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The Journal of Neuroscience, April 15, 2003, 23(8):3295
Longitudinal Magnetic Resonance Imaging Studies of Older Adults:
A Shrinking Brain
Susan M.
Resnick1,
Dzung L.
Pham1,
Michael A.
Kraut2,
Alan B.
Zonderman1, and
Christos
Davatzikos2
1 National Institute on Aging, Baltimore, Maryland
21224-6825, and 2 Department of Radiology, Johns Hopkins
University, Baltimore, Maryland 21287
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ABSTRACT |
Age-related loss of brain tissue has been inferred from
cross-sectional neuroimaging studies, but direct measurements of gray and white matter changes from longitudinal studies are lacking. We
quantified longitudinal magnetic resonance imaging (MRI) scans of 92 nondemented older adults (age 59-85 years at baseline) in the
Baltimore Longitudinal Study of Aging to determine the rates and
regional distribution of gray and white matter tissue loss in older
adults. Using images from baseline, 2 year, and 4 year follow-up, we
found significant age changes in gray (p < 0.001) and white (p < 0.001) volumes even
in a subgroup of 24 very healthy elderly. Annual rates of tissue loss
were 5.4 ± 0.3, 2.4 ± 0.4, and 3.1 ± 0.4 cm3 per year for total brain, gray, and white
volumes, respectively, and ventricles increased by 1.4 ± 0.1 cm3 per year (3.7, 1.3, 2.4, and 1.2 cm3, respectively, in very healthy). Frontal and
parietal, compared with temporal and occipital, lobar regions showed
greater decline. Gray matter loss was most pronounced for orbital and
inferior frontal, cingulate, insular, inferior parietal, and to a
lesser extent mesial temporal regions, whereas white matter changes
were widespread. In this first study of gray and white matter volume changes, we demonstrate significant longitudinal tissue loss for both
gray and white matter even in very healthy older adults. These data
provide essential information on the rate and regional pattern of
age-associated changes against which pathology can be evaluated and
suggest slower rates of brain atrophy in individuals who remain
medically and cognitively healthy.
Key words:
magnetic resonance imaging; aging; brain volumes; longitudinal studies; gray matter loss; white matter loss
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Introduction |
Stereological studies of neuron
counts in the human brain suggest that there is little neuron loss in
normal aging (Morrison and Hof, 1997 ; Pakkenberg and Gundersen, 1997).
The conclusions drawn from these postmortem studies, which are
necessarily cross-sectional, stand in contrast to data from in
vivo imaging studies, which reveal age differences in brain
volumes and CSF spaces (Gur et al., 1991; Coffey et al., 1992;
Pfefferbaum et al., 1994; Murphy et al., 1996; Courchesne et al.,
2000). One difficulty in interpreting both the postmortem and in
vivo imaging studies is the cross-sectional nature of the majority
of investigations. Estimates of age effects based on such designs are
confounded by secular changes in nutrition, medical care, and other
factors. Indeed, secular drifts in body and brain weight have been
documented (Miller and Corsellis, 1977), and rates of brain
changes can only be determined from longitudinal investigations.
An advantage of the use of in vivo imaging to study brain
changes is that repeated assessments can be performed over time. By
controlling for variability between individuals, within-individual comparisons can identify subtle changes over time. There have been few
longitudinal neuroimaging studies, and none have examined global and
regional changes in both gray and white matter using high-resolution
magnetic resonance imaging (MRI). One longitudinal MRI study reported
puzzling findings of longitudinal increases in some brain volume
measures and decreases in subarachnoid CSF over a 3-6 year interval
(Mueller et al., 1998). Low reliability (inter-rater agreement of 0.71)
for measurement of subarachnoid CSF on images of 4 mm thickness
suggested difficulties in identifying the interface between CSF
and brain parenchyma, leading to inaccuracies in measurement of
brain and sulcal CSF volumes. More recently, Tang and colleagues (2001)
reported a 2.1% annual rate of cerebral volume loss over 4.4. years,
using stereological techniques to estimate a global measure of cerebral
volume from MRI sections of 5 mm thickness.
Since 1994, we have been conducting a longitudinal brain imaging study
of older adults in the Baltimore Longitudinal Study of Aging (BLSA) to
identify brain changes that may be predictors of cognitive decline and
Alzheimer's disease. High-resolution MRI (1.5 mm thickness) and a
validated approach for image analysis (Goldszal et al., 1998; Resnick
et al., 2000) are used for quantitation of global and regional gray and
white volumes. We hypothesized that loss of both gray and white matter
would be detectable over the 4 year interval. Consistent with other
cross-sectional studies (Grant et al., 1987; Gur et al., 1991; Coffey
et al., 1992; Pfefferbaum et al., 1994) and our own data on 1 year
change (Resnick et al., 2000), we hypothesized that ventricular volume
would continue to increase over the course of the study. We also
examined the effects of age and sex on the rates of change for regional
brain volumes and ventricular volume. In this report, we provide the first evidence of substantial longitudinal loss of both gray and white
matter tissue volumes over a 4 year interval even in healthy elderly.
In addition, we show steady age-related increases in ventricular CSF
(V-CSF). The rate of increase in V-CSF is influenced by age but is
similar in male and female older adults.
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Materials and Methods |
Subjects. The present sample includes 92 participants
(50 men, 42 women) in the neuroimaging study of the BLSA (Resnick et al., 2000) who completed a baseline MRI study and assessments at year 3 (2 year interval) and year 5 (4 year interval). Three additional
participants were excluded from these analyses because they had
developed serious CNS pathology (but remained free of dementia).
Neuroimaging participants in this sample are a subset of BLSA
volunteers, aged 59-85 at baseline, who agreed to return annually and
who did not meet any of the following exclusionary criteria at initial
evaluation: CNS disease [epilepsy, stroke, bipolar illness, previous
diagnosis of dementia according to Diagnostic and Statistical Manual
(DSM)-III-R criteria (Spitzer and Williams, 1987)], severe
cardiovascular disease (myocardial infarction, coronary artery disease
requiring angioplasty or bypass surgery), severe pulmonary disease, or
metastatic cancer. Individuals showing signs of cognitive decline that
did not meet criteria for dementia (n = 4) and those
with past or current depression (n = 8) were included,
because these factors may be risk factors for dementing illness. All
participants remained free of dementia at year 5 follow-up, using
diagnostic procedures described previously (Kawas et al., 2000).
Demographic characteristics and measures of functional status are
presented in Table 1 for the entire
sample and for a subgroup of 24 participants who remained very healthy
(no medical conditions or cognitive impairment) at year 5 evaluation.
This research protocol was approved by the local institutional review board, and written informed consent was obtained from all participants in conjunction with each neuroimaging visit.
Image acquisition. MR acquisition procedures are detailed in
Resnick et al. (2000). MR scanning was performed on a GE Signa 1.5 Tesla scanner. The current results are based on a high-resolution volumetric "spoiled grass" (SPGR) series (axial acquisition;
repetition time = 35; echo time = 5; flip angle = 45;
field of view = 24; matrix = 256 × 256; number of
excitations = 1; voxel dimensions of 0.94 × 0.94 × 1.5 mm slice thickness). Two similarly configured scanners were used
interchangeably over the course of the data collection, which spanned
from February 1994 through July 1999. During this time period, there
were no major hardware upgrades, and all acquisitions were completed
before the LX software upgrade. Quality control scans were
performed daily.
Image analysis. Quantitative analysis of MR volumes is
accomplished using a semi-automated approach, with demonstrated
validity and high reliability (Goldszal et al., 1998). Images are first reformatted parallel to the plane containing the anterior and posterior
commissures. Extracranial tissue, the cerebellum, and brainstem
structures inferior to the mammillary bodies are removed by a single,
highly experienced, image-processing technician using a semi-automated
procedure, with high inter-rater and test-retest reliability (Goldszal
et al., 1998). The remaining tissue is classified, using an adaptive
Bayesian segmentation algorithm (Yan and Karp, 1995), into gray matter,
white matter, and CSF. V-CSF is defined by drawing a crude region of
interest to mask out any nonventricular CSF. After this step, all image
processing is fully automated and operator independent. The segmented
images provide quantitative volumetric measures of total gray, white,
and brain (gray + white) matter, as well as V-CSF volumes and
ventricle-to-brain ratios (VBRs). Stabilities over 1 year for these
measures are all >0.95. Sulcal CSF is not quantified, because the
interface between CSF and the cranium is difficult to determine
reliably on SPGR images. Moreover, our longitudinal analysis focuses on
direct measurements of changes in brain parenchyma, rather than
indirect measurements of such changes via sulcal spaces.
Regional analysis of volumes examined in normalized space, the RAVENS
approach (Goldszal et al., 1998), is used for quantification of
regional volumes and investigation of local brain changes. The
segmented images are transformed to the Talairach stereotaxic coordinate space (Talairach and Tournoux, 1988), using the elastic deformation algorithm of Davatzikos (1996). This approach allows quantitation of absolute volumes within the standard reference space
and applies a boundary constraint for the ventricles, which are often
enlarged in the elderly. Volumes of frontal, parietal, temporal, and
occipital brain regions, for gray and white matter, are determined
automatically within the Talairach coordinate space (Andreasen et al.,
1996), yielding stabilities over 1 year between 0.86 and 0.97 (Resnick
et al., 2000). In addition, the RAVENS approach yields average brain
maps for analysis of local differences between groups of subjects or
longitudinal change. Image intensities reflect the amount of expansion
or contraction relative to a template brain and correspond to the
distribution of gray, white, or CSF volumes in the average RAVENS maps.
For example, if a subject's ventricles are larger relative to other
subjects, this subject's RAVENS V-CSF map will be relatively brighter.
The same holds for gray matter and white matter structures.
Accordingly, regional volumetric measurements are performed via
regional analysis of the RAVENS maps. Effect sizes for differences in
intensities over time reveal local longitudinal changes.
Statistical analysis. Statistical analysis was performed
using SAS Version 6.12 on a DEC  computer running OpenVMS. Two and 4 year stabilities were estimated by Pearson product-moment
correlations. Mixed-effects regression was used to investigate
longitudinal changes and the effects of age (as a continuous
covariate), sex, and hemisphere on changes for individual brain
regions. Separate analyses were conducted for total brain, gray, and
white matter volumes, frontal, parietal, temporal, and occipital
volumes, ventricular volumes, and VBR. Height was entered as a
covariate for volume but not ratio measures. Longitudinal change was
included in each model by addition of time (baseline, year 3, year 5)
as a fixed-effects term. In addition, all two-way interactions,
excluding those with height, and three-way interactions of interest
were included in the initial model. A backward elimination procedure
was used, whereby all lower-order terms remained in the final model but nonsignificant interactions (p > 0.05) were
eliminated at each step until a final solution was reached (Morrell et
al., 1997). Annual rates of change were calculated from the slope of
volume versus age at assessment, providing estimates that can be
compared across the various brain volume measurements. Finally, the
differential effects of age and sex across different tissue types
(i.e., gray versus white), brain regions (i.e., frontal, parietal,
temporal, occipital), and hemispheres (right, left) were examined using multivariate ANOVA (MANOVA). In these analyses, sex and age
(<70 years versus 70-85 years at baseline) were grouping factors;
time (baseline, year 3, year 5) and tissue type or region were
repeated-measures factors. Tissue type and region were examined in
separate analyses, with the analysis of lobar volumes including both
gray and white matter. Two sets of additional analyses were performed.
The first excluded the four subjects with mild cognitive impairment. In the second, analyses of global and lobar measures were repeated, restricting the sample to the 24 individuals who remained free of
medical problems and did not meet criteria for mild cognitive impairment.
Statistical analysis of local gray and white matter volume changes was
performed using the RAVENS images and voxel-based paired t
tests as implemented in Statistical Parametric Mapping (SPM) 99 (Friston et al., 1995). Tissue outside the brain, as well as V-CSF, was
masked from each image before SPM analysis. Images were smoothed using
a 9 mm3 filter, chosen on the basis of our
previous validation experiments (Davatzikos et al., 2001) and the
spatial specificity of our normalization approach. Because we were
interested in identifying regions of absolute volumetric changes, we
did not adjust for global changes in brain volume. Longitudinal change
was calculated as baseline minus year 5 images, with significance set
at p < 0.001, uncorrected. Anatomic localization was
determined from our average gray matter map for the 92 subjects, using
standard anatomical references (Mai et al., 1997; Duvernoy, 1999).
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Results |
Stability over time
All measurements were highly stable over both 2 and 4 year
intervals, with test-retest correlations ranging from 0.94 to 0.99. Four year stabilities for brain and ventricular volumes (Fig. 1, top and bottom, respectively)
illustrate the high stability in the face of changing absolute volumes.
These scatterplots show declines in brain volumes and increases in
V-CSF for the majority of individuals. Although the magnitude of
decline in brain volume appears relatively consistent across subjects,
there is a tendency for greater V-CSF increases in individuals with
larger baseline volumes.
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Figure 1.
Scatterplots showing stability of brain
(top) and ventricular (bottom)
volumes over 4 years. Against a background of highly stable
measurement, brain volumes decrease and ventricular volumes increase
over the 4 year interval.
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Age effects on global and lobar volumes
Results of the mixed-effects
regression analyses are summarized in Tables 2 and
3 for the whole sample and very healthy subsample, respectively. Cross-sectional effects of age and sex on
brain and ventricular volumes were consistent with our previous report
of baseline and 1 year follow-up assessments (Resnick et al., 2000). As
indicated by the significant effects of time, longitudinal brain
changes reached statistical significance for all regions examined.
These findings indicated longitudinal decreases in brain volume
measurements and increases in VBR. For ventricular volume and VBR,
there was a significant age by time interaction, revealing a faster
rate of change in both measures with advancing age. In the presence of
this interaction, the main effect of time for V-CSF did not reach
significance, although significant longitudinal increases were evident
when the interaction was omitted from the model. Rates of change in
brain and ventricular volumes did not differ significantly for men and
women. Longitudinal changes remained significant excluding the four
individuals with mild cognitive impairment and for the analyses based
on 24 individuals free of medical problems and even very mild cognitive
change. Interestingly, cross-sectional effects of age did not
reach significance in the very healthy group, highlighting the greater
sensitivity of intra-individual measurements of longitudinal
change.
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Table 3.
Results of mixed-effects regression analyses for main
effects in subsample of 24 very healthy individuals
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Direct comparison of longitudinal tissue loss for gray versus white
matter was performed using repeated-measures MANOVA. Consistent with
the mixed-effects regression results, the overall loss of brain tissue
over time was highly significant:
F(2,88) = 146.0; p < 0.0001. However, there were no significant differences between gray and
white matter in the magnitude of tissue loss. In contrast to these
findings, analysis of the magnitudes of longitudinal tissue loss for
frontal, parietal, temporal, and occipital regions revealed significant
differences among regions (time × region: F(6,84) = 4.73; p < 0.001). To further examine this interaction, values were standardized
within each region using a z-transformation based on the
mean and SD at baseline for each region separately. Longitudinal tissue
loss was greater for frontal and parietal lobes than temporal and
occipital lobes (Fig. 2). The magnitudes of longitudinal changes in the lobar regions were not significantly influenced by age and sex. MANOVA restricted to the very healthy subsample showed significant longitudinal changes that did not differ
as a function of tissue type or lobar brain region.
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Figure 2.
Magnitudes of longitudinal change as a function of
brain (gray + white) region. To illustrate the significant
region-by-time interaction, mean longitudinal change for each lobar
brain region is plotted as a z-score, using the mean and
SD from lobar brain volumes at baseline to control for differences in
absolute volumes. Frontal and parietal brain volumes show greater
relative tissue loss compared with temporal and occipital
regions.
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The magnitudes of the annual rates of change for each of the volumetric
measurements are presented in Figure 3
for total brain, gray, white, and ventricular volumes and in Table
4 for lobar regions. Remarkably, this
community-dwelling sample of older adults shows an average loss of 5.4 cm3 of brain volume and increase of 1.4 cm3 in ventricular volume each year. (Note
that the discrepancy between volume loss and ventricular volume
increase is attributable to the fact that we have not measured the
sulcal CSF surrounding the cortex in this study.) Exclusion of the four
participants with mild cognitive impairment yielded nearly identical
rates of change for the global measures. The magnitude of brain tissue loss was somewhat reduced in the very healthy elderly, but these individuals still showed significant volume change. As described above,
the apparent trend toward greater decline in rates of change for white
compared with gray matter volume was not statistically significant when
compared directly using repeated-measures MANOVA.
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Figure 3.
Annual rates of change in brain, gray, white, and
ventricular volumes (cubic centimeters) for the entire sample,
the subgroup with some medical problems, and the subgroup of very
healthy individuals. Mean values are displayed at the axis ends. All
values are significant at p < 0.001 except gray (NS) and
white (p < 0.05) in the healthy group. Note that the
nonsignificant trend for gray matter tissue loss in the healthy
subgroup is significant in the more sensitive mixed-effects regression
analysis (see Results).
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Age effects on local gray and white matter tissue volumes
Voxel-based paired t tests were performed on the RAVENS
gray and white matter images, separately,
to examine localized loss of tissue over
4 years (Figs. 4, 5). Substantial gray
matter tissue loss (Fig. 6) is observed
in the regions of the interhemispheric and Sylvian fissures, affecting
cingulate and insular cortex, respectively. Consistent with our
cross-sectional observations (Resnick et al., 2000), the orbital and
inferior frontal cortex and the mesial temporal cortex, albeit to a
lesser extent, are also vulnerable to longitudinal tissue loss. In
addition, significant gray matter tissue loss is observed in the
inferior parietal region. There appears to be a lateralized pattern to
the gray matter volume loss, with greater change for the right compared
with left hemisphere in many regions. The right greater than left
asymmetry is most pronounced in inferior frontal and anterior temporal
regions, but shows a reversed pattern in the inferior parietal region. Local analysis of white matter tissue loss reveals widespread changes
throughout the brain. Although there is some asymmetry in the white
matter tissue loss, this asymmetry appears limited to the temporal
lobe, with greater loss of left than right temporal white matter.
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Figure 4.
Local changes in gray matter volume. Longitudinal
declines in tissue volumes over the 4 year interval are shown by the
color-coded t score values, calculated from a
voxel-based comparison between baseline and year 5 gray matter images.
Images are thresholded at p < 0.001 or
z = 3.18. To facilitate anatomic localization,
significant voxels are superimposed on transaxial slices of a map of
the average gray matter distribution of the baseline images after
segmentation and stereotaxic normalization. Brighter regions of the
gray matter image are more likely to contain gray matter.
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Figure 5.
Local changes in white matter volumes.
Longitudinal declines in white matter tissue volume from baseline to
year 5 are shown as t values for each voxel. See Figure
4 for a more detailed description.
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Figure 6.
Three-dimensional views of significant
longitudinal tissue loss in specific gray matter regions.
Three-dimensional views of the t statistic are shown
projected on the outer cortical surface of a representative subject in
our sample. The projection on the surface was performed by averaging
the value of the t statistic along the normal of each
surface vertex; only voxels with t statistic >3.18 were
included in this averaging procedure. The color bar shows the colors
corresponding to these calculated average t statistics.
Bottom, Views of the right and left hemispheres
highlight tissue loss in inferior frontal, insular, and posterior
temporal regions (right > left) and the inferior parietal
(left > right) region. Top, Gray matter volume
loss in the insula (right > left; inferior and coronal views),
orbital frontal cortex (inferior and sagittal views), and cingulate
cortex (sagittal and coronal views) are highlighted. In the inferior
view, the anterior temporal lobe is cut away to expose the surface of
the insular cortex.
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Discussion |
This report provides the first evidence of substantial
longitudinal declines in both gray and white matter brain volumes in older adults ranging in age from 59 to 85 years at baseline. Moreover, we provide the first quantitative data of the rates and regional distribution of local gray and white matter tissue loss in older adult
men and women. Although other investigations have indicated brain
volume loss in specific brain regions, such as the hippocampus (Kaye et
al., 1997; Jack et al., 1998; Mueller et al., 1998), or more global
volume loss (Chan et al., 2001; Tang et al., 2001), our findings reveal
significant longitudinal declines throughout the brain involving
multiple selected brain regions and both gray and white matter.
Previous cross-sectional imaging studies, primarily using older imaging
techniques, have yielded inconsistent results regarding differential
age effects on gray versus white matter (Pfefferbaum et al., 1994; Raz
et al., 1997; Guttmann et al., 1998). In a voxel-based analysis of
high-resolution MR images from 465 individuals ranging in age from 17 to 79 years, Good and colleagues (2001) reported cross-sectional
declines in global gray but not white matter volumes during adulthood.
However, this study included few individuals over age 65 years. Using
high-resolution MR images and a validated approach to image processing,
we previously reported cross-sectional age effects for both gray and
white matter volumes in adults aged 59 years and older (Resnick et al.,
2000). Consistently, our present longitudinal results from serial MRI evaluations show 4 year tissue loss for both gray and white matter even
in the very healthy subgroup of older adults. Although there was a
trend for greater longitudinal tissue loss in white compared with gray
matter, this difference was not statistically significant.
Rates of tissue loss were similar in men and women and in older and
younger adults. In contrast, the rates of increase in ventricular
volume, reflecting central brain atrophy, were significantly greater in
older compared with younger individuals. Although we (Resnick et al.,
2000) and others (Yue et al., 1997) have observed sex differences in
V-CSF in older adults, with older men having larger ventricles than
older women, trends toward sex differences in rates of V-CSF
enlargement did not reach significance when adjusted for baseline
ventricular volume. Although some studies have suggested that men may
show earlier increases in ventricular size (Kaye et al., 1992), the
magnitude and age at which sex differences appear remain unclear. We
have begun enrolling younger BLSA participants into our neuroimaging
study to investigate the age at which rates of brain atrophy accelerate
in both men and women.
Although tissue loss was distributed across gray and white matter,
analysis of lobar regions and voxel-based comparisons of local changes
revealed regional patterns of vulnerability to age-related tissue loss.
Consistent with other studies and our own cross-sectional findings on
lobar volumes of gray and white matter, frontal and parietal appeared
more vulnerable than temporal and occipital lobes. Voxel-based analyses
of local regions provided complementary information and indicated that
cingulate, insular, orbital, and inferior frontal cortex, and, to a
lesser extent, mesial temporal regions showed longitudinal changes in
gray matter. Relative vulnerability of insular and cingulate regions to
age-related gray matter loss is consistent with the cross-sectional
voxel-based analysis of Good and colleagues (2001), but our
observations of modest longitudinal volume loss in mesial temporal
structures is contrary to their report of relative preservation of
tissue in these regions. Differences in age distributions for the two
samples may account in part for these discrepancies. Region-based
analyses of hippocampal volumes have also yielded differing results
across studies, although recent longitudinal investigations of older
adults show small but significant tissue loss with age (Jack et al.,
1998; Mueller et al., 1998).
The local pattern of gray matter tissue loss observed in our study of
older adults is intriguing in light of the staging by Braak and Braak
(1997) of the deposition of amyloid in a nonselected series of autopsy
brains. Initial pathology appears in the basal neocortex, including
perirhinal and orbital cortex, and next spreads into adjacent
neocortical association areas and the hippocampal formation, followed
finally by its appearance in primary neocortical areas. Because
amyloid deposition increases as a function of age, the neurotoxic
consequences of amyloid may contribute to our observations of a
regional pattern of gray matter tissue loss. Longitudinal declines in
local white matter volumes were more widespread throughout the brain,
most likely reflecting demyelinating changes in older individuals
(Wiggins et al., 1988; Svennerholm et al., 1997). There was a notable
hemispheric asymmetry in white matter volume loss in the temporal lobe,
with greater left compared with right tissue loss.
One important limitation of our analysis of local gray and white matter
changes is that there is greater sensitivity for detection of age
effects in regions with more accurate stereotaxic transformation. Registration errors limit our ability to detect change in voxel-based analyses, and these difficulties will be most pronounced in regions of
high cortical variability. Recent progress in the development of new
algorithms for elastic deformation (Davatzikos et al., 2001; Shen and
Davatzikos, 2001) will result in substantial improvement in
registration accuracy and will enhance identification of change in
regions of greater anatomic variability. The next phase of our analysis
will use these new methods for detection of more highly localized age effects.
Nevertheless, the current approach identified substantial magnitudes of
age-related tissue loss. Annual rates of cerebral tissue loss were 5.4 cm3 (0.5% per year) and of V-CSF increase
were 1.4 cm3 (4% per year), with changes
over the 4-year interval of 21.6 and 5.6 cm3, respectively. This volume loss cannot
be explained by disproportionate changes in a limited number of
individuals. Inspection of Figure 1, top and bottom, indicates
that, against a background of highly stable measurement, longitudinal
decreases in brain tissue volume and increases in V-CSF occur across
almost all individuals in this age range. Although some argue that any
tissue loss reflects pathological changes associated with preclinical
dementia rather than normal aging, the uniformity of our findings
across individuals argues against this interpretation unless all are in
a preclinical stage of dementia. Thus, our data provide normative
values against which potential pathological increases in rates of
change can be evaluated. However, it should be cautioned that some of
these individuals will ultimately develop dementia. We would
hypothesize that those showing the fastest rates of change in mesial
temporal and orbital frontal regions are more vulnerable to disease,
given our observations of a regional pattern of age-related gray matter tissue loss and the distribution of progression of neuropathology associated with Alzheimer's disease (Braak and Braak, 1997). Thus, heightened vulnerability to disease may be indicated by accelerated change in specific regions against a background of age-related tissue loss.
Although our findings indicate that most older adults show some tissue
loss over time, there is substantial variability in the magnitude of
this change. The trend toward reduced volume loss in the very healthy
subsample suggests that brain atrophy may be reduced in individuals who
remain medically and cognitively healthy. Our sample is composed of
relatively healthy individuals, and they have shown, on average, little
cognitive change over this initial 4 year interval. However, with
continued longitudinal follow-ups we expect to detect cognitive change
and impairment in some portion of this aging sample. These follow-up
studies will determine the clinical relevance of the observed brain
changes and will identify which brain regions are associated with
cognitive and other behavioral changes. There is already substantial
evidence that loss of mesial temporal lobe tissue, particularly in
hippocampus and entorhinal cortex, is associated with memory impairment
and the development of Alzheimer's disease (Convit et al., 1997;
Petersen et al., 2000). Our findings indicate that other brain regions, e.g., orbital-frontal and insular cortex, also show significant longitudinal tissue loss and may predict other cognitive and functional impairments in elderly individuals. Finally, our findings from in
vivo MRI studies can help direct more focused neuropathological studies of specific brain regions. Such correlative neuropathological studies will be critical in elucidating the cellular basis of these
in vivo imaging findings and will help clarify the apparent paradox between volume loss observed on MRI and reports of minimal neuron loss with aging.
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FOOTNOTES |
Received Sept. 20, 2002; revised Jan. 17, 2003; accepted Jan. 21, 2003.
We gratefully acknowledge the assistance of Cynthia Wursta in image
processing, Dr. Dongrong Xu and Xiadong Tao in preparation of
three-dimensional displays of results, Beth Nardi for study coordination, and the Baltimore Longitudinal Study of Aging volunteers for their continued participation.
Correspondence should be addressed to Dr. Susan M. Resnick, Laboratory
of Personality and Cognition, Box 03, National Institute on Aging, 5600 Nathan Shock Drive, Baltimore, MD 21224-6825. E-mail: susan.resnick{at}nih.gov.
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TOP
ABSTRACT
Introduction
