ArticlePDF AvailableLiterature Review

Mapping Changes in the Human Cortex throughout the Span of Life

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In this review, the authors summarize the literature on brain morphological changes that occur throughout the human life span from childhood into old age. They examine changes observed postmortem and in vivo where various brain MRI analytic methods have been applied. They evaluate brain changes observed with volumetric image analytic methods and voxel-based morphometric methods that may be used to better localize where changes occur. The primary focus of the review is on recent studies using state-of-the-art cortical pattern-matching techniques to assess age-related changes in cortical asymmetries, gray matter distribution, and brain growth across various age spans. The authors attempt to integrate findings from the in vivo studies with results from postmortem studies and analyze the complicated question of when brain maturation stops and brain aging begins. Analyzing the regional patterns of change initiated at various ages may help elucidate relationships between changing brain morphology and changing cognitive functions that occur throughout life. Long-range longitudinal studies, correlations between imaging and postmortem data, and more advanced image acquisition and analysis technologies will be needed to fully interpret brain morphological changes observed in vivo in relation to development and aging.
Comparing gray matter across subjects. Gray matter is easier to compare across subjects if adjustments are first made for the gyral patterning differences across subjects. This adjustment can be made using cortical pattern matching (Thompson, Mega, and others 2000), which is illustrated here on example brain MRI data sets from a healthy control subject (left column) and from a patient with Alzheimer's disease (right column). First, the MRI images (stage 1) have extracerebral tissues deleted from the scans, and the individual pixels are classified as gray matter, white matter, or CSF (shown here in green, red, and blue colors; stage 2). After flattening a three-dimensional geometric model of the cortex (stage 3), features such as the central sulcus (light blue curve) and cingulate sulcus (green curve) may be reidentified. An elastic warp is applied (stage 4) moving these features, and entire gyral regions (pink colors), into the same reference position in "flat space." After aligning sulcal patterns from all individual subjects, group comparisons can be made at each two-dimensional pixel (yellow cross-hairs) that effectively compare gray matter measures across corresponding cortical regions. In this illustration, the cortical measure that is compared across groups and over time is the amount of gray matter (stage 2) lying within 15 mm of each cortical point. The results of these statistical comparisons can then be plotted back onto an average three-dimensional cortical model made for the group, and significant findings can be visualized as color-coded maps. Such algorithms bring gray matter maps from different subjects into a common anatomical reference space, overcoming individual differences in gyral patterns and shape by matching locations point-by-point throughout the cortex. This enhances the precision of intersubject statistical procedures to detect localized changes in gray matter (Thompson, Hayashi, de Zubicaray, Janke, Sowell, and others 2003).
… 
The arrows in these maps show the three-dimensional direction and distance of displacement between analogous surface points in the left and right hemispheres for a group of 62 normal controls between 7 and 30 years old. The base of each arrow represents the left hemisphere surface point location, and the tip of the arrow represents the analogous surface point location in the right hemisphere (a flipped and reflected version). Group differences (in millimeters) are mapped in color according to the color bar on the right. Note maximal asymmetry, up to 12-mm difference between analogous surface points is found in the peri-Sylvian region, shown enlarged to enhance detail in the top row. Displacement between left and right hemispheres is primarily in the anterior-posterior axis in most regions, more prominent in the peri-Sylvian region. Statistical maps are shown in the bottom row documenting the significance of displacement between analogous surface points in the left and right hemispheres according to the color bar (note white regions are P > 0.10). Note that we have assessed the significance of displacement in only the anterior-posterior direction because that is the primary direction of displacement in the peri-Sylvian region. The probabilities shown are for negative correlation coefficients (left more posterior than right surface point location), as few positive correlation coefficients reached statistical significance and they were all on the inferior surface of the brain not shown here (Sowell, Thompson, Peterson, and others 2002).
… 
Distance from center (DFC) age effect statistical maps (left, right, and top views) showing changes in DFC between childhood and adolescence (top) and between adolescence and adulthood (middle). Anatomically, the central sulcus (CS), Sylvian fissure (SF), and interhemispheric fissure (IF) are highlighted. In both images, shades of green to yellow represent positive Pearson's correlation coefficients (increased DFC or brain growth) and shades of blue, purple, and pink represent negative Pearson's correlation coefficients (decreased DFC or shrinkage) according to the color bar on the right (range of Pearson's correlation coefficients from-1 to +1). Regions shown in red correspond to correlation coefficients that have significant positive age effects at a threshold of P = 0.05 (brain growth), and regions shown in white correspond to significant negative age effects at a threshold of P = 0.05 (brain shrinkage). The images on the bottom display a statistical map of the Fisher's Z transformation of the difference between Pearson's correlation coefficients for the child-to-adolescent and the adolescent-to-adult contrasts (see color bar on far right representing Z scores from-5 to +5). Shades of green to yellow represent regions where the age effects are more significant in the adolescent-to-adult contrast (middle) than in the child-to-adolescent contrast (left). Highlighted in red are the regions where the difference between Pearson's correlation coefficients is statistically significant (P = 0.05). Shades of blue, purple, and pink represent regions where the age effects are more significant in the child-to-adolescent contrast than the adolescent-to-adult contrast. Highlighted in white are regions where these effects are significant at a threshold of P = 0.05. Note that the sign of the differences between contrasts is opposite to that in the difference map for the gray matter density contrasts because of the inverse relationship between gray matter density (negative effects) and late brain growth (positive effects) (Sowell, Thompson, and others 2001).
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372 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
Copyright © 2004 Sage Publications
ISSN 1073-8584
Understanding normative changes in brain structure
through the various stages of development and aging is
paramount to understanding cognitive changes through-
out the human life span. Considerable progress has been
made in these endeavors during the past few decades,
given the availability of noninvasive imaging tools such
as MRI. This revolutionary technological advance allows
us to study normally developing and aging individuals
because it is not harmful, and thus, it is ethically
employed, even in children. Furthermore, it provides the
unprecedented opportunity to study individuals at multi-
ple time points. Prior to the advent of these powerful
imaging tools, researchers were confined to infer brain
changes from postmortem data. Of obvious concern with
the postmortem studies is the normalcy of the partici-
pants studied after death and the notable scarcity of sam-
ples from the younger years of life. Regardless, results
from the few existing postmortem studies of cellular
changes over various age spans are indispensable for
interpreting results from the in vivo imaging studies.
With most existing imaging techniques, we observe
changes in MR signal values in brain tissue that are only
indirectly linked to the cellular makeup that comprises
the brain at any given point in time.
A detailed description of the nature of the MR signal
and what cellular constituents it represents is beyond the
scope of this review. Essentially, however, the source of
the MRI signal in all of the structural imaging studies
described in this review is associated with water, which
is more prominent in the cell bodies of the gray matter,
and fat, which is more prominent in the myelin sheath
comprising white matter. Changes in the amount of
water and/or fat within regional brain tissues that occur
with age are the primary source of age effects we
observe in studies of brain maturation and in studies of
degenerative changes that occur with normal aging when
MRI is used. As the reader will see in the following sec-
tions, the nature of change in MR signal and the under-
lying changes in cellular structure are critical to under-
standing when “maturation” stops and “aging” begins.
In addition to advancement in image acquisition
devices, computer technology and software development
have also advanced, producing more sophisticated meth-
ods for analyzing brain image data. Before quantitative
Mapping Changes in the Human
Cortex throughout the Span of Life
ELIZABETH R. SOWELL, PAUL M. THOMPSON, and ARTHUR W. TOGA
David Geffen School of Medicine
Laboratory of Neuro Imaging, Department of Neurology
University of California, Los Angeles
In this review, the authors summarize the literature on brain morphological changes that occur throughout
the human life span from childhood into old age. They examine changes observed postmortem and in vivo
where various brain MRI analytic methods have been applied. They evaluate brain changes observed with
volumetric image analytic methods and voxel-based morphometric methods that may be used to better
localize where changes occur. The primary focus of the review is on recent studies using state-of-the-art
cortical pattern-matching techniques to assess age-related changes in cortical asymmetries, gray matter
distribution, and brain growth across various age spans. The authors attempt to integrate findings from the
in vivo studies with results from postmortem studies and analyze the complicated question of when brain
maturation stops and brain aging begins. Analyzing the regional patterns of change initiated at various ages
may help elucidate relationships between changing brain morphology and changing cognitive functions
that occur throughout life. Long-range longitudinal studies, correlations between imaging and postmortem
data, and more advanced image acquisition and analysis technologies will be needed to fully interpret brain
morphological changes observed in vivo in relation to development and aging. NEUROSCIENTIST
10(4):372–392, 2004. DOI: 10.1177/1073858404263960
KEY WORDS Aging, Development, Magnetic resonance imaging (MRI), Cerebral cortex
This study was supported by the National Institute of Mental Health
(MH01733) and the National Institute of Drug Abuse (DA015878) to
ERS and the National Institute of Mental Health (5T32 MH16381), the
National Science Foundation (DBI 9601356), the National Center for
Research Resources (P41 RR13642), and the pediatric supplement of
the Human Brain Project, funded jointly by the National Institute of
Mental Health and the National Institute of Drug Abuse (P20
MH/DA52176), to AWT.
Address correspondence to: Elizabeth R. Sowell, PhD, University of
California, Los Angeles, Laboratory of Neuro Imaging, 710 Westwood
Plaza, Room 4-238, Los Angeles, CA 90095-1769 (e-mail: esowell
@loni.ucla.edu).
REVIEW
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 373
computerized algorithms were used to analyze change in
brain structure, MR image data was studied more quali-
tatively, with attempts to visually discriminate between
subjects of various ages based on prominent characteris-
tics such as sulcal depth, signal hyperintensities, or ven-
tricular size. These methods were used typically in older
patients (e.g., Kertesz and others 1998) and not to our
knowledge in normally developing children. The first
quantitative MRI studies in children measured T1 and T2
relaxation times using various imaging protocols (e.g.,
Holland and others 1986; Hassink and others 1992), pro-
viding information regarding the relative signal values
and appearance of different brain structures at different
ages. Next in the progression of technological advance-
ment in quantitative morphometry came volumetric
studies in which tissue segmentation was used to assess
total volumes of gray matter, white matter, and cere-
brospinal fluid (Jernigan, Archibald, and others 1991;
Pfefferbaum and others 1994; Blatter and others 1995).
More recently, volumetric studies have assessed local-
ized changes by defining regions of interest within MR
data sets on a slice-by-slice basis (Giedd, Vaituzis, and
others 1996; Bartzokis and others 2001; Jernigan and
others 2001; Sowell, Trauner, and others 2002). These
methods are considered a gold standard for regional vol-
ume assessment (to the extent that regional cortical def-
inition is valid and reliable) because they are not depend-
ent on image averaging across subjects, which can
require brain images to be spatially distorted and may
inaccurately match anatomy across subjects.
The main focus of this review will be on the exciting
new studies of normative brain development and aging
that have been accomplished with state-of-the-art brain-
mapping techniques. These studies have allowed us to
map structural changes over the entire cortical surface
and have considerably advanced our understanding of
the timing and localization of these alterations that occur
as part of the sculpting of the human brain at various
ages. Mapping techniques, such as voxel-based mor-
phometry (VBM) and cortical pattern matching, provide
an advantage over the more traditional volumetric stud-
ies because they can visualize changes occurring within
the brain and at the cortical surface, unbiased by observ-
able sulcal cortical boundaries necessary for making
anatomical delineations in the volumetric studies. In the
following paragraphs, we will describe cellular changes
observed in the postmortem literatures on development
and aging that likely underlie the changes we observe
with MRI. We will then describe changes in brain struc-
ture observed in brain-mapping studies of children and
adolescents and move ahead in time (with increasing
age) to describe structural changes observed in aging
populations. Then we will attempt to integrate these
postmortem and in vivo literatures in the hope of fur-
thering our understanding of distinctions between devel-
opment and aging. This review will focus on changes
that occur in the cerebral cortex because these have been
the focus of the most recent, cutting-edge brain-mapping
studies.
Postmortem Studies
Maturation
From postmortem studies, we know that myelination
begins near the end of the second trimester of fetal
development and extends beyond the second decade of
life (Yakovlev and Lecours 1967). Autopsy studies con-
sistently reveal that myelination occurs in a systematic
sequence with different brain regions myelinating at dif-
ferent times. Generally, myelination is thought to
progress from inferior to superior brain regions and from
posterior to anterior; that is, brain stem and cerebellar
regions myelinate prior to the cerebral hemispheres, and
the occipital lobes myelinate prior to the frontal lobes
(Yakovlev and Lecours 1967; Brody and others 1987).
This process is thought to reflect the regional pattern of
functional maturation of the brain.
More recently, Benes and colleagues (1994) examined
the brains of 164 subjects from 0 to 76 years of age,
including many subjects in the peripubertal age range.
Specifically, they studied the extent of myelination along
the surface of the hippocampal formation at the level of
the subiculum, presubiculum, and parasubiculum
(referred to as the superior medullary lamina) using a
computer-assisted quantitative technique. Benes and col-
leagues found a 95% increase in the area of myelination
of the superior medullary lamina in the first and second
decades of life. Notably, when the authors corrected their
measure for overall changes in cerebral size (e.g., area of
myelination expressed relative to brain weight), they still
found a 92% area increase between groups of subjects
aged 0 to 9 and 10 to 19 years. They describe in detail
the known connectivity of the subicular and presubicular
regions, indicating that at least some of the axons myeli-
nating in this region in adulthood could originate from
the cingulate gyrus. Thus, Benes and colleagues specu-
late that the functional significance of the increased
myelination that they observe could be related to corti-
colimbic integration thought to be involved in the regu-
lation of emotional behaviors with greater cognitive
maturity.
In addition to continuing myelination during child-
hood and adolescence, a regionally variable reduction in
synaptic density also occurs (Huttenlocher 1979;
Huttenlocher and de Courten 1987). The relationship
between findings in postmortem studies and findings
from in vivo studies is not yet clear. Although continuing
myelination and reductions in synaptic density are
known to occur throughout adolescence, which factors
contribute most to gross morphological changes observ-
able with neuroimaging are not known. As discussed in
detail by Giedd, Snell, and colleagues (1996), reductions
in synaptic density are not likely to account for the large-
volume decreases in gray matter structures observed in
vivo throughout adolescence (Jernigan, Trauner, and
others 1991; Caviness and others 1996; Reiss and others
1996; Sowell, Trauner, and others 2002). Rather, it
seems more likely that changes in myelination account
374 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
for overall brain size changes. Cell-packing density and
somal size can also account for structure size. These
variables are influenced by hydration levels, degree of
vascularity, hormones, and nutrition (Giedd, Snell, and
others 1996). Postmortem studies of total brain weight
have consistently shown dramatic increases during the
first 5 or 10 years of life but less dramatic increases into
the late teens and early 20s. This brain growth is fol-
lowed by a gradual decline beginning at about 45 or 50
years of age (Dekaban 1978; Ho and others 1980). The
question of whether myelination or synaptic pruning is
more responsible for changes in brain size or cortical
gray and white matter distributions may partly be
answered by findings from recent brain-mapping studies
described below.
Aging
As mentioned above, postmortem studies show that total
brain weight is relatively stable between the 20s and late
50s in humans and then gradually declines. The same
studies show that loss in total brain weight may be more
marked in the eighth decade and beyond (Dekaban 1978;
Ho and others 1980). Myelination continues in some
brain regions well into the fifth decade (Benes and oth-
ers 1994) and perhaps even beyond into senescence
(Yakovlev and Lecours 1967). At the same time, the total
length of myelinated fibers declines significantly
between the ages of 20 and 80 years, reflective of small
but not large nerve fiber loss (Marner and others 2003).
However, in the same postmortem sample, neocortical
neuron density remained relatively stable (Pakkenberg
and Gundersen 1997). This may be because the fibers
lost are small collaterals rather than the main axon,
which would explain the loss of neuronal fibers in the
absence of a comparable loss of neuronal density
(Pakkenberg and Gundersen 1997; Marner and others
2003). As noted almost a decade earlier (Terry and oth-
ers 1987), neuronal shrinkage, rather than cell loss,
accounts for the cortical volume loss observed during
the normal aging process. Regionally, the age-related
changes in neuronal size appear most prominent in
frontal and temporal lobes, with less dramatic changes in
the parietal cortices.
Summary of Postmortem Findings
As should be clear from the brief review above, myeli-
nation and synaptic pruning predominate changes in the
cortical neuropil during childhood and adolescence, and
during aging, loss of axonal fibers and neuronal shrink-
age predominate. However, the overlap in the age range
between developmental changes and changes that are
more specific to aging is considerable. There is no clear
pattern from these postmortem studies to time lock the
progressive changes from the regressive, degenerative
changes that occur during aging. Clearly, new and
improving cognitive abilities such as language acquisi-
tion, reading, fine motor skills, and problem solving are
a hallmark feature of human maturation, whereas some,
but perhaps not all, of these skills begin to decline as the
aging process continues. Thus, there must be a distinc-
tion between maturational and degenerative processes,
and examination of these questions using in vivo MRI is
the focus of the remainder of this review.
In Vivo Studies
Volumetric Image Analysis Findings
Maturation
Numerous volumetric MRI studies have focused on
brain developmental changes that occur during child-
hood and adolescence (Jernigan, Trauner, and others
1991; Pfefferbaum and others 1994; Caviness and others
1996; Giedd, Snell, and others 1996; Giedd, Vaituzis,
and others 1996; Reiss and others 1996; Sowell and
Jernigan 1998; Giedd and others 1999; Courchesne and
others 2000; Sowell, Trauner, and others 2002). The vol-
umetric studies to date have used various methods to
assess age effects on volume in various brain regions and
tissues. Tissue segmentation has been employed to
assess gray matter, white matter, and CSF differences
with age. Earlier studies in the literature tended to use
stereotaxic region definition schemes (Jernigan,
Trauner, and others 1991; Giedd, Snell, and others 1996;
Reiss and others 1996), frequently because the image
spatial resolution was low (i.e., 4 to 5 mm slice thick-
ness) relative to more recent studies in which high-
resolution
T1-weighted image volumes are assessed
(i.e., 1 to 1.5 mm slice thickness). In some of these stud-
ies, whole brain tissue volumes were assessed for age
effects (Caviness and others 1996; Courchesne and oth-
ers 2000), and others have employed manual region def-
inition on a slice-by-slice basis using cortical anatomical
landmarks (where observable) as boundaries (Giedd,
Vaituzis, and others 1996; Lange and others 1997;
Sowell and Jernigan 1998; Sowell, Trauner, and others
2002). Finally, automated lobar region definition
schemes have been used (Giedd and others 1999), in
which image-warping algorithms are used to map stan-
dardized lobar measures to each individual subject’s
brain.
Regional differences in the processes (e.g., myelina-
tion, synaptic pruning) that result in cortical gray and
white matter volume changes observed with MRI would
be expected given postmortem findings of regional dif-
ferences in the timing of progressive and regressive
events in brain maturation. In the earliest report of volu-
metric findings between childhood and young adult-
hood, Jernigan and Tallal (1990) reported that children
aged 8 to 10 years had significantly more cortical gray
matter relative to cerebral size than did young adults. In
a subsequent report in which more subjects were studied
and cortical and subcortical gray matter structures were
subdivided (cortical regions defined stereotaxically),
Jernigan, Trauner, and others (1991) found evidence for
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 375
an increase in size of the superior cranial vault, particu-
larly in the anterior region. Within the superior cranial
vault, the cortical gray matter appeared to be decreasing
with age whereas CSF in this region increased. The infe-
rior cortical gray matter volumes did not appear to
change across the age range. The authors proposed that
their observation of a thinning cortex in superior cortical
regions could be related to the processes that led to the
earlier reported reductions in synaptic density. Since
these early reports, cortical gray matter volume decreas-
es were reported by other groups (Pfefferbaum and oth-
ers 1994; Reiss and others 1996). Regionally, the most
notable changes during childhood and adolescence occur
in the more dorsal cortices. During adolescence, frontal
and parietal lobes show highly significant increases in
white matter along with concomitant decreases in gray
matter (Giedd and others 1999; Sowell, Trauner, and oth-
ers 2002). The more ventral cortices of the temporal
lobes change less dramatically between childhood and
adolescence (Jernigan, Trauner, and others 1991; Giedd
and others 1999; Sowell, Trauner, and others 2002).
Notably, gray matter thinning in the frontal cortex is
related to changing cognitive ability in normal children
and adolescents. We found significant correlations
between gray matter volume in the frontal lobe and chil-
dren’s performance on a verbal learning task (Sowell,
Delis, and others 2001).
Aging
Volumetric imaging studies in adult aging populations
have been very enlightening and are, again, a gold stan-
dard in beginning to understand the effects of increasing
age on brain tissue. Consistent in most studies are find-
ings of gray matter volume loss with age (Jernigan,
Archibald, and others 1991; Pfefferbaum and others
1994; Blatter and others 1995; Courchesne and others
2000; Bartzokis and others 2001; Ge and others 2002;
Bartzokis and others 2003). White matter volume loss
has also been consistently reported in these cross-sectional
samples (Jernigan, Archibald, and others 1991;
Pfefferbaum and others 1994; Blatter and others 1995;
Courchesne and others 2000; Bartzokis and others 2001;
Ge and others 2002; Bartzokis and others 2003).
Regionally, gray matter loss appears more prominent in
the frontal cortex than in other lateral cortical regions
(Raz and others 1997; Bartzokis and others 2001;
Jernigan and others 2001) and may be more specific to
nonorbital frontal regions (Salat and others 2001). White
matter loss may be more prominent than gray matter loss
(Jernigan and others 2001), but this depends on the start-
ing age ranges in the cross-sectional samples studied.
This is because white matter changes in adulthood
appear nonlinear, with white matter gain continuing into
approximately the mid-40s, followed by more prominent
loss (Bartzokis and others 2001; Ge and others 2002;
Sowell and others 2003). Again, frontal white matter vol-
umetric changes during aging appear more prominent
than those in other regions, such as the temporal lobes
(Bartzokis and others 2001; Jernigan and others 2001).
From the cross-sectional studies, age-related loss of
brain tissue can only be inferred, given that the same
individuals were not studied at multiple time points.
Recent longitudinal analyses in normative aging popula-
tions conducted by Resnick and colleagues (2000) have
yielded promising results. The only significant longitu-
dinal findings after 1 year were of ventricular size
increase, probably because the interscan interval was rel-
atively short. At 2- and 4-year follow-up, however, tissue
loss was highly significant within individuals (5.4%
total brain volume loss) and was more prominent in
frontal and parietal cortices than for temporal and occip-
ital cortices. These findings are generally consistent with
the cross-sectional results.
Mapping with VBM
Maturation
The question of spatial localization of maturational
changes cannot be fully addressed with volumetric
methods in which, typically, only gross lobar structures
can be reliably visually identified and manually defined.
Newer methods, initially used to evaluate functional
imaging data, have been employed to assess structural
effects during normal development on a voxel-by-voxel
basis (Paus and others 1999; Sowell, Thompson,
Holmes, Batth, and others 1999; Sowell, Thompson,
Holmes, Jernigan, and others 1999). We used VBM
(Ashburner and Friston 2000) to localize age-related
gray matter density reductions between childhood and
adolescence in 18 normally developing individuals
between 7 and 16 years of age (Sowell, Thompson,
Holmes, Batth, and others 1999). Essentially, VBM
entails automated spatial normalization of volumes into
a standard coordinate space and scaling of images so that
each voxel coordinate is anatomically comparable across
subjects. Tissue segmentation and spatial smoothing is
then used to assess localized differences in gray matter
and/or white matter. Results from these analyses
revealed that the gray matter volume reductions
observed in frontal and parietal lobes in the volumetric
studies of brain maturation resulted mostly from gray
matter density reductions in diffuse dorsal regions of
these cortices (Sowell, Thompson, Holmes, Batth, and
others 1999). The parietal cortex changed the most in
both the volumetric and VBM assessments of gray mat-
ter, and relatively little change occurred in the more ven-
tral cortices of the temporal and occipital lobes in these
normally developing children and adolescents (see Fig. 1).
In a similar study, Paus and colleagues (1999) used
VBM to assess white matter changes in subjects 4 to 17
years of age and found prominent white matter density
increases in the posterior limb of the internal capsule
and in the arcuate fasciculus in the temporo-parietal
region. The prominent findings in the parietal cortex,
relative to the frontal cortex, were not expected, given
376 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
the known posterior to anterior progression of matura-
tional cellular events. We fully expected frontal matura-
tion to have been well under way by age 17 and reflect-
ed in the VBM results.
We decided to test the hypotheses that frontal gray
matter changes must occur later in adolescence by con-
ducting a VBM study focusing on the adolescent to adult
age range. As described above, between childhood and
adolescence, cortical changes were diffusely distributed
in dorsal frontal and parietal regions (Sowell, Thompson,
Holmes, Batth, and others 1999). In striking contrast,
however, the pattern of cortical maturation between ado-
lescence and adulthood was localized to large regions of
dorsal, mesial, and orbital frontal cortex with relatively
little gray matter density reduction in the parietal lobes
or in any other cortical region (Sowell, Thompson,
Holmes, Jernigan, and others 1999) (see Fig. 1). These
results make sense in light of studies showing that the
frontal lobes are essential for such functions as response
inhibition, emotional regulation, planning, and organiza-
tion (Fuster 1997), which may not be fully developed in
adolescents.
Aging
To our knowledge, only one study has used VBM to
assess age effects in normal adults (Good and others
2001). In this study, 465 normal individuals between 17
and 79 years of age were assessed. Global gray matter
volume significantly declined with age, and regional
patterns suggested that above and beyond the global gray
matter loss, gray matter density reduction occurred in
regions including bilateral superior parietal regions, pre-
and postcentral gyri, insula/frontal operculum right cere-
bellum, and anterior cingulate. Accelerated loss was also
noted in the left inferior frontal gyrus and in some tem-
poral lobe regions. Generally, these results are consistent
with the volumetric studies in which parietal and frontal
regions may have more prominent gray matter volume
loss with aging. Good and colleagues (2001) noted
trends for nonlinear age effects in global white matter
volume, and regionally, cortical white matter loss was
most prominent in frontal and occipital regions. Other
studies have shown more prominent white matter loss
(Courchesne and others 2000; Bartzokis and others
2001; Jernigan and others 2001; Sowell and others
2003), but the oldest subjects studied by Good and col-
leagues were underrepresented relative to the other stud-
ies. Taken together, these studies suggest that white mat-
ter volume loss accelerates in the eighth and ninth
decades relative to the sixth and seventh.
Cortical Pattern Matching
Although the VBM approach has clear advantages over
the volumetric studies in which only gross lobar regions
are assessed, there are also disadvantages related to more
technical aspects of image averaging. The problem with
VBM is that typically, automated image registration
techniques are used to spatially normalize brain volumes
across subjects. Considerable variability exists in region-
al sulcal patterns across individuals, with variability
more pronounced the further the region is from the cen-
ter of the brain. The variability in sulcal patterns also dif-
fers by cortical region. Recent brain-mapping studies
have shown cortical variability of 10 to 20 mm, particu-
larly in the posterior temporal lobe regions in children
(Sowell, Thompson, Rex, and others 2002), adults (Narr
and others 2001), and the aged (Thompson and others
Fig. 1. Top, Child minus adolescent statistical map for the neg-
ative age effects representing gray matter density reductions
observed between childhood and adolescence. Bottom,
Adolescence and adulthood. These maps are three-dimensional
renderings of the traditional statistical maps shown inside the
transparent cortical surface rendering of one representative
subject’s brain. Lobes and the subcortical region were defined
anatomically on the same subject’s brain. Color coding is
applied to each cluster based on its location within the repre-
sentative brain. Clusters are shown in the frontal lobes (purple),
parietal lobes (red), occipital lobes (yellow), temporal lobes
(blue), and subcortical region (green) (Sowell, Thompson,
Holmes, Batth, and others 1999; Sowell, Thompson, Holmes,
Jernigan, and others 1999).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 377
1998), even after spatial normalization (see Fig. 2, vari-
ability). Thus, when brain volume data sets are normal-
ized without taking this variability into account, cortical
anatomical regions are not likely well matched across
subjects, particularly where sulcal pattern variability is
highest. The same methods that allow us to assess corti-
cal variability can be used to assess group differences in
gray matter density while accounting for the differences
in sulcal location across subjects.
Cortical pattern–matching methods allow us to match
cortical anatomy across subjects and account for the
interindividual differences in cortical patterns. They can
be used to encode both gyral patterning (as shown in Fig.
2) and gray matter variation. This may substantially
improve the statistical power to localize age-related
changes, relative to the VBM studies described above.
These cortical analyses discriminate the effects of gyral
shape variation from gray matter change, and they can
also be used to measure cortical asymmetries
(Thompson and others 1998; Sowell, Thompson, Rex,
and others 2002). Briefly, a three-dimensional geometric
model of the cortical surface is extracted from the MRI
scan (MacDonald and others 1994) and then flattened to
a two-dimensional planar format (Thompson and Toga
1997, 2002). A complex deformation, or warping trans-
form, is then applied that aligns the sulcal anatomy of
each subject with an average sulcal pattern derived for
the group (see Fig. 3). To improve sulcal alignment
across subjects, all sulci that occur consistently can be
manually defined on the surface rendering (see Fig. 4)
and used to restrict this transformation. Cortical pattern
matching adjusts for differences in cortical patterning
and shape across subjects. Cortical measures, such as
gray matter thickness or local brain size, can then be
compared across subjects and groups. Sulcal landmarks
are used as anchors, as homologous cortical regions are
better aligned after matching sulci than by just averaging
data at each point in stereotaxic space, as is done in tra-
ditional VBM (Ashburner and Friston 2000). Given that
the deformation maps associate cortical locations with
the same relation to the primary folding pattern across
subjects, a local measurement of gray matter density is
made in each subject and averaged across equivalent
cortical locations. To quantify local gray matter, we use
a measure termed gray matter density, used in many
prior studies to compare the spatial distribution of gray
matter across subjects. This measures the proportion of
gray matter in a small region of fixed radius (15 mm)
around each cortical point (Sowell, Thompson, and oth-
ers 2001; Thompson, Mega, Woods, and others 2001;
Thompson, Vidal, and others 2001; Sowell, Thompson,
Rex, and others 2002). Given the large anatomic vari-
ability in some cortical regions, high-dimensional elastic
matching of cortical patterns is used to associate meas-
ures of gray matter density from homologous cortical
regions across subjects (as shown in Fig. 4). One advan-
tage of cortical matching is that it localizes age effects
relative to gyral landmarks, as illustrated in elderly sub-
jects in Figure 5; it also averages data from correspon-
ding gyri, which would be impossible if data were only
linearly mapped into stereotaxic space. The effects of
age, gender, medication, disease, and other measures on
gray matter can be assessed at each cortical point.
Maturation
Using cortical pattern–matching techniques, we have
studied maturational changes in cortical sulcal asymme-
tries (Blanton and others 2001; Sowell, Thompson, Rex,
and others 2002), cortical gray matter asymmetries
(Sowell, Thompson, Peterson, and others 2002), cortical
gray matter density (Sowell, Thompson, and others
2001; Thompson, Vidal, and others 2001; Sowell,
Thompson, Peterson, and others 2002; Sowell and others
2003), and brain growth (Sowell, Thompson, and others
2001). Combined, these studies have highlighted region-
al patterns of cortical change with age during childhood
and adolescence that have not been appreciated with
other image analysis techniques.
Sulcal Asymmetries. Asymmetries in sulcal patterns
are of considerable interest, particularly in the peri-
Sylvian cortices given the functional lateralization of
language in this region (reviewed in Geschwind and
Fig. 2. Cortical surface variability maps in three dimensions
viewed from the right and the left showing variability in the aver-
age child (n = 14), the average adolescent (n = 11), and the
average young adult (n = 10). The color bar indicates patterns
of variability within each group as the root mean square magni-
tude (in mm) of displacement vectors required to map each
individual into the group average surface mesh. Note that this
map is representative of residual brain shape variability after
affine transformation into ICBM305 standard space. Higher
variability is observed in the postcentral gyrus and posterior
temporal regions in all three age groups, with relatively less
variability in precentral and anterior temporal gyri (Sowell,
Thompson, Rex, and others 2002).
378 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
Galaburda 1985). Postmortem studies have shown that in
adults, the Sylvian fissure is longer in the left hemi-
sphere than the right (Galaburda and others 1978; Ide
and others 1996), and in vivo vascular imaging studies
have shown that the Sylvian fissure angles up more dra-
matically at its posterior end in the right hemisphere than
the left (LeMay and Culebras 1972). Left hemisphere
peri-Sylvian asymmetries greater than right hemisphere
peri-Sylvian asymmetries (planum temporale length)
have also been observed in postmortem studies of
infants (Witelson and Pallie 1973), indicating that these
asymmetry patterns may be independent of maturational
change and the acquisition of language abilities
throughout infancy and childhood. Until our recent in
vivo imaging studies, little was known about the emer-
gence of cortical surface gyral and sulcal asymmetries in
normal development.
Age differences in structural asymmetries at the corti-
cal surface were mapped in groups of normally develop-
ing children (7 to 11 years), adolescents (12 to 16 years),
and young adults (23 to 30 years) using the novel surface-
based cortical pattern–matching image analytic methods
described above. We found that asymmetries in peri-
Sylvian cortices continued to develop between child-
hood and young adulthood (Sowell, Thompson, Rex and
others 2002). Although the normal left longer than right
Fig. 3. Top left, Three representative brain image data sets with the original MRI, tissue-segmented images, and surface renderings,
with sulcal contours shown in pink. Top right, Surface rendering of one representative subject with cutout showing tissue-segmented
coronal slice and axial slice superimposed within the surface. Sulcal lines are shown where they would lie on the surface in the cutout
region. Note the sample spheres over the right hemisphere inferior frontal sulcus (lower sphere) and on the middle region of the pre-
central sulcus (upper sphere) that illustrate varying degrees of gray matter density. In the blown-up panel, note that the upper sphere
has a higher gray matter density than does the lower sphere as it contains only blue pixels (gray matter) within the brain. The lower
sphere also contains green pixels (white matter) that would lower the gray matter proportion within it. In the actual analysis, the gray
matter proportion was measured within 15-mm spheres centered across every point over the cortical surface. Bottom, Sulcal anatom-
ical delineations are defined according to color. These are the contours drawn on each individual’s surface rendering according to a
reliable, written protocol (Sowell, Thompson, Rex, and others 2002).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 379
Sylvian fissure asymmetry was present in the children,
adolescents, and adults, it was much more pronounced in
adulthood, on average twice the magnitude of the asym-
metry observed in children. The asymmetry in the slope
of the Sylvian fissure also changed with age such that
the normal pattern of right more sloped than left
occurred without exception in the young adults studied
and significantly less frequently in the children. These
findings were consistent with the earlier postmortem lit-
erature. We observed similar asymmetry patterns in an
independent group of children and adolescents, and as in
our other report, Sylvian fissure asymmetry was more
prominent in the adolescents than in the children
(Blanton and others 2001). The dynamic age-related
changes in asymmetry seemed to occur as a result of
robust changes in the shape and location of the right
hemisphere Sylvian fissure. The slopes of the Sylvian
fissure and the superior temporal sulcus were roughly
parallel in children, adolescents, and adults in the left
hemisphere, but in the right hemisphere, the slope of the
superior temporal sulcus remained constant despite the
age-related upward slope of the Sylvian fissure (see Fig.
6). This suggests an increase in the surface area of the
posterior temporal lobes in the right hemisphere, result-
ing in the increased Sylvian fissure asymmetry observed
with increasing age (Sowell, Thompson, Rex, and others
2002). In a small-sample longitudinal study, we
observed prominent cerebral lobar growth in the lateral
temporo-parietal region in children studied at various 2-
to 4-year intervals between about 7 and 15 years of age
(Thompson, Giedd, and others 2000), and we also
observed brain growth in the inferior temporal cortex in
another study (Sowell, Thompson, and others 2001).
Together, these results suggest that brain growth in
regions surrounding the Sylvian fissure can affect its
morphology during development.
In another report, we focused on detailed three-
dimensional quantitative maps of brain surface and gray
matter density asymmetry patterns during normal ado-
lescent development (Sowell, Thompson, Peterson, and
others 2002). We studied two independent samples of
normally developing children, adolescents, and young
adults, totaling 83 subjects from two different research
groups. We found that the most prominent gray matter
asymmetry at the brain surface was in the posterior tem-
poral lobes, whether looking at children, adolescents, or
young adults (see Fig. 7). This finding was confirmed in
two independent samples of normal control subjects
scanned on different scanners by different research
groups, further establishing the validity of the results.
Age effects in gray matter asymmetry between the nor-
mal child and adolescent groups and between the ado-
lescent and young adult groups were not significant,
suggesting that the pattern of gray matter asymmetry is
established early in development. Right greater than left
gray matter asymmetry in the posterior, superior tempo-
ral sulcus has previously been reported in a large imag-
ing study of young adults (Watkins and others 2001).
White matter asymmetry in this region has also been
examined by another research group, showing left
greater than right white matter asymmetry in the primary
auditory cortex (Penhune and others 1996), a region
anterior to that observed here. Left greater than right
white matter asymmetry in the posterior temporal lobes
has been reported in the postmortem literature as well
(Anderson and others 1999). Although we measured
gray matter asymmetry, it is possible that we measured
less gray matter in the left hemisphere because there was
actually more white matter present with an opposite pat-
tern in the right hemisphere. Thus, the white matter
asymmetry findings from postmortem and in vivo sam-
ples may be consistent with the gray matter results
reported here and by other research groups (Watkins and
others 2001).
In the same group of control subjects, we assessed
total brain surface asymmetry by mapping the distance
from matched surface points in the left hemisphere to the
same locations in the reflected right hemisphere (ignor-
ing the large interhemispheric difference across the
brain) (Sowell, Thompson, Peterson, and others 2002).
Results from these analyses are shown in Figure 8. The
arrows show the magnitude and direction of asymmetry
at each brain surface point. Peak brain surface asymme-
Fig. 4. Analyzing cortical data. The schematic shows a
sequence of image-processing steps that can be used to map
how aging affects the cortex. The steps include aligning MRI
data to a standard space, tissue classification, and cortical pat-
tern matching as well as averaging and comparing local meas-
ures of cortical gray matter volumes across subjects. To help
compare cortical features from subjects whose anatomy differs,
individual gyral patterns are flattened and aligned with a group
average gyral pattern (a to f). Group variability (g) and cortical
asymmetry can also be computed. Correlations can be
mapped between age-related gray matter deficits and genetic
risk factors. Maps may also be generated that visualize linkages
between deficits and clinical symptoms, cognitive scores, and
medication effects. The only steps here that are currently not
automated are the tracing of sulci on the cortex. Some manual
editing may also be required to assist algorithms that delete
dura and scalp from images, especially if there is very little CSF
in the subdural space (Thompson, Hayashi, de Zubicaray,
Janke, Sowell, and others 2003).
380 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
try was observed in the peri-Sylvian region where the
distance between anatomically homologous surface
points in the left and sulcally matched right brain surface
was between 6 and 12 mm. The asymmetry was charac-
terized by posterior displacement of the left posterior
temporal and inferior parietal cortex relative to the right,
similar to the results from sulcal asymmetry patterns in
the same brain region (Blanton and others 2001; Sowell,
Thompson, Rex, and others 2002). Vector maps showed
that the direction of displacement between hemispheres
in all brain regions was primarily in the anterior-posterior
axis and arose primarily from the left being more poste-
rior than the right.
Gray Matter Changes. As described above, the VBM
studies have begun to shed light on the localization of
tissue changes within the developing brain. We have
applied cortical-matching techniques to assess matura-
tional changes in gray matter density (Sowell,
Thompson, and others 2001) that may not have been
appreciated with the gross anatomical matching of the
VBM studies. Thirty-five individuals between 7 and 30
years of age were assessed. Statistical maps for gray
matter density differences (Fig. 9) between children and
adolescents and between adolescents and adults reveal
distinct patterns as expected given earlier VBM results
(Sowell, Thompson, Holmes, Batth, and others 1999;
Fig. 5. Comparing gray matter across subjects. Gray matter is
easier to compare across subjects if adjustments are first made
for the gyral patterning differences across subjects. This adjust-
ment can be made using cortical pattern matching (Thompson,
Mega, and others 2000), which is illustrated here on example
brain MRI data sets from a healthy control subject (left column)
and from a patient with Alzheimer’s disease (right column).
First, the MRI images (stage 1) have extracerebral tissues
deleted from the scans, and the individual pixels are classified
as gray matter, white matter, or CSF (shown here in green, red,
and blue colors; stage 2). After flattening a three-dimensional
geometric model of the cortex (stage 3), features such as the
central sulcus (light blue curve) and cingulate sulcus (green
curve) may be reidentified. An elastic warp is applied (stage 4)
moving these features, and entire gyral regions (pink colors),
into the same reference position in “flat space.” After aligning
sulcal patterns from all individual subjects, group comparisons
can be made at each two-dimensional pixel (yellow cross-hairs)
that effectively compare gray matter measures across corre-
sponding cortical regions. In this illustration, the cortical meas-
ure that is compared across groups and over time is the
amount of gray matter (stage 2) lying within 15 mm of each cor-
tical point. The results of these statistical comparisons can then
be plotted back onto an average three-dimensional cortical
model made for the group, and significant findings can be visu-
alized as color-coded maps. Such algorithms bring gray matter
maps from different subjects into a common anatomical refer-
ence space, overcoming individual differences in gyral patterns
and shape by matching locations point-by-point throughout the
cortex. This enhances the precision of intersubject statistical
procedures to detect localized changes in gray matter
(Thompson, Hayashi, de Zubicaray, Janke, Sowell, and others
2003).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 381
Fig. 6. A, Asymmetry maps for the child, adolescent, and adult groups were created by subtracting the sulcal mesh averages of one
hemisphere from the mirror of the other hemisphere to create vectors representing displacement asymmetry (in millimeters) in the
superior-inferior and anterior-posterior directions shown in color. These maps not only illustrate the average sulcal asymmetry in color
but also show differences in sulcal shape profiles between the hemispheres because the right hemisphere is mapped onto the mirror
of the left hemisphere and vice versa. Thus, the color coding is identical in the two hemispheres, but the shape of the right hemisphere
sulci can be seen as distinct from the left hemisphere sulci. Note the left and right Sylvian fissures are close together (about 11 mm
displacement) in the children and more splayed (about 16 mm displacement) in the young adults. B, Ratio map of the asymmetry at
each point on each sulcus (i.e., the distance in millimeters between analogous points on sulcal curves in one brain hemisphere and a
mirror image of the opposite hemisphere) in the average child to the asymmetry at each point on each sulcus in the average adult.
Increases and decreases are represented in color, where red regions are indicative of increases in asymmetry with age and pink
regions are representative of decreases in asymmetry with age (according to the color bar on the right). Note the prominent increase
in asymmetry over the length of the Sylvian fissure. Only one hemisphere is represented, as it is a composite ratio measure of the right
and left hemispheres combined (C). Probability map of the difference in asymmetry between the child group and the adult group rep-
resented as P values (for age group effect on asymmetry between children and young adults) at each point along each sulcal curve
according to the color bar on the right. The age group effect on asymmetry is significant for the Sylvian fissure (P = 0.041) as deter-
mined with permutation tests (Sowell, Thompson, Rex, and others 2002).
Fig. 7. Shown here are ratio maps for groups of 25 children, 15 adolescents, and 16 young adults quantifying the amount of gray mat-
ter within a 15-mm sphere at each brain surface point in the left hemisphere as ratio to that of the analogous points in the right hemi-
sphere. According to the color bar, 1 (color coded in green shades) represents complete symmetry. Cooler colors (greater than 1) rep-
resent regions where there is more gray matter in the left hemisphere than in the right, and warmer colors (less than 1) represent
regions where there is more gray matter in the right hemisphere than in the left (Sowell, Thompson, Peterson, and others 2002).
382 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
Sowell, Thompson, Holmes, Jernigan, and others 1999).
Between childhood and adolescence, local gray matter
density loss was distributed primarily over the dorsal
frontal and parietal lobes. Similar effects of gray matter
density reduction were observed in a small, independent
longitudinal sample of normal adolescents who were
studied as controls for patients with schizophrenia
(Thompson, Vidal, and others 2001). Between adoles-
cence and adulthood, a dramatic increase in local gray
matter density loss was observed in the frontal lobes,
parietal gray matter loss was reduced relative to the ear-
lier years, and a relatively small, circumscribed region of
local gray matter density increase was observed in the
left peri-Sylvian region. Unlike in our previous reports,
with sulcal pattern matching, we were able to statistical-
ly map the significance of differences between child-to-
adolescent and adolescent-to-adult contrasts, finally
confirming that there are regions of accelerated gray
matter loss in the postadolescent age range, mostly in the
dorsal frontal cortices (see Fig. 9). These findings sug-
gest that changes in gray matter density between child-
hood and young adulthood may not be linear in nature.
Brain Growth. In this same group of 35 subjects, we
also assessed localized brain growth using our distance
from center (DFC) measure. It is a measure of radial
expansion measured from the center of each subject’s
brain roughly at the midline decussation of the anterior
commissure (i.e., x = 0, y = 0, z = 0) to each of the
65,536 matched brain surface points. Differences in the
length of the DFC line at each brain surface point
between groups (i.e., children and adolescents) suggest
local growth in that location, and statistical analyses at
each point can be conducted, much like with gray matter
density. We found statistically significant spatial and
temporal patterns of brain growth and surface contrac-
tion between childhood, adolescence, and young adult-
hood. Because the brain surfaces were scaled to remove
global size differences for these analyses, local brain
growth and contraction observed in these results must be
considered relative to global differences in brain size
between groups. Notably, the relative maps reveal little
local growth (increased DFC) occurring between child-
hood and adolescence (Fig. 10) once overall brain size
differences are controlled. When comparing the adoles-
cents to the adults, there was some regional specificity
with prominent local growth or increased DFC occur-
ring in the dorsal aspects of the frontal lobes bilaterally
in the same general region where we observed accelerat-
ed gray matter density reduction described above. Lateral
growth also appeared in the inferior, lateral temporo-
occip
ital junction bilaterally where the brain surface was
also significantly farther from the center of the brain in
the adults than in the adolescents. Finally, some growth
was also observed in the orbital frontal cortex, more
prominent in the left hemisphere. The difference
between
Fig. 8. The arrows in these maps show the three-dimensional
direction and distance of displacement between analogous
surface points in the left and right hemispheres for a group of
62 normal controls between 7 and 30 years old. The base of
each arrow represents the left hemisphere surface point loca-
tion, and the tip of the arrow represents the analogous surface
point location in the right hemisphere (a flipped and reflected
version). Group differences (in millimeters) are mapped in color
according to the color bar on the right. Note maximal asymme-
try, up to 12-mm difference between analogous surface points
is found in the peri-Sylvian region, shown enlarged to enhance
detail in the top row. Displacement between left and right hemi-
spheres is primarily in the anterior-posterior axis in most
regions, more prominent in the peri-Sylvian region. Statistical
maps are shown in the bottom row documenting the signifi-
cance of displacement between analogous surface points in
the left and right hemispheres according to the color bar (note
white regions are P > 0.10). Note that we have assessed the
significance of displacement in only the anterior-posterior
direction because that is the primary direction of displacement
in the peri-Sylvian region. The probabilities shown are for neg-
ative correlation coefficients (left more posterior than right sur-
face point location), as few positive correlation coefficients
reached statistical significance and they were all on the inferior
surface of the brain not shown here (Sowell, Thompson,
Peterson, and others 2002).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 383
correlation coefficients for the child-to-adolescent
and
adolescent-to-adult comparisons shown in Figure 10
confirmed the accelerated local growth in dorsal frontal
regions in the older age range and accelerated local
growth in the posterior temporo-occipital junction as
well.
Brain image data sets were also assessed without scal-
ing to adjust for brain size differences. The nonscaled
Fig. 9. Gray matter density age effect statistical maps (left, right, and top views) showing gray matter density changes between child-
hood and adolescence (top) and between adolescence and adulthood (middle). Anatomically, the central sulcus (CS), Sylvian fissure
(SF), and interhemispheric fissure (IF) are highlighted. In both images, shades of green to yellow represent negative Pearson’s corre-
lation coefficients (gray matter loss with increasing age) and shades of blue, purple, and pink represent positive Pearson’s correlation
coefficients (gray matter gain with age) according to the color bar on the right (range of Pearson’s correlation coefficients from
–1 to +1).
Regions shown in red correspond to correlation coefficients that have significant negative age effects at a threshold of P =
0.05 (gray matter loss), and regions shown in white correspond to significant positive age effects at a threshold of P = 0.05 (gray mat-
ter density gain). The images on the bottom display a statistical map of the Fisher’s Z transformation of the difference between
Pearson’s correlation coefficients for the child-to-adolescent and the adolescent-to-adult contrasts (see color bar on far right
representing Z scores from –5 to +5). Shades of green to yellow represent regions where the age effects are more significant in
the adolescent-t
o-adult contrast (middle) than in the child-to-adolescent contrast (left). Highlighted in red are the regions where the
difference between Pearson’s correlation coefficients is statistically significant (P = 0.05). Shades of blue, purple, and pink represent
regions where the age effects are more significant in the child-to-adolescent contrast than in the adolescent-to-adult contrast.
Highlighted in white are regions where these effects are significant at a threshold of P = 0.05 (Sowell, Thompson, and others 2001).
384 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
maps showed that continued brain growth occurred
between adolescence and adulthood in the most extreme
dorsal aspects of the posterior frontal lobes bilaterally
and in the posterior inferior temporal lobes bilaterally
whether or not brain size differences were controlled. As
shown in Figure 11, between adolescence and adulthood,
Fig. 10. Distance from center (DFC) age effect statistical maps (left, right, and top views) showing changes in DFC between childhood
and adolescence (top) and between adolescence and adulthood (middle). Anatomically, the central sulcus (CS), Sylvian fissure (SF),
and interhemispheric fissure (IF) are highlighted. In both images, shades of green to yellow represent positive Pearson’s correlation
coefficients (increased DFC or brain growth) and shades of blue, purple, and pink represent negative Pearson’s correlation coefficients
(decreased DFC or shrinkage) according to the color bar on the right (range of Pearson’s correlation coefficients from –1 to +1).
Regions shown in red correspond to correlation coefficients that have significant positive age effects at a threshold of P = 0.05 (brain
growth), and regions shown in white correspond to significant negative age effects at a threshold of P = 0.05 (brain shrinkage). The
images on the bottom display a statistical map of the Fisher’s Z transformation of the difference between Pearson’s correlation
coefficients for the child-to-adolescent and the adolescent-to-adult contrasts (see color bar on far right representing Z scores
from –5 to +5)
. Shades of green to yellow represent regions where the age effects are more significant in the adolescent-to-adult con-
trast (middle) than in the child-to-adolescent contrast (left). Highlighted in red are the regions where the difference between Pearson’s
correlation coefficients is statistically significant (P = 0.05). Shades of blue, purple, and pink represent regions where the age effects
are more significant in the child-to-adolescent contrast than the adolescent-to-adult contrast. Highlighted in white are regions where
these effects are significant at a threshold of P = 0.05. Note that the sign of the differences between contrasts is opposite to that in
the difference map for the gray matter density contrasts because of the inverse relationship between gray matter density (negative
effects) and late brain growth (positive effects) (Sowell, Thompson, and others 2001).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 385
large, diffuse regions of shrinkage or decreased DFC
were observed in frontal and parietal regions surround-
ing the frontal and temporal growth areas. This was in
contrast to the large regions of growth in frontal cortices
between childhood and adolescence, with shrinkage
occurring only in parietal and inferior temporal cortices
bilaterally. These regions of growth and shrinkage were
not as prominent in the analyses of scaled image data
sets when overall differences in brain size were correct-
ed. The analyses of nonscaled images do suggest that
much of the progressive maturational change that leads
to the subtle increase in total brain size occurs during the
years between childhood and adolescence. Only relative-
ly subtle growth occurs after adolescence in dorsal
frontal and posterior temporal cortices.
Relationships between Brain Growth and Gray
Matter Density Reduction. Notably, when comparing the
adolescents to the adults, significant gray matter density
loss in the frontal lobes was seen almost exclusively in
locations where positive age effects for DFC were
observed, with very little gray matter loss observed in
frontal regions that were not growing in this age range.
In the composite map shown in Figure 12, the regions of
significant gray matter loss overlapped nearly perfectly
onto the regions of frontal lobe brain growth in the cor-
relation map for DFC. It is interesting to note the corre-
spondence in the distributions of these two features of
brain development (brain growth and gray matter densi-
ty reduction) despite their irregular shapes and patterns
over the brain surface. Similar effects were observed in
the child-to-adolescent comparison composite map
where significant gray matter loss tended to be seen pri-
marily in regions where growth was observed, although
these effects were in different regions than those in the
adolescent-to-adult age range. The strong correspon-
dence in the age effects for gray matter density reduction
and increased brain growth in the frontal cortex may pro-
vide new insight for making inferences about the cellular
processes contributing to postadolescent brain maturation.
Aging
Fewer studies of aging than maturation have been con-
ducted using the cortical pattern–matching techniques
described in this review. We have conducted one com-
prehensive study of gray matter changes across the
human life span (7 to 87 years) in a group of 176 normal
individuals (Sowell and others 2003). Groups of elderly
normal subjects have been studied as controls for
patients with Alzheimer’s disease, shedding further light
on aging processes in cortical asymmetries (Thompson
and others 1998) and gray matter density changes
(Thompson, Hayashi, de Zubicaray, Janke, Rose and
others 2003).
Sulcal Asymmetry Patterns. Sulcal pattern asymmetry
measures in 10 normal elderly subjects (Thompson and
others 1998) were similar to those observed in normal
children, adolescents, and young adults (Sowell,
Thompson, Rex, and others 2002). Relatively small
asymmetries were observed in parieto-occipital, anterior
and posterior calcarine, and cingulate sulci (between 3-
and 4-mm difference between left and right hemi-
spheres). Robust asymmetries were observed in the
Sylvian fissure, with an upward slope of the right
hemisphere relative to the left and elongation of the
left hemisphere relative to the right. The distance
between matching
anatomical points in the left and
right Sylvian fissures was maximal at 14 mm. Brain sur-
face asymmetry was studied in a larger group of 20 eld-
erly control subjects, showing a similar pattern of poste-
Fig. 11. Differences between groups in distance from center
(DFC) shown in millimeters in color (according to the color bar)
between childhood and adolescence in both nonscaled (A) and
scaled image data sets (C). Differences between adolescents
and adults are also shown in nonscaled (B) and scaled images
(D). Anatomically, the central sulcus (CS) and Sylvian fissure
(SF) are highlighted. The maps in scaled image space allow an
assessment of the magnitude (in millimeters) of differences in
DFC shown as statistical maps in Figure 10. The same color
scale applies to both nonscaled and scaled images, and
regions of brain growth between the younger and older age
groups tested are shown in dark blue, purple, and pink, and
regions of shrinkage between the younger and older groups
tested are shown in red, yellow, green, and light blue. Note that
whether or not brain size correction is made with scaling, dor-
sal frontal lobes and posterior temporal lobes show evidence
for continued growth after adolescence. Other less robust
regions of brain growth or shrinkage are “scaled” out when
brain size correction is used to control individual differences
(Sowell, Thompson, and others 2001).
386 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
rior displacement of the left peri-Sylvian region relative
to the right (Thompson, Mega, Vidal, and others 2001).
In another study, 28 normal adults (mean age = 30.5
years) were assessed as controls for patients with schiz-
ophrenia, and Sylvian fissure asymmetry was also max-
imal at approximately 14 mm in the most posterior
extent of this structure (Narr and others 2001). Thus, in
all populations studied to date—children (Blanton and
others 2001; Sowell, Thompson, Rex, and others 2002),
adolescents (Sowell, Thompson, Rex, and others 2002),
young adults (Sowell, Thompson, Rex, and others 2002),
adults (Narr and others 2001), and elderly (Thompson
and others 1998)—sulcal asymmetries are most promi-
nent in the posterior Sylvian fissure with upward shifts
of the right relative to the left and elongation of the left
relative to the right.
Gray Matter Density. In a recent report (Sowell and
others 2003), we used cortical-matching algorithms to
create three-dimensional, nonlinear statistical, and peak
age maps of gray matter density change on the lateral
and interhemispheric brain surfaces across nine decades
(7 to 87 years) in 176 normal individuals. Significant,
nonlinear age effects were observed over large areas of
the most dorsal aspects of the frontal and parietal regions
on both the lateral and interhemispheric surfaces and in
the orbit frontal cortex (Fig. 13). Scatter plots of these
effects revealed a dramatic decline in gray matter densi-
ty between the ages of 7 and 60 years, with little or no
decline thereafter. A sample scatter plot of the quadratic
effect of age on gray matter density at one brain surface
point on the superior frontal sulcus is also shown in
Figure 13 and is similar to others in the dorsal frontal
and parietal regions (see Fig. 14). In the superior frontal
sulcus, there was a loss of gray matter density of approx-
imately 32% between the ages of 7 and 60 years, declin-
ing to only 5% loss between the ages of 40 and 87 years.
Notably, the most lateral aspects of the brain in the pos-
Fig. 12. Composite statistical maps (top) showing the correspondence in age effects for changes in distance from center (DFC) and
changes in gray matter in the child-to-adolescent contrast (A). Shown in green is the Pearson’s R map of all positive correlation coef-
ficients for DFC (shown also in Fig. 10), and in blue is the probability map of all regions of significant gray matter loss (surface point
significance threshold P = 0.05, as shown in Fig. 9). In red are regions of overlap in the gray and DFC statistical maps. A similar com-
posite map for the adolescent-to-adult age effects is also shown (B). Note the highly spatially consistent relationship between brain
growth and reduction in gray matter density. The shapes of the regions of greatest age-related change for the two maps (gray matter
and DFC) are nearly identical in many frontal regions in the adolescent-to-adult contrast. Very few regions of gray matter density reduc-
tion fall outside regions of increases in DFC. Shown in images in the lower part of this figure (left, right, and top views) are the differ-
ence between Pearson’s correlation coefficients for the age effects for gray matter density and the age effects for DFC between child-
hood and adolescence (C) and between adolescence and adulthood (D). These maps are similar to the difference between correlation
coefficients for age effects of gray matter and DFC shown in Figures 9 and 10 but instead highlight the correlation between regions
of greatest change in the two separate features of brain maturation measured here (DFC and gray matter density). The color bar rep-
resents corresponding Z scores ranging from –5 to +5 for the difference between correlation coefficients for DFC and gray matter.
Highlighted in red are regions of significant negative correlations between DFC and gray matter density (P = 0.05), showing that the
relationship between regions of greatest gray matter density reduction are statistically the same as the regions with the greatest brain
growth, particularly in the adolescent-to-adulthood years. Highlighted in white are the regions where the difference between correla-
tion coefficients for the gray matter and DFC maps is positive, indicating that the change with age is in the same direction for both
variables (i.e., increased DFC change goes with increased gray matter density change) (Sowell, Thompson, and others 2001).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 387
terior temporal and inferior parietal lobes bilaterally
show a different pattern, one in which the nonlinear age
effects were inverted relative to the age effects seen in
more dorsal cortices. A subtle increase in gray matter
density was observed until age 30, which remained sta-
ble until a precipitous decline was seen in later decades
(Figs. 13 and 14). In contrast to the percentage changes
reported above for the superior frontal sulcus, only a
12% decline in gray matter density was observed in the
superior temporal sulcus between the ages of 7 and 60
years, increasing to a 24% decline between the ages of
40 and 87 years. Intriguingly, the rapid decline in gray
matter density in these lateral regions occurred when the
age effects in the dorsal surfaces leveled off.
Interindividual variability in gray matter density was
accentuated in these regions, contributing to less signif-
icant age effects here than on the more dorsal surface of
the brain.
The age at base gray matter loss (lowest point in the
quadratic curve) or peak gray matter gain (in the poste-
rior temporal lobes) was estimated for each brain surface
point and mapped onto the average brain surface render-
ing (Fig. 15). Base gray matter density levels in the most
dorsal aspects of the parietal lobes seem to hit their nadir
earlier (at 40–50 years) than do the frontal lobes (at
50–60 years). The peak age maps show an intriguing pat-
tern of age effects in which the association cortices of
the frontal and parietal lobes show the most robust gray
matter density loss early in life, and primary auditory
(lateral surface) and visual cortices show a much shal-
lower decline over the life span (see regions color coded
in black in Fig. 15).
Fig. 13. This map (left frontal view) shows age effects on gray matter density on the lateral surface of the brain between childhood
and old age. Shades of green to yellow represent positive partial regression coefficients for the quadratic term (U-shaped curves with
respect to age), and shades of blue, purple, and pink represent negative partial regression coefficients (inverted U-shaped curves).
Regions shown in red correspond to regression coefficients that have significant positive nonlinear age effects at a threshold of P =
0.0000008, and regions shown in white correspond to significant negative nonlinear age effects at a threshold of P = 0.01. The pat-
tern of nonlinear age effects was similar in the left and right hemispheres (not shown) except that none of the negative nonlinear age
effects in the right posterior temporal lobe reached a threshold of P = 0.01. Scatter plots of age effects with the best-fitting quadrat-
ic regression line are shown for sample surface points in the superior frontal sulcus (top) and the superior temporal sulcus (bottom),
representative of the positive (U-shaped) and negative (inverted U-shaped) nonlinear age effects. Gray matter proportion within the
15-mm sphere surrounding the sample surface point (matched across subjects) is shown on the y-axis (Sowell and others 2003).
388 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
The regionally and temporally variable patterns of
aging on gray matter density probably reflect differences
in the underlying cellular architecture in those regions
and likely contribute to the well-documented variability
in cognitive functions associated with aging. We have
shown correlations between IQ and localized differences
in gray matter density between normal adult twins
(Thompson, Cannon, and others 2001). Although we
cannot directly measure myelin deposition, somal size,
or synaptic density using MRI, postmortem studies
described above indicate that these cellular changes
occur simultaneously throughout maturation and aging.
Summary of Cortical
Pattern–Matching Analyses
The cortical pattern–matching studies of maturation and
aging have shown various regional patterns of changing
peri-Sylvian asymmetries, gray matter changes, and
brain growth, depending on the age range studied. The
general pattern of asymmetry in the posterior temporal
lobe is remarkably similar across all subject samples and
age ranges studied, with longer left than right and more
sloped right than left Sylvian fissures (Thompson and
others 1998; Blanton and others 2001; Narr and others
2001; Sowell, Thompson, Rex, and others 2002).
However, there is an increase in the magnitude of this
asymmetry between childhood and adulthood, as shown
in one of the studies (Sowell, Thompson, Rex, and oth-
ers 2002), probably related to regional growth in cortical
structures surrounding the Sylvian fissure. Age effects
in this sulcal pattern asymmetry beyond young adult-
hood have not been studied, despite the fact that numer-
ous normal populations across the age span from child-
hood to old age have been assessed with the same image
analysis methods. This is largely because comparability
of data collected on different magnets with different
image acquisition protocols and by different research
groups has not been assessed under these circumstances,
rendering it difficult to make direct comparisons.
Experience-dependent plasticity and asymmetries in
behavioral function may be responsible for differential
maturational patterns between the two hemispheres. For
a recent, thorough review of the functional significance
of brain asymmetries, see Toga and Thompson (2003).
Age-related changes in cortical surface gray matter
density patterns have been consistent across populations
and vary depending on the age group studied. Gray mat-
ter density reduction has been observed during adoles-
cence both cross-sectionally (Sowell, Thompson, and
others 2001; Sowell and others 2003) and longitudinally
(Thompson, Vidal, and others 2001). These findings are
generally consistent with the volumetric imaging litera-
ture (Giedd and others 1999; Sowell, Trauner, and others
2002) and VBM studies (Sowell, Thompson, Holmes,
Batth, and others 1999; Sowell, Thompson, Holmes,
Jernigan, and others 1999). Regional patterns suggest
that gray matter density reduction in parietal cortices
begins earlier in childhood, followed by the frontal lobes
when more prominent gray matter loss occurs between
adolescence and adulthood (Sowell, Thompson, Holmes,
Batth, and others 1999; Sowell, Thompson, Holmes,
Jernigan, and others 1999; Sowell, Thompson, and oth-
ers 2001; Sowell and others 2003). In our previous
report, we speculated that cognitive functions subserved
by parietal association cortices (i.e., spatial relations)
may develop earlier than the executive functions associ-
ated with the frontal lobes (Sowell, Thompson, Holmes,
Jernigan, and others 1999). When assessed across the
age span between 7 and 87 years, gray matter density
decline is prominent across most dorsal cortical regions,
but the gray matter loss is nonlinear in nature. Peak loss
occurs in most dorsal brain regions between the ages of
50 and 70, after which there is a much more gradual
decline. Interestingly, gray matter density increases also
occur and are specific to primary language areas in the
posterior peri-Sylvian region (i.e., Wernicke’s area).
These changes have been observed in two independent
samples thus far, and studies across the human life span
suggest that gray matter increases are occurring until
approximately 30 years of age, followed by a more grad-
ual decline than that observed in other cortical regions
(Sowell, Thompson, and others 2001; Sowell and others
2003).
What is different about posterior peri-Sylvian regions
that the pattern of maturation and aging appears so dis-
parate from most other cortical regions? In our report of
these findings, we speculated that specific language
functions such as language comprehension skills are less
vulnerable to aging, whereas more anterior language
functions such as language production and word
retrieval deteriorate as a function of normal aging
(Sowell and others 2003). Perhaps the protracted pattern
Fig. 14. Shown is a surface rendering of a human brain (left
hemisphere; left is anterior, right is posterior) with scatter plots
for gray matter density at various points over the brain surface.
The graphs are laid over the brain approximately where the
measurements were taken. The axes for every graph are iden-
tical, and they are identical to the axes on graphs shown in
Figure 13 (Sowell and others 2003).
Volume 10, Number 4, 2004 THE NEUROSCIENTIST 389
of maturation and aging in these regions is related to the
pattern of vulnerable and relatively invulnerable lan-
guage capacities.
Finally, regional patterns of brain growth and shrink-
age have suggested that the most dorsal regions of the
frontal lobes continue to grow between adolescence and
adulthood (Sowell, Thompson, and others 2001). Brain
growth in these regions spatially and temporally coin-
cide with regional patterns of gray matter density reduc-
tion between childhood and young adulthood.
Regressive (i.e., synaptic pruning) and progressive (i.e.,
myelination) cellular events are known to occur concur-
rently in the brain during childhood, adolescence, and
young adulthood, both of which could result in the
appearance of gray matter density reduction or cortical
thinning on MRI. A reduction in the number of synaps-
es in the cortex could result in our observations of
reduced gray matter density. On its own, this process
would seem to result in a net brain volume loss (along
with an increase in CSF). Notably, however, we found
local brain growth in the same regions where gray mat-
ter density reduction was occurring. An increase in the
amount of myelin could also result in a reduction in the
amount of brain tissue that has a gray matter appearance
on MRI, given that nonmyelinated peripheral axonal and
dendritic fibers do not have typical white matter signal
values on T1-weighted MRI (Barkovich and others
1988). Increased myelination would seem to necessarily
result in a net brain volume increase, given that myelin
consists of space-occupying glial cells (Friede 1989).
This would be consistent with our data showing late
growth in frontal cortex concomitant with the cortical
gray matter density reduction.
Integration of Aging and Development with
In Vivo and Postmortem Studies
Integrating the results from maturational and aging stud-
ies is complicated. One of the ultimate goals is to deter-
mine when maturation stops and when the more degen-
erative changes with aging begin. From the studies
encompassing an age range between childhood and old
age, it is clear that assessment of gray matter density
alone cannot begin to disentangle this interesting ques-
tion. Clearly, the prominent gray matter density loss that
occurs between the ages of 7 and 20 has a different eti-
ology from the continued gray matter loss that occurs
between 40 and 60 years of age. We know from post-
mortem studies that myelination (at least in the mesial
temporal lobe) peaks at approximately 50 years of age,
and myelination is much more prominent between birth
and approximately 20 years of age than it is from 20 to
50 years (Benes and others 1994). Brain weight is max-
imal by approximately 20 years of age and does not
begin to decline until approximately 55 years of age
(Dekaban 1978). Synaptic density loss is prominent dur-
ing adolescence in a regionally variable pattern, but it
continues into old age as well (Huttenlocher and
Dabholkar 1997). In vivo volumetric studies show rela-
tively consistent gray matter loss in many cortical
regions beginning in late childhood (Jernigan, Trauner,
and others 1991; Giedd and others 1999; Sowell,
Trauner, and others 2002) and continuing apparently lin-
early through old age (Jernigan, Archibald and others
1991; Pfefferbaum and others 1994; Blatter and others
1995; Courchesne and others 2000; Bartzokis and others
2001; Ge and others 2002; Bartzokis and others 2003).
Fig. 15. Peak (or base) age maps for the nonlinear effects of age on gray matter. Shown in color, according to the color bar on the
right, is the mean age at which peak or base gray matter density is reached for each point on the lateral (top), interhemispheric (bot-
tom), and top (right) surface of the brain. Shown in black are regions where the partial correlation coefficient for the nonlinear age effect
did not reach significance at a level of P = 0.05. Age effects in these regions tended to be linear, rather than quadratic (Sowell and
others 2003).
390 THE NEUROSCIENTIST Mapping Cortical Change throughout Life
White matter volume, on the other hand, increases up
until approximately the mid-40s (Bartzokis and others
2001; Sowell and others 2003), which coincides with the
peak myelination observed at approximately age 50
(Benes and others 1994). It seems reasonable to specu-
late then that the gray matter loss observed during child-
hood and young adulthood results from a combination of
synaptic pruning and increased myelination (and per-
haps other cellular changes as well that result in local
brain size increases concomitant with gray matter reduc-
tion) (Sowell, Thompson, and others 2001), whereas the
gray matter loss that occurs later in adulthood may be
more due to a combination of continued late myelination
and perhaps decreased somal size (Terry and others
1987). The leveling of gray matter loss seen in late
adulthood in cortical pattern–matching studies (Sowell
and others 2003) may begin at a time when only degen-
erative changes (i.e., decreased somal size/atrophy) are
occurring in the absence of continued myelination and
synaptic pruning. Unfortunately, these changes would be
impossible to discriminate with the conventional image
acquisition protocols used in most of the structural
brain-imaging studies reviewed here. A study combining
postmortem and in vivo analyses of individuals across
the age span could be used to determine when gray mat-
ter loss observed in vivo coincides with gray matter thin-
ning associated with maturational versus atrophic
processes. Long-range longitudinal studies along with
cognitive assessments would also be ideal for disentan-
gling maturational from degenerative changes observed
in the in vivo studies.
Findings of localized gray matter density increases up
to approximately age 30 in posterior language cortices
are relatively new, although they have been replicated in
two independent samples (Sowell, Thompson, and others
2001; Sowell and others 2003). It is somewhat more dif-
ficult to speculate on what cellular changes could be
causing gray matter growth, given the postmortem stud-
ies that have been more helpful in explaining gray mat-
ter loss observed in most other cortical regions. It has
been speculated that increases in gray matter volume
could be related to a “second wave of over production of
synapses” (Giedd and others 1999), but there does not
appear to be much support for this hypothesis in the
human or animal literature. It is now generally well
accepted that neurogenesis does occur in the adult mam-
malian brain (Gould and Gross 2002). Adult-generated
cells with neuronal characteristics have been found in
the temporal neocortex in the monkey. It has been shown
that enriched environments enhance the survival of
newly generated cells (reviewed in Gould and Gross
2002). The only lateral cortical region in the brain to
show prominent gray matter increases into adulthood are
primary language cortices. Perhaps neuronal prolifera-
tion in humans could occur only in brain regions sub-
serving a prominent primary cognitive function, such as
language, involved in so many aspects of human cognition.
Conclusions and Future Directions
We have reviewed a variety of literatures that assess the
effects of aging on the morphology of the human brain.
Postmortem studies have shown increased myelination
and synaptic pruning during development, new neuron
proliferation in adulthood, and reduction in somal size
and neuronal fiber loss in aging. In vivo studies have
confirmed regional changes in gray and white matter tis-
sues that vary depending on the age studied and perhaps
the volumetric methods used to measure age-related
change. Recent cortical pattern–matching image analy-
sis techniques have expanded our knowledge of regional
changes, and exciting new results suggest that gray mat-
ter increases in specific brain regions continue into
adulthood. Parsing brain changes that accompany matu-
ration, when so many new cognitive functions are devel-
oping, and aging, when degenerative cognitive and brain
changes are occurring, is complicated with in vivo stud-
ies in which the resolution needed to detect cellular
changes is not available. Only by integrating the post-
mortem and in vivo literatures can we begin to hypothe-
size about the etiology of structural changes in matura-
tion and aging that look so similar in the in vivo measures.
Clearly, long-range serial studies of brain and cognitive
changes (accomplishable only in vivo) and combining
postmortem and imaging studies can begin to definitive-
ly answer these interesting questions. The technological
advance in brain image acquisition and image analysis is
staggering, and it may be that more detailed assessments
of brain morphological change in vivo will be possible in
the near future.
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... The copyright holder for this preprint this version posted November 19, 2021. ; https://doi.org/10.1101/2021.11.16.468806 doi: bioRxiv preprint 1 Introduction 1 The human brain is an intricate network whose complex wiring diagram can be reconstructed 2 in vivo from magnetic resonance imaging (MRI) data and abstracted in a connectome network 3 map (Park & Friston, 2013 The human brain undergoes developmental changes across the lifespan (Sowell et al., 2004). 19 While gray matter volume decreases non-linearly from childhood to old age, white matter 20 volume and the integrity of fiber connections follow an 'inverted U' shaped trajectory with 21 increases into mid-adulthood and a decline thereafter (Kochunov et al., 2012;Sowell et al., 22 2003). ...
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Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.
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Chapter
This chapter concerns mainly the gross and microscopic aspects of normal cerebral development during the second half of gestation, that is the period usually encountered by the pathologist. Its purpose is to provide a frame of reference for assessing normalcy in the brain of the fetus and of the newborn, to point out changes of borderline significance, and to establish base lines for the evaluation of gross or microscopic pathologic changes. The chapter does not provide an extensive review of normal embryology of the human central nervous system; developmental principles are cited only to the extent to which they are of help in interpreting abnormal tissue structure, and pertinent data are generally included in the respective chapters of the text.
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• The auditory regions in four normal brains were mapped and the full extent of the cytoarchitectonic subdivisions was measured for the presence of right-left asymmetries. It was found that asymmetries similar to those found in the planum temporale (left commonly larger than right) are also seen in auditory cytoarchitectonic area Tpt, an area of probable importance for language function. There is a strong positive correlation between the planum asymmetry and the asymmetry of Tpt. It is concluded that the previously described planum asymmetries probably reflect asymmetries in an auditory cytoarchitectonic area and therefore may represent, at least in part, the anatomic substrate for language lateralization.
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More than 20,000 autopsy reports from several general hospitals were surveyed for the purpose of selecting brains without a pathological lesion that had been weighed in the fresh condition. From this number, 2,773 males and 1,963 females were chosen for whom body weight, body height, and cause of death had been recorded. The data were segregated into 23 age groups ranging from birth to 86+ years and subjected to statistical evaluation. Overall, the brain weights in males were greater than in females by 9.8%. The largest increases in brain weights in both sexes occurred during the first 3 years of life, when the value quadruples over that at birth, while during the subsequent 15 years the brain weight barely quintuples over that at birth. Progressive decline in brain weight begins at about 45 to 50 years of age and reaches its lowest values after age 86 years, by which time the mean brain weight has decreased by about 11% relative to the maximum brain weight attained in young adults (about 19 years of age). Computed regression lines for brain weights versus body heights and body weights and for ratios of brain weights to body heights and weights versus age groups show clearly differential rates of change in brain weights which are less affected by sex.
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In this article, we extend earlier findings of age‐related changes in brain morphology on magnetic resonance images to include measurements of the mesial temporal lobe as well as asymmetry measures in regional cortical and subcortical structures. The earlier sample was increased to include 57 children and young adults aged 8 to 35 years. The participants were studied using quantitative image analytic techniques. Estimated volumes of limbic and anterior diencephalic structures increased significantly with age; although inferior lateral cortex, superior cortex, caudate nuclei, thalamus, and lenticular nuclei all decreased significantly with age. Of the 7 regions measured, only the lenticular nucleus and anterior diencephalon showed significant changes in asymmetry with increasing age. It is hypothesized that some of these changes may be related to the changing levels of gonadal steroid hormones known to occur during this age range. They may also have important implications for the study of late developing higher cognitive functions and the loss of behavioral plasticity.