Cortical thickness measured from MRI in the YAC128 mouse model
of Huntington's disease
Jason P. Lerch,a,⁎Jeffrey B. Carroll,bAdrienne Dorr,eShoshana Spring,aAlan C. Evans,d
Michael R. Hayden,cJohn G. Sled,aand R. Mark Henkelmana
aThe Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
bCentre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Neuroscience, University of British Columbia,
Vancouver, British Columbia, Canada
cCentre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia,
Vancouver, British Columbia, Canada
dMontreal Neurological Institute, McGill University, Montreal, Quebec, Canada
eClinical Integrative Biology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
Received 16 August 2007; revised 16 January 2008; accepted 7 February 2008
Available online 26 February 2008
A recent study found differences in localised regions of the cortex
between the YAC128 mouse model of Huntington's Disease (HD) and
wild-type mice. There are, however, few tools to automatically examine
shape differences in the cortices of mice. This paper describes an
algorithm for automatically measuring cortical thickness across the
entire cortex from MRI of fixed mouse brain specimens. An analysis of
localised difference in cortical thickness can be measured using MR
scans. Applying these methods to 8-month-old YAC128 mouse model
mice representing an early stage of HD, we found an increase in cortical
thickness in the sensorimotor cortex, and also revealed regions wherein
decreasing striatal volume correlated with increasing cortical thickness,
indicating a potential compensatory response.
© 2008 Elsevier Inc. All rights reserved.
A recent study by our group examined differences between the
YAC128 mouse model of Huntington's Disease (HD) and wild-type
mice using deformation-based analyses of high-resolution post-
mortem MR scans (Lerch et al., 2008). Along with the shrinkage of
the striatum (Vonsattel et al., 1985), we found a volume increase in
the sensorimotor cortex, a finding mirrored by a study showing that
prodromal human HD also features increased cortical thickness
(Paulsen et al., 2006) and exhibits compensatory functional
responses in the thalamocortical circuit (Feigin et al., 2006).
The tools to investigate shape changes in the mouse cortex are,
however, still limited. Histology and stereology-based techniques,
while extremely powerful and capable of providing cellular level
detail, are labour intensive and thus do not easily produce whole
cortex coverage. They are also subject to tissue shrinkage and
distortion from sectioning.
MR-based assessment of murine brain shape has recently gained
increasing traction (Badea et al., 2007; Pitiot et al., 2007; Ma et al.,
2005). The dominant methodology involves automated non-linear
alignment of all examined mice towards a common space and is
followed by either statistical analyses of the deformation fields or
mapping an anatomical atlas back to each animal to measure
structure volumes (Ma et al., 2005; Kovacevic et al., 2005; Chen
et al., 2006). Among anatomical measurements, the thickness of the
cortex in particular is endowed with biological significance, given
that this measure can approximate the path taken by the cortical
column, the prime computational unit of the cerebral cortex.
The last decade has produced a field of research based on
accurately extracting shape models of the human cortex from MR
images. As these techniques have become more sophisticated,
intriguing new insights into the anatomy of the cortex have been
gained, including mapping the pattern of thickening and thinning
during development (Sowell et al., 2004) and ageing (Salat et al.,
2004), in degenerative (Lerch et al., 2005) and psychiatric
disorders (Shaw et al., 2006b), and in relationships between
anatomy and behaviour (Shaw et al., 2006a; Walhovd et al., 2006).
Algorithms to measure cortical thickness from MRI follow one
of four prototypes. The simplest, and rarest due to the labour
intensive nature of the task, are manual measures using the digital
equivalent of callipers (Ross et al., 2001; Meyer et al., 1996). A
second set of methods which has gained increasing prominence
involves the extraction of the inner and outer cortical surfaces
using deformable models (MacDonald et al., 2000; Fischl and
NeuroImage 41 (2008) 243–251
E-mail address: email@example.com (J.P. Lerch).
Available online on ScienceDirect (www.sciencedirect.com).
1053-8119/$ - see front matter © 2008 Elsevier Inc. All rights reserved.
Dale, 2000; Kim et al., 2005). A third set of methods works
without explicit polyhedral models, instead computing image
boundaries to find the cortex then measuring the thickness of the
cortex at the voxel level (Jones et al., 2000; Preul et al., 2005). The
last type involves a mix of explicit surface parameterisation and
voxel-based cortical thickness methods (Thompson et al., 2005;
Miller et al., 2000).
considerably between individuals. The mouse cortex, on the other
hand, is lissencephalic and, given the similarities within inbred
strains, almost identical among individuals. This key difference
between mice and humans simplifies the creation of a cortical
thickness algorithm considerably:
1. The complex deformable model process employed to map the
convoluted human cortex is not necessary for the mouse.
2. The similarity across animals allows for near perfect image
registration, a task still impossible within human populations.
3. The ability to compute accurate registrations implies that the
definition of the cortex and its boundaries can be created by
mapping a pre-existing atlas to each animal.
These points suggest that a volume-based cortical thickness
algorithm with boundary definitions gained from registration
towards an anatomical atlas will be the easiest solution. Explicit
surface parameterisation does, however, provide one key advan-
tage: the creation of a surface coordinate system with a controllable
number of nodes. Rather than comparing cortical thickness at every
voxel, thickness statistics can instead be computed on a much
smaller set of vertices over the cortical surface, leading to obvious
advantages in controlling for multiple comparisons. Moreover, it
allows the data to be smoothed along the manifold, an anatomically
more sensible operation than using volumetric Gaussian kernels. A
combined method, using voxel-based cortical thickness measures
together with a surface coordinate system, suggests itself as an
ideal choice of algorithm. It has the further advantage that every
voxel in the cortex is part of a path between the inner and outer
The goal of this study, motivated by the intriguing finding of
shape differences in the YAC128 Huntington Disease model mice
as well as the lack of available tools to investigate the mouse
cortex, is to design a framework for automatically measuring
cortical thickness from mouse MRI and to use these new tools to
further investigate the YAC128 mouse model. These new methods
encompass a combination of mouse registration algorithms with
adaptations of tools designed to measure cortical thickness from
human MRI scans.
The proposed method estimates the thickness of the entire
cortical manifold. The basic procedure used follows: (1) MRIs of
all mice in the study are acquired. (2) A model independent
consensus representation of all mice in the study is created using
linear and non-linear registration techniques. (3) A previously
existing atlas with an associated segmentation of the cortex is then
mapped to the result of step 2. (4) The remapped cortical
boundaries are then in turn mapped to each of the individual mice.
(5) The thickness of the cortex is estimated. (6) Statistics on the
population are then computed to determine the relationship
between cortical thickness and the covariates of interest.
Specimen preparation and MR acquisition
The mice were anaesthetised with a combination of Ketamine
previously described sample preparation protocol for scanning was
were opened and animals were perfused through the left ventricle
with 30 mL of phosphate-buffered saline (PBS) (pH 7.4) at room
temperature (25 °C). This was followed by infusion with 30 mL of
iced 4% paraformaldehyde (PFA) in PBS. Following perfusion, the
heads were removed along with the skin, lower jaw, ears and the
cartilaginous nose tip. The remaining skull structures containing the
brain were allowed to postfix in 4% PFA at 4 °C for 12 h. Following
an incubation period of 5 days in PBS and 0.01% sodium azide at
15 °C, the skulls were transferred to a PBS and 2 mM ProHance©
15 °C. MR imaging occurred 12 to 21 days post-mortem.
A multi-channel 7.0 Tesla MRI scanner (Varian Inc., Palo Alto,
CA) with a 6-cm inner bore diameter insert gradient set was used to
acquire anatomical images of brains within skulls. Prior to
imaging, the samples were removed from the contrast agent
solution, blotted and placed into 13 mm diameter plastic tubes
filled with a proton-free susceptibility-matching fluid (Fluorinert
FC-77, 3M Corp., St. Paul, MN). Three custom-built, 14 mm
diameter solenoid coils with a length of 18.3 mm and over wound
ends were used to image three brains in parallel. Parameters used
in the scans were optimised for grey/white matter contrast: a T2-
averages, field-of-view of 12×12×25 mm and matrix size=432×
432×780 giving an image with 0.032 mm isotropic voxels. Total
imaging time was 11.3 h. Geometric distortion due to position of the
three coils inside the magnet and gradient was corrected using a
calibrated MR phantom (Henkelman et al., 2006).
Model-independent average creation and cortex segmentation
An unbiased model independent average of the MR scans is
created through the following procedure (Kovacevic et al., 2005;
Chen et al., 2006). All scans are linearly (3 rotations, 3
translations) registered towards a pre-existing atlas based on 20
male and 20 female adult C57Bl/6 mice (Spring et al., 2007), freely
available at www.mouseimaging.ca. All possible pairwise 12
parameter registrations (3 scales, 3 shears, 3 rotations, 3
translations) are then computed, and a transform is created for
each mouse through matrix averaging. After intensity averaging all
transformed scans to create the first population average, represent-
ing the average anatomy of the study sample after accounting for
overall brain-size differences, an iterative 6 generation multi-scale
non-linear alignment procedure is begun, initially registering each
mouse towards the 12 parameter registration average, and
subsequently towards the average determined from the previous
non-linear generation. All registrations are performed using the
mni_autoreg tools (Collins et al., 1995, 1994), which use an elastic
registration algorithm. The end-result is to have all scans in the
study deformed into a common space in an unbiased fashion.
The cortical labels were created on an average atlas of 20 male
and 20 female 12-week-old C57Bl6 mice (Spring et al., 2007). The
cerebral cortexwasmanuallysegmentedusingthesoftware package
html, Montreal Neurological Institute, Montreal, Canada). Segmen-
tation was performed slice by slice along the coronal orientation;
244J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
horizontal and sagittal orientations were used as guidance and all
three orientations were needed for manual correction. Careful
with the visual aid of online mouse brain histological atlases such as
the Allen Reference Atlas (Lein et al., 2007) and the High
Resolution Mouse Brain Atlas http://www.hms.harvard.edu/re-
search/brain/atlas.html (based on Sidman et al., 1971) as well as a
book-based histological atlas (Paxinos and Franklin, 2001).
The male–female average is then non-linearly aligned to the
population average under investigation, and the cortex segmenta-
tion resampled into the space of the population average. The
segmented cortex is then backpropagated along either the inverse
final non-linear transform from each mouse or the concatenation of
the inverse non-linear transform plus the inverse 12 parameter
transform. The former is chosen if overall brain sizes are to be held
constant, the latter if cortical thickness is to be measured without
any overall scaling of the brains.
The whole procedure for generating a cortical thickness map for
each mouse can be seen in Fig. 1. The first steps involve placing the
segmentation of the cerebral cortex into the space of each mouse so
that cortical thickness estimates can be computed (Figs. 1a, b). The
polygonal surface defining the cortex on the atlas is transformed to
each individual mouse, and the annotation of inside, outside and
resistant boundary (see section 2.3 for an explanation of the
boundaries) is stored on a rasterized grid that is transformed along
with the cortical surface. The exact algorithm is the following: The
definition of the cortex, and inside, outside and resistant boundaries
is stored independently in the space of the atlas. They are
concurrently transformed into the space of each mouse. Following
the transformation, the polygonal surface defining the cortex is
are then blurred with 0.5 mm Gaussian kernel. The rasterized grid
necessary to solve Laplace's equation for each mouse is then
the transformed and rasterized cortex, otherwise it is assigned the
of the three blurred boundary maps had the maximum value at that
Fig. 1. Measuringcorticalthickness.EachMRscan(a)hasthesegmentedcortexmappedtoit(b),thebluevoxelsrepresentingthecortex,thepurplevoxelstheinside
and outside boundaries (c). (d) shows a zoomed view of the potential field around the interhemispheric fissure, and (e) shows the streamlines traversing from the
inside boundary to the outside boundary, colour coded according to their length (i.e. thickness). (f) displays the entire cortical thickness mapped on to a manifold.
245J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
The technique for estimating cortical thickness is adapted from
Jones et al. (2000), wherein Laplace's Equation is used to create
streamlines between the inside and outside cortical surfaces, and
the length of these streamlines is used to measure thickness. The
entire thickness estimation procedure is illustrated in Fig. 1.
Laplace's Equation is a second order partial differential equation
for a scalar field ψ enclosed between two surfaces S and S'. It takes
In the original paper, three regions were defined: the cortex, the
the path taken by the cortical column, crossing perpendicular to the
1. The definition of cortex and the boundaries is mapped to each
mouse brain via the inverse registration procedure described
2. Laplace's equation is iteratively solved using the Jacobi
method, keeping the boundaries fixed.
4. Streamlines are computed at every voxel by integrating the
tangent vector field using Euler's method.
The same scheme is used for the mouse brain. A third, resistive,
boundary is added near the interhemispheric fissure. The standard
two boundary approach results in all potential lines terminating
where the inside and outside boundaries meet, which breaks the
analogy to cortical layers (see Fig. 2). The standard two boundaries
Jacobian relaxation takes the following form:
ðÞ ¼ ½wix þ Dx;y;z
þwix;y þ Dy;z
þwix;y;z þ Dz
ðÞ þ wix ? Dx;y;z
Þ þ wix;y ? Dy;z
Þ þ wix;y;z ? Dz
Where ψi+1(x,y,z) is the value of the potential at x,y,z in the ith
iteration. The third boundary modifies the above algorithm slightly.
and if it is, it is not included in the modification of ψi+1(x,y,z), and the
Jones et al. (2000).
Surface coordinate system
A surface coordinate system is used to compare cortical
thickness across a population of mice. The following procedure is
1. The laplacian potential field is generated on the average and
an intermediate surface (halfway between pial and white matter
boundaries) extracted for each hemisphere using marching
cubes with the half maximum potential line as the threshold.
2. The intermediate surface is simplified by means of an edge
collapsing algorithm to contain 18,000 polygons with the edge
using Amira (©Mercury Computer Systems, Inc.).
3. These surfaces are mapped to each subject using the same
registration procedure as the mapping of the Laplacian grid.
The same surface, generated in average space, is thus mapped to
registration, an inherent correspondence between vertices, as in
in mouse 2, even if the mapping process had displaced that vertex to
different points in space for each mouse.
The existence of an intermediate surface also allows for
smoothing of the thickness along the cortical manifold. Some
between groups of mice (Lerch and Evans, 2005), and following the
topology of the cortex provides better preservation of anatomical
information than using a Gaussian kernel in volume space (Lerch
and Evans, 2005; Chung et al., 2003). Here we employ diffusion
smoothing using the Laplace Beltrami operator as described in
Chung et al. (2003).
The above algorithm results in a map of 9000 cortical thickness
measures. Localising differences in cortical morphometry between
groups of mice can thus be performed in the same massively
univariate or multivariate ways as employed in human brain
imaging. Here we employ independent linear models relating
thickness to mouse genotype or other volumetric/behavioural
phenotypes. Multiple comparisons are controlled using the False
Discovery Rate (FDR) (Genovese et al., 2002), a technique which
limits the number of allowed false positives within the set of
Fig. 2. Laplacian potential at the interhemispheric fissure: (a), taken from Hof et al. (2000), shows a Nissl stained slice along with anatomical annotations,
including the 6 cortical layers. (b) is the Laplacian potential map using two boundaries, and (c) with the third resistive boundary added. Note how the potential
lines approximate cortical layers more closely with the addition of the resistive boundary.
246J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
Variance and power analysis
In order to understand the cortical analysis system described
herein, a group of 20 male C57BL/6 mice, first described in Spring
et al. (2007), were processed. The goal was to gain an under-
standing of the variance inherent in the thickness and surface area
measures and thus know their statistical power.
The results are shown in Fig. 3. The mean cortical thickness in
these mice was 0.89 mm±0.016 mm. Given two groups of 10
mice, one can expect to recover a 0.05 mm difference in cortical
thickness at α=0.005 (corresponding to a FDR of 5% in Spring
et al., 2007). The shape of the power analysis graph indicates that a
sufficient study size for cortical thickness analysis is around 6–9
mice per genotype.
YAC128 mouse model
HD is a neurodegenerative disorder characterised by motor
dysfunction, psychiatric disturbances and cognitive impairment that
is caused by a CAG trinucleotide expansion in the HD gene on
chromosome 4 (Huntington's Collaborative Research Group, 1993).
HD is classically associated with initial reduction in volume and
neuronal loss primarily localised to the striatum (Vonsattel et al.,
1985), but widespread brain atrophy has been described in early to
mid-stage HD in humans using magnetic resonance (MR) tech-
niques (Rosas et al., 2003).
The YAC128 mouse model of HD expresses the entire human
huntingtin (htt) gene with 120 CAG repeats (Slow et al., 2003).
This mouse model recapitulates many features of human HD
including progressive motor and cognitive deficits, striatal and
cortical atrophy with relative sparing of the hippocampus and
cerebellum. These animals demonstrate low interanimal variability
(Slow et al., 2003; Van Raamsdonk et al., 2005b,a). As in human
HD, the phenotype of the YAC128 mouse is progressive, with
cognitive deficits detectable as early as 2 months, followed by
motor deficits and specific striatal and cortical atrophy (Slow et al.,
2003; Van Raamsdonk et al., 2005b). Striatal atrophy in YAC128 is
associated with a decrease in neuronal count, and striatal neuronal
loss at later timepoints correlates strongly with earlier motor
deficits (Slow et al., 2003).
The analysis of cortical morphology presented here used the
and MR scanning were identical to the procedure described in
mapped to each individual mouse and cortical thickness measured
blurred with a 0.2 mm diffusion smoothing kernel.
The analysis addressed the following questions: does the
thickness of the cortex vary by genotype, does it relate to the size
of the striatum, and does the relationship between cortical thickness
and striatal volume differ between the wild-type and YAC128 mice.
The model employed was thus the classic ANCOVA linear model:
thickness ¼ b0þ b1Striatum þ b2Genotype
þ b3Genotype ? Striatum
where Striatum is the volume of the striatum in mm3centred on the
mean striatum volume of all mice, and
Tests were carried out at every vertex as well as for the mean
cortical thickness and overall surface area. Note that there was no
ð Þ þ e
Fig. 3. Mean cortical thickness(n=20) for 12-week-old male C57BL/6 mice is shown in (a) with the standard deviation in (b). The graph in part (c) shows the
expected powergiven twogroups, the y axis specifyingthe recoverablechangegiven the groupsizesshownon the x axis.The three lines belongto three different
estimates of standard deviation: standard deviation of mean cortical thickness (0.016 mm), the maximum variance from figure (b) (0.062 mm) and the median
variance of all vertices in (b) (0.007 mm).
247J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
Fig. 4. Differences in overall cortical thickness between the YAC128 mouse and wild-type controls; the top row shows the difference between groups in mm, the
bottom row the t-statistics of that difference. The sensorimotor cortex is thicker in the YAC128 mice, reaching a maximum difference of 0.19 mm. A
representative vertex is plotted, the solid lines showing the linear model fit between striatal volume and cortical thickness, the dashed lines the 5% confidence
interval. All effects are significant at a 10% false discovery rate.
Fig. 5. Differences in the relationshipbetween cortical thickness andstriatal volume; the top row shows the difference in regression slopesin mm, the bottom row
the t-statistics of the difference. There is a positive relationship between striatal volume and cortical thickness throughout the cortex in the wild-type mice. In the
YAC128 mice, however, the sensorimotor cortex shows an inverse relationship, with cortical thickness increasing as the volume of the striatum declines. A
representative vertex is plotted, solid lines representing the linear model fit and dashed lines the 5% confidence interval.
248 J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
between brain size and cortical thickness. This is true in mice
(R2=0.01 in the male–female data set described above) as well as
humans (Luders et al., 2006).
There was a significant positive correlation between striatal
volume and mean cortical thickness (p=0.02), and the YAC128
mice had thicker cortices than wild-type controls at the centred
striatal volume (p=0.04). There was no significant interaction
between striatal volume and cortical thickness. Overall surface area
also correlated with striatal volume (p=0.01), but there were no
group differences nor significant interactions.
The results of the per vertex tests are shown in Figs. 4 and 5.
YAC128 mice had significantly thicker sensorimotor cortices,
reaching a difference of 0.147±0.04 mm. Small increases were also
foundinthefrontal and entorhinalcortices. Onlytwosmallregions –
the anterior cingulate and the retrosplenial cortex – showed thinning
in the YAC128 mice. Significant interactions between genotype and
striatal volume were also found in the sensorimotor cortex, located
slightly anterior to the significant group differences, wherein the
wild-type mouse showed a positive correlation between striatal
volume and cortical thickness while the YAC128 mice featured
increasing cortical thickness with decreasing volume of the striatum.
At 8 months of age, the YAC128 mouse most closely represents
a late pre-clinical or very early clinical phase of Huntington's
Disease (Van Raamsdonk et al., 2005b). Motor learning deficits, as
measured on the rotarod, are present as of 2 months, and cognitive
function (as measured in the water maze) present at 8 months of
age (Van Raamsdonk et al., 2005b), but striatal atrophy can just
barely be detected (Lerch et al., 2008).
The findings of increased cortical thickness agree with previous
results from human preclinical subjects (Paulsen et al., 2006). The
are a compensatory response to striatal degeneration, indications of
abnormal cortical development, or some type of inflammatory
response preceding cell death in later stages of the disease. Lon-
gitudinal data or more timepoints are needed in order to adequately
provide a mechanism for this increase in cortical thickness; based on
human data, however, it is expected that in later stages of the disease
the cortex in the HD mice will indeed be thinner than their wild-type
counterparts (Rosas et al., 2002), and the YAC128 mouse model has
indeed been shown to have reduced cortical volume at 12 months of
age (Slow et al., 2003; Van Raamsdonk et al., 2005a).
The fact that, in the sensorimotor cortex, there is an inverse
correlation between striatal volume and cortical thickness only in the
YAC128 mice indicates that this could indeed be a compensatory
response. A recent study using functional MRI showed increasing
neuronal recruitment in early HD patients using a Simon task, which
tests response times under conditions of spatial incompatibility
between stimulus and response (Georgiou-Karistianis et al., 2007).
More importantly, that same study showed that increased activation
in Huntington's patients in the parietal lobes during a triggered finger
opposition task (Bartenstein et al., 1997). Increasing damage to the
striatum could thus cause increasing compensatory recruitment of
cortical areas (Georgiou-Karistianisetal.,2007; Paulsen etal.,2004).
Here we have presented an automated algorithm for measuring
cortical thicknessat every pointofthe cortex frommouse MRI.This
technique can greatly compliment the existing set of tools for
analysing differences in image deformations by providing a more
biologically relevant index of corticalchange. The applicationto the
YAC128 mouse model of Huntington's Disease showed an increase
in cortical thickness in response to striatal degeneration, a potential
these findings reaffirm the potential utility of MRI in monitoring
mouse models of neurodegenerative diseases and underline the
striking parallels to human HD exhibited by the YAC128 mouse.
The authors would like to thank Dr. Claude Lepage of the
Montreal Neurological Institute for valuable fixes to the code. This
work was supported by grants from the Michael Smith Foundation
for Health Research, the Canadian Institutes of Health Research,
the Huntington's Disease Society of America and the High Q
Foundation. J.P.L is supported by the Canadian institutes for
Health Research. J.B.C is supported by the Huntington Society of
Canada. M.R.H. is supported by the Canadian Institutes of Health
Research, the Huntington Society of Canada, the Hereditary
Disease Foundation and the Canadian Genetic Diseases Network.
M.R.H. is a Killam University Professor and holds a Canada
Research Chair in Human Genetics. The Mouse Imaging Centre
(MICe) acknowledges funding from the Canada Foundation for
Innovation and the Ontario Innovation Trust for providing facilities
along with The Hospital for Sick Children. Operating funds from
the Burroughs Wellcome Fund, the Canadian Institutes of Health
Research, the National Cancer Institute of Canada Terry Fox
Program Projects, the National Institutes of Health and the Ontario
Research and Development Challenge Fund are gratefully
acknowledged. R.M.H. holds a Canada Research Chair in Imaging.
The authors would like to express their appreciation to Jean Paul
Vonsattel and Elizabeth Aylward for their thoughtful suggestions.
Badea, A., Nicholls, P.J., Johnson, G.A., Wetsel, W.C., 2007. Neuroanato-
mical phenotypes in the reeler mouse. NeuroImage 34 (4), 1363–1374.
Bartenstein, P., Weindl, A., Spiegel, S., Boecker, H., Wenzel, R., Ceballos-
Baumann, A.O., Minoshima, S., Conrad, B., 1997. Central motor
Chen, X.J., Kovacevic, N., Lobaugh, N.J., Sled, J.G., Henkelman, R.M.,
Henderson, J.T., 2006. Neuroanatomical differences between mouse
Chung, M.K., Worsley, K.J., Robbins, S., Paus, T., Taylor, J., Giedd, J.N.,
Rapoport, J.L., Evans, A.C., 2003. Deformation-based surface morpho-
metry applied to gray matter deformation. NeuroImage 18 (2), 198–213.
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C., 1994. Automatic 3D
intersubject registration of MR volumetric data in standardized Talairach
space. J. Comput. Assist. Tomogr. 18 (2), 192–205.
Collins, D.L., Holmes, C.J., Peters, T.M., Evans, A.C., 1995. Automatic 3D
model-based neuroanatomical segmentation. Hum. Brain Mapp. 33,
Feigin, A., Ghilardi, M.F., Huang, C., Ma, Y., Carbon, M., Guttman, M.,
Paulsen, J.S., Ghez, C.P., Eidelberg, D., 2006. Preclinical Huntington's
disease: compensatory brain responses during learning. Ann. Neurol.
59 (1), 53–59.
Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral
cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U. S. A.
97 (20), 11050–11055.
Genovese, C.R., Lazar, N.A., Nichols, T., 2002. Thresholding of statistical
maps in functional neuroimaging using the false discovery rate.
NeuroImage 15 (4), 870–878.
249J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
Georgiou-Karistianis, N., Sritharan, A., Farrow, M., Cunnington, R., Stout,
J., Bradshaw, J., Churchyard, A., Brawn, T.L., Chua, P., Chiu, E.,
Thiruvady, D., Egan, G., 2007. Increased cortical recruitment in
Huntington's disease using a Simon task. Neuropsychologia 45 (8),
Henkelman, R.M., Dazaj, J., Lifshitz, N., Nieman, B.J., Tsatskis, S., Lerch,
J., Bishop, S., S,K., Sled, J.G., Chen, X.J., 2006. High throughput
microimaging of the mouse brain. ISMRM Proceedings.
Hof, P.R., Young, W.G., Bloom, F.E., Belichenko, P.V., Celio, M.R., 2000.
Comparative Cytoarchitectonic Atlas of the C57BL/6and 129/SvMouse
Brains. Elsevier Science, Amsterdam.
Huntington's Collaborative Research Group, 1993. A novel gene containing
a trinucleotide repeat that is expanded and unstable on Huntington's
disease chromosomes. The Huntington's Disease Collaborative
Research Group. Cell 72 (6), 971–983.
Jones, S.E., Buchbinder, B.R., Aharon, I., 2000. Three-dimensional
mapping of cortical thickness using Laplace's equation. Hum. Brain
Mapp. 11 (1), 12–32.
Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab'bagh, Y., MacDonald, D.,
Lee, J.M., Kim, S.I., Evans, A.C., 2005. Automated 3-D extraction and
evaluation of the inner and outer cortical surfaces using a Laplacian map
and partial volume effect classification. NeuroImage 27 (1), 210–221.
Kovacevic, N., Henderson, J.T., Chan, E., Lifshitz, N., Bishop, J., Evans,
A.C., Henkelman, R.M., Chen, X.J., 2005. A three-dimensional MRI
atlas of the mouse brain with estimates of the average and variability.
Cereb. Cortex 15 (5), 639–645.
Lein, E.S., Hawrylycz, M.J., Ao, N., Ayres, M., Bensinger, A., Bernard, A.,
Boe, A.F., Boguski,M.S., Brockway, K.S., Byrnes,E.J., Chen, L., Chen,
T.M., Chin, M.C., Chong, J., Crook, B.E., Czaplinska, A., Dang, C.N.,
Datta, S., Dee, N.R., Desaki, A.L., Desta, T., Diep, E., Dolbeare, T.A.,
Donelan, M.J., Dong, H.W., Dougherty, J.G., Duncan, B.J., Ebbert, A.J.,
Eichele, G., Estin, L.K., Faber, C., Facer, B.A., Fields, R., Fischer, S.R.,
Fliss, T.P., Frensley, C., Gates, S.N., Glattfelder, K.J., Halverson, K.R.,
Hart, M.R., Hohmann, J.G., Howell, M.P., Jeung, D.P., Johnson, R.A.,
Karr, P.T., Kawal, R., Kidney, J.M., Knapik, R.H., Kuan, C.L., Lake,
J.H., Laramee, A.R., Larsen, K.D., Lau, C., Lemon, T.A., Liang, A.J.,
Liu, Y., Luong, L.T., Michaels, J., Morgan, J.J., Morgan, R.J., Mortrud,
M.T., Mosqueda, N.F., Ng, L.L., Ng, R., Orta, G.J., Overly, C.C., Pak,
T.H., Parry, S.E., Pathak, S.D., Pearson, O.C., Puchalski, R.B., Riley,
Schaffnit, K., Shapovalova, N.V., Sivisay, T., Slaughterbeck, C.R.,
Smith, S.C., Smith, K.A., Smith, B.I., Sodt, A.J., Stewart, N.N., Stumpf,
K.R., Sunkin, S.M., Sutram, M., Tam, A., Teemer, C.D., Thaller, C.,
Thompson, C.L., Varnam, L.R., Visel, A., Whitlock, R.M., Wohnoutka,
P.E., Wolkey, C.K., Wong, V.Y., Wood, M., Yaylaoglu, M.B., Young,
R.C., Youngstrom, B.L., Yuan, X.F., Zhang, B., Zwingman, T.A.,
Jones, A.R., 2007. Genome-wide atlas of gene expression in the adult
mouse brain. Nature 445 (7124), 168–176.
Lerch, J.P., Evans, A.C., 2005.Cortical thicknessanalysis examined through
power analysis and a population simulation. NeuroImage 24 (1),
2005. Focal decline of cortical thickness in Alzheimer's disease identified
by computational neuroanatomy. Cereb. Cortex 15 (7), 995–1001.
Lerch, J.P., Carrol, J.B., Spring, S., Bertram, L.N., Schwab, C., Hayden,
M.R., Henkelman, R.M., 2008. Automated deformation analysis in the
YAC128 Huntington disease mouse model. NeuroImage 39 (1), 32–39.
Luders,E.,Narr, K.L.,Thompson,P.M., Rex,D.E.,Woods,R.P., Deluca,H.,
Jancke, L., Toga, A.W., 2006. Gender effects on cortical thickness and
the influence of scaling. Hum. Brain Mapp. 27 (4), 314–324.
Ma, Y., Hof, P.R., Grant, S.C., Blackband, S.J., Bennett, R., Slatest, L.,
McGuigan, M.D., Benveniste, H., 2005. A three-dimensional digital
atlasdatabase of the adultC57BL/6Jmousebrain by magneticresonance
microscopy. Neuroscience 135 (4), 1203–1215.
MacDonald, D., Kabani, N., Avis, D., Evans, A.C., 2000. Automated 3-D
extraction of inner and outer surfaces of cerebral cortex from MRI.
NeuroImage 12 (3), 340–356.
Meyer, J.R., Roychowdhury, S., Russell, E.J., Callahan, C., Gitelman, D.,
Mesulam, M.M., 1996. Location of the central sulcus via cortical
thickness of the precentral and postcentral gyri on MR. AJNR Am. J.
Neuroradiol. 17 (9), 1699–1706.
Miller, M.I., Massie, A.B., Ratnanather, J.T., Botteron, K.N., Csernansky,
J.G., 2000. Bayesian construction of geometrically based cortical thick-
ness metrics. NeuroImage 12 (6), 676–687.
Benjamin, M.L., Reynolds, N.C., Rao, S.M., 2004. fMRI biomarker of
early neuronal dysfunction in presymptomatic Huntington's disease.
AJNR Am. J. Neuroradiol. 25 (10), 1715–1721.
Paulsen, J.S., Magnotta, V.A., Mikos, A.E., Paulson, H.L., Penziner, E.,
Andreasen, N.C., Nopoulos, P.C., 2006. Brain structure in preclinical
Huntington's disease. Biol. Psychiatry 59 (1), 57–63.
Paxinos, G., Franklin, K., 2001. The Mouse Brain in Stereotaxic
Coordinates, 2nd ed. Academic Press, San Diego.
Pitiot, A., Pausova, Z., Prior, M., Perrin, J., Loyse, N., Paus, T., 2007.
Magnetic resonance imaging as a tool for in vivo and ex vivo anatomical
phenotyping in experimental genetic models. Hum. Brain Mapp. 28 (6),
2005. Morphometry demonstrates loss of cortical thickness in cerebral
microangiopathy. J. Neurol. 252 (4), 441–447.
Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H.,
van der Kouwe, A., Jenkins, B.G., Dale, A.M., Fischl, B., 2002.
Regional and progressive thinning of the cortical ribbon in Huntington's
disease. Neurology 58 (5), 695–701.
Rosas, H.D., Koroshetz, W.J., Chen, Y.I., Skeuse, C., Vangel, M.,
Cudkowicz, M.E., Caplan, K., Marek, K., Seidman, L.J., Makris, N.,
Jenkins, B.G., Goldstein, J.M., 2003. Evidence for more widespread
cerebral pathology in early HD: an MRI-based morphometric analysis.
Neurology 60 (10), 1615–1620.
Ross, M.E., Swanson, K., Dobyns, W.B., 2001. Lissencephaly with
cerebellar hypoplasia (LCH): a heterogeneous group of cortical
malformations. Neuropediatrics 32 (5), 256–263.
Salat, D.H., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa,
E., Morris, J.C., Dale, A.M., Fischl, B., 2004. Thinning of the cerebral
cortex in aging. Cereb. Cortex 14 (7), 721–730.
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N.,
Evans, A., Rapoport, J., Giedd, J., 2006a. Intellectual ability and cortical
development in children and adolescents. Nature 440 (7084), 676–679.
Castellanos, F.X., Rapoport, J., 2006b. Longitudinal mapping of cortical
thickness andclinical outcome in childrenandadolescents with attention-
deficit/hyperactivity disorder. Arch. Gen. Psychiatry 63 (5), 540–549.
Sidman, R.L., Angevin, J.B., Pierce, E.T., 1971. Atlas of the Mouse Brain
and Spinal Cord. Harvard University Press, Cambridge.
Slow, E.J., van Raamsdonk, J., Rogers, D., Coleman, S.H., Graham, R.K.,
Deng, Y., Oh, R., Bissada, N., Hossain, S.M., Yang, Y.Z., Li, X.J.,
Simpson, E.M., Gutekunst, C.A., Leavitt, B.R., Hayden, M.R., 2003.
Selective striatal neuronal loss in a YAC128 mouse model of Huntington
disease. Hum. Mol. Genet. 12 (13), 1555–1567.
Sowell, E.R., Thompson, P.M., Leonard, C.M., Welcome, S.E., Kan, E.,
Toga, A.W., 2004. Longitudinal mapping of cortical thickness and brain
growth in normal children. J. Neurosci. 24 (38), 8223–8231.
Spring, S., Lerch, J.P., Henkelman, R.M., 2007. Sexual dimorphism
revealed in the structure of the mouse brain using three-dimensional
magnetic resonance imaging. NeuroImage 35 (4), 1424–1433.
Thompson, P.M., Dutton, R.A., Hayashi, K.M., Toga, A.W., Lopez, O.L.,
Aizenstein, H.J., Becker, J.T., 2005. Thinning of the cerebral cortex
visualized in HIV/AIDS reflects CD4+ T lymphocyte decline. Proc.
Natl. Acad. Sci. U. S. A. 102 (43), 15647–15652.
Tyszka, J.M., Readhead, C., Bearer, E.L., Pautler, R.G., Jacobs, R.E., 2006.
Statistical diffusion tensor histology reveals regional dysmyelination
effects in the shiverer mouse mutant. NeuroImage 29 (4), 1058–1065.
Van Raamsdonk, J.M., Murphy, Z., Slow, E.J., Leavitt, B.R., Hayden, M.R.,
2005a. Selective degeneration and nuclear localization of mutant
250 J.P. Lerch et al. / NeuroImage 41 (2008) 243–251
huntingtin in the YAC128 mouse model of Huntington disease. Hum. Download full-text
Mol. Genet. 14 (24), 3823–3835.
Van Raamsdonk, J.M., Pearson, J., Slow, E.J., Hossain, S.M., Leavitt, B.R.,
Hayden, M.R., 2005b. Cognitive dysfunction precedes neuropathology
and motor abnormalities in the YAC128 mouse model of Huntington's
disease. J. Neurosci. 25 (16), 4169–4180.
Vonsattel, J.P., Myers, R.H., Stevens, T.J., Ferrante, R.J., Bird, E.D.,
Richardson Jr., E.P., 1985. Neuropathological classification of Hunting-
ton's disease. J. Neuropathol. Exp. Neurol. 44 (6), 559–577.
Walhovd, K.B., Fjell, A.M., Dale, A.M., Fischl, B., Quinn, B.T., Makris, N.,
Salat, D., Reinvang, I., 2006. Regional cortical thickness matters in
recall after months more than minutes. NeuroImage 31 (3), 1343–1351.
251 J.P. Lerch et al. / NeuroImage 41 (2008) 243–251