The concept of the brain atlas is not new1. Cartographic
approaches have been used for centuries to identify
and target specific regions in the brain and to establish
spatial relationships between a coordinate and a struc-
ture. Comprehensive maps of brain structure have been
created, at a variety of spatial scales, from anatomical
specimens2–5 and various histological preparations that
reveal regional cytoarchitecture6,7, myelination patterns8–10,
and protein and mRNA distributions. Most early and
some more recent atlases of the human brain were derived
from one, or at best a few, individual post-mortem speci-
mens3–5,11–14. Such atlases provide anatomical references
or represent a particular feature of the brain15,16, such as a
specific neurochemical distribution17 or the cellular archi-
tecture of the cerebral cortex6. For example, Brodmann’s
map (1909) exclusively describes the cytoarchitectonic
segregation of the cortex6, Dejerine’s map (1901) describes
fibre tract anatomy18, and the map by Schaltenbrand and
Wahren (1977) describes the thalamus14.
Beyond these traditional, anatomical atlases based
on post-mortem tissue, modern brain atlases are
being developed that incorporate flexible, computable
systems, which accommodate the sometimes considerable
variation in a population. The application of magnetic
resonance imaging (MRI) to acquire detailed descriptions
of anatomy in vivo is a driving force in brain mapping
research. Tomographic imaging has the advantage of
largely retaining the spatial integrity of the data by main-
taining the intrinsic three-axis registration and simple
volumetric coordinates19,20. The atlases derived from
these images are digital, allowing a wealth of compu-
tational algorithms to be applied to automatically align
new two- and three-dimensional imaging data into the
coordinate systems of these atlases21. Furthermore, there
is an increasing ability to image various structural (as
well as functional and chemical) features in the brain
such as nuclei, cytoarchitectural details and white mat-
ter tracts. Technological advances continue to improve
spatial and contrast resolution and have led to multispectral
characterization (using MRI) of brain anatomy, reflect-
ing features such as lipid content or water diffusivity in
different tissues. Recently, chemoarchitectural maps, which
describe receptor densities relative to their cytoarchitec-
tonic localization and functional attributes, have been
generated21,22 to bridge the gap between anatomical and
The transition from a static atlas representation to a
computational one has dramatically extended the atlas
concept. The brain atlas is now equivalent to a database
that incorporates a multitude of data points that are
organized, relational, extendable and testable. Initially,
the brain atlas was purely neuroanatomical, based on a
single representative example. Now it can include popu-
lation statistics on structure, gene expression, receptor
patterns or connectivity over time.
In this review, we describe the features and limita-
tions of traditional methods of creating brain atlases
and outline the requirements of a modern brain atlas.
So far, most atlasing efforts have consisted of grey
matter maps obtained from structural and functional
MRI (fMRI) data. Here, we discuss the background and
applications of human brain mapping, focusing on a
few selected examples of methods that have shown
rapid progress in recent years. These newer methods
can be used to generate statistical data on cortical
cytoarchitecture and receptor distributions, as well as
*Laboratory of Neuro
Imaging, Department of
Neurology, UCLA School
of Medicine, Los Angeles,
‡Johns Hopkins University
School of Medicine,
Department of Radiology and
Baltimore, Maryland 21205,
USA. §Institute of Medicine
and Brain Imaging Centre
West (BICW), Research
Centre Jülich, Germany.
||Department of Psychiatry
RWTH Aachen University,
¶C. & O. Vogt-Institute of
Brain Research, Heinrich-
Correspondence to A.W.T.
Approaches that place brain
images from multiple subjects
and devices into an anatomical
reference system with
coordinates (for volumetric
images) or two-dimensional
spherical or planar coordinates
(for cortical regions).
Towards multimodal atlases of
the human brain
Arthur W. Toga*, Paul M. Thompson*, Susumu Mori‡, Katrin Amunts§|| and
Abstract | Atlases of the human brain have an important impact on neuroscience. The
emergence of ever more sophisticated imaging techniques, brain mapping methods and
analytical strategies has the potential to revolutionize the concept of the brain atlas. Atlases
can now combine data describing multiple aspects of brain structure or function at different
scales from different subjects, yielding a truly integrative and comprehensive description of
this organ. These integrative approaches have provided significant impetus for the human
brain mapping initiatives, and have important applications in health and disease.
952 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
Subdivisions (named or
numbered) of the cerebral
cortex, called cytoarchitectonic
maps, based on cellular
features (size and shape of
cells, cell packing density in
different cortical layers, width
of layers) and identified in cell
Magnetic resonance imaging
(MRI). A non-invasive method
to obtain images of living
tissue. It uses radio-frequency
pulses and magnetic field
gradients; the principle of
nuclear magnetic resonance is
used to reconstruct images of
tissue characteristics (for
example, proton density, water
Multispectral imaging devices
measure multiple features of an
object at each spatial location,
such as optical reflectance
at different wavelengths, or
different relaxometric decay
constants (T1 and T2) in MRI.
Differences in the molecular
composition of cortical and
subcortical brain regions can
be assessed using enzyme- or
immunohistochemistry, in situ
autoradiography and so on,
revealing subdivisions with
patterns, such as expression of
Diffusion tensor imaging
(DTI). A technique developed in
the mid-1990s, based on MRI
in which diffusion constants of
water molecules are measured
along many (>6) orientations
and diffusion anisotropy is
characterized. It is used to
visualize the location,
orientation and anisotropy of
the brain’s white matter tracts,
and is sensitive to directional
parameters of water diffusion
in the brain.
Subdivisions (numbered or
named) of the cerebral cortex,
maps, based on features (for
example, stria of Gennari in the
visual cortex) of myelinization
(differential density of
myelinated fibres and fibre
bundles in different cortical
layers), and identified in myelin-
stained histological specimens.
white matter fibre tracts and projections, using tech-
niques such as diffusion tensor imaging (DTI). It is likely
that these methods will contribute to the future devel-
opment of advanced integrative atlases. Brain maps
that visualize distributed patterns of task-dependent
functional activity are now beginning to be related
to anatomical fibre connectivity computed from DTI
— each modality contributes to better hypotheses and
models regarding neural systems involved in cogni-
tive function. We go on to evaluate several existing
integrative, population-based international initiatives.
Algorithms that are used to analyse probabilistic and
time-varying atlases have led to important findings
regarding some of the diseases that affect the brain,
and have provided new perspectives on the effects of
age, gender and genetic factors. We discuss how these
atlases are evolving rapidly as developments in imaging
modalities, post-mortem mapping and computational
analysis of images are combined to detect new features
that were not observable (or measurable) in the past.
The evolution of brain atlases
Lessons from the past. Early research on the cellular
composition of the brain culminated in the develop-
ment of atlases of the human cerebral cortex, which
were pioneered by Brodmann6,23, Flechsig8, Vogt and
Vogt10, and von Economo and Koskinas7. These studies
continued until the 1960s (REFS 24–26). The cortex was
segregated into numerous structurally defined areas,
based on regional cytoarchitecture (identified mainly
by the number of cortical layers, laminar patterns of
cell packing density and the shape of neuronal cell
bodies) or myeloarchitecture (identified mainly by the
degree of myelination and the presence or absence of
myelinated fibre bundles in the cerebral cortex). These
types of architecture were studied by visual inspection
of Nissl- or myelin-stained histological sections in
However, when maps by different authors of this
classical period are considered, a number of problems
become apparent. For example, the drawings do not
provide the sulcal pattern of a single real brain or a well-
defined ‘average’ brain but of an imagined ‘ideal’ brain (for
example, Brodmann’s schematic drawing of a brain6); so,
it is impossible to compare different maps in a common
reference space as they are not registered in a stereotaxic
system, and brain shape and sulcal contours vary greatly
between maps. The number of cortical areas also
varies between the different maps, as the delineation proce-
dure for areal boundaries was highly observer-dependent
and not assessed quantitatively or statistically. Furthermore,
with few exceptions, the drawings or descriptions do not
show the positions of areas and their borders in the corti-
cal sectors that are hidden in the sulci. This amounts to
nearly two-thirds of the total surface area of the cortex27.
Any desig nation of cortical areas in the sulcal part of the
cortex5 is pure supposition, and not supported by original
observations. Finally, most classical maps9,10 do not match
the high degree of cortical segregation that has more
recently been shown by functional imaging — particularly
in the multimodal association cortices28–31.
Indeed, architectonic brain mapping based on visual
inspection has been severely criticized24,32 because of its
lack of clearly stated and objectively verifiable criteria.
This led to a decrease in the general interest in architec-
tonic atlases for decades. More recently, axonal tracing
techniques have been combined with architectonic
observations to produce atlases of selected cortical
regions in non-human primates and other mammals28,33.
Unfortunately, this experimental approach cannot be
used in studies of the human brain. Meanwhile, stereo-
taxic atlases of the human brain, based on imaging data,
began to be published (mostly in book form), primarily
in response to the needs of neurosurgeons. These atlases
compensate for some of the problems of the classical
brain maps — particularly the lack of a spatial reference
system. However, stereotaxic atlases tend to focus on
subcortical structures and provide only sparse informa-
tion about the cortex14. Furthermore, they lack quantita-
tive information on the intersubject variability of areal
boundaries11, intersubject variability in gross anatomy,
left–right asymmetries and the criteria to identify
The driving force for modern brain atlases. The
current resurgence of interest in brain atlases that provide
architectonic maps of the human cerebral cortex has been
largely motivated by the introduction of functional imag-
ing techniques such as positron emission tomography (PET)
and fMRI. Researchers using these techniques invari-
ably want to define — as far as possible with the limited
resolution of PET or fMRI — the location of neural
activity and determine whether the focus of activity is
associated with a portion of a specific cortical area, an
entire cortical area or overlapping areas.
The development of functional imaging techniques
has therefore relied on the evolution of structural
imaging. These advances have included both in vivo,
structural MRI and high-resolution imaging of three-
dimensional reconstructed histological sections. An
example of the use of these techniques has been the
development of the concept of a ‘cortical area’, which has
an important role in functional brain mapping. Cortical
areas can be defined structurally on the basis of either
macroscopic or microscopic (architectonic) criteria,
although architectonic borders vary considerably in rela-
tion to macroscopic landmarks34–37. The concept of ‘brain
mapping’ benefits from the combination of functional
imaging of a distinct and well-defined function with a
microstructurally defined architectural atlas, because the
correlation between function and its underlying cyto- or
myeloarchitecture can then be tested by using the archi-
tectural map for the definition of regions of interest in
The correlation between the (micro)structure and
the function of an area has been established for many
cortical regions, in particular for primary sensory and
motor areas7,8,38–41. Many functional units in the cortex
are consistently found in humans and other primates,
such as primary visual area V1, or the motion-sensitive
area MT/V5, in the middle temporal gyrus. The spatial
layout of these regions in the cortex is also somewhat
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 953
© 2006 Nature Publishing Group
0 20 40 60 80100
Cortical depth (%)
0 20 40 60 80100
Cortical depth (%)
consistent across individuals38,39,42,43. Visual areas V1–V5
were initially defined electrophysiologically, based
on their highly selective responses to visual stimuli
with specific intensities, orientations or directions of
motion29,30,38,44,45. These classifications allow functional
imaging studies to relate their observations to known
anatomical units that were initially defined using
electrophysiology or direct histological mapping.
Requirements for modern brain atlases. Based on
the criticisms of architectonic brain mapping24,32, the
requirements of basic and clinical brain research and
the current progress in structural and functional brain
imaging techniques, the criteria for ideal brain atlases can
be established. They should include a multimodal micro-
structural approach that combines different independent
methods, such as cytoarchitectonic, myeloarchitectonic,
chemoarchitectonic and modern fibre-tracking approaches
along with macroscopic, in vivo and tomographic
imaging methods, and use observer-independent
methods46–49 to define cortical boundaries — so-called
architectonic parcellation. Brain atlases should contain
data on interindividual variations in the geometry and
topology of architectonic areas and should allow the
use of algorithms that perform adequate linear (such as
scaling, rotating, translation and shearing) or nonlinear
(based, for example, on an elastic model50,51) alignment
of individual brains and their structures to a standard
spatial reference system. Nonlinear transformations,
which apply local contractions or dilations, are often
required (as well as linear transformations such as local
rotations and shearing) to equate, or deform, an indi-
vidual subject’s anatomy to match the shape of a brain
atlas21. In addition, efforts should be made to establish
population brain maps (probability maps), which define
the probabilistic position and borders of cortical areas
based on studies of a large number of subjects across
various population groups. Finally, these atlases should
use a database format that provides easy access to the
original data on which the atlas is based (for example,
anonymized images from individual subjects, or models
of specific brain structures). Alternatively, the statistical
maps and anatomical delineations in the atlas should be
readily importable into software and tool boxes that are
commonly used when analysing new brain images. Once
imported into other image analysis software, probabilistic
maps of cortical regions or fibre pathways can be used
to define regions of interest on new images, to provide
a priori constraints or search regions for new statistical
analyses, or simply to provide visual overlays of data
from an alternative modality.
New architectonic atlas approaches
The limitations of classical brain maps led to the evolu-
tion of new concepts for generating atlases34,43,52,53. As a
result of these conceptual developments, an objective,
observer-independent procedure for the parcellation of
the cerebral cortex has recently been introduced (FIG. 1).
This technique is based on the measurement of the grey-
level index (GLI) as an indicator of the volume fraction
of cell bodies throughout the cortical layers from the
Figure 1 | Observer-independent procedure for cytoarchitectonic parcellations.
a | Two mean cortical profiles (red and blue curves in the insets) sampled from the grey-
level index (GLI) image of a cell body-stained histological section (coronal plane)
through the superior and the middle temporal gyri of a human brain. Each mean profile
is the average of 20 equally spaced, individual profiles (red lines in panel b, top), which
were sampled from the rectangular regions of interest (boxes centred around the red or
blue arrows). The shape of each profile is described as a feature vector46, and is a
measure of the cytoarchitecture of the area. High GLI values indicate high volume
densities of cell bodies. b | Top panel shows the sliding window procedure used to
establish the distance function (bottom panel). The cortical region of interest is divided
into a set of profiles (positions shown in red). The positions of the profiles are
consecutively numbered starting from n = 1 (at left margin) to k. A sliding window
consists of two cortical segments (yellow, to the left and to the right of a central profile)
made up of two neighbouring groups of n individual profiles. As an example, sliding
windows are shown at positions n = 21 and n = 180. When a certain profile position has
been analysed, the sliding window is moved one step (profile) to the next position. The
red arrows indicate the direction of the movement of the sliding window across the
cortical ribbon. The blue and black arrows indicate positions where the feature vectors
show significant changes. TE1.0, TE1.1 and TE2 are distinct cytoarchitectonic areas of
the human auditory cortex64,82. Bottom panel shows the Mahalanobis distance, which
indicates the dissimilarity in laminar pattern between two cortical segments and can be
calculated from the feature vectors from each profile. The Mahalanobis distance at each
position of the sliding window is plotted. Significant maxima at positions n = 40 (blue
arrow), n = 97 (black arrow), n = 305, n = 497 and n = 586 indicate the positions of
putative areal borders. Performing this procedure in serial histological sections of
several (usually ten) brains, three-dimensional reconstructed histological data sets of
post-mortem brains and their areas are registered to common standard reference
space, and cytoarchitectonic probability maps are calculated for each area.
954 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
Warping of the individual brains and their cortical areas to the
reference system (e.g., MRI volume of an individual in vivo brain)
Receptor imaging approach
Multimodal atlas and database of the human brain
Observer-independent mapping of receptor
Comparison with cyto- and myeloarchitecture
Quantitative in vitro receptor autoradiography:
• Deep freezing of tissue slabs comprising cross-sections
of whole hemispheres
• Serial sectioning (20 μm) using a cryostat microtome
• Incubation with tritiated ligands
• Film autoradiography
• Contrast enhancement colour and coding/quantification
• Cell body and myelin staining of adjacent sections
Three-dimensional MRI of post-mortem brains prior to
Linear and non-linear correction of histological distortions
with their MRI volumes as reference
Three-dimensional reconstruction of processed brains
and their cortical areas
Observer-independent mapping of
• Paraffin embedding
• Serial sectioning (20 μm) of complete human brains
• Staining (Gallyas 1979, Merker 1983)
A technique or apparatus used
in neurosurgery or brain
imaging studies to localize a
specific anatomical locus using
coordinates; used, for example,
for directing the tip of a surgical
instrument (such as a needle or
electrode) to a known location.
Distinct from structural imaging
techniques such as
computerized tomography or
MRI that assess anatomical
structure, functional imaging
techniques (for example,
positron emission tomography,
functional MRI and electro-
encephalography) are sensitive
to physiological processes such
as neuronal activation,
electromagnetic properties of
living tissue, blood flow or
The multimodal association
cortices of the parietal and
frontal lobes integrate
somatosensory, auditory and
visual information for higher-
order cognitive processing.
A medical imaging technique
that uses injected radiolabelled
tracer compounds in
conjunction with mathematical
reconstruction methods to
produce a three-dimensional
image or map of functional
processes in the body, such as
glucose metabolism, blood
flow or receptor distributions.
An approach to characterize
fine-scale anatomy using
multiple histological and
revealing different aspects of
cellular organization or
Using this approach, three-
dimensional trajectories of
white matter tracts can be
reconstructed. The algorithm is
based on fibre orientation
information obtained from
diffusion tensor imaging.
surface to the white matter border in cell body-stained
histological sections, and subsequent multivariate
analysis of changes in the laminar pattern46. Using this
procedure, areal borders can be more reproducibly
mapped than in previous studies, which were based on
visual inspections34. Three-dimensional reconstructions
of post-mortem brains, including cytoarchitectonically
defined cortical areas, are then elastically registered or
‘warped’ to the MRI volume of an in vivo brain that
is also used as a spatial reference for the registration
of functional imaging data50,54,55. This strategy (FIG. 2)
allowed the classical maps to be corrected56 and provided
a quantitative description of the intersubject variability
of cytoarchitectonic areas34–37,40,57,58. It has also facilitated
linear or nonlinear registration of individual brains
and their structures to a spatial reference system and
the discovery of hitherto unknown cytoarchitectonic
areas such as intraparietal areas hIp1 and hIp2 (REF. 59),
areas of the secondary somatosensory cortex OP1–4
(REF. 60) and area hOc5 of the extrastriate cortex61.
This integrated approach has also been used to create
cytoarchitectonic probability maps of the human cortex22,34–
36,40,52,53,56,58,62–70. The resulting probability maps have been
used for the anatomical interpretation of functional
imaging observations40,41,45,52,70–75. For example, it has
been shown that left area 45 is more involved in semantic
processing than left area 44 (REF. 71). So, schematic
drawings of maps and areal borders have been replaced
with probability and maximum probability maps55 (FIG. 3).
This allows the visualization of the intersubject varia-
bility in cytoarchitecture and quantitative definition of
the presence and extent of cytoarchitectonic borders and
cortical areas in all voxels of the reference brain or space.
Architectonic probability maps from a number of
post-mortem brains can be spatially warped onto an
individual or ‘average’ brain (represented by a high-
resolution MRI volume) as a common spatial reference
system. This provides a powerful tool for the anatomical
interpretation of functional imaging data, which can also
be developed into a topographically organized multimodal
database of the human brain.
Molecular architectonics. Histochemical and immuno-
histochemical methods have greatly improved our
knowledge of the regional chemoarchitecture of the
cerebral cortex, mainly in animals. However, the often
demanding requirements of these methods regard-
ing tissue preservation and fixation have limited their
application in human post-mortem brains. Moreover, a
large-scale analysis of serial sections of whole human
brain hemispheres is hampered by the time-consuming
procedures, regionally unequal staining quality, lack of
precise reproducibility and high costs. However, analy-
sis of large brain sections is a necessary prerequisite in
Figure 2 | Summary of procedures for generating a multimodal probabilistic atlas. Probabilistic atlases are
generated by entering cytoarchitectonic, receptor architectonic and functional imaging data into a common spatial
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 955
© 2006 Nature Publishing Group
y = 22x = 13
A map that depicts the
likelihood of a particular
feature. It can be used to show
how frequently, in percentage,
a given anatomical structure is
found in a specific location
across a population of subjects.
Based on a sample of brains
that have been parcellated
using cytoarchitectonic criteria;
they display the statistical
likelihood, or relative
frequency, that a particular
voxel in stereotaxic space
contains a given
cytoarchitectonic unit (for
example, a cortical area, or a
Maximum probability maps
Summary maps in which each
voxel of the three-dimensional
space has been assigned to
defined unit with the highest
probability. They are
calculated on the basis of
probability maps, such as
maps, to generate a map with
The three dimensional
equivalent of a pixel. A pixel is
a picture element, and a voxel
is a volume element.
many mapping projects. Recent developments in the
detection of neurotransmitter receptors have overcome
this limitation and allowed the molecular mapping of
human brains22,34,76–80. Neurotransmitter receptors are
important for signal transduction in the brain; many
types of receptor are expressed in any given cortical
area, and even within a single neuron or glial cell22. So,
information on regional differences in receptor density
(receptor architecture) is an important feature of a
modern brain atlas.
The distribution patterns of neurotransmitter recep-
tors can be assessed using quantitative in vitro receptor
autoradiography. This method is based on the binding of
radioactively labelled ligands selective for binding sites of a
defined receptor type or subtype, and subsequent analysis
of binding site densities. The analysis of numerous
receptor types in the same brain region is also feasible,
and allows insight into the balance of neurotransmitter
systems. Receptor autoradiography has numerous advan-
tages. Most importantly, it is possible to study brain
tissues without major loss in binding properties, up to a
maximum post-mortem delay of approximately 18–28
hours (depending on the post-mortem conditions and
receptor types). It also yields strictly quantifiable results,
and allows analysis of large serial sections through com-
plete, undissected hemispheres. Finally, it is possible to
label numerous different receptors in immediately adja-
cent cryostat sections (10–20 μm thick), and to match
cytoarchitectonic and myeloarchitectonic data derived
from alternating sections of the same brains that have
been used for receptor labelling.
Receptor autoradiography has been used to study
the regional distribution patterns (that is, differences
between brain areas) and laminar distribution patterns
(that is, differences between cortical layers) of selected
neurotransmitter receptor types and subtypes in the
human cortex22,34,40,69,70,79–82. It has been shown that local-
ized changes in the laminar distribution patterns and/or
mean densities (which are averaged over all cortical areas)
of receptor binding sites resemble cytoarchitectonic
borders22,34. For example, the border between the primary
(V1) and secondary (V2) visual cortices is clearly visible
due to the considerably higher muscarinic M2 receptor
density in V1 than in V2 (REFS 22,34) (FIG. 4a,b). The V1/
V2 border is also visible in myelin-stained sections owing
to the unique presence of a heavily myelinated sublayer
(Gennari’s stripe) in area V1 (REFS 22,34). Compared with
V2, α2-adrenergic, GABAA (γ-aminobutyric acid type A)
and serotonergic 5-HT2 receptors are also present at a
higher density in V1, whereas glutamatergic kainate
and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole
propionic acid) receptors are present at a lower density
in V1. Such receptor architectonic features have led to
the discovery of cortical areal borders and intracortical
areal subdivisions that had not previously been detected
in cytoarchitectonic or myeloarchitectonic studies.
These include the subdivision of the primary motor
area 4 (REF. 40), as well as the TE1.0 and TE1.1 (temporal
cortex82) subdivisions of the primary auditory cortex,
which were revealed by the distribution pattern of
receptors (FIG. 4c). So far, this receptor autoradiography
approach has also been used to examine various
neurotransmitter receptors in more than 50 different
cyto architectonically defined cortical areas. Receptor
mapping not only adds multimodal data about the
molecular basis of their areas and nuclei to brain atlases,
but it is also a powerful tool for the identification of
cyto- or myeloarchitectonically undetectable borders
of cortical areas. Finally, as receptors have a key role in
neurotransmission, receptor mapping enriches brain
atlases with functionally relevant data that are directly
related to anatomical criteria such as cyto- or myelo-
White matter maps
In the past, most atlas efforts have focused on grey
matter structure and function. Multimodal atlases,
however, are incomplete without information about
white matter. The white matter has an important role
in connecting different regions of the brain. Perhaps
surprisingly, our understanding of human brain
connectivity is still limited. One possible reason for this
is that it is extremely complex: given a specific number, n,
Figure 3 | Cytoarchitectonic probability maps of the
cortex. a | Probability maps of the primary visual cortex V1
in a sagittal section (left) and the rostral part (Brodmann’s
area 45 (BA45); right) of Broca’s speech region in a coronal
section. x and y refer to the spatial location of the sections
in Montreal Neurological Institute (MNI) space54. The
colour scale indicates the probabilities that the areas are
present in a certain voxel of the reference space. b | Right
lateral and occipital views of the maximum probability map
in which each voxel of the three-dimensional space has
been assigned to the cortical area with the highest
probability. Cortical areas shown include areas 44 and 45
(Broca’s region), primary motor (4) and premotor (6) areas,
the somatosensory cortex (areas 3a, 3b, 1 and 2), inferior
posterior parietal association areas (PFt, PFop, PF, PFm,
PGa, PGp), parietal opercular areas (OP 1–4), mesial
superior parietal area (7mes), auditory areas (TE 1–3) and
visual areas (V1, V2 and V5/MT).
956 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
fmol mg–1 protein
310445 579 713
fmol mg–1 protein
One of most widely used MRI
methods, in which the contrast
is based on a selection of MRI
producing an image that
weights signal by the
relaxometric parameter, T1, of
each tissue (that is, the
longitudinal relaxation time). In
the brain, T1-weighting causes
fibre tracts (nerve connections)
to appear white, cortex and
basal nuclei to appear grey,
and cerebrospinal fluid to
The measurement of relaxation
parameters in nuclear MRI,
such as the longitudinal (T1) or
transverse (T2) decay
constants that characterize
signal decay from excited
nuclei. By detecting subtle
differences in relaxation times,
relaxometry is capable of
differentiating various tissue
types in the brain.
The directional dependency of
water diffusion at each point in
the brain can be summarized
using measures such as
fractional anisotropy. High
anisotropy values indicate
heavily myelinated white
matter, whereas decreased
anisotropy is often a sign of
of distinct functional locations in the cortex, there are
as many as n2 possibilities for the presence or absence of
interconnections (as connections can be intraregional
and be in either direction between two regions). Another
reason is that the available tools to investigate brain
connectivity in vivo are inadequate, as most existing
methods are based on invasive techniques such as
chemical tracers and lesion-based studies, which are less
practical for population-based studies and inappropriate
for humans. In addition, conventional MRI has also been
powerless on this front. As shown in FIG. 5a, the white
matter seems to be homogeneous in T1-weighted images.
Even using relaxometry to derive quantitative measures
that depend on tissue type, conventional MRI has been
unable to provide sufficient contrast to decipher fibre
tract organization in the white matter.
However, this situation has changed since DTI was
developed in the mid-1990s (REF. 83). The contrast of
this MRI technique is based on directionality of water
motion (random Brownian diffusion) in the brain, and
it can provide two types of information. First, it can
generate a so-called anisotropy map83–86 (FIG. 5b). If the fibre
architecture surrounding water molecules has a coherent
orientation, as in the case of axonal bundles, water tends
Figure 4 | Correlation between receptor autoradiography and myeloarchitectonic borders. The regional and
laminar distribution patterns of muscarinic cholinergic M2 receptors (a) and myelin fibres (b) were visualized in
neighbouring coronal sections through the human occipital cortex. The borders (arrows) between the primary visual
cortex V1 and the adjacent secondary visual cortex V2 are visible by considerable differences in receptor densities (a) and
the abrupt disappearance of Gennari’s stripe (b, asterisk) at the border. The regional and laminar distribution patterns of
muscarinic cholinergic M2 receptors were visualized in a coronal section through a human hemisphere by agonist
([3H]oxotremorine-M) binding (c). Area 3b is the primary somatosensory cortex63 and areas TE1.0 and TE1.1 (REF. 64) are
subdivisions of the primary auditory cortex. Arrows show the borders with adjacent areas. Considerable regional and
laminar variations in binding site densities are also visible in the other cortical regions. The colour scales in panels a and c
indicate the M2 receptor binding site densities in fmol mg–1 protein.
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 957
© 2006 Nature Publishing Group
Diffusion of a substance (for
example, water) that is greater
in certain preferred directions,
such as along the axons of a
Diffusion of a substance (for
example, water) that is uniform
in all directions.
Information that can be
modelled mathematically as a
matrix, or tensor, at each
location in an object. Diffusion
tensor imaging produces
signals with at least six
independent parameters at
each anatomical point (the
diffusion tensor); tensor
calculus can then be used to
estimate diffusion parameters
in any specific direction.
to diffuse preferentially along that orientation, in a process
called anisotropic diffusion. If the molecular environment is
random, water motion is also random, yielding isotropic
diffusion. Therefore, the anisotropy map can reveal which
brain regions have a more orderly structure (high diffu-
sion anisotropy). Not surprisingly, many regions of the
white matter have coherent fibre orientation with respect
to the image resolution, and so have high anisotropy, as
can be seen in FIG. 5b. The second type of information
obtainable from DTI is the orientation of these ordered
structures83,87–91; colour is often used to symbolize this
information. In FIG. 5c, red, green and blue colours repre-
sent fibres running along the right–left, anterior–posterior
and superior–inferior axes, respectively. The rich anatomi-
cal and spatial information conveyed by this orientation
map can be clearly appreciated. Pixel-by-pixel informa-
tion on fibre orientation can also be extrapolated to
reconstruct three-dimensional trajectories of prominent
fibre tracts based on a fibre-tracking approach92–98 or a
probability-based approach99. FIGURE 6 shows examples of
such a three-dimensional fibre-tract reconstruction based
on DTI data, which is remarkably similar to known white
matter anatomy based on post-mortem samples100.
The orientation information from DTI provides
essential contrast in the white matter and opens up
new opportunities for brain mapping and atlasing. Just
as the cortical folding pattern provides clues to identify
anatomical locations and map functions, the orientation
contrast can be used to identify various functional units
in the white matter. Combined with three-dimensional
reconstruction, coordinates of specific white matter tracts
can also be mapped. FIGURE 7 shows several examples of
DTI-based white matter maps. Using a high-quality
single-subject dataset, various white matter tracts have
been identified and a white matter atlas generated85,90,101–105.
By normalizing DTI data from multiple subjects into a
common template coordinate system, a probabilistic
map of white matter anatomy can be created for normal
and diseased patient populations, allowing for quantita-
tive comparisons of white matter anatomy106–108 (FIG. 7b).
FIGURE 7c,d show a manually reconstructed map with
parcellated white matter tracts based on DTI data. Once
the white matter is parcellated into different tracts, these
can serve as a valuable framework to map anatomical and
functional information. If one is interested in the shape
and size of a specific white matter tract, the fibre-tracking
approach can be applied to create population-based
maps103 (FIG. 7e). Using these mapping techniques, axonal
degeneration after stroke or surgery can be followed in a
tract-specific manner and these changes can be correlated
with functional outcomes109. Therefore, we can investigate
such questions as ‘which white matter tracts are affected
by stroke?’110,111, or ‘what is the functional outcome of
degeneration in a specific white matter tract?’112. DTI
can also be used to generate connectivity maps (FIG. 7f) by
identifying brain regions associated with a specific white
matter tract113. Such connectivity maps could provide
important clues to deduce which cortical areas are likely
to be affected when white matter injuries occur.
Although DTI is a powerful technique for brain
mapping, it has several limitations. First, it generates
tensor-valued information at each pixel, accumulating large
amounts of complicated anatomical information. Data
analysis methodology is not yet sufficiently developed
to harness this detail, making it challenging to quantify
morphology from these datasets. Second, with current
Figure 5 | Comparison of a conventional T1-weighted image and DTI-based contrasts. These images show the
same slice level from the same subject. a | The T1-weighted image shows the detailed anatomy of the cortex but the white
matter appears homogeneous. b | The anisotropy map shows the regions that have high diffusion anisotropy, a hallmark of
axonal fibres with a coherent orientation within a pixel. c | For high anisotropy regions, we can map the fibre orientation
using colours. In this map, red, green and blue represent fibres running along the right–left, anterior–posterior and
superior–inferior axes, respectively. Fibres running along oblique angles are represented by a mixture of the three
Figure 6 | Comparison between a post-mortem brain
sample and the results of DTI-based three-
dimensional tract reconstruction. a | Post-mortem
sample showing 4 main association fibres: the superior
longitudinal fasciculus (SLF), inferior longitudinal
fasciculus (ILF), inferior fronto-occipital fasciculus (IFO)
and uncinate fasciculus (UNC). b | These tracts can be
reconstructed from in vivo human DTI data and
presented with different colours. There is excellent
agreement between the two. Panel a reproduced, with
permission, from REF. 196 © (1997) Univ. of Iowa.
958 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
Partial volume effects
This refers to the blurring of
intensity differentiations used
to classify contributing tissue
types (grey matter, white
matter and cerebrospinal
fluid). It is the results of pixels
placed over a region that
contains multiple tissue types.
The smaller the pixel size the
less frequently this is
imaging resolution (2–3 mm), only the macroscopic
anatomy of large white matter tracts can be studied. For
example, the notion of ‘fibre orientation at each pixel’
is based on the assumption that there is only one fibre
bundle with the same orientation in a given pixel.
Currently, many new approaches alternative to the
tensor-based method are being proposed, which can
extract more anatomical information (such as identifying
multiple fibre populations) from each pixel114–117. Although
these approaches have successfully demonstrated their
effectiveness, with current imaging resolution, the infor-
mation inherent in diffusion measurement is macro-
scopic and there is no way to obtain cellular-level or even
synaptic connectivity information.
This resolution issue is also related to the validity of
DTI-based tract reconstruction results. Although good
overall agreement has been reported between the three-
dimensional reconstruction results and existing anatomical
knowledge, there are frequent false-negative and false-
positive results due to noise and partial volume effects. In
the future, it will be important to develop approaches to
reliably extract anatomically and clinically valuable infor-
mation from such contaminated data. Although this is a
challenging task, several effective approaches have been
suggested, such as knowledge-based tract reconstruc-
tion (namely, reconstructing only anatomically known
fibres)92, probabilistic tracking99 and population-average
Nevertheless, DTI can advance our knowledge of the
correlations between white matter anatomy and func-
tion by providing a refined white matter map to which
we can register various experimental findings. Finally,
macroscopic DTI information can be compared directly
with microscopic post-mortem data on fibre tracts118–120,
yielding an integrative atlas system that evaluates both
methods with considerable synergistic effects.
Figure 7 | Examples of various methods of mapping the white matter based on DTI. a | For each individual dataset,
many white matter structures can be discretely identified and anatomical labels can be assigned to them. b | Normal or
abnormal variability of white matter anatomy can be investigated by using a pixel-based population-averaging approach.
This image was created using data from 30 healthy volunteers normalized to a common template using a 12 parameter
affine transformation. c | Various structures are parcellated manually or by using three-dimensional tract reconstruction
into individual tract families based on orientation (colour) information. d | This parcellation/reconstruction allows a three-
dimensional white matter parcellation map to be created. e | The morphology of a specific white matter tract can be
investigated, based on a three-dimensional tract reconstruction technique followed by group averaging. Here, the inferior
fronto-occipital fasciculus (IFO) was reconstructed from the normal population and mapped onto a common template.
f | This approach can also be used to identify cortical regions associated with a specific tract, where the trajectory of the
IFO is extrapolated to identify associated cortical regions, and a probabilistic cortical map is created from the 30 subjects.
These kinds of map pave the way to investigate white matter anatomy and function in a systematic and quantitative
manner. ACR, anterior corona radiata; ALIC, anterior limb of internal capsule; ATR, anterior thalamic radiation; CC, corpus
callosum; CG, cingulum; CPT, corticopontine tract; CST, corticospinal tract; EC, external capsule; Fmajor, forceps major;
Fminor, forceps minor; FX, fornix; GCC, genu corporis callosi; ILF, inferior longitudinal fasciculus; PLIC, posterior limb of
internal capsule; PTR, posterior thalamic radiation; RLIC, retrolentricular part of internal capsule; SCC, splenium corporis
callosi; SFO, superior fronto-occipital fasciculus; SLF, superior longitudinal fasciculus; SS, sagittal striatum; ST, stria
terminalis; TAP, tapetum. Panel a modified, with permission, from REF. 20 © (2005) Elsevier Science.
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 959
© 2006 Nature Publishing Group
Multimodality and population-based atlases
Multimodal mapping. Before the advent of brain map-
ping methods1, atlasing efforts of various research
groups were typically independent of one another.
Most atlases had a different spatial scale and resolu-
tion, utilized different data structures, described
different structural or functional characteristics and
were inherently incompatible with the others. Although
each of those brain mapping strategies has its unique
advantage, the brain atlases that are derived from a
single method will be limited, unless certain integra-
tive approaches such as spatial normalization are
implemented. An integrated, comprehensive approach
requires these diverse mapping methods to be combined
Characterizing a single subject with multiple imaging
devices clearly combines the strengths of each imaging
modality. Data from single subjects, pre-mortem and
post-mortem, provide a unique view of the relationship
between in vivo imaging and histological assessment. For
example, Mega et al.121 scanned patients in the terminal
stages of Alzheimer’s disease using both MRI and PET.
These data were combined with a stain of neurofibrillary
tangles and post-mortem three-dimensional histological
images that show the gross anatomy122. This multimodal,
but single-subject, atlas of Alzheimer’s disease relates
the anatomical and histopathological underpinnings
to in vivo metabolic and perfusion maps of this disease.
Recent work on neurodegenerative diseases has also
warped histological data into MRI scans to better iden-
tify regions (such as the CA fields of the hippocampus
and the basal nucleus of Meynert123) that are relevant to
understanding the pathology.
Population-based atlases. Modern atlases not only incor-
porate information from multiple modalities — they
also incorporate information from multiple members
of a population. Normal anatomical complexity
and variability between human brains are so great that
group-specific patterns of anatomy and function are
often obscured. Reports of structural differences in
the brain linked to gender, IQ and handedness are a
topic of intense controversy124,125, and it is even less clear
how these factors affect disease-specific abnormalities.
The importance of these linkages has propelled com-
putational anatomy to the forefront of brain imaging
investigations. In particular, population atlases can
be compiled into subpopulations to represent specific
disease types, and subsequently stratified by age, gender,
handedness or genetic factors.
To distinguish abnormalities from normal variants,
a realistically complex mathematical framework
is required to encode information on anatomical
variability in homogeneous populations126. Warping
algorithms can be used to equate individual brain datasets
with an atlas template, and the applied transformations
can be studied to examine patterns of anatomical varia-
tion and detect pathology. Cortical anatomy is altered
in schizophrenia127, Alzheimer’s disease128,129 and in
various developmental disorders, including fetal alcohol
syndrome130, autism131 and Williams syndrome132. By
using specialized strategies for averaging anatomy across
individuals, specific features of anatomy emerge that are
not observed in individual representations due to their
considerable variability; population-specific patterns of
cortical organization or asymmetry can then be mapped
Population-based brain atlases54,133 provide an
expandable framework to synthesize the results of
disparate imaging studies. These atlases use novel
analytical tools to combine data across subjects, modali-
ties and time and to detect group-specific features not
apparent in individual scans. A notable example is the
pattern of anatomical asymmetry in the major cortical
gyri and sulci, such as the prominent asymmetries in
the perisylvian language-related cortices, which are well
known134 but not easily discernible in individual brain
scans. The average patterns clearly emerge after averaging
computational models of the cortical sulci across a
population124. So, a computational framework can be
used to answer neuroscientific questions regarding the
development of these asymmetries135, and how they are
modulated by gender136, aging or in psychiatric illnesses
such as schizophrenia37,137,138. Furthermore, population-
based atlases can be stratified into subpopulations to
reflect a particular (clinical or demographic) subgroup1.
Design of appropriate reference systems for brain map-
ping data presents considerable challenges, as these
systems must capture how brain structure and function
vary in large populations, across age and gender, in
different disease states, across imaging modalities and
even across species139.
As imaging data from different studies can now
be compared in a common coordinate system, large
databases of functional imaging data — and associated
meta-data on experimental paradigms and findings
— are now beginning to be assembled, along with tools
developed to interact with them140. Notable examples
of these neuroinformatics efforts include the ICBM53
(see International Consortium for Brain Mapping in
Online links box), the ADNI (see Alzheimer’s Disease
Neuroimaging Initiative in Online links box) consor-
tium project to build a data repository of MRI, PET
and other clinical and biomarker data on aging and
Alzheimer’s disease, the NIMH paediatric imaging
study (which is scanning nearly 1000 children every
2 years for 10 years141,142), and the Finnish twin
registry, whose scans have been used to identify genetic
influences on brain structure125,143,144. Over the next
decade, population-based atlases are also likely to
gain widespread applicability in genetic studies. In an
exciting development, genetic linkage data have been
incorporated into brain imaging studies to discover
previously unknown effects on the brain of variations
at specific genetic loci145 (FIG. 8e). Given their power to
store and compute statistics on expected rates of brain
development and degeneration for different clinical
populations, dynamic brain atlases are also likely to be
used in drug trials or studies of factors that influence
disease expression and therapeutic response146, and
studies on the effects of specific medications such as
antipsychotics or mood stabilizers147–149.
960 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
a Statistical atlas based on healthy subjects
b Normal brain development
Grey matter volume
c Atlases of disease
1.5 years later (same patient)
Average deficit (%)
Average deficit (%)
Average deficit (%)
d Environmental influences
e Genetic influences
Infectious disease (HIV/AIDS) Genetic linkage map (DISC1)
Image processing algorithms are also making digital
brain atlases more versatile. For example, probabilistic
atlases retain information on cross-subject variations
in brain structure and function. These atlases are
powerful new tools with broad clinical and research
applications52,124,150,151. Deformable brain atlases, which
are essentially labelled templates of anatomy that can
be locally dilated, stretched and scaled to match scans
from new subjects152, are adaptable in that they can be
individualized to reflect the anatomy of new subjects.
The deformation can transfer all the accumulated data
in the atlas onto the new subject’s scan, thereby quantify-
ing the degree of deviation in their anatomy, relative to a
Figure 8 | Brain atlases that represent specific subpopulations. Population-based atlases can be stratified to compare
various populations, revealing the impact of development or disease on specific features of the brain. a | Statistical maps
show the changing distribution of cortical grey matter (over the human lifespan (n = 176; data from Sowell et al. (2003)130).
b | Based on longitudinal MRI scans of children developing normally, dynamic maps can show the rate and anatomical
sequence of cortical grey matter maturation by fitting an age-adjusted mean to these trajectories154. c–d | Such maps can be
used to reconstruct time-lapse sequences that visualize the spread of cortical atrophy at different stages of Alzheimer’s
disease (c), changes associated with chronic drug abuse (d, top), or in the course of infectious illnesses such as HIV/AIDS
(d, bottom) 161,182,195. Atlases could provide statistical measures of disease burden for drug trials, offering insight into the
systems involved and factors that influence them. e | Individual deviations from these average patterns can also be analysed:
twin studies have revealed aspects of brain structure (such as frontal grey matter) that are under extremely strong genetic
control188. In the new field of imaging genomics, genetic linkage studies have been extended to brain mapping data to
identify the statistical linkage between specific genetic variations (single nucleotide polymorphisms) and anatomical
variations in the form of a statistical map186. DISC1, disrupted in schizophrenia 1; rs229, denotes a single nucleotide
polymorphism marker (rs751229) located in DISC1. The colour bar denotes the correlation between intrapair differences in
brain matter density with the number of alleles in common at this genetic marker locus in twins discordant for schizophrenia.
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 961
© 2006 Nature Publishing Group
Finally, dynamic brain atlases can also be used to
reference and analyse dynamic, time-varying data.
Recent studies have analysed longitudinally collected
MRI scans to compute probabilistic information on
growth rates156–158 (FIG. 8a), lifelong normal changes130
(FIG. 8a) or normative statistics on rates of degenerative
tissue loss in drug abuse (FIG. 8d), aging and dementing
diseases149,159–161 (FIG. 8c). A complementary development,
which has advanced the power of multi-subject atlasing
projects, is the widespread use of analysis techniques
such as statistical parametric mapping (SPM) (BOX 1).
Disease-specific atlases. Surprisingly few atlases of
neuropathology use a standardized three-dimensional
coordinate system to integrate data across patients,
techniques and acquisitions. Atlases with a well-
defined coordinate space54,163, together with algorithms
to align data with them21, have enabled the pooling
of brain mapping data from multiple individuals and
sources, including large patient populations. Automated
algorithms can then utilize atlas descriptions of
anatomical variance to guide image segmentation164–166,
tissue classification167,168, functional image analysis169,170
and pathology detection171. An obvious challenge in
integrating pathological data from tissue specimens
results from the destructive sectioning and histologi-
cal staining procedures. Even so, the advent of whole
human head cryosectioning techniques (in which the
intact blockface is imaged photographically) allows
spatially coherent three-dimensional volumetric images
to be acquired. Stained pathological material could,
in principle, be aligned with these canonical volumes
using three-dimensional image deformation strategies
that have proven useful in creating cytoarchitectural
Statistical representations of anatomy resulting
from the application of atlasing strategies to specific
subgroups of diseased individuals have revealed the
profile of structural brain deficits in a number of
diseases. These include studies of Alzheimer’s dis-
ease161, HIV/AIDS132,172, epilepsy172,146, unipolar depres-
sion173, childhood and adult-onset schizophrenia174–177,
attention-deficit/hyperactivity disorder178, fetal alcohol
syndrome179, Tourette’s syndrome180, bipolar disorder181,149,
autism131,149, Williams syndrome132 and methamphetamine
abusers182. Findings from these population atlases
have often led to unexpected neuroscientific find-
ings, such as a spreading wave of cortical changes in
Without methods to overcome the problems of ana-
tomical variability, the statistical power to resolve disease
and treatment effects is seriously undermined. First, owing
to normal anatomical variation, diseased and healthy indi-
viduals overlap on most anatomical measures. Second,
these difficulties are exacerbated by disease-related change
such as atrophy128,149,159,183,184 or other progressive and
dynamic anatomical changes. To fully capitalize on neuro-
imaging data from the diseased brain, an appropriately
complex mathematical framework is needed to address
these challenges. Only then can brain maps be compared
across patients and across time133,160,182,184,185.
Atlases have also revealed how single genes can affect
brain structure186. When they have supported existing
models of disease, such as the sequence of spreading
pathology in Alzheimer’s disease, atlas-based descrip-
tions of variance offer statistics on degenerative rates and
can elucidate clinically relevant features at the systems
level. Atlases have identified differences in atrophic
patterns between Alzheimer’s disease and Lewy Body
dementia187, and differences in atrophic rates between
clinically-defined subtypes of psychosis188–190.
Based on well-characterized patient groups,
population-based atlases contain composite maps and
visualizations of structural variability, asymmetry and
group-specific differences. Pathological change can
be tracked over time, and generic features resolved,
allowing these atlases to offer biomarkers for a variety
of pathological conditions, as well as morphometric
measures for genetic studies or drug trials.
Conclusions and perspectives
The evolution of brain atlases has seen tremendous
advances; they can now accommodate observations from
multiple modalities and from populations of subjects
Box 1 | Statistical parametric mapping
Initially proposed as a method to analyse functional or metabolic images from multiple subjects in a common coordinate
space161,192, statistical parametric mapping (SPM) is now widely used by the brain mapping community to detect and
identify activated brain regions in functional imaging studies. SPM relates patterns of activation to experimental or
subject-specific parameters, and can be used to infer patterns of functional connectivity from time-series data, or to
analyse anatomical images193. Atlasing efforts in the early 1990s pointed to the value of registering data from multiple
subjects to a common coordinate space. Once aligned, imaging signals could be combined across subjects for statistical
analysis, producing a statistic (for example, expressing a group difference) at any given voxel location in the canonical
space. Because many millions of voxels are typically tested in a brain mapping study, statistical solutions to the multiple
comparisons problem were developed based on the mathematical likelihood that clusters of certain sizes and
magnitudes would occur by chance when no signal was present. SPM rapidly became a widely used software package for
collating and analysing brain imaging data (see Statistical Parametric Mapping in Online links box), partly because it
implemented powerful mathematical formulae — based on new findings in the theory of Gaussian random fields194 — for
inferring whether functional activations were present in multisubject studies. The SPM approach shares some affinity
with atlasing efforts in that it reports statistical findings in a common coordinate space. In contrast to SPM, which
typically analyses registered images on a voxel-by-voxel basis, computational atlasing efforts can also include
computational anatomical work that models individual brain structures as geometrical surfaces and curves, such as
cortical surface modelling and fibre tract modelling. Ultimately these approaches are complementary.
962 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
Toga, A. W. & Mazziotta, J. C. in Brain Mapping: the
Methods (eds Toga, A. W. & Mazziotta, J.C.) 3–25
(Academic, San Diego, 1996).
Duvernoy, H. M. The Human Brain (Springer, New
Ono, M., Kubik, S. & Abernathey, C. D. Atlas of the
Cerebral Sulci (Thieme, Stuttgart, 1990).
This widely used atlas reports anatomical
descriptions and trends in individual variabilities of
cortical sulci. It serves as a reminder of the
challenges in developing multisubject reference
systems for human brain mapping.
Talairach, J. & Szikla, G. Atlas d’Anatomie
Stereotaxique du Telencephale: Etudes Anatomo-
Radiologiques (Masson & Cie, Paris, 1967)
Talairach, J. & Tournoux, P. Co-planar Stereotaxic
Atlas of the Human Brain (Thieme, New York, 1988).
This atlas influenced the brain mapping field by
providing a principled method for spatially
transforming anatomical datasets into a
coordinate-based reference system based on the
anterior commissure–posterior commisure line.
The atlas presents post-mortem data from a single
Brodmann, K. in Some Papers on the Cerebral Cortex,
translated as: On the Comparative Localization of the
Cortex 201–230 (Thomas, Springfield, Illinois, 1960).
von Economo, C. & Koskinas, G. N. Die
Cytoarchitektonik der Hirnrinde des Erwachsenen
Menschen (Springer, Berlin, 1925) (in German).
Flechsig, P. Anatomie des menschlichen Gehirns und
Rückenmarks auf myelogenetischer Grundlage
(Thieme, Leipzig, 1920) (in German).
Smith, G. E. A new topographical survey of the human
cerebral cortex, being an account of the distribution of
the anatomically distinct cortical areas and their relation-
ship to the cerebral sulci. J. Anat. 41, 237–254 (1907).
10. Vogt, C. & Vogt, O. Allgemeinere ergebnisse unserer
hirnforschung. J. Psychol. Neurol. 25, 292–398
(1919) (in German).
11. Mai, J., Assheuer, J. & Paxinos, G. Atlas of the Human
Brain (Academic, San Diego, 1997).
12. Matsui, T. & Hirano, A. An Atlas of the Human Brain for
Computerized Tomography (Igako-Shoin,Tokyo, 1978).
13. Schaltenbrand, G. & Bailey, P. Introduction to
Stereotaxis with an Atlas of the Human Brain (Thieme,
Stuttgart & New York, 1959).
14. Schaltenbrand, G. & Wahren, W. Atlas for Stereotaxy of
the Human Brain 2nd edn (Thieme, Stuttgart, 1977).
15. Van Buren, J. M. & Borke, R. C. Variations and
Connections of the Human Thalamus Vols 1 & 2
(Springer, New York, 1972).
16. Van Buren, J. M. & Maccubin, D. An outline atlas of
human basal ganglia and estimation of anatomic
variants. J. Neurosurg. 19, 811–839 (1962).
17. Mansour, A., Fox, C. A., Burke, S., Akil, H. & Watson, S. J.
Immunohistochemical localization of the cloned mu
opioid receptor in the rat CNS. J. Chem. Neuroanat. 8,
18. Dejerine, J. Anatomie des Centres Nerveux (Rueff,
19. Damasio, H. Human Brain Anatomy in Computerized
Images (Oxford Univ. Press, Oxford & New York, 1995).
20. Mori, S., Wakana, S., Nagae-Poetscher, L. M. &
van Zijl, P. C. MRI Atlas of Human White Matter
(Elsevier Science, Amsterdam, 2005).
21. Toga, A. W. Brain Warping (Academic, San Diego, 1998).
The author surveys the many approaches and
computational algorithms for deforming brain
imaging data from multiple subjects or modalities
to match an atlas or other standardized coordinate
22. Zilles, K. et al. Architectonics of the human cerebral
cortex and transmitter receptor fingerprints: reconciling
functional neuroanatomy and neurochemistry. Europ.
Neuropsychopharmacol. 12, 587–599 (2002).
23. Brodmann, K. Physiologie des Gehirns. Neue Dtsch
Chir. 11, 85–426 (1914) (in German).
24. Bailey, P. & von Bonin, G. The Isocortex of Man (Univ.
Illinois Press, Urbana, 1951).
25. Sanides, F. Die Architektonik des menschlichen
Stirnhirns (Springer, Berlin & New York, 1962)
collected at different laboratories. The probabilistic
systems described here show promise for identifying
patterns of structural, functional and molecular variation
in large image databases, for pathology detection in indi-
viduals and groups and for determining the effects of age,
gender, handedness and other demographic or genetic
factors on brain structures in space and time. Integrating
these observations to enable statistical comparison has
already provided a deeper understanding of the relation-
ship between brain structure and function. Importantly,
the utility of an atlas depends on appropriate coordinate
systems, registration, and deformation methods to allow
the statistical combination of multiple observations in an
agreed, but expandable, digital reference framework.
In this review, we highlighted two sources of data that
will have an increasingly important role in integrative brain
atlases: molecular architectonics and DTI. Once stored in
a population-based atlas, information from these tech-
niques can help to interpret more conventional functional
and structural brain maps by integrating them with data
on molecular content, physiology and fibre connections
— a development that can help to formulate and test new
types of neuroscientific models. A goal of systems neuro-
science is to establish brain systems that underlie cogni-
tive processes and the factors that influence them. DTI
data on fibre connectivity, stored in an atlas coordinate
system, can offer a rigorous computational basis to test
how identifiable anatomical systems (for example, visual,
limbic or corticothalamic pathways) interact. This atlas
information can be invoked as regions of interest that
are incorporated into the statistical design of functional
brain mapping studies (for example, with fMRI or electro-
encephalography), even when underlying fibre connec-
tions are not evident in the data being collected for a
particular study. Molecular architectonic mapping also
provides a complementary perspective in which known
neurotransmitter and receptor pathways — the physiology
and molecular features of which are now well understood
— can be associated with functional subdivisions of the
cortex, identified with tomographic imaging. For example,
an fMRI study of inhibitory cognitive processes in drug
abusers might be informed by other modalities of data on
limbic–prefrontal connectivity (from DTI), or on cortical
monoamine receptor distributions (from architectonic
mapping). In each of these contexts, the coordinate system
of the atlas, and the transformations that equate different
modality data in the same reference frame, provide the
means to build and test systems-level models of cognition
or disease, incorporating data from traditionally separate
domains of neuroscience.
As brain atlases begin to incorporate data from
thousands of subjects, new questions in basic and clini-
cal neuroscience can be addressed that were previously
out of reach. For example, quantitative genetic studies are
underway to link functional, structural and connectivity
information with variations in candidate genetic polymor-
phisms that could influence them. As polygenic disorders
involve the interaction of multiple genetic variations, each
with a small effect on the overall phenotype, digital atlases
provide the ideal setting to mine large numbers of images
computationally with hybrid techniques from computa-
tional anatomy and quantitative genetics (such as linkage
and association studies in which a statistic is computed at
each voxel location in the brain191).
Another area of expansion is in the processing of clini-
cal data from therapeutic trials, to determine factors that
combat or modify disease progression. Statistical atlases,
containing time-varying data, have revealed unforeseen
but characteristic brain changes in several dementias and
neuropsychiatric illnesses. The population-based atlases
of the future will provide the necessary statistical power to
identify demographic, genetic and environmental factors
that influence therapeutic response. Most important of
all, brain atlases are now being enriched with data from
newer technologies, such as DTI, fMRI and modern
high-throughput cytoarchitectural methods. These
efforts are yielding whole new avenues of research into
the functional organization of the brain that will be of
interest not just to specialists in neuroimaging, but to all
basic and clinical neuroscientists.
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 963
© 2006 Nature Publishing Group
26. Sarkisov, S. A., Filimonoff, I. N., Kononowa, E. P.,
Preobrachenskaja, I. S. & Kukuew, L. A. Atlas of the
Cytoarchitectonics of the Human Cerebral Cortex
(Medgiz, Moscow, 1955).
27. Zilles, K., Armstrong, E., Schleicher, A. &
Kretschmann, H. J. The human pattern of gyrification
in the cerebral cortex. Anat. Embryol. 179, 173–179
28. Nelissen, K., Luppino, G., Vanduffel, W., Rizzolatti, G.
& Orban, G. A. Observing others: multiple action
representation in the frontal lobe. Science 310,
29. Sereno, M. I. et al. Borders of multiple visual areas in
humans revealed by functional magnetic resonance
imaging. Science 268, 889–893 (1995).
30. Bremmer, F. et al. Polymodal motion processing in
posterior parietal and premotor cortex: a human fMRI
study strongly implies equivalencies between humans
and monkeys. Neuron 29, 287–296 (2001).
31. Zeki, S. et al. A direct demonstration of functional
specialization in human visual cortex. J. Neurosci. 11,
32. Lashley, K. S. & Clark, G. The cytoarchitecture of the
cerebral cortex of atlases: a critical examination of
architectonic studies. J. Comp. Neurol. 85, 223–306
33. Luppino, G. & Rizzolatti, G. The organization of the
frontal motor cortex. News Physio. Sci. 15, 219–224
34. Zilles, K., Schleicher, A., Palomero-Gallagher, N. &
Amunts, K. in Brain Mapping: The Methods 2nd edn
(eds Toga, A. W. & Mazziotta, J. C.) 573–602
(Academic, Amsterdam, 2002).
35. Amunts, K. et al. Broca’s region revisited:
cytoarchitecture and intersubject variability. J. Comp.
Neurol. 412, 319–341 (1999).
36. Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T.
& Zilles, K. Brodmann’s areas 17 and 18 brought into
stereotaxic space — where and how variable?
NeuroImage 11, 66–84 (2000).
37. Zilles, K. et al. Quantitative analysis of sulci in the
human cerebral cortex: development, regional
heterogeneity, gender difference, asymmetry,
intersubject variability and cortical architecture. Hum.
Brain Mapp. 5, 218–221 (1997).
38. Tootell, R. B. H. et al. Functional analysis of primary
visual cortex (V1) in humans. Proc. Natl Acad. Sci.
USA 95, 811–817 (1998).
39. Talavage T. M. et al. Tonotopic organization in human
auditory cortex revealed by progressions of frequency
sensitivity. J. Neurophysiol. 91, 1282–1296 (2004).
40. Geyer, S. et al. Two different areas within the primary
motor cortex of man. Nature 382, 805–807 (1996).
41. Young, J. P. et al. Regional cerebral blood flow
correlations of somatosensory areas 3a, 3b, 1, and 2
in humans during rest: a PET and cytoarchitectural
study. Hum. Brain Mapp. 19, 183–196 (2003).
42. Hagler, D. & Sereno, M. I. Spatial maps in frontal
and prefrontal cortex. Neuroimage 29, 567–577
43. Van Essen, D. C. Surface-based approaches to spatial
localization and registration in primate cerebral
cortex. NeuroImage 23, S97–S107 (2004).
This review article describes many of the
neuroanatomical, technical and informatics issues
involved in integrating cortically-derived
neuroimaging data across subjects and modalities.
44. Tootell, H. et al. Functional analysis of primary visual
cortex (V1) in humans. Proc. Natl Acad. Sci. 95,
45. Larsson, J. et al. Neuronal correlates of real and
illusory contour perception: functional anatomy with
PET. Europ. J. Neurosci. 11, 4024–4036 (1999).
46. Schleicher, A., Amunts, K., Geyer, S., Morosan, P. &
Zilles K. Observer-independent method for
microstructural parcellation of cerebral cortex: a
quantitative approach to cytoarchitectonics.
Neuroimage 9, 165–177 (1999).
47. Schleicher, A. et al. A stereological approach to human
cortical architecture: identification and delineation of
cortical areas. J. Chem. Neuroanat. 20, 31–47
48. Schleicher, A. et al. Quantitative architectonic analysis:
a new approach to cortical mapping. Anat. Embryol.
210, 373–386 (2005).
49. Annese, J. & Toga, A. W. in Brain Mapping: The
Methods (eds Toga, A. W. & Mazziotta, J. C.) 537–564
(Academic, San Diego, 2002).
50. Hömke, L. in Numerical Linear Algebra with
Applications 215–229 (Wiley, Copper Mountain,
51. Thompson, P. & Toga, A. W. in Handbook of Medical
Image Processing (ed. Bankman, I.) 159–170
(Academic, San Diego, 2000).
52. Roland P. E. & Zilles K. Brain atlases — a new research
tool. Trends Neurosci. 17, 458–467 (1994).
53. Mazziotta, J. C. et al. A probabilistic atlas and
reference system for the human brain: international
consortium for brain mapping (ICBM). Philos. Trans. R.
Soc. Lond., B, Biol. Sci. 356, 1293–1322 (2001).
A description of an international consortium
project that was set up to develop a probabilistic
reference system for the human brain,
incorporating statistical information on the
variations in human brain structure and function in
a population of 7000 subjects.
54. Evans, A. C., Collins, D. L. & Milner, B. An MRI-based
stereotactic brain atlas from 300 young normal
subjects. Soc. Neurosci. Abstr. 408 (1992).
55. Eickhoff, S. et al. A new SPM toolbox for combining
probabilistic cytoarchitectonic maps and functional
imaging data. NeuroImage 25, 1325–1335 (2005).
56. Grefkes, C., Geyer, S., Schormann, T., Roland, P. &
Zilles, K. Human somatosensory area 2: observer-
independent cytoarchitectonic mapping,
interindividual variability, and population map.
NeuroImage 14, 617–631 (2001).
57. Eickhoff, S., Schleicher, A., Zilles, K. & Amunts, K.
The human parietal operculum. I. Cytoarchitectonic
mapping of subdivisions. Cereb. Cortex 16, 254–267
58. Geyer, S., Schleicher, A. & Zilles, K. Areas 3a, 3b, and
1 of human primary somatosensory cortex: 1.
Microstructural organization and interindividual
variability. NeuroImage 10, 63–83 (1999).
59. Choi, H.-J. et al. Cytoarchitectonic identification and
probabilistic mapping of two distinct areas within the
anterior ventral bank of the human intraparietal
sulcus. J. Comp. Neurol. 495, 53–69 (2006).
60. Eickhoff, S. B., Weiss, P. H., Amunts, K., Fink, G. R. &
Zilles, K. Identifying human parieto-insular vestibular
cortex using fMRI and cytoarchitectonic mapping.
Hum. Brain Mapp. 27, 611–621 (2006).
61. Malikovic, A. et al. Cytoarchitectonic analysis of the
human extrastriate cortex in the region of V5/MT+:
a probabilistic, stereotaxic map of area hOc5. Cereb.
Cortex 7 April 2006 (doi:10.1093/cercor/bhj181).
62. Amunts, K. et al. Cytoarchitectonic mapping of the
human amygdala, hippocampal region and entorhinal
cortex: intersubject variability and probability maps.
Anat. Embryol. 210, 342–352 (2005).
63. Geyer, S., Schormann, T., Mohlberg, H. & Zilles, K.
Areas 3a, 3b, and 1 of human primary somatosensory
cortex. 2. Spatial normalization to standard
anatomical space. NeuroImage 11, 684–696 (2000).
64. Morosan, P. et al. Human primary auditory cortex:
cytoarchitectonic subdivisions and mapping into a
spatial reference system. NeuroImage 13, 684–701
65. Rademacher, J. et al. Probabilistic mapping and
volume measurement of human primary auditory
cortex. NeuroImage 13, 669–683 (2001).
66. Rademacher, J. Bürgel, U. & Zilles, K. Stereotaxic
localization, intersubject variability and interhemispheric
differences of the human auditory thalamocortical
system. NeuroImage 17, 142–160 (2002).
67. Roland, P. E. & Zilles, K. The developing european
computerized human brain database for all imaging
modalities. NeuroImage 4, 39–47 (1996).
68. Roland, P. E. et al. Cytoarchitectural maps of the
human brain in standard anatomical space. Hum.
Brain Mapp. 5, 222–227 (1997).
69. Zilles, K. & Palomero-Gallagher, N. Cyto-, myelo- and
receptor architectonics of the human parietal cortex.
NeuroImage 14, 8–20 (2001).
70. Zilles, K. Mapping of human and macaque
sensorimotor areas by integrating architectonic,
transmitter receptor, MRI and PET data. J. Anatomy
187, 515–537 (1995).
71. Amunts, K. et al. Analysis of the neural mechanisms
underlying verbal fluency in cytoarchitectonically
defined stereotaxic space — the roles of Brodmann
areas 44 and 45 NeuroImage 22, 42–56 (2004).
72. Bodegård, A. et al. Object shape differences reflected
by somatosensory cortical activation. J. Neurosci. 20,
73. Bodegård, A. et al. Somatosensory areas in man
activated by moving stimuli. Cytoarchitectonic
mapping and PET. NeuroReport 11, 187–191
74. Eickhoff, S., Amunts, K., Mohlberg, H. & Zilles, K. The
human parietal operculum. II. Stereotaxic maps and
correlation with functional imaging results. Cereb.
Cortex 16, 268–279 (2006).
75. Horwitz, B. et al. Activation of Broca’s area during the
production of spoken and signed language: a
combined cytoarchitectonic mapping and PET
analysis. Neuropsychologia 41, 1868–1876 (2003).
76. Pazos, A., Probst, A. & Palacios, J. M. Serotonin
receptors in the human brain — III. Autoradiographic
mapping of serotonin-1 receptors. Neuroscience 21,
77. Pazos, A., Probst, A. & Palacios, J. M. Serotonin
receptors in the human brain — IV. Autoradiographic
mapping of serotonin-2 receptors. Neuroscience 21,
78. Vogt, B. A., Plager, M. D., Crino, P. B. & Bird, E. D.
Laminar distributions of muscarinic acetylcholine,
serotonin, GABA and opioid receptors in human
posterior cingulate cortex. Neuroscience 36,
79. Zilles, K. in From Monkey Brain to Human Brain (eds
Dehaene, S., Duhamel, J.-R., Hauser, M. & Rizzolatti, G.)
41–56 (MIT Press, Cambridge, Massachusetts, 2005).
80. Zilles, K., Palomero-Gallagher, N. & Schleicher, A.
Transmitter receptors and functional anatomy of the
cerebral cortex. J. Anat. 205, 417–432 (2004).
81. Geyer, S., Schleicher, A. & Zilles, K. The somatosensory
cortex of human: cytocarchitecture and regional
distributions of receptor-binding sites. NeuroImage 6,
82. Morosan, P., Rademacher, J., Palomero-Gallagher, N.
& Zilles, K. in The Auditory Cortex: Towards a
Synthesis of Human and Animal Research (eds König,
R., Heil, P., Budinger, E. & Scheich, H.) 27–50
(Lawrence Erlbaum, Mahwah, New Jersey, 2005).
83. Basser, P. J., Mattiello, J. & LeBihan, D. Estimation of
the effective self-diffusion tensor from the NMR spin
echo. J. Magn. Reson. B 103, 247–254 (1994).
84. Conturo, T. E., McKinstry, R. C., Akbudak, E. &
Robinson, B. H. Encoding of anisotropic diffusion with
tetrahedral gradients: a general mathematical
diffusion formalism and experimental results. Magn.
Reson. Med. 35, 399–412 (1996).
85. Pierpaoli, C. & Basser, P. J. Toward a quantitative
assessment of diffusion anisotropy. Magn. Reson.
Med. 36, 893–906 (1996).
86. Ulug, A. M., Bakht, O., Bryan R. N. & van Zijl, P. C. M.
Mapping of Human Brain Fibers Using Diffusion
Tensor Imaging. Proc. Int. Soc. Mag. Reson. Med. 4,
87. Douek, P., Turner, R., Pekar, J., Patronas, N. &
Le Bihan, D. MR color mapping of myelin fiber
orientation. J. Comput. Assist. Tomogr. 15, 923–929
88. Hsu, E. W. & Mori, S. Analytical interpretations of
NMR diffusion measurements in an anisotropic
medium and a simplified method for determining
fiber orientation. Magn. Reson. Med. 34, 194–200
89. Nakada, T. & Matsuzawa, H. Three-dimensional
anisotropy contrast magnetic resonance imaging of
the rat nervous system: MR axonography. Neurosci.
Res. 22, 389–398 (1995).
90. Makris, N. et al. Morphometry of in vivo human white
matter association pathways with diffusion weighted
magnetic resonance imaging. Ann. Neurol. 42,
A monumental work in which DTI-based image
contrast was correlated with white matter
anatomy in a comprehensive manner for the first
91. Pajevic, S. & Pierpaoli, C. Color schemes to represent
the orientation of anisotropic tissues from diffusion
tensor data: application to white matter fiber tract
mapping in the human brain. Magn. Reson. Med. 42,
92. Conturo, T. E. et al. Tracking neuronal fiber pathways
in the living human brain. Proc. Natl Acad. Sci. USA
96, 10422–10427 (1999).
93. Jones, D. K., Simmons, A., Williams S. C. & Horsfield,
M. A. Non-invasive assessment of axonal fiber
connectivity in the human brain via diffusion tensor
MRI. Magn. Reson. Med. 42, 37–41 (1999).
94. Mori, S., Crain, B. J., Chacko, V. P. & van Zijl, P. C. M.
Three dimensional tracking of axonal projections in
the brain by magnetic resonance imaging. Annal.
Neurol. 45, 265–269 (1999).
95. Xue, R., van Zijl, P. C. M, Crain, B. J., Solaiyappan, M.
& Mori, S. In vivo three-dimensional reconstruction of
rat brain axonal projections by diffusion tensor
imaging. Magn. Reson. Med. 42, 1123–1127
964 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group
96. Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J. &
Aldroubi, A. In vitro fiber tractography using DT-MRI
data. Magn. Reson. Med. 44, 625–632 (2000).
97. Poupon, C. et al. Regularization of diffusion-based
direction maps for the tracking of brain white matter
fascicules. NeuroImage 12, 184–195 (2000).
98. Lazar, M. et al. White matter tractography using
diffusion tensor deflection. Hum. Brain Mapp. 18,
99. Behrens, T. E. et al. Non-invasive mapping of
connections between human thalamus and cortex
using diffusion imaging. Nature Neurosci. 6, 750–757
100. Ludwig, E. & Klingler, J. Atlas Cerebrei Human: the
Internal Structure of the Brain Demonstrated on the
Basis of Macroscopical Preparations (Karger, Basel,
101. Stieltjes, B. et al. Diffusion tensor imaging and axonal
tracking in the human brainstem. NeuroImage 14,
102. Catani, M., Howard, R. J., Pajevic, S. & Jones, D. K.
Virtual in vivo interactive dissection of white matter
fasciculi in the human brain. NeuroImage 17, 77–94
103. Mori, S. et al. Imaging cortical association tracts in
human brain. Magn. Reson. Med. 47, 215–223
104. Wakana, S., Jiang, H., Nagae-Poetscher, L. M., Van Zijl,
P. C. & Mori, S. Fiber tract-based atlas of human white
matter anatomy. Radiology 230, 77–87 (2004).
A comprehensive atlas of the human white matter
based on two-dimensional and three-dimensional
visualization of DTI data.
105. Mamata, H. et al. High-resolution line scan diffusion
tensor MR imaging of white matter fiber tract
anatomy. Am. J. Neuroradiol. 23, 67–75 (2002).
106. Alexander, D. C., Pierpaoli, C., Basser, P. J. & Gee, J. C.
Spatial transformations of diffusion tensor magnetic
resonance images. IEEE Trans. Med. Imaging 20,
107. Jones, D. K. et al. Spatial normalization and averaging
of diffusion tensor MRI data sets. Neuroimage 17,
108. Xu, D., Mori, S., Shen, D., van Zijl, P. C. & Davatzikos, C.
Spatial normalization of diffusion tensor fields. Magn.
Reson. Med. 50, 175–182 (2003).
109. Pagani, E., Filippi, M., Rocca, M. A. & Horsfield, M. A.
A method for obtaining tract-specific diffusion tensor
MRI measurements in the presence of disease:
application to patients with clinically isolated
syndromes suggestive of multiple sclerosis.
NeuroImage 26, 258–265 (2005).
110. Pierpaoli, C. et al. Water diffusion change in
Wallerian degeneration and their dependence on
white matter architecture. NeuroImage 13,
111. Thomalla G. et al. Diffusion tensor imaging detects
early Wallerian degeneration of the pyramidal tract
after ischemic stroke. NeuroImage 22, 1767–1774
112. Lee, J. S., Han, M. K., Kim, S. H., Kwon, O. K. & Kim, J. H.
Fiber tracking by diffusion tensor imaging in
corticospinal tract stroke: topographical correlation with
clinical symptoms. NeuroImage 26, 771–776 (2005).
113. Johansen-Berg, H. et al. Changes in connectivity
profiles define functionally distinct regions in human
medial frontal cortex. Proc. Natl Acad. Sci. USA 101,
114. Frank, L. R. Anisotropy in high angular resolution
diffusion-weighted MRI. Magn. Reson. Med. 45,
115. Tournier, J. D., Calamante, F., Gadian, D. G. &
Connelly, A. Direct estimation of the fiber orientation
density function from diffusion-weighted MRI data
using spherical deconvolution. NeuroImage 23,
116. Tuch, D. S., Reese, T. G., Wiegell, M. R. & Wedeen, V. J.
Diffusion MRI of complex neural architecture. Neuron
40, 885–895 (2003).
117. Wedeen, V. J., Hagmann, P., Tseng, W. Y., Reese, T. G.
& Weisskoff, R. M. Mapping complex tissue
architecture with diffusion spectrum magnetic
resonance imaging. Magn. Reson. Med. 54,
118. Bürgel, U., Schormann, T., Schleicher, A. & Zilles, K.
Mapping of histologically identified long fiber tracts
in human cerebral hemispheres to the MRI volume of a
reference brain: position and spatial variability of the
optic radiation. NeuroImage 10, 489–499 (1999).
119. Rademacher, J., Engelbrecht, V., Bürgel, U.,
Freund, H. J. & Zilles, K. Measuring in vivo myelination
of human white matter fiber tracts with magnetization
transfer MR. NeuroImage 9, 393–406 (1999).
120. Rademacher, J. et al. Variability and asymmetry in the
human precentral motor system. A cytoarchitectonic
and myeloarchitectonic brain mapping study. Brain
124, 2232–2258 (2001).
121. Mega, M. S. et al. Mapping pathology to metabolism:
coregistration of stained whole brain sections to PET in
alzheimer’s disease. NeuroImage 5, 147–153 (1997).
122. Toga, A. W., Ambach, K. L., Quinn, B. C., Hutchin, M.
& Burton, J. S. Postmortem anatomy from
cryosectioned whole human brain. J. Neurosci.
Methods 5, 239–252 (1994).
123. Teipel, S. J. et al. Measurement of basal forebrain
atrophy in Alzheimer’s disease using MRI. Brain 128,
124. Toga, A. W. & Thompson, P. M. Mapping brain
asymmetry. Nature Rev. Neurosci. 4, 37–38 (2003).
125. Toga, A. W. & Thompson, P. M. Genetics of brain
structure and intelligence. Ann. Rev. Neurosci. 28,
126. Grenander, U. & Miller, M. I. Computational Anatomy:
an Emerging Discipline. Technical Report, Dept.
Mathematics, Brown Univ. (1998).
This highly-cited article was influential in
stimulating mathematical developments in the field
of computational anatomy. A new framework is
proposed that represents anatomical variation by
defining statistics and probability measures on
three-dimensional elastic or fluid mappings that
deform a canonical template of anatomy.
127. Narr, K. L. et al. Mapping morphology of the corpus
callosum in schizophrenia. Cereb. Cortex 10, 40–49
128. Thompson, P. M. et al. Cortical variability and
asymmetry in normal aging and alzheimer’s disease.
Cereb. Cortex 8, 492–509 (1998).
129. Thompson, P. M., Mega, M. S. & Toga, A. W. in
Brain Mapping: the Disorders (eds Toga A. W. &
Mazziotta, J. C.) 131–177 (Academic, San Diego,
130. Sowell, E. R. et al. Mapping cortical change across the
human life span. Nature Neurosci. 6, 309–315
131. Levitt, J. G. et al. Proton magnetic resonance
spectroscopic imaging of the brain in childhood
autism. Biol. Psychiatry 54, 1355–1366 (2003).
132. Thompson, P. M. et al. Cortical complexity and
thickness are increased in Williams Syndrome.
J. Neurosci. 25, 4146–4158 (2005).
133. Mazziotta, J. C., Toga, A. W., Evans, A. C., Fox, P. &
Lancaster, J. A probabilistic atlas of the human brain:
theory and rationale for its development. NeuroImage
2, 89–101 (1995).
134. Geschwind, N. & Galaburda, A. M. Cerebral
lateralization. Biological mechanisms, associations,
and pathology: III. A hypothesis and a program for
research. Arch. Neurol. 42, 634–656 (1985).
135. Sowell, E. R. et al. Brain abnormalities in early-onset
schizophrenia spectrum disorder observed with
statistical parametric mapping of structural magnetic
resonance images. Am. J. Psychiatry, 157,
136. Luders, E. et al. Mapping cortical gray matter in the
young adult brain: effects of gender. NeuroImage 26,
137. Crow, T. J. Handedness, language lateralisation and
anatomical asymmetry: relevance of protocadherin
XY to hominid speciation and the aetiology of
psychosis. Point of view. Br. J. Psychiatry 181,
138. Narr, K. L. et al. Mapping cortical thickness and gray
matter concentration in first episode schizophrenia.
Cereb. Cortex 15, 708–719 (2005).
139. Van Essen, D. C. Surface-based approaches to spatial
localization and registration in primate cerebral
cortex. NeuroImage 23, S97–S107 (2005).
140. van Horn, J. D. Neuroimaging databases as a resource
for scientific discovery. Int. Rev. Neurobiol. 66, 55–87
141. Giedd, J. N. et al. Brain development during childhood
and adolescence: a longitudinal MRI study. Nature
Neurosci. 2, 861–863 (1999).
142. Rapoport, J. L., Addington, A. & Frangou, S. The
neuro developmental model of schizophrenia: what can
very early onset cases tell us? Curr. Psychiatry Rep. 7,
143. Thompson, P. M. et al. Early cortical change in
Alzheimer’s disease detected with a disease-specific
population-based brain atlas. Cereb. Cortex 11, 1–16
144. Cannon, T. D. et al. Early and late neurodevelopmental
influences in the prodrome to schizophrenia:
contributions of genes, environment and their
interactions. Schizophr. Bull. 29, 653–669 (2003).
145. Cannon, T. D. et al. Association of DISC1/TRAX
haplotypes with schizophrenia, reduced prefrontal gray
matter and impaired short- and long-term memory.
Arch. Gen. Psychiatry 62, 1205–1213 (2005).
146. Lin, J. J. 3D Pre-operative maps of hippocampal
atrophy predict surgical outcomes in temporal lobe
epilepsy. Neurology 65, 1094–1097 (2005).
147. Moore, G. J., Bebchuk, J. M., Wilds, I. B., Chen, G. &
Manji, H. K. Lithium-induced increase in human brain
grey matter. Lancet 356, 1241–1242 (2000).
148. Lieberman, D. Z. & Goodwin, F. K. Use of olanzapine in
the treatment of bipolar I disorder. Expert Rev.
Neurother. 4, 759–767 (2004).
149. Leow, A. et al. Brain structural mapping using a novel
hybrid implicit/explicit framework based on the level-
set method. NeuroImage 24, 910–927 (2005).
150. Kikinis, R. et al. A digital brain atlas for surgical
planning, model-driven segmentation, and teaching.
IEEE Trans. Vis. Comput. Graph. 2, 232–241 (1996).
151. Mangin, J. F. et al. Brain morphometry using 3D
moment invariants. Med. Image Anal. 8, 187–196
152. Collins, D. L., Peters, T. M., Evans, A. C. An automated
3D non-linear image deformation procedure for
determination of gross morphometric variability in the
human brain. Proc. Visualization. Biomed. Comput. 3,
153. Thompson, P. M. et al. Detection and mapping of
abnormal brain structure with a probabilistic atlas of
cortical surfaces. J. Comput. Asst. Tomogr. 21,
154. Chung, M. K. Cortical thickness analysis in autism with
heat kernel smoothing. NeuroImage 25, 1256–1265
155. Carmichael, O. T. et al. Mapping ventricular changes
related to dementia and mild cognitive impairment in
a large community-based cohort. IEEE Int. Symp.
Biomed. Imaging 315–318 (2006).
156. Gogtay, N. et al. Dynamic mapping of human cortical
development during childhood and adolescence. Proc.
Natl Acad. Sci. 101, 8174–8179 (2004).
157. Sowell, E. R., Thompson, P. M. & Toga, A. W. Mapping
changes in the human cortex throughout the span of
life. Neuroscientist 10, 372–392 (2004).
158. Thompson, P. M. et al. Growth patterns in the developing
brain detected by using continuum-mechanical tensor
maps. Nature 404, 190–193 (2000).
159. Lerch, J. P. et al. Focal decline of cortical thickness in
Alzheimer’s disease identified by computational
neuroanatomy. Cereb. Cortex 15, 995–1001 (2005).
160. Janke, A. L. et al. 4D deformation modeling of cortical
disease progression in Alzheimer’s dementia. Magn.
Reson. Med. 46, 661–666 (2001).
161. Thompson, P. M. et al. Dynamics of gray matter loss in
Alzheimer’s disease. J. Neurosci. 23, 994–1005 (2003).
162. Friston, K. J. et al. Spatial registration and
normalisation of images. Hum. Brain Mapp. 2, 16
163. van Essen, D. C. A population-average, landmark- and
surface-based (PALS) atlas of human cerebral cortex.
NeuroImage 28, 635–662 (2005).
164. Fischl, B. et al. Sequence-independent segmentation
of magnetic resonance images. NeuroImage 23,
165. Pitiot, A., Delingette, H., Thompson, P. M. & Ayache, N.
Expert knowledge-guided segmentation system for
brain MRI. NeuroImage 23, S85–S96 (2004).
166. Zijdenbos, A. P., Lerch, J. P., Bedell, B. J. & Evans, A. C.
Brain imaging in drug R&D. Biomarkers 10, S58–S68
The authors describe a recent extension of the
atlas concept to include large-scale processing of
images from drug trials, analysing images that have
been mapped into a stereotaxic coordinate space.
167. Duncan, J. S. et al. Geometric strategies for
neuroanatomic analysis from MRI. NeuroImage 23,
168. Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A.,
Rottenberg, D. A. & Leahy, R. M. Magnetic resonance
image tissue classification using a partial volume
model. NeuroImage 13, 856–876 (2001).
169. Dinov, I. D. et al. Analyzing functional brain images in
a probabilistic atlas: a validation of sub-volume
thresholding. J. Comput. Asst. Tomog. 24, 128–138
170. Rasser, P. E. et al. Functional MRI BOLD response to
Tower of London performance of first-episode
NATURE REVIEWS | NEUROSCIENCE
VOLUME 7 | DECEMBER 2006 | 965
© 2006 Nature Publishing Group
schizophrenia patients using cortical pattern
matching. NeuroImage 26, 941–951 (2005).
171. Thompson, P. et al. Mapping hippocampal and
ventricular change in Alzheimer’s disease. NeuroImage
22, 1754–1766 (2004).
172. Lin, C. L. et al. Characterization of perioperative
seizures and epilepsy following aneurysmal
subarachnoid hemorrhage. J. Neurosurg. 99,
173. Ballmaier, M. et al. Localizing gray matter deficits in
late onset depression using computational cortical
pattern matching methods. Am. J. Psychiatry 161,
174. Cannon, T. D. et al. Cortex mapping reveals
regionally specific patterns of genetic and disease-
specific gray-matter deficits in twins discordant for
schizophrenia. Proc. Natl Acad. Sci. USA 99,
175. Narr, K. L. et al. Abnormal gyral complexity in first
episode schizophrenia. Biol. Psychiatry 55, 859–867
176. Thompson, P. et al. Mapping hippocampal and
ventricular change in Alzheimer’s disease. NeuroImage
22, 1754–1766 (2004).
177. Vidal, C. et al. Dynamically spreading frontal and
cingulate deficits mapped in adolescents with
Schizophrenia. Arch. Gen. Psychiatry 63, 25–34
178. Sowell, E. R. et al. Cortical abnormalities in children
and adolescents with attention deficit hyperactivity
disorder. Lancet 362, 1699–1707 (2003).
179. Sowell, E. R. et al. Regional brain shape abnormalities
persist into adolescence after heavy prenatal alcohol
exposure. Cereb. Cortex 12, 856–865 (2002).
180. Sowell, E. R. et al. Gray matter thickness
abnormalities mapped in children with Tourette
Syndrome. Soc. Neurosci. Abstr. 800.15 (2004).
181. Gogtay, N. et al. Dynamic mapping of cortical brain
development in pediatric bipolar illness. Int. Conf. Org.
Hum. Brain Mapp. 344 (2004).
182. Thompson, P. et al. Structural abnormalities in the
brains of human subjects who use methamphetamine.
J. Neurosci. 24, 6028–6036 (2004).
183. Mega, M. S. et al. Sulcal variability in the Alzheimer’s
brain: correlations with cognition. Neurology 50,
184. Miller, M. Computational anatomy: shape, growth and
atrophy comparison via diffeomorphisms. NeuroImage
23, S19–S33 (2004).
185. Studholme, C. et al. Deformation tensor morphometry
of semantic dementia with quantitative validation.
NeuroImage 21, 1387–1398 (2004).
186. Cannon, T. D. et al. Mapping heritability and
molecular genetic associations with cortical features
using probabilistic brain atlases: methods and initial
applications to schizophrenia. Neuroinformatics 4,
187. Ballmeier, M. et al. Comparing gray matter loss
profiles between dementia with Lewy bodies and
Alzheimer’s disease using cortical pattern matching:
diagnosis and gender effects, NeuroImage 23,
188. Thompson, P. M., Mega, M. S., Vidal, C.,
Rapoport, J. L. & Toga, A. W. in Lect. Notes Comput.
Sci. 2082, 488–501 (2001).
189. Thompson, P. M. et al. Mapping adolescent brain
change reveals dynamic wave of accelerated gray
matter loss in very early-onset schizophrenia. Proc.
Natl Acad. Sci. USA 98, 11650–11655 (2001).
190. Vidal, C. N. et al. dynamically spreading frontal and
cingulate deficits mapped in adolescents with
schizophrenia. Arch. Gen. Psychiatry 63, 25–34
191. Worsley, K. J. et al. A unified statistical approach for
determining significant signals in images of cerebral
activation. Hum. Brain Mapp. 4, 58–73 (1996).
192. Friston, K. J. Statistical parametric mapping: ontology
and current issues. J. Cereb. Blood Flow Metab. 15,
193. Friston, K. J. et al. Statistical parametric maps in
functional imaging: a general linear approach. Hum.
Brain Mapp. 2, 189–210 (1995).
194. Ashburner, J. & Friston, K. J. Voxel-based
morphometry — the methods. NeuroImage 11,
195. Thompson, P. M. et al. Thinning of the cerebral cortex
in HIV/AIDS reflects CD4+ T-lymphocyte decline. Proc.
Natl Acad. Sci. 102, 15647–15652 (2005).
196. Williams, T. H., Gluhbegoric, N. & Jew, J. Y. The human
brain: dissections of the real brain. Virtual Hospital,
University of Iowa [online], <http://www.vh.org/
This work was supported by research grants from the National
Institutes of Health (NIH), Roadmap Initiative for
Bioinformatics and Computational Biology, National Center
for Research Resources, the National Institute of Mental
Health (NIMH) and the National Institute of Neurological
Disorders and Stroke, and by Human Brain Project grants to
the International Consortium for Brain Mapping, funded
jointly by NIMH and the National Institute on Drug Abuse
and one funded by the National Institute of Aging (NIA).
Additional support was provided by the National Institute for
Biomedical Imaging and Bioengineering, the National Center
for Research Resources and the NIA, the National Library of
Medicine and the Biomedical Informatics Research Network
(BIRN, http://www.nbirn.net), which is funded by the National
Center for Research Resources at the NIH. Other funds came
from the German Ministry of Science BMBF, the Helmholtz
Association of Research Centres and from various grants from
the German Research Foundation DFG and the European
Union. We thank the many collaborators and doctoral stu-
dents in our laboratories. Special thanks to P. Roland from
the Karolinska Institute Stockholm for an exciting collabora-
tion of many years, and to N. Palomero-Gallagher for her
enthusiasm in the receptor project.
Competing interests statement
The authors declare no competing financial interests.
The following terms in this article are linked online to:
Alzheimer’s disease | autism | schizophrenia | Williams
Toga’s laboratory: http://www.loni.ucla.edu
Alzheimer’s Disease Neuroimaging Initiative:
International Consortium for Brain Mapping:
Statistical Parametric Mapping:
Access to this links box is available online.
966 | DECEMBER 2006 | VOLUME 7
© 2006 Nature Publishing Group