Department of Radiology, Johns Hopkins University School of Medicine and , 1 Laboratory of Personality and Cognition, National Institute on Aging, Baltimore, MD, USA
Christos Davatzikos, Department of Radiology, JHOC 3230, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA. Email: hristos{at}jhu.edu.
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Abstract |
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Introduction |
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The characterization of signal changes with aging or disease provides important information that is complementary to morphometric studies of regional brain volumes. The deposition of plaques and tangles in Alzheimer's disease is likely directly to influence the signal properties of affected tissue, since it changes the tissue composition. For example, changes in T2* proton signal in amyloid plaques have been identified with postmortem MRI and a high field magnet (Benveniste et al., 1999). In contrast, morphometric studies only indirectly measure these changes via displacements that are observed at adjacent tissue boundaries and that allow for the definition of regions of interest and volumetric measurements (Goldszal et al., 1998
; Collins et al., 1999
) or for the calculation of shape deformation fields (Bookstein et al., 1989
; Miller et al., 1993
; Davatzikos et al., 1996
; Thompson et al., 1997
; Freeborough and Fox, 1998a
; Gaser et al., 1999
; Ashburner and Friston, 2000
; Davatzikos, 2000
). However, such shape measurements are limited in many respects. In particular, marked shape changes are likely to occur at relatively late stages of the development of disease, when neuronal death and brain atrophy are observed. Moreover, these indirect shape changes may be small, depending on how close the affected tissue is to tissue boundaries and other features that can be reliably identified in tomographic images. Thus, quantification of global and local changes in tissue composition characteristics, reflected by characteristics of the magnetic resonance signal, may provide additional information. This unique and complementary information may enhance the preclinical detection of memory impairment and Alzheimer's disease.
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Materials and Methods |
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To quantify the signal characteristics of WM and GM, images were first segmented into GM, WM and CSF using an automated algorithm for tissue classification (Yan and Karp, 1995; Goldszal et al., 1998
). We first quantified global signal characteristics by determining the average grey and white matter intensities throughout the whole brain. To eliminate global signal scaling effects, a contrast ratio (CR) was calculated as the ratio of the difference between white and grey matter intensities to their average value. Moreover, in order to remove the effects of magnetic field inhomogeneities, we applied an iterative inhomogeneity correction algorithm that has been published and tested in the literature (Pham and Prince, 1999
). Longitudinal age changes, in contrast, were examined using mixed effects regression analysis as implemented by SAS v. 8.1 under OpenVMS. Age at baseline evaluation, sex and time (baseline, year 3, year 5) were entered as predictors and global contrast for each time point was the dependent measure.
The interpretation of global brain changes is inherently limited due to the high degree of functional specialization throughout the brain. To examine regionally specific effects of age, a voxel-wise regional signal analysis of the magnetic resonance images was performed, focusing on WM signal intensities. Images were first spatially normalized using a three-dimensional elastic warping method (Davatzikos, 1997) that placed the images into stereotaxic coordinate space (Talairach and Tournoux, 1988
) and accounted for inter-individual variability in overall brain shape and size. Our elastic transformation was particularly designed to account for enlargement of the ventricles that is prominent in elderly subjects. Magnetic resonance signal characteristics at each WM voxel in the stereotaxic space were then determined. A regional contrast ratio (rCR) was calculated as:
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The significance of age differences and longitudinal changes in WM-rCR at each voxel location was examined via voxel-wise t-tests and paired t-tests, respectively. We examined age differences (5969 versus 7085 years at baseline) and longitudinal change over 4 years for the entire sample, as well as separately for men and women.
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Results |
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Discussion |
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Our observations also demonstrate that degenerative age effects on WM connectivity are not uniform throughout the brain, but rather they have specific regional patterns. Most pronounced was the difference between left and right hemispheres, with the left hemisphere showing more rapid changes and larger age differences than the right hemisphere. This interhemispheric difference was pronounced primarily in frontal and temporal regions, and less pronounced in parietal association regions. Finally, occipital regions did not display any significant age differences or longitudinal changes, which is consistent with the relative preservation of functional activity in primary visual regions with aging (Murphy et al., 1996). Our findings are complementary to recent studies using diffusion (Bozzao et al., 2001
; O'Sullivan et al., 2001
) and perfusion diffusion (Bozzao et al., 2001
) imaging, which have indicated changes in magnetic resonance signal characteristics with normal and abnormal aging. A complete characterization of such changes will most likely require a combined approach that examines all aspects of the magnetic resonance signal. One of the unique elements of our study is the use of high-dimensional spatial normalization transformations, which enabled a voxel-wise statistical analysis, rather than the coarser region-of-interest-based analysis.
An important finding of our study is the lack of associations between age changes in signal intensity and brain atrophy. This indicates that measures of tissue characteristics provide unique and complementary information to widely used morphometric measures. Measurements based on rWM-CR show stronger associations with age and can potentially be more sensitive than volumetric measures as indicators of preclinical disease, because they reflect changes in the underlying tissue composition. Volumetric changes are likely to occur later than signal changes, when loss of tissue causes displacement of well-identified features, such as tissue boundaries.
The extent to which subtle changes in periventricular regions contributed to local changes in these regions is unclear. It is important to note that our analysis was restricted to relatively healthy elderly subjects of the BLSA, who typically display only mild age-related periventricular signal abnormalities, often called hyperintensities' due to their bright appearance in T2-weighted images. Any more extreme periventricular signal abnormalities did not affect our signal analysis, because these regions are typically classified as GM in T1 images and were therefore excluded from the analysis that was restricted to WM points. The local analysis is, by definition, restricted locally, so any such signal abnormalities will affect only the voxels in which they appear. Misclassification of WM signal abnormalities as GM was also not likely to affect the global analysis, because these regions account for a very small percentage of the total WM volume. One goal of our continued follow-up studies is to characterize the progression of signal changes and to determine whether regions initially showing subtle changes are those that are more likely to contain hyperintensities' with advancing age.
In summary, our study is the first documentation of longitudinal age and region-dependent changes in magnetic resonance signal characteristics of WM fibers, reflecting underlying degenerative effects of aging. Due to the limitations of structural magnetic resonance imaging, our study does not delineate the specific neurobiological processes underlying these changes. However, our findings highlight the potential utility of a novel approach to analysis of magnetic resonance images and suggest clear directions for more detailed in vivo imaging (e.g. magnetic resonance spectroscopy) and postmortem neuropathological examinations. Continued longitudinal follow-ups of this sample will determine whether changes in tissue composition provide information useful in the preclinical identification of individuals vulnerable to memory problems and Alzheimer's disease.
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Acknowledgments |
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References |
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