroimaging. As much of theutility of neuroimaging relies on the extent to which this challenge
can be met, accounting for the variability is of prime importance.
In the following section, we will examine some of the parameters
known to influence variation in brain development.
Influences on Developmental Trajectories of Brain
Anatomy during Childhood and Adolescence
Genes and Environment
By comparing similarities between monozygotic (MZ) twins, whoshare 100% of the same genes, and dizygotic (DZ) twins, whoshare 50% of the same genes, we can estimate relative contributions of genetic and nongenetic influences on trajectories ofbrain development. To pursue this question, we are conducting
a longitudinal neuroimaging study of twins and currently haveacquired 600 scans from 90 MZ and 60 DZ twin pairs. Structural
equation modeling (SEM) is used to assess age 3 gene 3 environment interactions and other epistatic phenomena that challenge conventional interpretation of twin data. SEM describes
the interacting effects as (A) additive genetic, (C) shared environmental, or (E) unique environmental factors (Neale and Cardon,
1992). For most brain structures examined, additive genetic
effects (i.e., ‘‘heritability’’) are high and shared environmental
effects are low (Wallace et al., 2006). Additive genetic effects
for total cerebral and lobar volumes (including GM and WM subcompartments) ranged from 0.77 to 0.88; for the caudate, 0.80;
and for the corpus callosum, 0.85. The cerebellum has
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Reviewa distinctive heritability profile with an additive genetic effect of
only 0.49, although wide confidence intervals merit cautious
interpretation. Highly heritable brain morphometric measuresprovide biologic markers for inherited traits and may serve as
targets for genetic linkage and association studies (Gottesman
and Gould, 2003).
Multivariate analyses allow assessment of the degree to which
the same genetic or environmental factors contribute to multiple
neuroanatomic structures. Like the univariate variables, theseinterstructure correlations can be parceled into relationships ofeither genetic or environmental origin. This knowledge is vitally
important for interpretation of most of the twin data, including
understanding the impact of genes that may affect distributedneural networks, as well as interventions that may have global
brain impacts. Shared effects account for more of the variancethan structure-specific effects, with a single genetic factor
accounting for 60% of variability in cortical thickness (Schmitt
et al., 2007). Six factors account for 58% of the remaining variance, with five groups of structures strongly influenced by thesame underlying genetic factors. These findings are consistent
with the radial unit hypothesis of neocortical expansion proposedby Rakic (Rakic, 1995) and with hypotheses that global, genetically mediated differences in cell division were the driving force
behind interspecies differences in total brain volume (Darlington
et al., 1999; Finlay and Darlington, 1995; Fishell, 1997). Expanding the entire brain when only specific functions might beselected for is metabolically costly, but the number of mutations
required to affect cell division would be far less than that required
to completely change cerebral organization.
Age-related changes in heritability may be linked to the timingof gene expression and related to the age of onset of disorders.
In general, heritability increases with age for WM and decreases
for GM volumes (Wallace et al., 2006), whereas heritability
increases for cortical thickness in regions within the frontal
cortex, parietal, and temporal lobes (Lenroot et al., 2009). Knowledge of when 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 have different
prevalence, age of onset, and symptomatology between males
and females, sex differences in typical developmental brain
trajectories are highly relevant for studies of pathology. Robust
sex differences in developmental trajectories are noted for nearly
all structures, with GM volume peaks generally occurring 1–3
years earlier in females (Lenroot et al., 2007). To assess the relative contributions of sex chromosomes and hormones, our groupis studying subjects with anomalous sex chromosome variations(e.g., XXY, XXX, XXXY, XYY) (Giedd et al., 2007), as well as
subjects with anomalous hormone levels (e.g., congenital
adrenal hyperplasia, familial male precocious puberty, Cushing
syndrome) (Merke et al., 2003, 2005).
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 apoli732 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

poprotein E (apoE), which modulates risk for Alzheimer’s
disease. Carriers of the 4 allele of apoE have increased risk,
whereas carriers of the 2 allele are possibly at decreased risk.
To explore whether apoE alleles have distinct neuroanatomic
signatures identifiable in childhood and adolescence, we examined 529 scans from 239 healthy subjects aged 4–20 years(Shaw et al., 2007c). Although there were no significant IQ-genotype interactions, there was a stepwise effect on cortical thickness in the entorhinal and right hippocampal 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 adultonset disorders.
Summary/Discussion
Maturational themes relevant to both health and illness include
the importance of considering developmental trajectories andthe high variability of measures across individuals. Despitehigh individual variation, several statistically robust patterns of
average maturational changes are evident. Specifically, WM
volumes increase and GM volumes follow an inverted U developmental trajectory with peaks latest in high association areas suchas dorsolateral prefrontal cortex. These anatomic changes are
consistent with electroencephalographic, functional MRI, postmortem, and neuropsychological studies indicating an
increasing ‘‘connectivity’’ in the developing brain. ‘‘Connectivity’’characterizes several neuroscience concepts. In anatomic
studies, connectivity can mean a physical link between areasof the brain that share common developmental trajectories. In
studies of brain function, connectivity describes the relationshipbetween different parts of the brain that activate together during
a task. In genetic studies, it refers to different regions that are
influenced by the same genetic or environmental factors. All ofthese types of connectivity increase during adolescence.
Characterizing developing neural circuitry and the changing relationships among disparate brain components is one of the mostactive areas of neuroimaging research as detailed by Power et al.
(2010) (this issue of Neuron).
Although other higher association areas also mature relatively
late, the developmental course of the dorsolateral prefrontal
cortex has most prominently entered discourse affecting thesocial, legislative, judicial, parenting, and educational realms
because of its involvement in judgment, decision making, andimpulse control. It is also consistent with a growing body of literature indicating a changing balance between earlier-maturing
limbic system networks (which are the seat of emotion, andlater-maturing frontal lobe networks) and later-maturing frontal
systems (Casey et al., 2010a [this issue of Neuron]). The
frontal/limbic relationship is highly dynamic. Appreciating theinterplay between limbic and cognitive systems is imperative
for understanding decision making during adolescence.
Psychological tests are usually conducted under conditionsof ‘‘cold cognition’’—hypothetical, low-emotion situations.
However, real-world decision making often occurs under conditions of ‘‘hot cognition’’—high arousal, with peer pressure andreal consequences. Neuroimaging investigations continue todiscern the different biological circuitry involved in hot and cold
cognition and are beginning to map how the parts of the brain

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involved in decision making mature. For instance, adolescentsshow exaggerated nucleus accumbens activation to reward
compared to children but not a difference in orbital frontal activation (Galvan et al., 2006). Prolonged maturation of the PFC has
also been shown to be related to age-related improvement inmemory for details of experiences (in contrast to earlier maturing
medial temporal lobe structures subserving nonexperiential
memories) (Ofen et al., 2007).
The ‘‘journey as well as the destination’’ tenet highlights thefundamentally dynamic nature of brain and cognitive development in children. Adolescence is a particularly critical stage ofneural development, and the relationship between typical maturational changes and the onset of psychopathology in this age
group is an area of active investigation. The onset of several
classes of psychiatric illness in the teen years (e.g., anxiety
and mood disorders, psychosis, eating disorders, and substance abuse) (Kessler et al., 2005) may be related to the many
brain changes occurring during this time (Paus et al., 2008).
More broadly, understanding the mechanisms and influenceson structural and functional brain development across childhood
may help us to harness the brain’s developmental plasticity tohelp guide interventions for clinical disorders and for elucidating
the path to promote optimal healthy development.
SUPPLEMENTAL INFORMATION
Supplemental Information includes methodologic considerations and can befound with this article online at doi:10.1016/j.neuron.2010.08.040.
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