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Mapping brain maturationArthur W. Toga, Paul M. Thompson and Elizabeth R. Sowell

Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South,

Suite 225, Los Angeles, CA 90095-7332, USA

Human brain maturation is a complex, lifelong process

that can now be examined in detail using neuroimaging

techniques. Ongoing projects scan subjects longitudin-

ally with structural magnetic resonance imaging (MRI),

enabling the time-course and anatomical sequence of

development to be reconstructed. Here, we review

recent progress on imaging studies of development.

We focus on cortical and subcortical changes observed

in healthy children, and contrast them with abnormal

developmental changes in early-onset schizophrenia,

fetal alcohol syndrome, attention-deficit–hyperactivity

disorder (ADHD) and Williams syndrome. We relate

these structural changes to the cellular processes that

underlie them, and to cognitive and behavioral changes

occurring throughout childhood and adolescence.

Introduction

The dynamic course of brain maturation is one of the mostfascinating aspects of the human condition. Althoughbrain change and adaptation are part of a lifelong process,the earliest phases of maturation – during fetal develop-ment and childhood – are perhaps the most dramatic andimportant. Indeed, much of the potential and many of thevulnerabilities of the brain might, in part, depend on thefirst two decades of life.

The cortex and subcortical gray-matter nuclei developduring fetal life in a carefully orchestrated sequence of cellproliferation, migration and maturation. This leads to ahuman brain with w100 billion neurons at birth.However, the brain of a newborn child is only one-quarterto one-third of its adult volume, and it continues to growand specialize according to a precise genetic program, withmodifications driven by environmental influences, bothpositive and negative. With stimulation and experience,the dendritic branching of neurons greatly increases, as dothe numbers of synaptic connections. As layers ofinsulating lipids are laid down on axons through theprocess of myelination, the conduction speed of fibers thatinterconnect different brain regions increases w100-fold.This exuberant increase in brain connections is followedby an enigmatic process of dendritic ‘pruning’ and synapseelimination, which leads to a more efficient set ofconnections that are continuously remodeledthroughout life.

Synaptic changes and myelination

When Huttenlocher began to demonstrate this successionof events in the human brain, magnetic resonance imaging

Corresponding author: Toga, A.W. ([email protected]).

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(MRI) was still in its infancy. His histological work in the1980s paved the way in showing that the time-course forsynaptic blooming and pruning in the human brain variesenormously by brain region. For example, in the visualcortex, synaptic overproduction reaches a maximum atabout the fourth postnatal month. Then synapse elimin-ation starts, and this continues until preschool age, bywhich time synaptic density has reached the adult level.But in the medial prefrontal cortex, an area of the braininvolved in executive, attentional and regulatory func-tions, the peak occurs at 3–4 years of age, and substantialdecline does not occur until mid-to-late adolescence [1].

Early work by Yakovlev and Lecours documented theprogression of myelination in the developing human brain[2]. More recent work by Benes et al. with much largersamples showed similar results, with myelination con-tinuing well into the third decade of life [3]. Interestingly,the spatial and temporal pattern of these cellular changesseemed to parallel developmental changes in synapticdensity. Simply put, myelination of the most dorsalregions of the brain responsible for higher cognitivefunctions seemed to continue well into adolescence, andmore ventral and deep brain structures – some of whichare responsible for relatively more primitive functions –were myelinated earlier.

However, the sequence of myelination might be morecomplex than this. A problem with many interpretationsof the work by Yakovlev and Lecours [2], and of similarstudies of myelogenesis, is that dorsal and ventral systemsdo not ultimately myelinate to the same degree: onaverage, dorsal cortex, at its most mature, is lessmyelinated than ventral cortex. Therefore, if myelinationtook the same time-course, then at any given time areasthat myelinate more fully would appear more myelinatedthan those that myelinate less. Therefore, when thedegree of myelination is observed on histologically stainedsections or in MRI scans of the brain, one must alsoconsider the ultimate extent of myelination for eachsystem. Recent research studying patterning molecules,and rates of proliferation and neuronal migration, show amuch more complex pattern of cortical maturation,occurring primarily from a rostral-lateral-ventral poletoward a dorsal-medial-caudal pole (reviewed in [4]).

This histological evidence suggested that braindevelopment is a dynamic process of progressive andregressive changes. But histology gave only very frag-mented evidence for late brain maturation, given thedearth of post-mortem data from childhood and adolescentyears. By contrast, MRI can non-invasively documentthese large-scale processes of brain development, can

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provide insight into the sequence and timing of thesedevelopmental processes in longitudinal experiments, andcan document how they occur in living subjects.

Initial brain-imaging studies

The first quantitative structural brain-imaging studies innormal children using MRI were conducted in the late1980s and early 1990s. Terry Jernigan and colleaguesshowed that young adults actually had less cortical graymatter than children, despite the fact that adults hadsomewhat larger overall total brain volumes [5]. Theyfound that gray-matter volumes generally declined afterage 7, perhaps because the advancement of white matter(i.e. myelination) throughout childhood began to overtakethe overall rate of brain volume expansion, causing a netdecrement in the amount of tissue appearing gray (orunmyelinated) on MRI. They then determined that thetiming of gray-matter loss was different for different brainregions: loss was first observed in the deep motor nuclei inearly childhood, then in the parietal and frontal lobes ataround puberty [6]. Although MRI did not assess synapticdensity per se, this was the first in vivo evidence to supportthe post-mortem findings of Huttenlocher and of Yakovlevand Lecours.

Anatomical parcellation and mapping

The aforementioned studies used a method called volu-metric parcellation. In this approach, the brain issubdivided into several, separate anatomical regionswith different functions (e.g. the frontal lobe, the deepmotor nuclei or the hippocampus), and their volumes aremeasured (Figure 1). Parcellation enables the compilationof growth curves that document how regional volumesvary with age. Results are typically illustrated usingscatterplots but regional measurements are limited byanatomical structures (i.e. sulcal landmarks) and can alsobe reliably visualized and defined using MRI.

In the late 1990s, a few teams began to make composite3D maps of developing brain structures [4,7–12] showing,for example, the average pattern of age-related change ingray-matter thickness between childhood and youngadulthood. More recently, these mapping techniqueshave become more popular because they provide more

MRI scan Tissue classifi

(a) (b)

Figure 1. Typical processing steps in an analysis of MRI brain scans. (a) A typical coronal

classification approach to classify image voxels as gray matter (green), white matter (bl

surrounding the brain have been digitally edited from the image. (c) Parcellation of the br

lobe (yellow). This subdivision of anatomy is performed with the aid of a cortical surfac

Once partitioned in this way, the volumes of each tissue type in the major lobes can be co

white matter in the brain and of CSF in the ventricles and cortical sulci can be computed

used to calculate shape, size and other statistics, and can also be subdivided into small

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visual detail and group statistics regarding the rates ofchange and their regional variation. Time-lapse anima-tions also can be computed to illustrate the changes.

Many of the basic functions of the brain, such as vision,hearing, speech, planning and emotional control areprimarily handled in the cortex, so much of the workmapping brain development has focused on the cortex,although some groups have studied subcortical graymatter. The goal of cortical mapping is to create groupaverage maps of cortical features of interest such as gray-matter thickness, gray-matter density, cortical shape,average sulcal patterning and hemispheric asymmetries,all of which change during development (for methods, see[13]). Next, statistics are defined that help localize ageeffects on these measurements, such as localizedreductions or increases in gray-matter density or thick-ness, and brain growth.

Changes in gray and white matter

The earliest cross-sectional pediatric brain MRI studies ofnormal developmental changes showed that gray-mattervolumes generally declined after 6–7 years of age andcontinued to decrease during adolescence, whereas white-matter volumes increased linearly over time. However, inone of the first studies to compile growth curves for thevolumes of different lobes of the brain as subjects aged (i.e.studies that were longitudinal rather than cross-sectional)[14], there was a clear linear increase in white matter upto age 20, whereas there were non-linear changes incortical gray matter. Giedd et al. demonstrated a pre-adolescent increase, with developmental curves peakingat w12 years for the frontal and parietal lobe, and at w16years for the temporal lobe. After that, gray-matterloss occurs.

More recently, another developmental study quantifiedhuman cortical development by measuring gray-matterdensity in each lobe, point by point [4]. This map wasconstructed from serial brain MRI scans of 13 childrenfollowed over a ten year period. Children were scannedevery two years for eight years from the time they wererecruited, and were given a structured diagnostic inter-view at each visit to confirm lack of a psychiatric disorder.

cation Parcellation into lobes

(c)

section from a T1-weighted MRI scan of the brain. (b) The result of applying a tissue-

ue) or cerebrospinal fluid (CSF; red). Non-brain tissues such as scalp and meninges

ain into the frontal lobe (blue), parietal lobe (green), occipital lobe (red) and temporal

e model on which sulcal landmarks separating the lobes can be reliably identified.

mputed and growth curves established for each major lobe. The quantity of gray and

and compared across subjects and over time. Maps of these tissue types (b) can be

er regions to determine the amount of each tissue type in each lobe (c).


(a)

(b)

5.5

5.0

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4.0

3.5

3.0

2.5

2.0

1.5

1.0

< 2 mm2.0–2.5 mm2.5–3.0 mm3.0–3.5 mm3.5–4.0 mm

Figure 2. Cortical thickness maps. (a) An in vivo average cortical-thickness map

created from 45 normally developing children at their first scan [12]. The brain

surface is color coded according to the bar on the right, where thickness is shown in

millimeters. The average thickness map can be compared to an adapted version of

the 1929 cortical thickness map of von Economo [15] (b). Color coding has been

applied over his original stippling pattern, respecting the boundaries of his original

work, to highlight the similarities between the two maps. Reproduced, with

permission, from [12].


This study measured which brain regions changebetween 4 and 21 years of age and illustrated thesestatistics as a time-lapse movie. This revealed a shiftingpattern of gray-matter loss, appearing first (at w4–8 yearsof age) in dorsal parietal and primary sensorimotorregions near the interhemispheric margin, and spreadinglaterally and caudally into temporal cortices and ante-riorly into dorsolateral prefrontal areas. The first areas tomature were those with the most basic functions, such asthose processing the senses and movement. Areasinvolved in spatial orientation and language (parietallobes) followed, around the age of puberty (11–13 years).Areas with more advanced functions – integratinginformation from the senses, reasoning and other ‘execu-tive’ functions (e.g. prefrontal cortex) – matured last, inlate adolescence. This sequence also provided evidencethat phylogenetically older cortical areas mature earlierthan the more recently evolved higher-order associationcortices, which integrate information from earliermaturing cortex.

Cortical thickness

In another longitudinal study, changes in cortical thick-ness were measured (in millimeters) in a group of 45normally developing children studied between 5 and 11years of age [12]. Each child was studied twice with a two-year scanning interval. Maps of cortical thickness werecreated for each child, and the average cortical-thicknessmaps were remarkably similar to maps created from post-mortem data by von Economo [15]. The average corticalthickness for the normally developing children and fromthe post-mortem data are shown in Figure 2. As can beseen on the medial brain surface, the cortex is thickest inthe most dorsal aspects of the frontal and parietal lobes(w4–5 mm), and thinnest in the visual cortices of theoccipital lobes surrounding the calcarine sulcus (2.0–2.5 mm). These data revealed cortical thinning of w0.15–0.30 mm per year in normally developing children, mostprominently in right dorsal frontal and bilateral parietalregions. Cortical thickening was also observed withincreases of w0.10–0.15 mm per year in the classicallanguage regions of the temporal and frontal lobes(Figure 3). The brain grew at a rate of w0.4–1.5 mm peryear in many regions, most prominently in the frontal andoccipital regions. This pattern of results is similar to thoseobserved in cross-sectional and longitudinal data ofnormal brain maturation [4,16], but the new methodsenabled changes in cortical thickness to be assessed inmillimeters for the first time. Additionally, this studyconfirmed that dynamic but distinct changes occur inposterior temporal and frontal language regions, wheregray matter continues to increase in thickness. Wespeculate that cortical changes in these regions could berelated to changing language abilities that continue after5 years of age (e.g. changes in reading ability) [12];functional and structural imaging studies are underwayto test these hypotheses.

Cognitive correlates

Changes in cortical thickness during normal developmentrelate to cognitive changes as children and adolescents


mature. In the same 45 normally developing childrenalready described, cortical thinning in the left dorsalfrontal and parietal lobes correlated with improvedperformance on a test of general verbal intellectualfunctioning [12,17]. Greater left hemisphere gray-matterthinning was associated with improved performance onthe vocabulary subtest of a standardized IQ measure [17](Figure 4). When permutation analyses were conducted tocorrect for multiple comparisons in the brain–behavioranalyses, change in cortical thickness was correlated withchange in the behavioral measure only in dorsal frontaland parietal cortex of the left hemisphere. Cortex in theseregions tends to thin with increased age. Thus, the resultsof negative relationships between cortical thickness andvocabulary improvements are consistent with what wouldbe expected. Considering these data, it seems reasonableto speculate that the brain changes are related to cognitivechanges during development, although few studies haveaddressed this issue.

Cortical changes throughout life

The brain-mapping studies of normal brain maturationdescribed here have been limited by the age ranges of the


Left Right

0.300.250.200.150.100.050.00

–0.05–0.10–0.15–0.20–0.25–0.30

Figure 3. Annualized rate of change in cortical thickness. The average rate of change in cortical thickness is shown in millimeters according to the color bar on the right

(maximum gray-matter loss is shown in shades of red and maximum gray-matter gain is shown in shades of blue). Forty-five children were studied twice (two-year scan

interval) between 5 and 11 years of age. Reproduced, with permission, from [12].


subjects studied. They have clearly shown regional andtemporal patterns of dynamic maturational changecontinuing through childhood and adolescence. However,they could not enable conclusions to be made about theendpoint in cortical sculpting (which probably resultsfrom increased myelination and synaptic pruning)because they did not assess individuals O30 years ofage. This issue was addressed in a study of a large sampleof normal individuals (nZ176) across the span of life (7–87years of age) [11]. Significant, non-linear age effects wereobserved over large areas of the most dorsal aspects of thefrontal and parietal regions on both the lateral andinterhemispheric surfaces and in the orbitofrontal cortex.Scatterplots of these effects revealed a dramatic decline ingray-matter density between 7 and 60 years of age, withlittle or no decline thereafter. A sample scatterplot of thequadratic effect of age on gray-matter density at onebrain-surface point on the superior frontal sulcus is shownin Figure 5, and is similar to plots from the dorsal frontal

Figure 4. Brain-behavior maps for vocabulary and cortical thickness. P values are for n

shown in figure 2 of [12]) and change in vocabulary raw scores (time 2 minus time 1).

vocabulary improvement) are color-coded, and regions in white showed no significant a

any of the regions of interest, and are not shown here. Reproduced, with permission, fr


and parietal regions. Notably, the most lateral aspects ofthe brain in the posterior temporal and inferior parietallobes bilaterally showed a distinct pattern of gray-matterchange, one in which the non-linear age effects wereinverted relative to the age effects in more dorsal cortices.A subtle increase in gray-matter density was observeduntil w30 years of age, and it then remained stable until aprecipitous decline in later decades. As these dataillustrate, the trajectory of maturational changes mightcontinue beyond adolescence and into young adulthood,and can be assessed only using an extended age range.The question of when brain maturational changestraverse into the more degenerative changes of agingbecomes even more poignant when considering these data(for a detailed discussion, see [18]). The last systems tomature are also among the first to degenerate in disorderssuch as Alzheimer’s disease; their high degree of plasticitythroughout life might make them more vulnerable toneurodegeneration [19–21].

P = 0.10–0.05

P = 0.05–0.01

P <0.01

egative correlations between change in cortical thickness (time 2 minus time 1, as

Negative P values (i.e. regions where greater thinning was associated with greater

ssociation. Positive correlations were not significant in the permutation analyses for

om [12].


(b)

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0.70

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r de

nsity

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Superior frontal sulcus

0 10 20 30 40 50 60 70 80 90

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0.000100.000080.000060.000040.000020.00000

–0.00002–0.00004–0.00006–0.00008–0.00010

Nonlinearregressioncoefficient

P <0.01 (inverted U curve)P <0.0000008 (U curve)

5 years

20 years

Age

Gray-mattervolume

Figure 5. Mapping brain change over time. Brain changes in development can be identified by fitting time-dependent statistical models to data collected from subjects cross-

sectionally (i.e. across a group of subjects at a particular time), longitudinally (i.e. following individual subjects as they aged), or both. Measurements such as cortical

thickness are then plotted onto the cortex using a color code. (a,b) Trajectory of gray-matter loss over the human lifespan, based on a cohort of 176 subjects aged 7–87 years

[11]. Plots superimposed on the brain in (b) show how gray-matter density decreases for particular regions; (a) highlights example regions in which the gray-matter density

decreases rapidly during adolescence (the superior frontal sulcus) or follows a more steadily declining time-course during lifespan (the superior temporal sulcus). (c,d)




(a) Tensor maps of growth (b) Tensor-based morphometry

3–6 years

Localgrowth (%)

Healthychildren(n = 20)

Autisticchildren(n = 20)

Areas thinnerin autism

SR

t statistic

Log (Jacobian)

80

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Figure 6. Tensor maps of growth and tensor-based morphometry. (a) The corpus callosum (indicated by a green box) of a healthy three-year-old girl in a sagittal section from

a 3D MRI scan. Using a follow-up scan thee years later, an elastic deformation field is computed that digitally aligns, or warps, the anatomy of the earlier time-point to match

its shape at the later time point. The amount of local stretching of the anatomy is color-coded, indicating fastest growth rates (red) in the anterior corpus callosum. In a related

approach (b), maps can be compiled to represent the average expansion factor required to deform an average corpus callosum shape elastically onto each subject in a set of

healthy children and matched autistic children. This identifies areas of the corpus callosum that are thinner in autistic children, pinpointing where abnormal white-matter

growth might lead to the disorder in early childhood. The letters ‘S’ and ‘R’ denote the splenium and rostrum of the corpus callosum, respectively. Data in (a) is adapted, with

permission, from [22]; (b) is adapted, with permission, from [53].


Subcortical changes during development

Most brain-mapping studies of development have focusedon the cortex, but developmental changes in subcorticalstructures have also been characterized. For example, onestudy mapped a front-to-back wave of growth through thecorpus callosum at 3–15 years of age [22] (Figure 6). Thecontinuous myelination of interhemispheric fibers resultsin increased axonal conduction speed and transmission ofinformation between the brain hemispheres. Perhaps themain surprise from the subcortical studies is theprogressive volume loss in the deep gray-matter nuclei –especially the caudate – at around puberty. In contrast tothe basal ganglia, temporal lobe gray-matter structures(the amygdala and hippocampus) seem to increase involume during childhood and adolescence.

Relationship to cellular changes

We cannot conclude directly what cellular changes areinvolved in the dynamic maturational processes describedhere. MRI measures changes in the volume and density ofbrain structures, but lacks the resolution to characterizethe cellular mechanism (e.g. dendritic remodeling, celldeath, synaptic pruning or myelination) of such changes.Moreover, gray-matter volume and thickness reflect notonly the dendritic and synaptic processes that occur inneurons but also the complex architecture of neurons, glia(i.e. myelin) and vasculature. However, we can say thatthe gray-matter changes described in this review correlatewell with the post-mortem sequence of regionally variableincreased synaptic pruning and myelination duringadolescence and early adulthood.

Trajectory of cortical gray-matter density in 13 children scanned longitudinally every two

is defined as the proportion of tissue segmenting as gray matter within a 15-mm-diame

ranges from 0 to 1, and is highly correlated with cortical thickness [52].


Fetal and neonatal MRI

Mapping fetal and neonatal brains presents another set ofpractical and technical challenges. Aside from the obviousdifficulty in obtaining motion-artifact-free images, atthese ages the degree of myelination produces consider-ably less contrast in images obtained using the mostcommonly applied T1 and T2 weighted pulse sequences.Nevertheless, considerable insight into the earliestmyelination and differentiation of the telencephalon anddiencephalon can be seen by imaging from fetal stages inutero through the first six months after birth [23]. Theseimaging results are consistent with post-mortemmeasures of increased myelination before birth [24–26].A general caudal-to-rostral progression can be appreciatedin this age group. Furthermore, morphological differen-tiation of several structures such as the internal capsule(a fan-like structure of white matter separating thelentiform nucleus from the caudate and dorsal thalamus)precedes changes in white-matter, often by many months.Utilizing specific magnetic resonance pulse sequences toidentify white matter, in an approach called diffusiontensor imaging (DTI), can also help characterize thechanges associated with myelination. Neil et al. [27]found dramatic and generalized changes in waterapparent diffusion coefficients and diffusion anisotropy.These diffusion parameters, measurable using DTI, arelocally sensitive to the degree of myelination (whichprovides resistance to water diffusion) and to fiberorientation (which constrains the principal directions ofwater diffusion to be aligned with axons). Gilmore et al.[28] found significant increases in fractional anisotropy inthe anterior and posterior regions (genu and splenium) ofthe corpus callosum during development that reflect

years for eight years [4]. The units used in (a) and (d) are gray-matter density, which

ter sphere centered at each point on the cortical surface. This widely-used measure



underlying changes to water content and cytoarchitectureduring maturation.

Developmental disorders

Statistical maps can also be created to assess group effectssuch as brain structure differences between healthychildren and those with neuropsychiatric disorders suchas schizophrenia, bipolar illness or attention-deficit–hyperactivity disorder (ADHD), those with genetic dis-orders of brain development such as Williams syndrome(WS; a mental retardation disorder), and those exposed toteratogens during brain development, such as childrenwith fetal alcohol syndrome (FAS). The resulting maps canreveal where in the brain differences are located, howsignificant they are, and – depending on the study design –whether they are stable or progressive [29].

Statistically significant reductions or increases inmeasured gray- or white-matter structures are commonlyfound in children with developmental disorders, anddifferent brain abnormalities have been observed indifferent disorders. However, most of the abnormalitiesobserved in the quantitative MRI studies of children withdevelopmental disorders have been relatively subtle. Thatis, the brain structural abnormalities could not typicallybe observed within individual children, but rather onlywhen studied in groups of children with a particulardisorder compared with non-affected or medication-matched controls.

Childhood-onset schizophrenia

Beginning in 1992, scientists led by Judith Rapoport atthe National Institute of Mental Health employed MRItechnology to scan a group of w50 teenagers repeatedly astheir schizophrenia developed and a group of O500healthy children as controls. The collected data wereanalyzed using brain-mapping methods developed todetect subtle changes in the cortex [30]. Healthy subjectslost gray matter at a subtle rate of 1–2% per year in theparietal cortices, with very little detectable change in theother lobes of the brain. By contrast, the childhood-onsetschizophrenia patients showed a rapid progressive loss ofgray matter in superior frontal and temporal cortices,reaching 3–4% per year in some regions. This pattern wasseen in both boys and girls. Early deficits in parietal brainregions that support language and associative thinkingspread forwards into the temporal lobes, supplementarymotor cortices and frontal eye fields. The deficits spreadanatomically over a period of 5 years, consistent with thecharacteristic neuromotor, sensory and visual searchimpairments in the disease. In temporal cortices, includ-ing primary auditory regions, severe gray-matter loss wasabsent at disease onset but later became pervasive.Patients with the worst brain tissue loss also had theworst symptoms, which included hallucinations, delu-sions, bizarre and psychotic thoughts, hearing voicesand depression.

The notion of schizophrenia as a dynamically emergingdisease of neurodevelopment is still controversial. Thespreading patterns of deficits corroborate the idea that thenormal developmental process of dendritic and synapticpruning might be abnormally accelerated or derailed in


schizophrenia [31]. Even so, they might represent aseparate process entirely that begins in adolescence –twins studies have hinted that the earliest deficits,occurring in the parietal cortex, might have a non-genetictrigger [32]. Some histological studies suggest that thereis decreased neuropil in schizophrenia, but autopsymaterial is scarce and findings are inconsistent. As innormal development, the observed cortical changes mightalso be glial or vascular in origin, rather than purelyneuronal [33]. Intracortical contrast might also be affectedby changes in myelination [34], and by changes in lipidmetabolism during the use of atypical antipsychoticagents. It is possible that in schizophrenia, changes ingray-matter density also reflect, at least in part, increas-ing intracortical myelination. Still, four dimensional mapssuch as these offer a biological marker of diseaseprogression for drug trials, where brain mapping canassess how well antipsychotic medications opposeadvancement of the disease [35].

Williams syndrome

Brain imaging has also helped to identify circumscribedalterations in brain structure that are associated withdevelopmental disorders that have genetic causes. Wil-liams syndrome (WS), for example, is a developmentaldisorder associated with a deletion of w20 genes in the7q11.23 region of chromosome 7. WS subjects have mild tomoderate mental retardation, but have remarkableproficiencies in language skills, social drive and musicalability. Figure 7 shows a range of brain regions affected bythis genetic deletion: in general, the cortex is thinner,except in perisylvian language regions, where it is thickerby 5–10%; cortical complexity is also significantlyincreased [36]. In post-mortem data from WS subjects,Holinger et al. [37] observed larger cells in Heschl’s gyrusin the primary auditory area – a finding congruent withthe cortical thickening seen here. WS subjects also havedisproportionately reduced white matter, so cortical cellsfitting over a smaller white-matter area can ‘pile up’, andthe cortex can thicken owing to a crowding effect. SomeWnt signaling genes, which direct early cell differentiationand segmentation, are in the deleted 7q11.23 chromo-somal region, suggesting a genetic basis for the corticaldysmorphology observed on MRI. Other genes deleted inWS might normally contribute to fissure formation in thehuman cortex, and their absence might account for theabnormal cortical thickening. Ongoing work is examiningthe changes in folding complexity in the same regions thatshow thickened cortex, to ensure that this thickening isnot simply a consequence of increased gyrus formation(e.g. microgyria often appears as thickened cortex, whenin fact it is thinner, four-layered cortex). If corticalthickness and complexity were positively correlated ingeneral, the frontal and parietal regions of greatergyrification found in studies of WS (e.g. [38]) would beexpected to show greater cortical thickness, but this wasnot found. There is, therefore, no simple relationshipbetween thickness and complexity, and careful study ofthese measures in larger samples is warranted. Alter-natively, the cortical thickening might represent anadaptive response to the genetic deletion, perhaps even


(a)

(b)

(c)

WS versus controls

ADHD versus controls

FAS versus controls

Figure 7. Differences in gray-matter density between subjects with three neurodevelopmental disorders. The percentage differences in gray-matter density between subjects

with Williams syndrome (WS) (a), attention-deficit–hyperactivity disorder (ADHD) (b), fetal alcohol syndrome (FAS) (c) and their respective normally developing control

groups are color-coded. In all maps, warmer colors represent positive differences, indicating an increase in the patient group (arbitrarily coded as 1) relative to the control

group (arbitrarily coded as 0), with red representing the largest group difference. Note that the maximum value varies on the three color bars, depending on the maximum

group difference from each comparison. Adapted, with permission, from [36,41,46].


from increased use or overuse of specific cortical networks.Identification of such neuroanatomical characteristics ofWS is important for better understanding of the scope andtiming of the cortical anomaly in WS.

Attention-deficit–hyperactivity disorder

ADHD is a much more common, and less debilitating,disorder of childhood than childhood-onset schizophreniaand WS. Nonetheless, volumetric structural brain-ima-ging studies of ADHD patients have demonstrated subtlereductions in total brain volume, and in volumes of theright frontal lobe and caudate nucleus [39,40]. To furtherunderstanding of brain abnormalities in ADHD, brainmapping studies were conducted in 27 children andadolescents with ADHD and in 46 age-matched andgender-matched control subjects [41]. As predicted,abnormal morphology was observed in the frontal corticesof ADHD subjects, with reduced regional brain sizelocalized primarily to inferior portions of dorsal prefrontalcortices bilaterally. Brain size was also reduced in anteriortemporal cortices bilaterally. Gray-matter density wasprominently increased in large portions of the posteriortemporal and inferior parietal cortices bilaterally(Figure 7). The etiology and cognitive and behavioralsequelae of this increased gray-matter density and


regionally reduced brain size are not yet understood, butthese regions of the brain are known to be part of anaction–attention network probably involved in the symp-tomatology of ADHD (discussed in [41]).

Fetal alcohol syndrome

Brain mapping has also identified anomalies in develop-mental disorders with known environmental causes. Fetalalcohol syndrome (FAS) is a severe neurodevelopmentaldisorder observed in children whose mothers abusedalcohol during pregnancy [42,43]. Not all women whoabuse alcohol during pregnancy have children with FAS,but it is quickly becoming more evident from animal andboth post-mortem and in vivo human studies that thebrain is especially vulnerable to the teratogenic effects ofalcohol. Children with FAS display prenatal growthdeficiency, developmental delay, craniofacial anomalies(i.e. microcephaly, epicanthal folds and short palpebralfissures) and limb defects. Cognitive deficits in thesechildren are significant and mental retardation is fre-quently observed, but a clear neuropsychological profilehas yet to be discovered [44].

In vivo quantitative MRI studies have confirmed brainmorphological abnormalities in children prenatallyexposed to alcohol. In addition to the microcephaly



frequently observed in these children, volumetric MRIstudies have shown that within the cortex, only theparietal lobes were significantly reduced in volume aboveand beyond the generalized brain size reduction. White-matter hypoplasia (i.e. incomplete development) was moresignificant than gray-matter hypoplasia, and hippocampalvolume was relatively intact [45]. These volumetricfindings prompted use of cortical mapping techniques toassess brain abnormalities on a more local level. Localizedabnormalities in brain size and gray-matter density wereidentified in a group of 21 children, adolescents and youngadults with severe prenatal alcohol exposure comparedwith 21 age-matched and gender-matched non-exposedsubjects [46]. The resulting brain maps confirmed parietallobe size reductions observed in the volumetric studies,and revealed frontal lobe size reductions that had not beenpreviously observed. In fact, frontal lobe abnormalitieswere predicted given these children’s deficits on neurop-sychological tests of executive functioning [47]. Further-more, gray-matter density increases were observedbilaterally in the inferior parietal and posterior temporallobes (Figure 7). These mapping studies suggest that braingrowth continues to be adversely affected long after theprenatal insult of alcohol exposure to the developingbrain, and the regions most prominently affected areconsistent with the behavioral and neuropsychologicaldeficits in these children.

Similarities among the developmental disorders

It is interesting to note the regional correspondence ofgray-matter density abnormalities observed in three of thefour neurodevelopmental disorders reviewed here. Asshown in Figure 7, gray-matter density increases wereobserved in perisylvian (language) cortices in ADHD [41],WS [36] and FAS [46]. The magnitude of increase in graymatter relative to their respective control groups variesacross the disorders, with maxima of w30% in ADHD,10% in WS and 50% in FAS. The spatial extent of thesegray-matter density increases also varies across thedisorders, and is bilateral in ADHD and FAS, but moreprominent in the right hemisphere in WS. These disordersare distinct in terms of their etiologies and neurocognitivedeficit profiles, although various levels of languageimpairment are observed in all three disorders. Facialdysmorphology is present in both FAS and WS. Ofparticular relevance might be that ADHD diagnoses arecommon among children with FAS [48] and WS [49].Perhaps the similar profile of gray-matter densityabnormalities observed in these three groups are relatedto the common symptoms of ADHD. Functional andstructural imaging studies evaluating children that haveADHD, WS with ADHD, WS without ADHD, FAS withADHD, and FAS without ADHD might yield interestingresults, although they would be difficult given the relativerarity of WS in particular. These examples are illustrativeonly and summarize visualizations derived from differentstudies (e.g. case-control studies of FAS, WS and ADHD).Further studies are required in which the magnitudes andspatial patterns of the effects in the different patientgroups are directly compared. Such direct comparisonswould have to take into account possible confounding


factors, because the children were assessed on differentscanners at different imaging centers.

Genetic influences on the developing brain

In the quest to understand what factors contribute to thetrajectory of brain development in normally developingchildren and those with neurodevelopmental disorders,genetic and imaging methods can be combined to answerquestions about the influence of genes and environment onbrain structure – the so-called nature–nurture debate.With this in mind, twins or family members with differentdegrees of genetic affinity can be compared. Of course,influences of nature and nurture in the determination ofindividual brain structure are not independent. None-theless, twin designs can reveal the degree to whichheredity is involved.

Twin studies

Probably the best known twin database is the Finnishtwin registry, which covers information on twin births inFinland from 1940 and has been a resource to manyepidemiological studies recognized worldwide. These dataenabled investigation of which brain regions developunder tight genetic control and which are more influencedby the environment. Before that, twins had been studiedusing MRI and some similarities in the volumes of brainstructures had been noted in identical twins, with weakerresemblance in fraternal twins. In 2001, utilizing thisdatabase, the first study of twins using brain mappingrevealed aspects of brain structure that are largelyinherited [50].

Forty healthy adult subjects, consisting of ten mono-zygotic and ten dizygotic twin pairs (five male pairs andfive female pairs in each group) were drawn from a twincohort consisting of all the same-sex twins born in Finlandbetween 1940 and 1957 for which both members of eachpair were alive and still residing in Finland. All thesubjects were scanned and 3D maps of gray matter andmodels of cortical surface anatomy were derived. Mono-zygotic pairs were then matched with the dizygotic pairs.Gray-matter volumes in the frontal parts of the brain weremore closely matched in the identical twins than in twinswho were less similar genetically. One interpretation ofthe findings is that areas of the brain involved in processand rule learning (e.g. frontal lobe structures) are undermore tight genetic control, at least in terms of grossstructural development, than areas of the brain that maplife experience and content (the temporal and parietallobes).

The finding that the gross anatomy of the frontal cortexdevelops under such strong genetic control is also ofpractical use for genetic studies of disease. Thesegenetically mediated structural differences have evenbeen linked, using genetic brain-mapping methods, withknown genetic markers that are overrepresented inpatients with frontal lobe deficits [51].

Limitations of brain-imaging studies

As we have shown, MRI used in both cross-sectional andlongitudinal studies gives precious data on normal andpathological brain development. Key information is now



coming from the large-scale analysis of data from childrenscanned longitudinally using MRI. Moreover, advances inMRI techniques other than anatomical MRI are nowoffering additional perspectives on the functional develop-ment of the brain. Functional MRI studies are nowemerging as a tool to study brain development, as arefiber-mapping studies using DTI to track connections andhow they develop during maturation.

However, several limitations need to be acknowledged.Some are technical. For example, because it requiresconsiderable statistical power to detect systematic differ-ences in brain structure, sample size is a major issue,especially when rare disorders are considered. MRI datacan be heterogeneous. Scan databases are only nowbecoming large enough to stratify images of braindevelopment by symptom profiles, therapeutic responseand currently identified risk factors.

MRI has some limits in resolving distinctions betweengray and white matter that are relevant when assessingmaturational changes. Recent algorithms for measuringcortical thickness [13] rely on definition of a boundarybetween gray and white matter, or at least on a proportionof tissue identified as gray matter (in the case of

(a) Hemispheric asymmetry

(c) Schizophrenia

Loss(%/y

1614121086420

0

–1

–2

–3

–4

–5

Healthyadolescents

Schizophreniasubjects

Boys

Child Adolescent Adult

Girls

Figure 8. Statistical maps of cortical structure. Various maps can be made that describe di

(a) and how strongly genes and environmental influence brain structure (b). Panel (a) sh

adults [55]. In these maps, the differences in asymmetry in the three age groups can be ev

Note the angle is larger in the adults than in the children. The color coding enables the

group to be visualized. Asymmetry measures can also be extended to rest of the cortical s

groups of identical (monozygotic, MZ) and fraternal (dizygotic, DZ) twins. Some brain

temporal lobe regions, such as the dorsolateral prefrontal cortex and temporal poles (re

(i.e. a greater proportion of the inter-subject variance is explained by non-genetic facto

longitudinally over five years. Also shown are maps of the considerably faster loss rates

scanned at the same ages and intervals. The frontal cortex underwent a selective rapid los

might be, in some respects, an exaggeration of changes that normally occur in adolesc

shown (d) by comparing average profiles of gray-matter density between 12 Alzheimer’s

71.4G0.9 years). In Alzheimer’s disease, gray-matter loss sweeps forward in the brain from

patients (c), the frontal cortices lose gray matter the fastest. The letters ‘W’ and ‘T’ deno


gray-matter density). As gray matter becomes moremyelinated, the border between gray and white changes,and can be displaced toward the pia. One factor in thecortical thinning seen in adolescence is not absolutediminution of the cortical gray matter, but rather a changein the gray–white segmentation boundary because ofincreasing intracortical myelin with age, which mightshift the gray–white junction further into the corticalmantle. Other anatomical features might also affect theaccurate segmentation of gray and white matter onmagnetic resonance images. The inner band of Baillarger,for instance, is so richly myelinated that in brain sectionsit is visible with the naked eye, close to the gray–whiteinterface in layer 5b; this might affect the ability to definean accurate cortical boundary in conventional magneticresonance images. Further studies with improved tissueclassification algorithms, tissue relaxometry and high-field imaging (providing increased resolution and con-trast-to-noise ratio) are likely to resolve some ofthese limitations.

Other issues concern the interpretation given to somedata. The key question is: what does a change in thevolume of a brain region mean, especially when one

(b) Gray-matter correlation between twins Perfectlycorrelated (%)

rateear)

Averagedeficit

(d) Alzheimer’s disease

0

–5

–10

–15

100908070605040302010

0

Identical twins (MZ) Fraternal twins (DZ)

Early deficit

1.5 years later (same subjects)

W

T

r2

fferent aspects of cortical anatomy. These include maps of gyrus-pattern asymmetry

ows the increasing gyrus-pattern asymmetry in groups of children, adolescents and

aluated as the angle of the left relative to the right Sylvian fissure in each age group.

displacement in millimeters between the left and right hemisphere within each age

urface and expressed in millimeters. Panel (b) shows correlations in gray matter for

regions develop under tight genetic control, and these include many frontal and

d). Other regions are more strongly influenced by the environment as they develop

rs). (c) Average maps of gray-matter loss rates for healthy boys and girls, scanned

in age-matched and gender-matched subjects with childhood-onset schizophrenia

s of gray matter (up to 3–4% per year faster in patients than controls). These changes

ence [10,54]. By contrast, deficits occurring as Alzheimer’s disease progresses are

disease patients (mean age 68.4G1.9 years) and 14 age-matched controls (mean age

limbic to frontal cortices in concert with cognitive decline, but in the schizophrenia

te Wernicke’s area and the temporal cortex, respectively.



considers pathology (Figure 8)? Even if the volume andshape of brain structures are determined, to identify theunderlying cellular cause of these differences one mustrely on using additional sources of information, such aspost-mortem tissue. Nevertheless, these MRI studies arelikely to help us better understand the links betweenchanges in the brain and cognitive development, inaddition to the clinical course of development andtreatment of illnesses.

AcknowledgementsThis work was funded by grants from the National Institute forBiomedical Imaging and Bioengineering, the National Center forResearch Resources, and the National Institutes on Aging, Neuro-degenerative Diseases and Mental Health (grant numbers PO1EB001955, U54 RR021813, MO1 RR000865 and P41 RR13642 toA.W.T., and R21 EB01651, R21 RR019771 and P50 AG016570 toP.M.T.), and from the National Institute of Mental Health, the NationalInstitute of Drug Abuse and the March of Dimes (K01 MH01733, R21DA15878, R01 DA017831 and MOD 5FY03-12 to E.R.S.).

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