Abstract

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 (±standard deviation) age of 68.3 years (±11.5) and mean Mini Mental State Examination (MMSE) score of 21.8 (±4.8) were recruited. There were 13 control subjects with a mean age of 66.7 years (±6.4) and MMSE of 29.1 (±0.7). 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.

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 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 AD-related 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.

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: χ2 = 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 × 1.1 × 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 × 256 and a field of view of 270 × 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 × 208 and a filed of view of 230 × 187 mm2, which resulted in a voxel size of 0.9 × 0.9 × 3.6 mm3.

The diffusion-weighted data were collected with a spin-echo single-shot 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 × 128 with a field of view of 230 × 230 mm2, the resulting voxel size was 1.8 × 1.8 × 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.

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).

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).

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 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 T1-weighted 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 T1-weighted 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.

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).

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.

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.

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) T9 
   x y z  
Frontal lobe       
    Superior frontal gyrus Left −43 17 47 5.25 
    Superior frontal gyrus Left −20 24 53 5.14 
    Precentral gyrus Right 31 −14 70 6.15 
    Middle frontal gyrus Left −35 25 40 7.23 
Parietal lobe       
    Superior parietal lobule Left −13 −72 59 6.38 
    Inferior parietal lobule Left 39 −40 −65 39 5.58 
    Precuneus Left −12 −50 41 5.57 
    Precuneus Left −14 −69 40 4.6 
    Precuneus Left 19 −40 −75 39 5.84 
Region Side BA Coordinates (mm) T9 
   x y z  
Frontal lobe       
    Superior frontal gyrus Left −43 17 47 5.25 
    Superior frontal gyrus Left −20 24 53 5.14 
    Precentral gyrus Right 31 −14 70 6.15 
    Middle frontal gyrus Left −35 25 40 7.23 
Parietal lobe       
    Superior parietal lobule Left −13 −72 59 6.38 
    Inferior parietal lobule Left 39 −40 −65 39 5.58 
    Precuneus Left −12 −50 41 5.57 
    Precuneus Left −14 −69 40 4.6 
    Precuneus Left 19 −40 −75 39 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 = right hemisphere); y, the anterior to posterior distance relative to the anterior commissure (positive = anterior); z, superior to inferior distance relative to the anterior–posterior commissure line (positive = superior). T9, T value with 9 degrees of freedom; BA, Brodmann area.

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).

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) T9 
   x y z  
Frontal lobe       
    Superior frontal gyrus Right 30 −7 68 7.03 
    Superior frontal gyrus Left −15 72 6.13 
    Middle frontal gyrus Left −43 11 32 8.22 
    Middle frontal gyrus Right 15 −3 57 6.83 
    Middle frontal gyrus Right 33 18 33 5.13 
Temporal lobe       
    Superior temporal gyrus Left 22 −56 −26 10.7 
    Superior temporal gyrus Left 22 −57 −40 10 9.69 
    Superior temporal gyrus Right 22 59 −22 8.67 
    Inferior temporal gyrus Right 20 55 −29 −18 7.99 
    Inferior temporal gyrus Right 19 46 −53 7.05 
    Inferior temporal gyrus Right 37 47 −48 −7 6.45 
Parietal lobe       
    Postcentral gyrus Right 43 51 −17 21 7.1 
    Postcentral gyrus Right 33 −35 49 4.77 
    Inferior parietal lobule Right 40 55 −25 29 5.12 
    Subgyral Right 40 27 −38 55 5.94 
    Precuneus Right 10 −65 40 4.68 
Occipital lobe       
    Precuneus Right 31 −68 18 8.43 
    Cuneus Right 23 −75 14 5.86 
Region Side BA Coordinates (mm) T9 
   x y z  
Frontal lobe       
    Superior frontal gyrus Right 30 −7 68 7.03 
    Superior frontal gyrus Left −15 72 6.13 
    Middle frontal gyrus Left −43 11 32 8.22 
    Middle frontal gyrus Right 15 −3 57 6.83 
    Middle frontal gyrus Right 33 18 33 5.13 
Temporal lobe       
    Superior temporal gyrus Left 22 −56 −26 10.7 
    Superior temporal gyrus Left 22 −57 −40 10 9.69 
    Superior temporal gyrus Right 22 59 −22 8.67 
    Inferior temporal gyrus Right 20 55 −29 −18 7.99 
    Inferior temporal gyrus Right 19 46 −53 7.05 
    Inferior temporal gyrus Right 37 47 −48 −7 6.45 
Parietal lobe       
    Postcentral gyrus Right 43 51 −17 21 7.1 
    Postcentral gyrus Right 33 −35 49 4.77 
    Inferior parietal lobule Right 40 55 −25 29 5.12 
    Subgyral Right 40 27 −38 55 5.94 
    Precuneus Right 10 −65 40 4.68 
Occipital lobe       
    Precuneus Right 31 −68 18 8.43 
    Cuneus Right 23 −75 14 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 = right hemisphere); y, the anterior to posterior distance relative to the anterior commissure (positive = anterior); z, superior to inferior distance relative to the anterior–posterior commissure line (positive = superior). T9, T value with 9 degrees of freedom; BA, Brodmann area.

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.

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 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 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 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 T2-weighted 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.

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 Ludwig-Maximilian 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.

References

Andersen
RA
Asanuma
C
Essick
G
Siegel
RM
Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule
J Comp Neurol
 , 
1990
, vol. 
296
 (pg. 
65
-
113
)
Ashburner
J
Friston
KJ
Voxel-based morphometry—the methods
Neuroimage
 , 
2000
, vol. 
11
 (pg. 
805
-
821
)
Ashburner
J
Neelin
P
Collins
DL
Evans
A
Friston
K
Incorporating prior knowledge into image registration
Neuroimage
 , 
1997
, vol. 
6
 (pg. 
344
-
352
)
Awad
IA
Johnson
PC
Spetzler
RF
Hodak
JA
Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. II. Postmortem pathological correlations
Stroke
 , 
1986
, vol. 
17
 (pg. 
1090
-
1097
)
Awad
IA
Spetzler
RF
Hodak
JA
Awad
CA
Carey
R
Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. I. Correlation with age and cerebrovascular risk factors
Stroke
 , 
1986
, vol. 
17
 (pg. 
1084
-
1089
)
Basser
PJ
Pierpaoli
C
Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI
J Magn Reson B
 , 
1996
, vol. 
111
 (pg. 
209
-
219
)
Bozzali
M
Falini
A
Franceschi
M
Cercignani
M
Zuffi
M
Scotti
G
Comi
G
Filippi
M
White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging
J Neurol Neurosurg Psychiatry
 , 
2002
, vol. 
72
 (pg. 
742
-
746
)
Braffman
BH
Zimmerman
RA
Trojanowski
JQ
Gonatas
NK
Hickey
WF
Schlaepfer
WW
Brain MR: pathologic correlation with gross and histopathology. 1. Lacunar infarction and Virchow-Robin spaces
AJR Am J Roentgenol
 , 
1988
, vol. 
151
 (pg. 
551
-
558
)
Braffman
BH
Zimmerman
RA
Trojanowski
JQ
Gonatas
NK
Hickey
WF
Schlaepfer
WW
Brain MR: pathologic correlation with gross and histopathology. 2. Hyperintense white-matter foci in the elderly
AJR Am J Roentgenol
 , 
1988
, vol. 
151
 (pg. 
559
-
566
)
Brodal
A
Neurological anatomy in relation to clinical medicine
1981
New York
Oxford University Press
(pg. 
5
-
10
)
Brun
A
Englund
E
A white matter disorder in dementia of the Alzheimer type: a pathoanatomical study
Ann Neurol
 , 
1986
, vol. 
19
 (pg. 
253
-
262
)
Cavada
C
Goldman-Rakic
PS
Posterior parietal cortex in rhesus monkey: II. Evidence for segregated corticocortical networks linking sensory and limbic areas with the frontal lobe
J Comp Neurol
 , 
1989
, vol. 
287
 (pg. 
422
-
445
)
Chimowitz
MI
Estes
ML
Furlan
AJ
Awad
IA
Further observations on the pathology of subcortical lesions identified on magnetic resonance imaging
Arch Neurol
 , 
1992
, vol. 
49
 (pg. 
747
-
752
)
Choi
SJ
Lim
KO
Monteiro
I
Reisberg
B
Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: a preliminary study
J Geriatr Psychiatry Neurol
 , 
2005
, vol. 
18
 (pg. 
12
-
19
)
Collette
F
Olivier
L
Van der Linden
M
Laureys
S
Delfiore
G
Luxen
A
Salmon
E
Involvement of both prefrontal and inferior parietal cortex in dual-task performance
Brain Res Cogn Brain Res
 , 
2005
, vol. 
24
 (pg. 
237
-
251
)
Corbetta
M
Frontoparietal cortical networks for directing attention and the eye to visual locations: identical, independent, or overlapping neural systems?
Proc Natl Acad Sci USA
 , 
1998
, vol. 
95
 (pg. 
831
-
838
)
Cronin-Golomb
A
Rizzo
JF
Corkin
S
Growdon
JH
Visual function in Alzheimer's disease and normal aging
Ann N Y Acad Sci
 , 
1991
, vol. 
640
 (pg. 
28
-
35
)
de Lacoste
MC
Kirkpatrick
JB
Ross
ED
Topography of the human corpus callosum
J Neuropathol Exp Neurol
 , 
1985
, vol. 
44
 (pg. 
578
-
591
)
de Lacoste
MC
Woodward
DJ
The corpus callosum in nonhuman primates. Determinants of size
Brain Behav Evol
 , 
1988
, vol. 
31
 (pg. 
318
-
323
)
Demetriades
AK
Functional neuroimaging in Alzheimer's type dementia
J Neurol Sci
 , 
2002
, vol. 
203–204
 (pg. 
247
-
251
)
Fellgiebel
A
Wille
P
Muller
MJ
Winterer
G
Scheurich
A
Vucurevic
G
Schmidt
LG
Stoeter
P
Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffusion tensor imaging study
Dement Geriatr Cogn Disord
 , 
2004
, vol. 
18
 (pg. 
101
-
108
)
Friedland
RP
Brun
A
Budinger
TF
Pathological and positron emission tomographic correlations in Alzheimer's disease
Lancet
 , 
1985
, vol. 
1
 pg. 
228
 
Friston
KJ
Frith
CD
Liddle
PF
Frackowiak
RS
Functional connectivity: the principal-component analysis of large (PET) data sets
J Cereb Blood Flow Metab
 , 
1993
, vol. 
13
 (pg. 
5
-
14
)
Giannakopoulos
P
Hof
PR
Michel
JP
Guimon
J
Bouras
C
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
 , 
1997
, vol. 
25
 (pg. 
217
-
245
)
Good
CD
Johnsrude
IS
Ashburner
J
Henson
RN
Friston
KJ
Frackowiak
RS
A voxel-based morphometric study of ageing in 465 normal adult human brains
Neuroimage
 , 
2001
, vol. 
14
 (pg. 
21
-
36
)
Grady
CL
Furey
ML
Pietrini
P
Horwitz
B
Rapoport
SI
Altered brain functional connectivity and impaired short-term memory in Alzheimer's disease
Brain
 , 
2001
, vol. 
124
 (pg. 
739
-
756
)
Griswold
MA
Jakob
PM
Heidemann
RM
Nittka
M
Jellus
V
Wang
J
Kiefer
B
Haase
A
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Magn Reson Med
 , 
2002
, vol. 
47
 (pg. 
1202
-
1210
)
Hampel
H
Teipel
SJ
Alexander
GE
Horwitz
B
Teichberg
D
Schapiro
MB
Rapoport
SI
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
 , 
1998
, vol. 
55
 (pg. 
193
-
198
)
Hanyu
H
Imon
Y
Sakurai
H
Iwamoto
T
Takasaki
M
Shindo
H
Kakizaki
D
Abe
K
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
 , 
1999
, vol. 
6
 (pg. 
195
-
203
)
Head
D
Buckner
RL
Shimony
JS
Williams
LE
Akbudak
E
Conturo
TE
McAvoy
M
Morris
JC
Snyder
AZ
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
 , 
2004
, vol. 
14
 (pg. 
410
-
423
)
Horwitz
B
McIntosh
AR
Haxby
JV
Furey
M
Salerno
JA
Schapiro
MB
Rapoport
SI
Grady
CL
Network analysis of PET-mapped visual pathways in Alzheimer type dementia
Neuroreport
 , 
1995
, vol. 
6
 (pg. 
2287
-
2292
)
Ishii
K
Imamura
T
Sasaki
M
Yamaji
S
Sakamoto
S
Kitagaki
H
Hashimoto
M
Hirono
N
Shimomura
T
Mori
E
Regional cerebral glucose metabolism in dementia with Lewy bodies and Alzheimer's disease
Neurology
 , 
1998
, vol. 
51
 (pg. 
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
Mild cognitive impairment and Alzheimer disease: regional diffusivity of water
Radiology
 , 
2001
, vol. 
219
 (pg. 
101
-
107
)
Le Bihan
D
Moonen
CT
van Zijl
PC
Pekar
J
DesPres
D
Measuring random microscopic motion of water in tissues with MR imaging: a cat brain study
J Comput Assist Tomogr
 , 
1991
, vol. 
15
 (pg. 
19
-
25
)
Lu
M
Mitsias
PD
Ewing
JR
Soltanian-Zadeh
H
Bagher-Ebadian
H
Zhao
Q
Oja-Tebbe
N
Patel
SC
Chopp
M
Predicting final infarct size using acute and subacute multiparametric MRI measurements in patients with ischemic stroke
J Magn Reson Imaging
 , 
2005
, vol. 
21
 (pg. 
495
-
502
)
McIntosh
AR
Grady
CL
Ungerleider
LG
Haxby
JV
Rapoport
SI
Horwitz
B
Network analysis of cortical visual pathways mapped with PET
J Neurosci
 , 
1994
, vol. 
14
 (pg. 
655
-
666
)
McKhann
G
Drachman
D
Folstein
M
Katzman
R
Price
D
Stadlan
EM
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
 , 
1984
, vol. 
34
 (pg. 
939
-
944
)
Mendez
MF
Cherrier
MM
Cymerman
JS
Hemispatial neglect on visual search tasks in Alzheimer's disease
Neuropsychiatr Neuropsychol Behav Neurol
 , 
1997
, vol. 
10
 (pg. 
203
-
208
)
Minoshima
S
Giordani
B
Berent
S
Frey
KA
Foster
NL
Kuhl
DE
Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease
Ann Neurol
 , 
1997
, vol. 
42
 (pg. 
85
-
94
)
Ohm
TG
Muller
H
Braak
H
Bohl
J
Close-meshed prevalence rates of different stages as a tool to uncover the rate of Alzheimer's disease-related neurofibrillary changes
Neuroscience
 , 
1995
, vol. 
64
 (pg. 
209
-
217
)
Petrides
M
Pandya
DN
Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns
Eur J Neurosci
 , 
1999
, vol. 
11
 (pg. 
1011
-
1136
)
Pfefferbaum
A
Sullivan
EV
Increased brain white matter diffusivity in normal adult aging: relationship to anisotropy and partial voluming
Magn Reson Med
 , 
2003
, vol. 
49
 (pg. 
953
-
961
)
Polidori
C
Zeng
YC
Zaccheo
D
Amenta
F
Age-related changes in the visual cortex: a review
Arch Gerontol Geriatr
 , 
1993
, vol. 
17
 (pg. 
145
-
164
)
Pruessmann
KP
Weiger
M
Scheidegger
MB
Boesiger
P
SENSE: sensitivity encoding for fast MRI
Magn Reson Med
 , 
1999
, vol. 
42
 (pg. 
952
-
962
)
Raz
N
Gunning-Dixon
FM
Head
D
Dupuis
JH
Acker
JD
Neuroanatomical correlates of cognitive aging: evidence from structural magnetic resonance imaging
Neuropsychology
 , 
1998
, vol. 
12
 (pg. 
95
-
114
)
Resnick
SM
Goldszal
AF
Davatzikos
C
Golski
S
Kraut
MA
Metter
EJ
, et al.  . 
One-year age changes in MRI brain volumes in older adults
Cereb Cortex
 , 
2000
, vol. 
10
 (pg. 
464
-
472
)
Rose
SE
Chen
F
Chalk
JB
Zelaya
FO
Strugnell
WE
Benson
M
Semple
J
Doddrell
DM
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
 , 
2000
, vol. 
69
 (pg. 
528
-
530
)
Sandson
TA
Felician
O
Edelman
RR
Warach
S
Diffusion-weighted magnetic resonance imaging in Alzheimer's disease
Dement Geriatr Cogn Disord
 , 
1999
, vol. 
10
 (pg. 
166
-
171
)
Schaltenbrand
G
Spuler
H
Wahren
W
The anatomy of the corpus callosum determined electrically during stereotactic stimulation in man
Confin Neurol
 , 
1972
, vol. 
34
 (pg. 
169
-
175
)
Schumacher
EH
Hendricks
MJ
D'Esposito
M
Sustained involvement of a frontal-parietal network for spatial response selection with practice of a spatial choice-reaction task
Neuropsychologia
 , 
2005
, vol. 
43
 (pg. 
1444
-
1455
)
Sjobeck
M
Haglund
M
Englund
E
White matter mapping in Alzheimer's disease: A neuropathological study
Neurobiol Aging
 , 
2006
, vol. 
27
 (pg. 
673
-
680
)
Stahl
R
Dietrich
O
Teipel
S
Hampel
H
Reiser
MF
Schoenberg
SO
Diffusion tensor imaging zur Erfassung axonaler Degeneration bei Morbus Alzheimer
Radiologe
 , 
2003
, vol. 
43
 (pg. 
566
-
575
)
Sugihara
S
Kinoshita
T
Matsusue
E
Fujii
S
Ogawa
T
Usefulness of diffusion tensor imaging of white matter in Alzheimer disease and vascular dementia
Acta Radiol
 , 
2004
, vol. 
45
 (pg. 
658
-
663
)
Sullivan
EV
Adalsteinsson
E
Hedehus
M
Ju
C
Moseley
M
Lim
KO
Pfefferbaum
A
Equivalent disruption of regional white matter microstructure in ageing healthy men and women
Neuroreport
 , 
2001
, vol. 
12
 (pg. 
99
-
104
)
Sunderland
S
The distribution of commissural fibres in the corpus callosum in the macaque monkey
J Neurol Psychiatry
 , 
1940
, vol. 
3
 (pg. 
9
-
18
)
Takahashi
S
Yonezawa
H
Takahashi
J
Kudo
M
Inoue
T
Tohgi
H
Selective reduction of diffusion anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla magnetic resonance imaging
Neurosci Lett
 , 
2002
, vol. 
332
 (pg. 
45
-
48
)
Talairach
J
Tournoux
P
Co-planar stereotaxic atlas of the human brain
1988
New York
Thieme Medical Publishers
Tan
YL
Chen
BH
Yang
JD
Zhang
J
Wang
YC
Chai
SH
Wang
ZY
Li
QH
Localization of functional projections from corpus callosum to cerebral cortex
Chin Med J (ENGL ED)
 , 
1991
, vol. 
104
 (pg. 
851
-
857
)
Teipel
SJ
Flatz
WH
Heinsen
H
Bokde
AL
Schoenberg
SO
Stockel
S
Dietrich
O
Reiser
MF
Moller
HJ
Hampel
H
Measurement of basal forebrain atrophy in Alzheimer's disease using MRI
Brain
 , 
2005
, vol. 
128
 (pg. 
2626
-
2644
)
Teipel
SJ
Hampel
H
Alexander
GE
Schapiro
MB
Horwitz
B
Teichberg
D
Daley
E
Hippius
H
Moller
HJ
Rapoport
SI
Dissociation between corpus callosum atrophy and white matter pathology in Alzheimer's disease
Neurology
 , 
1998
, vol. 
51
 (pg. 
1381
-
1385
)
Verheul
HB
Berkelbach van der Sprenkel
JW
Tulleken
CA
Tamminga
KS
Nicolay
K
Temporal evolution of focal cerebral ischemia in the rat assessed by T2-weighted and diffusion-weighted magnetic resonance imaging
Brain Topogr
 , 
1992
, vol. 
5
 (pg. 
171
-
176
)
West
RL
An application of prefrontal cortex function theory to cognitive aging
Psychol Bull
 , 
1996
, vol. 
120
 (pg. 
272
-
292
)