1 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, and 2 Alzheimer Memorial Center, Dementia Research Section and Memory Clinic, Department of Psychiatry, Ludwig-Maximilian University, Munich, Germany
Address correspondence to Alan C. Evans, McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal QC Canada H3A 2B4. Email: alan{at}bic.mni.mcgill.ca.
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Abstract |
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Key Words: Alzheimer's disease automated 3-D image analysis cortical thickness MRI neuroinformatics neuropathology
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Introduction |
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MRI provides insight into the temporal sequence of AD-related regional atrophy, using region specific as well as global search algorithms. Within region specific protocols, recent effort has focused on using MRI to detect early morphological changes in AD pathology; attention has specifically focused on developing precise segmentation protocols for the early affected temporal lobe structures such as the entorhinal cortex (EC) or the hippocampus (HC) (Van Hoesen, 1995; de Leon et al., 1997
; Mori et al., 1997
; Krasuski et al., 1998
; De Toledo-Morrell et al., 2000
).
In comparison, whole brain imaging analysis allows detection of changes throughout the entire cerebrum. So far, the methodology of whole brain analysis approaches has been restricted to voxel-based morphometry (VBM) (Ashburner and Friston, 2000), implemented volumetrically (Baron et al., 2001
) across the cortex (Thompson, 1989
; Thompson et al., 2001
), or as deformation analysis in longitudinal datasets (Good et al., 2002
). In VBM, the concepts of gray matter density and gray matter concentration are central for the interpretation of the results; however, voxel density at any one point for any one subject is, unlike cortical thickness, meaningless.
Using specific algorithms to analyze cortical thickness across the entire cortex is a complementary method to the established research paradigms, offering a direct quantitative index of cortical atrophy that can be applied to single subjects and to group analysis. Cortical atrophy is reflected in a loss of gray matter which will result in a reduction of cortical thickness. Measuring cortical thickness across the entire cerebrum establishes a marker for the AD-related cortical atrophy.
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Materials and Methods |
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MRI examinations were performed on a 1.5 T Siemens Magnetom Vision MRI scanner (Siemens, Erlangen, Germany). All subjects were investigated with a volumetric T1-weighted sagittal oriented MRI sequence (TR = 11.6 ms, TE = 4.9 ms, resolution = 0.94 x 0.94 x 1.2 mm). The rectangular field of view (FOV) for the sagittal images was 256 mm (SI) x 204 mm (AP). Additionally, an axial-oriented fast FLAIR sequence (TR = 9000 ms, TE = 110 ms, resolution = 0.94 x 0.94 x 6 mm) was obtained. For the purpose of this study, only the T1-weighted images entered further processing. The native MRI were registered into standardized stereotaxic space using a linear transformation (Collins et al., 1994). Simultaneously, the images were corrected for non-uniformity artifacts (Sled et al., 1998
). The registered and corrected volumes were segmented into white matter, gray matter, cerebrospinal fluid and background using an advanced neural net classifier (Zijdenbos et al., 2002
). The white and gray matter surface were then fitted using deformable models (MacDonald et al., 2000
), resulting in two surfaces with 81 920 polygons each. The surface deformation algorithm works by first fitting the white matter surface, then expanding outward to find the gray matter CSF intersection. One characteristic of this procedure is that each vertex of the white matter surface is closely related to its gray matter surface counterpart; cortical thickness can thus be defined as the distance between these linked vertices. The relevant parts of the processing pipeline are shown schematically in Figure 1.
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Statistical analysis was performed at every vertex, regressing cortical thickness against clinical state, MMSE scores, or time from baseline. Multiple time-points were used where available in order to provide greater stability in the estimation of the fixed effects than a purely cross-sectional model would have allowed. Linear mixed models using the restricted maximum likelihood (REML) estimation method were employed to account for the within subject correlations present due to the repeated acquisitions in the AD cohort (Pinheiro and Bates, 2000). Mixed models extend linear models by incorporating random effects, which can best be regarded as an additional error term. The model used is the following:
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To illustrate with an example, the matrices for the MMSE analysis would be:
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The resulting statistical maps were thresholded using the false discovery rate (FDR) theory (Genovese et al., 2002) at a q value of 0.05 after pooling the P-values from all regressions run in this analysis. The interpretation of the maps is therefore that, on average, 5% of the results shown across all regressions are false positives. The figures also show the regression slope, which is the change in millimeters with each unit of measurement (group difference, point of MMSE, or in years).
Cortical thickness methods applied to human magnetic resonance images can be divided into three broad categories. The first measures thickness on a voxel by voxel level, and is illustrated by two papers solving a partial differential equation across the cortex (Jones et al., 2000; Yezzi and Prince, 2003
). The second set of methods use advanced versions of the marching cubes algorithm, creating two cortical surfaces with variable tesselation (cf. Zeng et al., 1999
; Miller et al., 2000
). The last group uses deformable models to create white and grey matter cortices, and includes our work outlined above and that of Fischl and colleagues (Fischl and Dale, 2000
; Rosas et al., 2002
). One characteristic of using deformable models is that the number of polygons will always be identical across subjects, allowing for the easy creation of a surface coordinate system that can be used to run statistical analyses of cortical thickness at every vertex of the surface.
Our cortical thickness algorithm has been validated by comparison to manual measurements (Kabani et al., 2001). Furthermore, extensive analysis of the precision of our cortical thickness analysis framework was recently preformed (Lerch and Evans, 2004
). Two tests were run: a study of reliability, where 19 scans of the same subject were processed separately to assess for methodological variability, and a population simulation. In the latter, 50 normal scans were processed. Twenty-five of them had the right superior temporal gyrus (rSTG) artificially thinned by one six-neighbor dilation of the white matter in that region, and that change recovered statistically. In the 19 repeat scans the mean standard deviation across all vertices was 0.27 mm. The population simulation resulted in 93% sensitivity in areas of perfect overlap of the rSTG along with 100% specificity (no single vertex outside of the rSTG was found to be significant between the two groups). This simulation study provides confidence that results shown using the AD data described in this paper are valid.
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Results |
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The results from the analysis clearly show significant differences in cortical thickness between the two groups. The average thickness across the entire cortex was significantly thinner in AD patients (3.1 ± 0.28 mm) compared with controls (3.74 ± 0.32 mm; t = 3.8, P < 0.0007), resulting in an average difference of 0.47 mm after removal of age effect by regression. Furthermore, the resulting maps of atrophy clearly show region specificity of thickness decline in AD. The most significant changes were found in the medial temporal lobes, the anterior and posterior cingulate region, the frontal lobes, the inferior parietal lobes, the orbitofrontal cortex, and the visual association cortex (Figs 24, Table 2). Most of these effects were found bilaterally except for the insula region, where only the left hemisphere appeared significant.
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The entire medial temporal lobe appeared to be severely affected. The average difference between cortical thickness in AD and in the control group ranged from 0.5 to 1.3 mm, with the strongest differences emerging in the area of the parahippocampal gyrus (PHG). This finding is consistent with the results from recent studies suggesting that the PHG, especially the entorhinal cortex (EC), is affected early in the course of AD (Krasuski et al., 1998; Van Hoesen et al., 2000
; Callen et al., 2001
). To investigate cortical thickness decline in this area in our subjects more closely, manual segmentation of the structures of the PHG was used to trace the EC in the MRI of all subjects using a recently developed protocol (Pruessner et al., 2002
). The labels of the EC from all subjects were then used to create a customized probabilistic map of the EC for the subjects in this study, which was overlaid onto the t-statistics map from the thickness analysis. This allowed an accurate evaluation of the decline in cortical thickness within the EC in our study sample. The result of this procedure is shown in Figure 5, indicating that the most striking difference between the two groups occurred in the anterior portion of the EC in the left hemisphere, with a difference of 1.25 ± 0.22 mm in cortical thickness between the two groups. The lateral temporal lobes also showed significant interaction with clinical state, with a peak of 0.8 ± 0.22 mm in the left medial temporal gyrus (BA 21).
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Regression of Cortical Thickness versus MMSE
In order to investigate whether cortical thickness measures vary with disease progression in the AD patients, a regression of cortical thickness against the MMSE score was performed in the AD patients at every vertex of the surface model. Mean cortical thickness was marginally associated with MMSE scores (t = 2.24, P = 0.06). Regionally, however, it was found that lower MMSE scores were associated with significantly thinner cortical thickness in the bilateral PHG, the left superior temporal gyrus, left insula, and left anterior cingulate gyrus (Fig. 6).
Regression of Cortical Thickness versus Progression
Further analysis of the effect of disease progression was performed by regressing cortical thickness against the time difference from baseline in the follow-up scans within the AD cohort. There was a significant correlation between time from baseline and thickness (t = 3.47, P = 0.006) featuring a decline of 0.18 mm per year. Significant regional correlations were found in the anterior temporal lobes including the para-hippocampal gyrus, the anterior frontal lobes, and the anterior cingulate (Fig. 7). A post-hoc test was also conducted to ascertain the rate of thinning in cortical areas that featured an initial group difference versus those where the Alzheimer's patients did not have significantly thinner cortex than the normal controls. Both these areas declined significantly with time from baseline; the rate was faster in cortical regions which were also significantly thinner in the patients (0.21mm per year, t = 4, P = 0.004) than the rest of the cortex (0.16mm per year, t = 3.4, P = 0.009).
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Discussion |
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Our results clearly show AD related decline in cortical thickness in multiple areas of the brain, many of which have been reported in previous MR studies. Cortical thickness of the medial temporal lobes was most severely reduced in AD patients, and, within the medial temporal lobes, the parahippocampal gyrus was most affected. This is consistent with previous MR and histopathological studies showing that this region of the brain is affected early and profoundly in the course of the disease (Juottonen et al., 1999; Xu et al., 2000
). The results from this study extend previous findings by showing that in the left hemisphere, the posterior portions of the PHG also seem to be strongly affected in AD. For the anterior portion of the PHG, in the area of the entorhinal cortex, the most significant differences occurred in the left hemisphere, though the effects were largely bilateral.
Cortical atrophy in AD is not limited to the medial temporal lobe; the remaining limbic system, the lateral temporal lobes, and certain associative visual areas correlate significantly with the disease as well. The finding of significant cortical thickness decline in the area of the visual association cortex is consistent with other studies showing this region to be strongly affected by AD (Cronin-Golomb et al., 1991; Thulborn et al., 2000
), as are the results in the lateral temporal lobes (Busatto et al., 2003
). Within the limbic system the orbito-frontal cortex (Van Hoesen et al., 2000
) and the anterior and posterior cingulate cortices (Valla et al., 2001
; Matsuda et al., 2002
) have also been shown to correlate with the disease in ways that support the results of this study. Combined with spared areas of the cortex, such as the motor, sensory and primary visual cortices (BA17) (Buckner et al., 2000
; Rizzo et al., 2000
; Prvulovic et al., 2002
), a pattern of atrophy in AD emerges that is focal to multiple cortical areas while centered on the medial temporal lobes.
The results from the MMSE regression within the AD group indicate that the decline in the PHG is correlated with the MMSE score (Fields et al., 1992). Significant associations between MMSE scores and cortical thickness were found in bilateral medial temporal areas as well as temporal, anterior cingulate, and insular regions of the left hemisphere. The results demonstrate that cortical thickness analysis can be used to link clinical information with decline in particular cortical areas when available. In the case of MMSE scores, the significant findings with cortical atrophy were left hemisphere dominant. Progression as measured by time from baseline in repeated scans showed a more significant effect than the MMSE analysis, indicating continuing atrophy that is not entirely captured by changes in cognitive state as captured by the MMSE. Progressive atrophy was particularly strong in the anterior frontal and temporal lobes as well as the posterior cingulate. Cortical areas which were significantly thinner in the patients thinned at a faster rate than those that were not, though this difference was small and would therefore suggest that the entire cortex is declining with disease progression. The longitudinal aspect of our analysis should be explored in greater detail and compared to non-linear registration methods that have recently been used in the analysis of AD populations and normal aging (Fox et al., 2000
; Scahill et al., 2002
, 2003
).
AD is commonly subdivided into six stages representing advancing pathology as defined by the progression of neurofibrillary tangles (NFTs) and senile placques (Braak and Braak, 1991, 1996
). The patients used in this study were diagnosed with probable AD according to NINCDS-ARDRA criteria and thus believed to be in stage V or VI (Nagy et al., 1999
). These two end stages, known as the neocortical stages, feature plaque and NFT involvement in virtually all subdivisions of the cerebral cortex with a particular emphasis on association areas and the medial temporal lobes. NFT presence is strongly correlated with neuronal loss and cortical atrophy (Gomez-Isla et al., 1997
; Grignon et al., 1998
). Moreover, increased duration of NFT presence is associated with increased atrophy (Grignon et al., 1998
). This leads to the hypothesis that, in patients diagnosed with probable AD, cortical areas involved earlier in the disease progression will show greater atrophy than areas involved later (Gomez-Isla et al., 1997
). Our results, which reveal greatest MRI detectable atrophy in areas implicated earlier (such as the entorhinal cortex), largely support this hypothesis. The ability to stage AD based on in-vivo imaging should be investigated further. Moreover, some of the findings in this study may be due to the small sample size, since occasional atypical representation of AD (such as the visuo-spatial variant; cf. Cronin-Golomb et al., 1991
) may have an undue influence on our results. This analysis should therefore be replicated in a larger sample.
The major difference between previous findings and the results obtained in this study lies in the use of the cortical thickness analysis technique. This strategy has a number of advantages when compared with other MR methods used to investigate AD with MRI. Cortical thickness analysis allows searching for associations between the depth of the cortex and sociodemographic, clinical or psychological variables across the entire surface of the brain. It shares this advantage with other global search algorithms like VBM and deformation field analysis. Unlike VBM, however, it is based on the description of the actual thickness of the cortex in millimeters and thus allows meaningful quantitative description of the results (difference in cortical thickness in millimeters). The major disadvantage is that the focus is entirely on the cortex; changes in subcortical structures, white matter or cerebro-spinal fluid will not be picked up.
Finally, cortical thickness analysis is fully automated and thus reaches 100% operator independence. The results are operator-independent and do not rely on the correct interpretation of regional anatomical variations, in contrast to manual segmentation protocols.
Cortical thickness analysis may serve as a surrogate marker for the neuronal loss that accompanies the histopathological changes in the cortex which occur in AD and their temporal ordering. For example, the highly significant findings in the PHG, in the area of the EC, support the view (Juottonen et al., 1999; Xu et al., 2000
) about the importance of medial temporal lobe morphometry in the diagnosis of AD. Future studies can compare the relative efficacy of cortical thickness measures with volumetric analyses of the hippocampus (Pruessner et al., 2001
).
Continuing work will investigate associations between cortical thickness and other sociodemographic or clinical variables. Owing to its fully automatic implementation, the method can be used to characterize the healthy and pathological ranges of cortical thickness in specific age groups. The development of normative data for specific age and disease groups will allow direct comparison of individual subjects with cortical thickness norms in health and disease. This process has the potential of aiding in the early diagnosis of dementia. Furthermore, unlike commonly used region of interest measures, cortical thickness analysis provides coverage of the complete cerebrum, and could thus be used for differential diagnosis of the various types of dementia as well. Finally, future studies will have to show the value of this new method in monitoring of the progression of dementia across the entire cortex.
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Acknowledgments |
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