Cerebral Cortex October 2007;17:2276--2282
doi:10.1093/cercor/bhl136
Advance Access publication December 12, 2006
Fiber Connections between the Cerebral
Cortex and the Corpus Callosum in
Alzheimer’s Disease: A Diffusion Tensor
Imaging and Voxel-Based Morphometry
Study
Djyldyz Sydykova1, Robert Stahl2, Olaf Dietrich2, Michael
Ewers1, Maximilian F. Reiser2, Stefan O. Schoenberg2,
Hans-Jürgen Möller1, Harald Hampel1 and Stefan J. Teipel1
Regional cortical atrophy in Alzheimer’s disease (AD) most likely
reflects the loss of cortical neurons. Several diffusion tensor
imaging studies reported reduced fractional anisotropy (FA) in the
corpus callosum in AD. The aim of this study was to investigate the
association between reduced FA in the corpus callosum and gray
matter atrophy in AD. Thirteen patients with AD with a mean
(6standard deviation) age of 68.3 years (611.5) and mean Mini
Mental State Examination (MMSE) score of 21.8 (64.8) were
recruited. There were 13 control subjects with a mean age of 66.7
years (66.4) and MMSE of 29.1 (60.7). We used voxel-based
morphometry of gray matter maps and region of interest--based
analysis of FA in the corpus callosum. FA values of the anterior
corpus callosum in AD patients were significantly correlated with
gray matter volume in the prefrontal cortex and left parietal lobes.
FA values of the posterior corpus callosum were significantly
correlated with gray matter volume in the bilateral frontal,
temporal, right parietal, and occipital lobes. In control subjects,
no correlations were detected. Our findings suggest that decline of
FA in the corpus callosum may be related to neuronal degeneration
in corresponding cortical areas.
white matter and characterizes the microarchitecture of local
brain tissue.
Consistent with the loss of interhemispherically projecting
neurons, diffusion tensor--based studies have reported reduced
FA in the anterior and posterior corpus callosum in patients
with AD (Rose et al. 2000; Bozzali et al. 2002; Stahl et al. 2003;
Head et al. 2004). However, it is still unknown whether the ADrelated diffusion changes in the corpus callosum reflect regional
loss of callosally projecting neurons in the cortical gray matter.
In the present study, we investigated correlations between
FA in the corpus callosum and cortical gray matter volume using
automated voxel-based morphometry (VBM) (Ashburner and
Friston 2000) in AD patients and healthy elderly controls. A
correlation between a decline in fiber tract integrity in the
corpus callosum and gray matter atrophy in corresponding
cortical areas would support the notion that a decline in FA
reflects a loss of intracortical projecting neurons in AD.
Keywords: Alzheimer’s disease, corpus callosum, cortical fibers
diffusion tensor imaging, fractional diffusion anisotropy
voxel-based morphometry
Introduction
Neuropathological studies in Alzheimer’s disease (AD) have
shown neurofibrillary tangles, senile plaques, and neuronal and
synaptic loss not only in areas of the mesial temporal lobe but
also in a subset of intracortical projecting neurons in neocortical association areas that maintain interhemispheric connections through the corpus callosum (Ohm et al. 1995;
Giannakopoulos et al. 1997). These changes involve not only
the neuronal somata in the cortical gray matter but also the
neuronal fibers in the subcortical white matter (Brun and
Englund 1986). Neural degeneration in AD starts in the neural
periphery with loss of synaptic functional and structural integrity, redistribution of cell organelles and elements of
cytoskeleton from axons and dendrites to the neuronal soma,
and axonal and dendritic degeneration (Brun and Englund
1986). Impaired integrity of nerve fibers leads to less constrained diffusive motion of water molecules. Diffusion tensor
imaging is a novel technique that allows measurement of the
subcortical fiber tract integrity in vivo (Le Bihan et al. 1991).
From the diffusion tensor, the fractional anisotropy (FA), a
quantitative measure describing the anisotropy of water diffusion, can be calculated at each voxel (Basser and Pierpaoli
1996). The FA reflects the integrity of neuronal fibers in the
Ó The Author 2006. Published by Oxford University Press. All rights reserved.
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1
Alzheimer Memorial Center, Dementia and Neuroimaging
Section, Department of Psychiatry, Ludwig-Maximilian
University, D-80366 Munich, Germany and 2Department of
Clinical Radiology, University Hospitals—Grosshadern,
Ludwig-Maximilian University, D-81377 Munich, Germany
Subjects and Methods
Subjects
Thirteen patients with AD mean (±standard deviation [SD]) age 68.3
(±11.5) years (6 women and 7 men) were recruited from the Department of Psychiatry, Alzheimer Memorial Center, Ludwig-Maximilian
University Munich, Germany. The mean Mini Mental State Examination
(MMSE) score was 21.8 (±4.8). Patients fulfilled the criteria of the
National Institute of Neurological and Communicative Disease and
Stroke and the Alzheimer’s Disease and Related Disorders Association
for the diagnosis of clinically probable AD (McKhann et al. 1984). The
clinical assessment included detailed medical history, neurological and
neuropsychological examinations, and laboratory tests (routine hematology and biochemistry screen, thyroid function tests). Major systemic,
psychiatric, or neurological illnesses were carefully investigated and
excluded in all subjects by clinical and neurological examinations, blood
testing (complete blood count, sedimentation rate, electrolytes, glucose, blood urea nitrogen, creatinine, liver-associated enzymes, cholesterol, high-density lipoprotein, triglycerides, antinuclear antibodies,
rheumatoid factor, HIV, serum B12, folate, thyroid function tests, and
urine analysis), and psychiatric examination. Patients were particularly
screened to exclude the presence of major cerebrovascular disease.
Only subjects were included which had no more than 3 subcortical
white matter hyperintensities as examined on T2-weighted magnetic
resonance imaging (MRI) scans exceeding 10 mm in diameter.
Thirteen healthy control subjects (7 women and 6 men, mean [±SD]
age 66.7 [±6.4] years) with MMSE 29.1 (±0.7) were recruited. The
controls did not complain about cognitive problems, and there was no
evidence of cognitive deficits as measured by neuropsychological
testing using the Consortium to establish a Registry for Alzheimer’s
Disease battery.
Patient and control groups were matched for age (Student’s T-test,
t = 0.42, degrees of freedom [df] = 24, P = 0.68) and gender (Pearson
Chi-Quadrat test: v2 = 0.07, df = 1, P = 0.78). As expected, there was
a significant difference in MMSE scores between AD and control groups,
with 21.7 (SD = 4.8) in patients with AD and 29.0 (SD = 0.9) in the
control group (Mann--Whitney U = 6.0, P = 0.001).
All patients and controls were only examined after they had given
their written informed consent. The study was approved by the Ethical
Committee of the Medical Faculty of the University Munich.
MRI Acquisition
We performed MRI examinations of the brain on a 1.5-T MRI scanner
(Magnetom Sonata Maestro Class, Siemens Medical Solutions, Erlangen,
Germany) using a new 8-channel phased-array head coil and integrated
hardware and software solutions of parallel acquisition technique.
For the structural data, we applied a high-resolution T1-weighted
magnetization-prepared rapidly acquired gradient echo (MPRAGE)
sequence with a spatial resolution of 1.1 3 1.1 3 1.1 mm3 and echo
time/time to inversion/time repetition (TE/TI/TR) of 3.9/800/1570 ms.
A total of 160 sagittal slices with a matrix size of 256 3 256 and a field
of view of 270 3 270 mm2 were measured. In order to identify white
matter lesions, 36 T2-weighted axial slices were acquired using a conventional sequence (TE/TR/TI: 66/8340/2500 ms) with a matrix size of
256 3 208 and a filed of view of 230 3 187 mm2, which resulted in
a voxel size of 0.9 3 0.9 3 3.6 mm3.
The diffusion-weighted data were collected with a spin-echo singleshot sequence (TE/TR 71/6000 ms); diffusion gradients in 6 different
spatial directions were applied as described by Basser and Pierpaoli
(1996). The b values were 0 and 1000 s/mm2. The images had a matrix
size of 128 3 128 with a field of view of 230 3 230 mm2, the resulting
voxel size was 1.8 3 1.8 3 3.6 mm3. Thirty-six axially orientated slices
were acquired. Ten measurements were performed and averaged.
During each course, each subject was scanned without changing their
position in the scanner.
The diffusion-weighted MRI scans were performed with parallel
imaging. Test measurements were performed in a single healthy control
to visually assess image quality, signal-to-noise, and artifacts for the
modified sensitive encoding (Pruessmann et al. 1999) and generalized
autocalibrating partially parallel acquisition (GRAPPA) (Griswold et al.
2002) algorithm. The GRAPPA algorithm revealed fewer ghosting
artifacts (N/2 artifacts) and a better signal-to-noise, which resulted in
a better image quality. Using a parallel-imaging acceleration factor of 3,
considerable ghosting artifacts degraded the image. Therefore, for all
following examinations we used the GRAPPA reconstruction algorithm
with an acceleration factor of 2 and 24 reference lines (autocalibration
signals) in the k-space center.
Data Analysis
Anisotropy Measurement
From the diffusion-weighted sequence, the values for FA in each voxel
were calculated with software developed in-house (Interactive Data
Language, version 5.4, Research Systems Inc., Boulder, CO). The resulting maps as well as the T2-weighted images (those of the diffusion
tensor sequence with a b value of 0) and the MPRAGE images were
converted separately into 3-dimensional volume data sets.
Regions of interest (ROIs) in the anterior and posterior corpus
callosum with a pixel number 20 carefully placed bilaterally in 3
consecutive slices on which these structures were completely shown
(Fig. 1) on the T2-weighted images, as it was reported previously (Bozzali
et al. 2002). The correct ROI position was visually compared with the
corresponding layers of the MPRAGE data sets.
Cortical Gray Matter Measurement
The regional gray matter volume was determined using VBM with
Matlab 6.5 (MathWorks, Natick, MA) through statistical parametrical
mapping (SPM 2, Wellcome Department of Imaging Neuroscience,
London, UK, see also http://www.fil.ion.ucl.ac.uk/spm). An optimized
VBM protocol was followed for preprocessing and subsequent analysis
of imaging data. This method has previously been described in detail
(Ashburner and Friston 2000; Good et al. 2001).
Normalized Group-Specific Template and Priors
After manual realignment of the T1-weighted scans, a group-specific
template was created from the scans of the AD subjects. First, each
Figure 1. Location of the ROIs in the anterior and posterior corpus callosum. ROIs in
the corpus callosum, at which the FA was determined. This ROI identification method
was proposed by Bozzali et al. (2002).
structural MRI was normalized to the standard T1-weighted MRI
template (Ashburner et al. 1997; Ashburner and Friston 2000). Normalized scans were then smoothed (12-mm full width at half maximum
isotropic Gaussian kernel) and averaged to obtain a group-specific T1weighted template. All structural MRI scans positioned in native space
were then normalized to this template. Afterward, group-specific
average maps were created for gray matter, white matter, and
cerebrospinal fluid. These maps, called prior images, carry the a priori
information on the tissue distribution for Bayesian segmentation of MRI
scans. In addition, gray matter images were smoothed with a 12-mm
kernel and averaged to obtain a group-specific gray matter template.
Optimized Normalization and Segmentation
The native MRI scans were segmented using the group-specific T1weighted template and gray matter, white matter, and cerebrospinal
fluid priors. The original structural images were then normalized. The
optimized normalization procedure involves an iterative normalization
of gray matter maps from native to standard space and aims to reduce
any contribution from nonbrain voxels and to afford optimal spatial
normalization of gray matter. The normalized gray matter maps were
resliced to a final voxel size of 1.0 mm3 and smoothed with a 12-mm
full width at half maximum isotropic Gaussian kernel. Additionally,
the partitioned gray matter images were modulated by the Jacobian
determinants from spatial normalization to correct volume changes
introduced during the nonlinear spatial transformations (Ashburner and
Friston 2000). The modulated gray matter images were used afterward
for further statistical analysis.
Statistical Analysis
For statistical analysis, we employed the general linear model on a voxel
basis. The voxel-based analysis is a measurement of the gray matter
density throughout the brain. The output of the method is a statistical
parametrical map showing where gray matter density differs significantly among groups. Prior to regression analysis, scans were proportionally scaled to the global mean threshold at 40% of global intensity to
reduce the influence of any remaining nonbrain tissue. Proportional
scaling to the global mean allows detection of voxels with a relatively
accelerated loss or a relative preservation of gray matter (i.e., more or
less than the global loss). Results were thresholded at an uncorrected
P value <0.001 and an extended threshold of 50 contiguous voxels
was applied.
Cerebral Cortex October 2007, V 17 N 10 2277
Independent regression models were calculated for FA in the anterior
and posterior corpus callosum as independent predictor variables of
gray matter density. The analyses were repeated using the MMSE score
as the covariate to control for general cognitive function and age as the
covariate to control for age effects on cognitive functions. In the AD
group, we controlled the results for MMSE score and age and in the
control group for age.
Results
For the AD group, the FA values in the anterior corpus callosum
were significantly positively correlated with gray matter volume
in the bilateral prefrontal cortex, superior temporal gyrus with
left side predominance, left postcentral, and right lingual gyrus.
After controlling for MMSE score and age, the FA values of the
anterior corpus callosum showed significant correlations with
gray matter volume in the bilateral prefrontal cortex and left
parietal lobe (Table 1, Fig. 2).
The FA values in the posterior corpus callosum in patients
with AD were significantly positively correlated with reduced
gray matter volume the bilateral frontal, bilateral temporal lobes,
right hippocampus, right parietal lobe, and insula. After controlling for MMSE and age as covariates, the FA of the posterior
corpus callosum was significantly correlated with gray matter
volume in the bilateral frontal and temporal lobes and right
parietal and occipital cortices (Table 2, Fig. 2).
In control subjects, significant positive correlations were
detected between the FA values in the anterior corpus callosum
and the cortical gray matter volume in the left superior
temporal gyrus, bilateral parietal lobe, and right posterior
cingulate gyrus. Controlling for age as a covariate in control
subjects changed the results. We found no significant positive
correlations between FA in the anterior corpus callosum and
cortical gray matter volume.
FA values in the posterior corpus callosum in control subjects
were significantly positively correlated with gray matter density
in the frontal lobe, predominantly in the left hemisphere, left
temporal, and left parietal lobes. Controlling for age as a covariate in control subjects changed the results. We found no
significant positive correlations between FA in the posterior
corpus callosum and cortical gray matter volume.
Table 1
Correlation between FA of the anterior corpus callosum and cortex, controlling for MMSE
score and age, in patients with AD
Region
Side
BA
Coordinates (mm)
x
y
T9
z
Frontal lobe
Superior frontal gyrus
Superior frontal gyrus
Precentral gyrus
Middle frontal gyrus
Left
Left
Right
Left
8
6
6
8
43
20
31
35
17
24
14
25
47
53
70
40
5.25
5.14
6.15
7.23
Parietal lobe
Superior parietal lobule
Inferior parietal lobule
Precuneus
Precuneus
Precuneus
Left
Left
Left
Left
Left
7
39
7
7
19
13
40
12
14
40
72
65
50
69
75
59
39
41
40
39
6.38
5.58
5.57
4.6
5.84
Note: The threshold value was set at P # 0.001, uncorrected. The cluster extension,
representing the number of contiguous voxels passing the height threshold, was set at $50.
Brain regions are indicated by Talairach and Tournoux coordinates x, y, and z (Talairach and
Tournoux 1988): x, the medial to lateral distance relative to midline (positive 5 right
hemisphere); y, the anterior to posterior distance relative to the anterior commissure (positive 5
anterior); z, superior to inferior distance relative to the anterior--posterior commissure line
(positive 5 superior). T9, T value with 9 degrees of freedom; BA, Brodmann area.
2278 Fiber Connections in Alzheimer’s Disease
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Sydykova et al.
Discussion
In this study, we report correlations between FA in the corpus
callosum and cortical gray matter volume in AD patients and
healthy elderly subjects.
Several studies have shown a notable decrease of corpus
callosum area in patients with AD, consistent with the loss of
intracortical projecting fibers (Friedland et al. 1985; Minoshima
et al. 1997; Hampel et al. 1998; Ishii et al. 1998; Demetriades
2002; Teipel et al. 2005). Diffusion tensor imaging studies have
reported significantly reduced FA values in the anterior (Head
et al. 2004) and posterior corpus callosum in AD (Rose et al.
2000; Bozzali et al. 2002; Takahashi et al. 2002; Stahl et al. 2003;
Fellgiebel et al. 2004; Sugihara et al. 2004; Choi et al. 2005).
The corpus callosum represents specific projections from
cortical areas in an anterior--posterior topology. In the rhesus
monkey, prefrontal cortical fibers were mapped in the genu and
anterior third of the body of the corpus callosum (Sunderland
1940). Data from the human brain suggest a similar organization
(Schaltenbrand et al. 1972; de Lacoste et al. 1985; Tan et al.
1991). There is a slight difference in the anterior topology of the
corpus callosum between the human and the monkey brain.
This can be explained by a rostral displacement of the prefrontal
fibers during evolution. In the human brain, fibers stemming
from frontal cortical areas project through the rostrum and
genu, whereas primary sensorimotor, posterotemporal, parietal,
and occipital cortical areas are represented in the body and the
splenium of the corpus callosum (de Lacoste et al. 1985).
In agreement with these observations (Sunderland 1940; de
Lacoste et al. 1985; de Lacoste and Woodward 1988), we found
correlations between FA values in the anterior corpus callosum
and gray matter volume in the bilateral frontal cortex in AD
patients. Additionally, FA values of the anterior corpus callosum
were significantly correlated with the left parietal lobe gray
matter volume. This correlation probably reflects a simultaneous
neurodegeneration along functional systems in AD patients.
Several studies have reported functional connections between
frontal and parietal lobes that play an important role in the
control of spatially guided behavior (Cavada and Goldman-Rakic
1989; Andersen et al. 1990; Corbetta 1998; Petrides and Pandya
1999; Collette et al. 2005; Schumacher et al. 2005). Previous
studies have shown that AD pathology progresses along such
intracortical networks, for example, along the dorsal visual
stream (Friston et al. 1993; McIntosh et al. 1994; Horwitz et al.
1995).
These studies were based on the assumption that memory
and other cognitive abilities are the result of integrated activity
of networks including multiple brain regions, rather than
activity in an isolated brain region, and the interactions within
these networks are disrupted by the neurodegeneration of AD
(Friston et al. 1993; McIntosh et al. 1994; Horwitz et al. 1995;
Grady et al. 2001). Hence, direct fiber connections between the
anterior corpus callosum and the frontal lobe and functional
network connectivity between the frontal and parietal cortices
could explain the correlation between the anterior corpus
callosum and the parietal lobe revealed in this study.
Fiber tracts of the posterior corpus callosum in patients with
AD after controlling for age and for MMSE score correlated with
the volume in the bilateral frontal, temporal, right parietal, and
bilateral occipital lobes. Fiber connectivity between the splenium and the temporo-parieto-occipital cortex detected in our
study agrees with previous findings from electrophysiological
Figure 2. Correlation between gray matter volume intensity and FA value in the anterior and posterior corpus callosum in patients with AD. Reduced gray matter volume intensity
with decreased FA in the corpus callosum (red: anterior and green: posterior corpus callosum), controlling for MMSE score and age, in patients with AD. Color-coded SPM (T) map
projected on the normalized rendered brain surface from the MRI scan of a healthy subject. Cluster extension set at $50 contiguous voxels passing the significance threshold of
P \ 0.001.
stimulation and postmortem examinations (Sunderland 1940;
de Lacoste et al. 1985). The significant correlation between the
decline in the posterior corpus callosum fiber integrity and
volume of the right parietal lobe gray matter may underlie the
impaired ability of spatial and object attention in AD that have
been reported in previous studies (Awad, Johnson, et al. 1986;
Cronin-Golomb et al. 1991; Mendez et al. 1997). The correlations between the posterior corpus callosum and the frontal
lobe may again reflect a simultaneous neurodegeneration of
the frontal and parietal lobes in AD. The correlation between
posterior corpus callosum may reproduce direct fiber connections between 1) posterior corpus callosum and the parietal
lobe and 2) the frontal and parietal cortices.
When controlling for dementia severity as measured by
MMSE, the result pattern was changed. The FA values of the
anterior corpus callosum were correlated with gray matter
density of the temporal lobes in addition to the gray matter
density of the frontal and parietal lobes. The FA values of the
posterior corpus callosum were only significantly correlated
with the gray matter density of the temporal lobes on both sides
and left parietal lobe but not with the gray matter density of the
frontal and the occipital lobes (Fig. 2). Therefore, the correlations that were not detected after controlling for dementia
severity may reflect the effect of the dementia severity in
healthy elderly subjects.
Several diffusion MRI studies (Hanyu et al. 1999; Sandson et al.
1999; Kantarci et al. 2001; Stahl et al. 2003) have demonstrated
that in addition to cortical gray matter changes, microscopic
white matter changes occur in patients with AD, which cannot
be detected by using conventional MRI. These findings agree
with neuropathological studies reporting partial loss of myelin
and axons together with hyaline fibrosis of arterioles and
Cerebral Cortex October 2007, V 17 N 10 2279
Table 2
Correlation between FA of the posterior corpus callosum and cortex, controlling for MMSE
score and age, in patients with AD
Region
Side
BA
Coordinates (mm)
x
y
T9
z
Frontal lobe
Superior frontal gyrus
Superior frontal gyrus
Middle frontal gyrus
Middle frontal gyrus
Middle frontal gyrus
Right
Left
Left
Right
Right
6
6
9
6
9
30
15
43
15
33
7
1
11
3
18
68
72
32
57
33
7.03
6.13
8.22
6.83
5.13
Temporal lobe
Superior temporal gyrus
Superior temporal gyrus
Superior temporal gyrus
Inferior temporal gyrus
Inferior temporal gyrus
Inferior temporal gyrus
Left
Left
Right
Right
Right
Right
22
22
22
20
19
37
56
57
59
55
46
47
26
40
22
29
53
48
2
10
2
18
4
7
10.7
9.69
8.67
7.99
7.05
6.45
Parietal lobe
Postcentral gyrus
Postcentral gyrus
Inferior parietal lobule
Subgyral
Precuneus
Right
Right
Right
Right
Right
43
3
40
40
7
51
33
55
27
10
17
35
25
38
65
21
49
29
55
40
7.1
4.77
5.12
5.94
4.68
Occipital lobe
Precuneus
Cuneus
Right
Right
31
23
4
6
68
75
18
14
8.43
5.86
Note: The threshold value was set at P # 0.001, uncorrected. The cluster extension,
representing the number of contiguous voxels passing the height threshold, was set at $50.
Brain regions are indicated by Talairach and Tournoux coordinates x, y, and z (Talairach and
Tournoux 1988): x, the medial to lateral distance relative to midline (positive 5 right
hemisphere); y, the anterior to posterior distance relative to the anterior commissure (positive 5
anterior); z, superior to inferior distance relative to the anterior--posterior commissure line
(positive 5 superior). T9, T value with 9 degrees of freedom; BA, Brodmann area.
smaller vessels in the absence of focal lacunes or infarcts in
about 60% of pure AD cases (Brun and Englund 1986). White
matter changes were not directly related to the severity and
localization of cortical pathology (Brun and Englund 1986;
Sjobeck et al. 2006), but preferentially involved the frontal
white matter, whereas AD pathology was concentrated in the
posterior portion of the brain (Sjobeck et al. 2006). Similarly,
diffusion imaging studies (Bozzali et al. 2002) suggest that white
matter pathology is not homogeneously distributed in AD, but
rather involves brain regions that project to the association
cortices (i.e., corpus callosum and white matter of the frontal,
temporal, and parietal lobes), with a relative sparing of other
white matter areas. These changes are thought to be related to
both AD-related microvascular pathology and Wallerian degeneration and would result in increased diffusivity and decreased fiber directionality. In our study, we included only
subjects who had no more than 3 subcortical white matter
hyperintensities exceeding 10 mm in diameter, as examined on
the T2-weighted MRI scans. T2-weighted MRI is sensitive for
changes in local water content of cerebral white matter due to
edema and demyelination as shown in clinical--pathological
correlation studies. In the acute and subacute period after the
onset of a stroke, the T2-weighted image may not provide
a sensitive measurement of lesion size but is used as surrogate
measure of final infarct size (Verheul et al. 1992; Lu et al. 2005).
Clinicopathological studies suggest that almost all lacunes and
infarcts as well as chronic ischemic lesions detected postmortem can be identified using T2-weighted MRI (Awad,
Spetzler, et al. 1986; Braffman et al. 1988a, 1988b; Chimowitz
et al. 1992). It can, however, not be excluded that the size of
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Sydykova et al.
a lesion identified in T2-weighted MRI underestimates the
extent of the underlying white matter alteration. Therefore,
our group of patients may have included some patients with
larger damage to the white matter than visualized by T2weighted MRI. In the absence, however, of MRI evidence for
stroke-like lesions or a high number of lacunes, the FA reductions in the corpus callosum most likely reflect AD-related
microstructural changes of the white matter and not mainly the
effect of cerebrovascular disease.
In the control group, we observed correlations between gray
matter volume in the superior temporal gyrus, the parietal and
occipital lobes, and the posterior cingulate gyrus and FA values
in the anterior corpus callosum and between gray matter
volume in the left frontal, left temporal, and left parietal cortices
and FA values in the posterior corpus callosum. These correlations were entirely explained by the effect of age: after
controlling for age there were no significant correlations
between FAs in the corpus callosum and cortical gray matter
in healthy elderly controls. Aging is associated with complex
patterns of cognitive decline and alterations of brain structure
(Good et al. 2001; Head et al. 2004). Many neuropathological
and in vivo neuroimaging studies showed that normal aging is
characterized by a substantial and extensive vulnerability of the
cerebral cortex (West 1996; Raz et al. 1998; Good et al. 2001;
Head et al. 2004). Previous MRI studies have examined an effect
of aging in healthy adults (Raz et al. 1998; Good et al. 2001;
Pfefferbaum and Sullivan 2003). In our study, we controlled the
aging effect in healthy subjects. Without controlling for age in
healthy subjects, we found significant correlations between
gray matter volume in the superior temporal gyrus, parietal and
occipital lobes, and posterior cingulate gyrus and FA values in
the anterior corpus callosum and between gray matter volume
in the left frontal, left temporal, and left parietal cortices with FA
values in the posterior corpus callosum. These results are in
agreement with previous studies that have reported reduction
of the gray matter volume in the prefrontal and temporal gray
matter (Raz et al. 1998), parietal (Resnick et al. 2000; Good et al.
2001) and occipital lobes (Polidori et al. 1993), cingulate gyrus
(Good et al. 2001), as well as the anterior and posterior corpus
callosum (Teipel et al. 1998; Sullivan et al. 2001; Pfefferbaum and
Sullivan 2003) in healthy subjects. When controlling for age, we
did not detect any correlation between gray matter volume and
FA in the corpus callosum. These findings suggest that these
correlations detected before controlling for aging indeed reflect
an age-associated effect in elderly healthy subjects.
The lack of an effect in the healthy subjects after controlling
for age and the presence of an effect after controlling for age
and dementia severity in AD supports the notion that, analogous
to the study of experimentally induced brain lesions in animals,
the system-specific neuropathology of AD can serve as a lesion
model to track the morphological substrate of connectivity
within cortical networks (Brodal 1981).
In conclusion, this is the first study on correlations between
FA values in the corpus callosum and cortical gray matter
volume in AD patients and healthy control subjects. Results
from our study support the notion that decline of FA in the
corpus callosum may be related to neuronal degeneration in
corresponding cortical areas.
Notes
The authors thank Felician Jancu (Ludwig-Maximilian University,
Munich, Germany) for his technical assistance. All authors had full
access to all of the data in the study and take responsibility for the
integrity of the data and the accuracy of the data analysis. Part of this
work was supported by grants from the Medical Faculty of the LudwigMaximilian University (Munich, Germany) to SJT, from the Hirnliga e. V.
(Nürmbrecht, Germany) to DS and SJT, from the German Competency
Network on Dementias (Kompetenznetz Demenzen) funded by the
Bundesministerium für Bildung und Forschung, Germany, and by an
unrestricted grant from Jannssen-Cilag GmbH (Neuss, Germany).
Conflict of Interest : None declared.
Address correspondence to Stefan J. Teipel, MD, Alzheimer Memorial
Center, Dementia and Neuroimaging Section, Department of Psychiatry,
Ludwig-Maximilian University, Nussbaumstrasse 7, D-80336 Munich,
Germany. Email: [email protected].
References
Andersen RA, Asanuma C, Essick G, Siegel RM. 1990. Corticocortical
connections of anatomically and physiologically defined subdivisions
within the inferior parietal lobule. J Comp Neurol. 296:65--113.
Ashburner J, Friston KJ. 2000. Voxel-based morphometry—the methods.
Neuroimage. 11:805--821.
Ashburner J, Neelin P, Collins DL, Evans A, Friston K. 1997. Incorporating
prior knowledge into image registration. Neuroimage. 6:344--352.
Awad IA, Johnson PC, Spetzler RF, Hodak JA. 1986. Incidental subcortical
lesions identified on magnetic resonance imaging in the elderly. II.
Postmortem pathological correlations. Stroke. 17:1090--1097.
Awad IA, Spetzler RF, Hodak JA, Awad CA, Carey R. 1986. Incidental
subcortical lesions identified on magnetic resonance imaging in the
elderly. I. Correlation with age and cerebrovascular risk factors.
Stroke. 17:1084--1089.
Basser PJ, Pierpaoli C. 1996. Microstructural and physiological features
of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn
Reson B. 111:209--219.
Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, Scotti G,
Comi G, Filippi M. 2002. White matter damage in Alzheimer’s disease
assessed in vivo using diffusion tensor magnetic resonance imaging. J
Neurol Neurosurg Psychiatry. 72:742--746.
Braffman BH, Zimmerman RA, Trojanowski JQ, Gonatas NK, Hickey WF,
Schlaepfer WW. 1988a. Brain MR: pathologic correlation with gross
and histopathology. 1. Lacunar infarction and Virchow-Robin spaces.
AJR Am J Roentgenol. 151:551--558.
Braffman BH, Zimmerman RA, Trojanowski JQ, Gonatas NK, Hickey WF,
Schlaepfer WW. 1988b. Brain MR: pathologic correlation with gross
and histopathology. 2. Hyperintense white-matter foci in the elderly.
AJR Am J Roentgenol. 151:559--566.
Brodal A. 1981. Neurological anatomy in relation to clinical medicine.
New York: Oxford University Press. p. 5--10.
Brun A, Englund E. 1986. A white matter disorder in dementia of the
Alzheimer type: a pathoanatomical study. Ann Neurol. 19:253--262.
Cavada C, Goldman-Rakic PS. 1989. Posterior parietal cortex in rhesus
monkey: II. Evidence for segregated corticocortical networks linking
sensory and limbic areas with the frontal lobe. J Comp Neurol.
287:422--445.
Chimowitz MI, Estes ML, Furlan AJ, Awad IA. 1992. Further observations
on the pathology of subcortical lesions identified on magnetic
resonance imaging. Arch Neurol. 49:747--752.
Choi SJ, Lim KO, Monteiro I, Reisberg B. 2005. Diffusion tensor imaging
of frontal white matter microstructure in early Alzheimer’s disease:
a preliminary study. J Geriatr Psychiatry Neurol. 18:12--19.
Collette F, Olivier L, Van der Linden M, Laureys S, Delfiore G, Luxen A,
Salmon E. 2005. Involvement of both prefrontal and inferior parietal
cortex in dual-task performance. Brain Res Cogn Brain Res. 24:237--251.
Corbetta M. 1998. Frontoparietal cortical networks for directing
attention and the eye to visual locations: identical, independent, or
overlapping neural systems? Proc Natl Acad Sci USA. 95:831--838.
Cronin-Golomb A, Rizzo JF, Corkin S, Growdon JH. 1991. Visual function
in Alzheimer’s disease and normal aging. Ann N Y Acad Sci. 640:28--35.
de Lacoste MC, Kirkpatrick JB, Ross ED. 1985. Topography of the human
corpus callosum. J Neuropathol Exp Neurol. 44:578--591.
de Lacoste MC, Woodward DJ. 1988. The corpus callosum in nonhuman
primates. Determinants of size. Brain Behav Evol. 31:318--323.
Demetriades AK. 2002. Functional neuroimaging in Alzheimer’s type
dementia. J Neurol Sci. 203--204:247--251.
Fellgiebel A, Wille P, Muller MJ, Winterer G, Scheurich A, Vucurevic G,
Schmidt LG, Stoeter P. 2004. Ultrastructural hippocampal and white
matter alterations in mild cognitive impairment: a diffusion tensor
imaging study. Dement Geriatr Cogn Disord. 18:101--108.
Friedland RP, Brun A, Budinger TF. 1985. Pathological and positron
emission tomographic correlations in Alzheimer’s disease. Lancet.
1:228.
Friston KJ, Frith CD, Liddle PF, Frackowiak RS. 1993. Functional
connectivity: the principal-component analysis of large (PET) data
sets. J Cereb Blood Flow Metab. 13:5--14.
Giannakopoulos P, Hof PR, Michel JP, Guimon J, Bouras C. 1997.
Cerebral cortex pathology in aging and Alzheimer’s disease: a quantitative survey of large hospital-based geriatric and psychiatric
cohorts. Brain Res Brain Res Rev. 25:217--245.
Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak
RS. 2001. A voxel-based morphometric study of ageing in 465 normal
adult human brains. Neuroimage. 14:21--36.
Grady CL, Furey ML, Pietrini P, Horwitz B, Rapoport SI. 2001. Altered
brain functional connectivity and impaired short-term memory in
Alzheimer’s disease. Brain. 124:739--756.
Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J,
Kiefer B, Haase A. 2002. Generalized autocalibrating partially parallel
acquisitions (GRAPPA). Magn Reson Med. 47:1202--1210.
Hampel H, Teipel SJ, Alexander GE, Horwitz B, Teichberg D, Schapiro
MB, Rapoport SI. 1998. Corpus callosum atrophy is a possible
indicator of region- and cell type-specific neuronal degeneration in
Alzheimer disease: a magnetic resonance imaging analysis. Arch
Neurol. 55:193--198.
Hanyu H, Imon Y, Sakurai H, Iwamoto T, Takasaki M, Shindo H, Kakizaki
D, Abe K. 1999. Regional differences in diffusion abnormality in
cerebral white matter lesions in patients with vascular dementia of
the Binswanger type and Alzheimer’s disease. Eur J Neurol. 6:195--203.
Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE,
McAvoy M, Morris JC, Snyder AZ. 2004. Differential vulnerability of
anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion
tensor imaging. Cereb Cortex. 14:410--423.
Horwitz B, McIntosh AR, Haxby JV, Furey M, Salerno JA, Schapiro MB,
Rapoport SI, Grady CL. 1995. Network analysis of PET-mapped visual
pathways in Alzheimer type dementia. Neuroreport. 6:2287--2292.
Ishii K, Imamura T, Sasaki M, Yamaji S, Sakamoto S, Kitagaki H,
Hashimoto M, Hirono N, Shimomura T, Mori E. 1998. Regional
cerebral glucose metabolism in dementia with Lewy bodies and
Alzheimer’s disease. Neurology. 51:125--130.
Kantarci K, Jack CR, Jr., Xu YC, Campeau NG, O’Brien PC, Smith GE,
Ivnik RJ, Boeve BF, Kokmen E, Tangalos EG, Petersen RC. 2001. Mild
cognitive impairment and Alzheimer disease: regional diffusivity of
water. Radiology. 219:101--107.
Le Bihan D, Moonen CT, van Zijl PC, Pekar J, DesPres D. 1991. Measuring
random microscopic motion of water in tissues with MR imaging:
a cat brain study. J Comput Assist Tomogr. 15:19--25.
Lu M, Mitsias PD, Ewing JR, Soltanian-Zadeh H, Bagher-Ebadian H, Zhao
Q, Oja-Tebbe N, Patel SC, Chopp M. 2005. Predicting final infarct size
using acute and subacute multiparametric MRI measurements in
patients with ischemic stroke. J Magn Reson Imaging. 21:495--502.
McIntosh AR, Grady CL, Ungerleider LG, Haxby JV, Rapoport SI, Horwitz
B. 1994. Network analysis of cortical visual pathways mapped with
PET. J Neurosci. 14:655--666.
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM.
1984. Clinical diagnosis of Alzheimer’s disease: report of the
NINCDS-ADRDA Work Group under the auspices of Department
of Health and Human Services Task Force on Alzheimer’s Disease.
Neurology. 34:939--944.
Mendez MF, Cherrier MM, Cymerman JS. 1997. Hemispatial neglect on
visual search tasks in Alzheimer’s disease. Neuropsychiatr Neuropsychol Behav Neurol. 10:203--208.
Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. 1997.
Metabolic reduction in the posterior cingulate cortex in very early
Alzheimer’s disease. Ann Neurol. 42:85--94.
Cerebral Cortex October 2007, V 17 N 10 2281
Ohm TG, Muller H, Braak H, Bohl J. 1995. Close-meshed prevalence rates
of different stages as a tool to uncover the rate of Alzheimer’s
disease-related neurofibrillary changes. Neuroscience. 64:209--217.
Petrides M, Pandya DN. 1999. Dorsolateral prefrontal cortex: comparative
cytoarchitectonic analysis in the human and the macaque brain and
corticocortical connection patterns. Eur J Neurosci. 11:1011--1136.
Pfefferbaum A, Sullivan EV. 2003. Increased brain white matter diffusivity in normal adult aging: relationship to anisotropy and partial
voluming. Magn Reson Med. 49:953--961.
Polidori C, Zeng YC, Zaccheo D, Amenta F. 1993. Age-related changes in
the visual cortex: a review. Arch Gerontol Geriatr. 17:145--164.
Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. 1999. SENSE:
sensitivity encoding for fast MRI. Magn Reson Med. 42:952--962.
Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD. 1998. Neuroanatomical correlates of cognitive aging: evidence from structural
magnetic resonance imaging. Neuropsychology. 12:95--114.
Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ,
et al. 2000. One-year age changes in MRI brain volumes in older
adults. Cereb Cortex. 10:464--472.
Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, Benson M, Semple J,
Doddrell DM. 2000. Loss of connectivity in Alzheimer’s disease: an
evaluation of white matter tract integrity with colour coded MR
diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 69:528--530.
Sandson TA, Felician O, Edelman RR, Warach S. 1999. Diffusion-weighted
magnetic resonance imaging in Alzheimer’s disease. Dement Geriatr
Cogn Disord. 10:166--171.
Schaltenbrand G, Spuler H, Wahren W. 1972. The anatomy of the corpus
callosum determined electrically during stereotactic stimulation in
man. Confin Neurol. 34:169--175.
Schumacher EH, Hendricks MJ, D’Esposito M. 2005. Sustained involvement of a frontal-parietal network for spatial response selection with
practice of a spatial choice-reaction task. Neuropsychologia.
43:1444--1455.
Sjobeck M, Haglund M, Englund E. 2006. White matter mapping in
Alzheimer’s disease: A neuropathological study. Neurobiol Aging.
27:673--680.
2282 Fiber Connections in Alzheimer’s Disease
d
Sydykova et al.
Stahl R, Dietrich O, Teipel S, Hampel H, Reiser MF, Schoenberg SO. 2003.
Diffusion tensor imaging zur Erfassung axonaler Degeneration bei
Morbus Alzheimer. Radiologe. 43:566--575.
Sugihara S, Kinoshita T, Matsusue E, Fujii S, Ogawa T. 2004. Usefulness of
diffusion tensor imaging of white matter in Alzheimer disease and
vascular dementia. Acta Radiol. 45:658--663.
Sullivan EV, Adalsteinsson E, Hedehus M, Ju C, Moseley M, Lim KO,
Pfefferbaum A. 2001. Equivalent disruption of regional white matter
microstructure in ageing healthy men and women. Neuroreport.
12:99--104.
Sunderland S. 1940. The distribution of commissural fibres in the
corpus callosum in the macaque monkey. J Neurol Psychiatry. 3:
9--18.
Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, Tohgi H. 2002.
Selective reduction of diffusion anisotropy in white matter of
Alzheimer disease brains measured by 3.0 Tesla magnetic resonance
imaging. Neurosci Lett. 332:45--48.
Talairach J, Tournoux P. 1988. Co-planar stereotaxic atlas of the human
brain. New York: Thieme Medical Publishers.
Tan YL, Chen BH, Yang JD, Zhang J, Wang YC, Chai SH, Wang ZY, Li QH.
1991. Localization of functional projections from corpus callosum to
cerebral cortex. Chin Med J (ENGL ED). 104:851--857.
Teipel SJ, Flatz WH, Heinsen H, Bokde AL, Schoenberg SO, Stockel S,
Dietrich O, Reiser MF, Moller HJ, Hampel H. 2005. Measurement of
basal forebrain atrophy in Alzheimer’s disease using MRI. Brain.
128:2626--2644.
Teipel SJ, Hampel H, Alexander GE, Schapiro MB, Horwitz B, Teichberg
D, Daley E, Hippius H, Moller HJ, Rapoport SI. 1998. Dissociation
between corpus callosum atrophy and white matter pathology in
Alzheimer’s disease. Neurology. 51:1381--1385.
Verheul HB, Berkelbach van der Sprenkel JW, Tulleken CA, Tamminga
KS, Nicolay K. 1992. Temporal evolution of focal cerebral ischemia in
the rat assessed by T2-weighted and diffusion-weighted magnetic
resonance imaging. Brain Topogr. 5:171--176.
West RL. 1996. An application of prefrontal cortex function theory to
cognitive aging. Psychol Bull. 120:272--292.