Patterns of Age-related Shrinkage in Cerebellum and Brainstem Observed In Vivo Using Three-dimensional MRI Volumetry

Andreas R. Luft1, Martin Skalej, Jörg B. Schulz2, Dorothea Welte, Rupert Kolb, Katrin Bürk2, Thomas Klockgether2 and Karsten Voigt

Department of Neuroradiology and , 2 Department of Neurology, University of Tübingen, Hoppe Seyler-Strasse 3, 72076 Tübingen, Germany


    Abstract
 Top
 Footnotes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
This study investigates the time course and regional differences in age-related volume loss in cerebellum and brainstem. Threedimensional (3D) magnetic resonance imaging (MRI) volumetry was used to measure the volumes of 11 regions in the cerebellum and three regions in the brainstem in 48 healthy volunteers (age 19.8–73.1 years). Landmark-adjusted lattices were used to divide the cerebellum into three radial (lobules I–V = lingula/lobulus/culmen, lobules VI–VII = declive/folium/tuber, lobules VIII–X = pyramis/ uvula/nodulus) and three transverse subdivisions (vermis, medial, lateral hemisphere). The radial sectors extended laterally throughout the vermis and the medial hemisphere. The brainstem was divided into midbrain, metencephalon (pons and tegmentum pontis) and medulla. Total cerebellar volume marginally declined with age using a linear regression model. An exponential model better described the age dependency of total cerebellar volume. The curve predicted that the volume remained stable until age 50 years and declined thereafter. Volume loss in the cerebellar vermis was striking. Shrinkage in the medial hemisphere was markedly less and only the inferior sector showed a trendwise negative association with age. The lateral hemisphere was not affected by age. No age effects were found for total brainstem volume, metencephalon and medulla. Only the mid-brain showed a trend for age-related shrinkage. The mediolateral gradient of decreasing age effects is similar to the histological pattern of alcoholic cerebellar atrophy (although our subjects were non-alcoholics according to DSM-IIIR criteria and laboratory data) suggesting that a common factor is involved in both processes. In search for a cause of the regional vulnerability, vascular, functional, structural and molecular/genetic factors may be considered.


    Introduction
 Top
 Footnotes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Post-mortem and in vivo imaging studies have shown that the human brain shrinks with age. Volume loss varies greatly among different brain regions (Raz, 1996Go; Haug, 1997Go). The cerebellum seems to be affected by age (Ellis, 1920Go; `Sullivan et al., 1995Go; Raz 1997Go), although its volume loss is less as compared to the cerebrum. In some subject samples age effects on the cerebellum were insignificant (Hayakawa et al., 1989Go; Escalona et al. 1991Go). Inside the cerebellum, some regions seem to be more prone to age-related decrease than others. Histology and computerized tomography studies reported pronounced volume loss in the anterior vermis (Koller et al., 1981Go; Torvik et al., 1986Go). In magnetic resonance imaging (MRI) studies, predominant shrinkage was observed in the posterior vermis (Schaefer et al., 1991Go; Shah et al., 1991Go; Raz et al., 1992Go; Raz, 1996Go). The hemispheres also showed shrinkage with age (Raz et al., 1998Go). In contrast, brainstem volume does not decline with age (Raz, 1996Go). Only for the midbrain region have some studies reported volume loss (Doraiswamy et al., 1992Go; Weis et al., 1993Go).

Regional volume loss does not only occur in physiological ageing. Various hereditary and non-hereditary disorders produce distinct patterns of atrophy in cerebellum and brainstem (Escourolle et al., 1982Go; Bürk et al., 1996Go). Precise quantification of brain volumes using modern imaging techniques may reveal patterns of volume loss with specificity for certain conditions related to age or disease. These patterns may be used for differential diagnosis (Schulz et al., 1998Go).

Considering the recent advances in the knowledge of functional as well as morphological compartmentalization of the cerebellum, we believe that a precise characterization of the regional patterns of cerebellar and brainstem age-related atrophy is necessary. A second objective of this study is to investigate the time course of cerebellar volume loss. Using semi-automated, three-dimensional MRI volumetry, we measured regional volumes in brainstem and cerebellum in a prospectively collected sample of subjects. The results will be compared to patterns of (pathological) cerebellar atrophy. Possible causes of differential vulnerability in the cerebellum will be addressed.


    Materials and Methods
 Top
 Footnotes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Forty-eight healthy volunteers (26 males, 22 females, mean age 39.8 years, age range 19.8–73.1) were examined after achieving informed and written consent. All subjects were Caucasian, living in southern parts of Germany. Individuals with diabetes were excluded from the sample. All subjects were examined by experienced neurologists (K.B., J.B.S., T.K.). Individuals with neurological symptoms or atherosclerotic disease in the head or neck were not included in this study. The subjects were questioned about recent and remote drinking habits. None of the volunteers reported to drink or have drunken more than socially accepted. None of the subjects met DSM-IIIR criteria for alcohol abuse. Additionally, the subjects were screened for elevated {gamma}-glutamyltransferase ({gamma}-GT, reference range: 5–30 U/l) and abnormal mean corpuscular volume (MCV, reference range: 86–98 µm3) to exclude alcohol-related liver dysfunction. Finally, T1 and proton-density-weighted MR images of all subjects were inspected by an experienced neuroradiologist (M.S.). None of the subjects showed morphological abnormalities in the central nervous system.

Using a 1.5 T unit (Magnetom Vision, Siemens, Erlangen, Germany) with standard head coil, two MRI sequences were acquired. (i) A three-dimensional fast low angle shot (FLASH) sequence producing isotropic high-resolution T1-weighted images (TR = 15 ms, TE = 5 ms, flip angle = 30°, 1 NEX, slice thickness 0.9 mm; pixel size 0.9 x 0.9 mm2). (ii) A two-dimensional turbo spin echo (TurboSE) sequence was acquired twice with interleaved slice positions to obtain a gapless set of images (proton density-contrast, TR = 5800 ms, TE = 15 ms, 2 NEX, slice thickness 2 mm, gap 2 mm). The total scan time was 45 min (positioning 5 min; FLASH 15 min; TurboSE 2 x 12.5 min). A cushion with head-shaped cutout held the patient's head to minimize motion artefacts. The subjects were instructed to keep their eyes closed during image acquisition. FLASH images were used for cerebellar and brainstem volumetry. The total intracranial volume (TICV) was estimated using TurboSE images.

Volumetric Processing

All data were transferred to a Unix workstation (SGI Indigo R4400) for postprocessing. Proprietary software (Welte, 1994Go) was used for volumetric measurements. Volumetry consisted of three steps. (i) Structural boundaries not defined by different signal intensities were manually traced. To improve reliability, all manual procedures were highly standardized using anatomical landmarks and planes rather than freehand tracing wherever possible. (ii) Contrast-defined boundaries were automatically segmented using a region-growing algorithm. Starting from a rater-defined seedpoint, this algorithm advanced in all three dimensions checking whether neighbouring voxel-intensities were within an intensity range (defined by the rater using a histogram which was calculated from the whole image set). (iii) Finally, volumes were calculated considering partial volume effects at the edge of the segmented structure (Welte, 1994Go; Luft et al., 1996Go, 1998bGo).

The brainstem was segmented first. The superior, inferior and posterior boundaries were defined interactively. Each boundary was marked by a plane, which was adjusted for two landmarks (Fig. 1aGo). For the superior boundary, an axial plane was aligned for the mamillary body and the posterior commissure. This plane was parallel-shifted downward by one-third of the height of the midbrain to avoid ‘escape' of the region-growing algorithm into the diencephalon. This shift also excluded the red nucleus and part of the substantia nigra from the brainstem volume (Fig. 1bGo). The inferior boundary was defined by a plane parallel to the mamillary body—posterior commissure plane, which was aligned for the posterior rim of the foramen magnum. The posterior boundary was marked by a coronal plane through the posterior commissure and the obex. This plane was displaced posteriorly up to the dorsal edge of the inferior colliculus. After interactive presegmentation, the brainstem was region-growing segmented and its volume was calculated.





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Figure 1. Landmark-based presegmentation and automated region-growing-based segmentation of cerebellum and brainstem are shown. (a,b) Several planes were adjusted for two landmarks to define the boundaries and regions in the brainstem. (c,d) Region-growing-based segmentation identified the full delineation of brainstem and cerebellum. (e,f) A three-dimensional lattice was used to subdivide the cerebellum into 11 regions.

 
Presegmentation of the cerebellum included subtraction of the brainstem volume from the original images. Subtraction resulted in a blackened brainstem area, which was then excluded by the regiongrowing algorithm. Thus, no volume was double-counted. The cerebrocerebellar boundary was not recognized by automatic region-growing segmentation and was therefore redrawn manually on every image showing the cerebellum (no stable landmarks were found to approximate this boundary). Subsequently, automated region-growing segmentation was applied (Fig. 1c,dGo) and the volume was calculated.

Compartmentalization of Brainstem and Cerebellum

Compartmentalization was achieved by one-pixel wide planes that were positioned in the segmented dataset (brainstem and cerebellum respectively) using landmarks. The intensity of pixels in these planes was set to black to produce a boundary for the region-growing algorithm. Because the pixels of these planes were excluded from the regional volume, the sum of all regional volumes was slightly smaller than the volume of the parent structure (Table 1Go).


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Table 1
 
The brainstem was divided into midbrain, pons/tegmentum pontis/ cerebellar peduncles and medulla. The boundaries between these regions were defined by two planes. The first plane was aligned for the upper rim of the pons and the lower edge of the inferior colliculus. The second plane was adjusted in parallel to the first, passing through the lower rim of pons (Fig. 1aGo).

The cerebellar peduncles which were part of the middle brainstem region could not be consistently separated using landmarks. An indirect measure of the volume of the peduncles was obtained by by correcting the whole middle brainstem region for pons volume. The pons volume was approximated by measuring the mid-sagittal area of the pons. This area is a good estimate of pontine volume assuming that the pons is rotation-symmetrical (Raz et al., 1992Go). Correction means that the quotient of total metencephalic volume and pons area was calculated (both standardized for the average volume/area over all subjects µ, to allow the division of two variables of different unit) with


(1)

Using this ratio as a dependent variable, age effects on the peduncles were estimated.

In the cerebellum, 11 regions were evaluated (Fig. 1e,fGo). In the vermis, three radial sectors, V1, V2 and V3, were separated using the primary and prepyramidal fissures as landmarks. The sectors represented lobules I–V, VI–VII, VIII–X respectively. The lateral boundary of the vermis was set at the indentation between vermis and tonsils. The radial sectors extended laterally into the medial hemisphere region (mH1, mH2, mH3). The ‘medial hemisphere' was defined as one-quarter of the total width of the hemisphere. The remaining three-quarters comprised the lateral hemisphere region. After t-testing revealed no asymmetries, left and right regional volumes were averaged for regression analysis.

Axial proton-density-weighted image sets were used to measure the total intracranial volume (TICV). Brain and cerebrospinal fluid (CSF) — both of high intensity on proton-density-weighted images — were manually separated from diploe, retro-orbital fat and musculature. The TICV included brain and CSF volumes caudally delimited by the foramen magnum.

Statistical Analysis

The reliability of the region-growing technique was reported in a previous study using phantoms of known volume (Luft et al., 1996Go). The whole protocol including presegmentation was evaluated by calculating intraclass correlation coefficients. Two raters, blinded for the other one's results, measured cerebellar and brainstem volume in 15 subjects. The coefficients were 0.93 for the cerebellum and 0.91 for the brainstem.

To analyse effects of age, a linear regression model was applied with the absolute volumes of seven cerebellar regions (V1, V2, V3, averaged left and right lH, mH1, mH2, mH3) and three brainstem regions as vectors of dependent variables. For each subject, left and right regional cerebellar volumes were averaged after testing for asymmetry using paired t-tests. Age, sex and TICV were introduced as independent variables. The total intracranial volume, TICV, was treated as a confounder to correct for differences in physiognomy. All interactions of the independent variables were non-significant and were therefore excluded from the model (Pedhazur, 1982Go). Stepwise elimination of independent variables was used to obtain the final model (elimination criterion P > 0.1). Partial correlation coefficients corrected for total cerebellar volume were calculated for (i) the association between age and one region and (ii) between two regional volumes. Their significance was tested under a Bonferroni-corrected level (Bonferroni-correction for n = 28 simultaneous tests for seven cerebellar regions and age; n = 6 tests for three brainstem regions and age).

The magnitude of age effect on different regions was compared by testing two partial correlation coefficients for significant difference. For this purpose, Steiger's Z* method (Steiger, 1980Go) was used, which compares two correlation coefficients with a common index (dependent correlation, e.g. rV1,age and rlH,age). To estimate the power of this test, a Monte Carlo simulation was performed (J.H. Steiger, personal communication). Assuming 48 cases, the power of the test was between 70 and 75%. Six tests to compare the lateral hemisphere correlation coefficient with the coefficients of the other cerebellar regions were performed (using a Bonferroni correction for n = 6 tests).

For total cerebellar volume, the temporal characteristics of atrophy were analysed. To test for non-linear age-dependency, the term age2 was added to the model (Kleinbaum et al., 1998Go). The non-linear model was compared with the linear using a partial F-test. Since the quality of this prediction may be questioned (see below), an exponential model was fitted to test for biphasic aging. The following curve was fitted (parameters a and b were defined arbitrarily, A0 and k were estimated using iterative fitting).


(2)

The rationale behind this curve is based on the assumption that shrinkage begins at a certain age and builds up until age A0. Provided that not every structural element (e.g. neurons, glia cells, afferent/efferent axons, dendrites — see Discussion) degenerates, the volume should converge at value b.


    Results
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 Footnotes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
The average cerebellar volume was 134 ml, ranging between 100 and 171 ml (Table 1Go). The volume of both lateral hemisphere regions was ~60% of total cerebellar volume. Among the radial segments, the caudoventral segments (V3 and mH3­) were larger than the superior and posterior segments.

The linear regression model for total cerebellar volume (TCV) included age and the total intracranial volume (TICV) as independent variables (Table 2Go). While the TCV was strongly correlated with the TICV (P < 0.001), the association with age was weak (Punivariate = 0.069). The quadratic model better described the dependency between age and total cerebellar volume (partial F-test: P < 0.05). In the quadratic model, shrinkage began around age 50 years (Fig. 2aGo). For young age groups, the quadratic model predicted an increase in volume between age 20 and 30 years, which seemed unlikely to be real. Subsequently, the model described by formula (2) was fitted (Fig. 2aGo). This exponential curve confirmed volume loss to begin around age 50 years. The degree of loss continued to increase until age 65 [parameter A0 in formula (2), point of inflection]. Thereafter, the decline in volume became slower to converge to a ‘virtual' volume of 90 ml. The point of convergence was beyond human life-expectancy. This model produced a better fit than the quadratic model (partial F-test: P < 0.05).


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Table 2 Linear regression models for different volumes in cerebellum and brainstem
 



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Figure 2. (a) Depicts three regression functions between total cerebellar volume and age (solid line: exponential model; dotted line: quadratic model; dashed line: linear model). The exponential model produced the best fit. (be) The plots show linear regression functions for cerebellar regions V1 (lobules I–V of the vermis), V2 (lobules VI–VII) and V3 (lobules VIII–X). To demonstrate effects of age only, other independent covariates are omitted in these plots.

 
Using stepwise elimination of independent variables, different linear models were obtained for the cerebellar regions (Table 2Go). The lateral hemisphere region and the superior segment of the medial hemisphere (mH1) were only associated with TICV (P < 0.001). TICV did not meet the inclusion criterion of Punivariate = 0.1 for regions mH2, V1, V2 and V3. Age remained in the linear model for all regions except lH and mH1. However, univariate effects of age reached 5% significance for vermal regions only (P <= 0.005). The inferior segment of the medial hemisphere (mH3) showed a trendwise association with age (P = 0.053). Gender was a significant predictor for the regional volumes mH2 and vermis-2 (P < 0.05). TICV had no effect on this association, which is demonstrated by direct comparison of group means of the ratios mH2/TICV and V2/TICV (Fig. 3Go). Women had larger TICV-corrected volumes mH2 and V2 than men.



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Figure 3. The figure demonstrates gender differences in regional volumes vermis-2 (lobules VI and VII) and the corresponding medial hemisphere region (mH2). Woman have significantly larger regions than men even after controlling for the total intracranial volume.

 
To compare the magnitude of age effects upon different cerebellar regions, partial correlation coefficients controlling for TCV were calculated (Table 3Go). Vermal regions shrank more than lateral regions (Fig. 4Go). For all vermal regions correlation coefficients were negative and significant when Bonferroni correction for 28 tests was not applied. They did not reach the Bonferroni-corrected 5% level of significance. In the medial hemisphere, the coefficients were still negative, but nonsignificant. The lateral hemisphere showed an increase with age relative to the TCV. Its correlation coefficient was positive (P < 0.05), but did not reach Bonferroni-corrected significance. The partial correlation coefficient of the lateral hemisphere was compared to the coefficients of the other regions (Table 4Go). When Bonferroni correction was not used, all vermal coefficients and the coefficients for mH2 and mH3 were significantly different from the lateral hemisphere coefficient (Table 4Go). A trendwise difference was observed for mH1–lH (Table 4Go). After Bonferroni correction, only the differences lH–V1 and lH–V2 remained significant.


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Table 3 Partial correlation coefficients (corrected for total cerebellar volume)
 


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Figure 4. Partial correlation coefficients between cerebellar volumes and age that are corrected for the total cerebellar volume are plotted (star indicates significance P < 0.05, non-Bonferroni corrected). While the vermal regions show marked negative correlation with age, the regions of the medial hemisphere shrank less, and the lateral hemisphere gained volume relative to the total cerebellum.

 

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Table 4 Steiger's Z-test results for difference in age–volume correlation coefficients
 
Table 3Go also lists partial correlation coefficients between two regional volumes. Significant positive correlations were observed among vermal regions and between vermal and medial hemisphere regions with the same index (P < 0.05). The lateral hemisphere was negatively correlated with all other volumes except mH1 (P < 0.01).

Average brainstem volume was 34ml ranging between 26.9 and 43.4 ml (Table 1Go). The total brainstem volume as well as pons/tegmentum and medulla demonstrated no age-related decline (Fig. 5Go). Only for the midbrain was a partial correlation coefficient (corrected for brainstem volume) of –0.31 obtained (P < 0.05), which did not meet Bonferroni-corrected significance. No age-related shrinkage was observed in the cerebellar peduncles. Gender was not a significant predictor of either total or regional brainstem volumes.



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Figure 5. The figure depicts the partial correlation coefficients between brainstem volumes and age. Trendwise shrinkage is noted only for the midbrain region.

 
The total intracranial volume was significantly smaller in women than in men (P < 0.01). It was not correlated with age.


    Discussion
 Top
 Footnotes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Study Design and Limitations

Age effects on regional volumes in the brain are generally small and interindividually variable. An unknown number of confounding factors may be of importance. Large samples of normal subjects of different origin, race and social background are therefore required to identify effects of age. A valid characterization of age effects and their regional expression, can best be achieved in meta-analytic studies as emphasized by Raz and co-workers (Raz et al., 1998Go). The present data should be considered as an integral part of the enlarging database on brain atrophy.

Magnetic Resonance Imaging and Volumetry

High-resolution MR images were used in this study. Brainstem and cerebellar volumes were determined using T1-weighted images with an isometric voxel size of 0.9 mm3. Lower resolution PD-weighted MR images (voxel size 0.9 x 0.9 x 2 mm3) were used to measure intracranial volumes. PD-weighting produced similar pixel intensitites for CSF and brain matter which facilitated segmentation of cranial contents. Lower spatial resolution does not adversely affect the precision of the measurement for large volumes, such as the intracranial volume (Luft et al., 1996Go).

Confounding Variables

The design of this study is cross-sectional. When ageing is studied in a cross-sectional sample of subjects, interindividual variability is of concern. To reduce variability, absolute volumes should be corrected for physiognomical parameters and factors affecting brain size but unrelated to age. Several physiognomical parameters may be considered. Most authors correct for body height (Raz et al., 1997Go) or TICV (Escalona et al., 1991Go; Schaefer et al., 1991Go; Shah et al., 1991Go; Doraiswamy et al., 1992Go). A few studies present uncorrected data (Hayakawa et al., 1989Go). In choosing the proper parameter, two caveats have to be considered. The results would be overcorrected if, for example, the volume of the cerebral hemispheres is used for correction. Age effects on cerebellar volume would be blurred, because both the cerebellum and the cerebral hemispheres shrink with age. In contrast, failure to correct for an effect which affects cerebellar volume but is unrelated to ageing will result in false (over or under) estimation of age effects. The TICV showed no age dependency in our data and was therefore considered a stable parameter for correction. Body height — a possible alternative — is influenced by degeneration of intervertebral discs and vertebral bone (e.g. compression fractures) (Galloway et al., 1990Go) and must therefore be considered unstable.

Time-course Analysis

To analyze the time-course of age-related cerebellar volume loss, three regression models were used. First, the linear model was fitted to assess the simplest relationship between age and volume. However, it has been reported previously that cerebral ageing follows a non-linear course (Haug, 1997Go). Changes are minimal until mid-age, but progressively accumulate thereafter. Therefore, a second-order model — the simplest possible nonlinear relationship — was applied and produced a better fit. The parabolic form of the second-order function would predict a gain in volume until age 40–50 before the shrinkage of advanced age begins. An increase in volume is conceivable for pre-school age only; between age 4 and 18 years cerebellar volume was shown to remain stable (Jernigan et al., 1991Go; Giedd et al., 1996Go). For subjects above age 20, an increase in volume is very unlikely. Also, progressive worsening of shrinkage predicted by the downslope arm of the parabola is unrealistic. On a histological level, it has been shown that some structural elements degenerate while others remain unaffected (Torvik et al., 1986Go; Haug, 1997Go). Therefore, we do not think that the volume loss is theoretically indefinite. It more likely verges upon a baseline — although this stage may be well beyond life expectancy, it should still be considered, because it postulates a point of inflection on the downward slope of the regression curve. A model that fits this rationale is described by formula (2). This model produced the highest quality fit as compared to linear and quadratic models (evaluated by partial F-test).

Statistical Analysis

Age effects on two regions in cerebellum or brainstem are most likely dependent. Therefore, testing for differences of age effects requires a test that manages dependent samples. Steiger's Z method fulfils these requirements and was used for similar problems previously (Raz et al., 1992Go, 1997Go). Since this test, as well as tests for significant correlation, were performed multiple times, Bonferroni-corrected significance levels were used. For correlation analysis, the significance level was corrected for 28 tests (all correlations between age and seven cerebellar regions). For Steiger's test, significance levels were corrected for six tests. These six tests were performed simultaneously to analyse the mediolateral gradient of decreasing age effect: The negative age–volume correlations of three vermal and three medial hemisphere regions were compared with the positive correlation of the lateral hemisphere. Bonferroni's correction ideally operates on independent samples; it holds for dependent samples too (Caraux and Gascuel, 1992Go), but may result in too stringent significance criteria. The alternative procedure, the Dunn–Sidak adjustment, is even more stringent. The significance of our results may therefore be underestimated when Bonferroni correction is used. Therefore, test results that do not meet Bonferroni-corrected significance levels are also mentioned in this article.

Cerebellar Atrophy

Histology

In the cerebrum, it is assumed that degenerative changes in the white matter account for macroscopic atrophy (Meier-Ruge et al., 1992Go; Peters et al., 1994Go; Haug, 1997Go). This has not, to our knowledge, been shown for the cerebellum. Dlugos et al. reported atrophy of the molecular layer in aged rat cerebella (Dlugos and Pentney, 1994Go). The spiny branchlets of Purkinje cells (PCs) have been shown to degenerate with age (Fujisawa and Nakamura, 1982Go). A loss of PCs has been reported by different authors in mice (Sturrock, 1989aGo) and humans (Ikunai, 1928Go; Hall et al., 1975Go; Torvik et al., 1986Go). Nairn and co-workers reported a correlation between the PC number and gross cerebellar volume (Nairn et al., 1989Go). Since PCs contribute little to cerebellar cortical mass (Ito, 1984Go), their role in macroscopic atrophy is questionable. However, PC axons comprise the major part of cerebellar white matter, especially of folial white matter (Victor et al., 1959Go). Folial white matter reduction may therefore significantly contribute to the loss of overall volume. To our knowledge, in vivo imaging has not yet been able to differentiate between grey matter and folial white matter in the cerebellum, to allow quantification of folial white matter.

Features of Macroscopic Ageing in the Cerebellum

Age-related degeneration is assumed to start between 50 and 60 years of age, as reported in autopsy (Ellis, 1920Go), CT (Nishimiya, 1988Go), MRI (Jernigan et al., 1991Go; Pfefferbaum et al., 1994Go) and cell-count studies (Hall et al., 1975Go; Torvik et al., 1986Go). Consistent with these findings, our data indicate a non-linear decline in total cerebellar volume. A biphasic time-course of atrophy was also observed in the cerebral hemispheres (Haug and Eggers, 1994Go). A second feature of cerebellar ageing is its variability. In a number of studies, cerebellar atrophy was present in ~30% of elderly people, while the remainder had normal or only slightly reduced volumes (Koller et al., 1981Go; Kryst et al., 1986Go; Torvik et al., 1986Go). This feature was not observed in our data.

Regional Age-related Atrophy

Regional differences in cerebellar atrophy have been reported in autopsy studies (Ellis, 1920Go). The anterior lobe seems more vulnerable than the rest of the cerebellum. Histological studies have shown an increased cell loss in the superior lobules of the vermis (Torvik et al., 1986Go). In general, the vermis seems to be more affected by age than the hemispheres (Koller et al., 1981Go). In vivo magnetic resonance volumetry studies obtained heterogeneous results (Raz, 1999Go). While some authors reported about equal volume loss in cerebellar hemispheres and vermis (Raz et al., 1998Go), others found the hemispheres to be less affected (Escalona et al., 1991Go; Deshmukh et al., 1997Go). Our results support the latter. However, this discrepancy may be the result of methodological differences among the studies, especially the definition of regions in the cerebellum. If the hemisphere is not divided into a medial and a lateral part, the whole region may very well show age-related shrinkage. In our data, the sum of regions 1H, mH1, mH2, mH3 has an age–volume correlation coefficient of –0.27 (P = 0.075). This is similar to a median effect size for the hemisphere of –0.29 calculated in a meta-analysis by Raz (Raz, 1999Go). The combination of hemispheric regions blurs the mediolateral gradient suggested by the comparison of vermal and lateral hemisphere regions in our data. Therefore, the findings of others which seem discrepant at first glance may not be contradictory to our results. Other possible explanations for discrepant results in cerebellar volumetry are racial or social differences in the subject samples of different studies (N. Raz, personal communication). Such differences cannot be controlled. Only large meta-analysis can overcome these shortages.

Patterns of Pathological Atrophy

The pattern of volume loss observed in the present study is similar to alcoholic cerebellar atrophy (Victor et al., 1959Go; Torvik and Torp, 1986Go; Karhunen et al., 1994Go). This similarity raises the question whether the volume loss results from normal ageing or whether it is caused by alcohol use which is below the threshold of our exclusion criteria. Social drinking habits were not excluded in our subject sample. Torvik and co-workers were confronted with the same problem when they found a similar pattern of atrophy in a group of chronic alcoholic patients and controls (Torvik and Torp, 1986Go). The authors subsequently investigated a larger group of normal individuals which were selected following strict exclusion criteria for alcohol disease. Cerebellar atrophy with vermal predominance was still found (Torvik et al., 1986Go). The similarity between age-related and alcoholic cerebellar atrophy (Freund, 1984Go) suggests a common mechanism. Cerebellar tissue may either be attacked by a common metabolic factor or it may suffer a nutritional deficit, which is present in the elderly as well as in alcoholics. Vitamin deficiencies have been proposed as a cause of alcoholic cerebellar atrophy (Victor et al., 1959Go) and also are frequent in the aged population.

However, assuming that a metabolic or nutritional factor causes cerebellar atrophy does not explain the mechanism of regional vulnerability.

Causes of Regional Vulnerability

CSF Flow

A recent article by Cavanagh and colleagues (Cavanagh et al., 1997Go) argues that tissue exposed to high CSF flow (such as the cerebellar vermis and, in particular, its anterior portion below the cistern of the great cerebral vein) is more affected by toxins dissolved in the CSF. With regard to alcoholic cerebellar atrophy, such toxins may be ethanol or acetaldehyde (Hunt, 1996Go). The CSF flow model gives one possible explanation for the pattern of age-related atrophy observed in this study.

Vascular Factors

Raz et al. discuss regional haemodynamic differences as a cause of age-related atrophy (Raz et al., 1998Go). Vascular compromise is known to induce cerebral atrophy (DeGirolami et al., 1994Go). In the elderly, the incidence of decreased flow is expectedly higher secondary to atherosclerosis. Such reduction in blood flow will first affect vascular watershed areas. In the cerebellum, watershed areas were identified in the deep white matter (which may be protected by high collateralization), the lateral hemispheres (SCA–PICA watershed), and the petrosal surface (SCA–AICA–PICA watersheds) (Gillilan, 1969Go; Savoiardo et al., 1987Go; Cormier et al., 1992Go). Accordingly, the lateral hemisphere region would be highly exposed to vascular atrophy, which is not consistent with our findings. However, other haemodynamic factors — as yet unidentified — may intervene. Since interindividual variability in cerebellar vasculature is high (Naidich et al., 1976Go; Cormier et al., 1992Go), identification of a link between atrophy and vascularity will require studies that directly correlate regional atrophy and vascular anatomy in the same individual.

Structural Factors

If white matter loss is responsible for macroscopic atrophy, as discussed above, then regional differences in grey-to-white matter ratio may be a source for regional differences in volume loss. Braitenberg and Atwood identified the regions with large amounts of folial white matter: the anterior vermal region and a sagittal band in the mid-hemisphere (Braitenberg and Atwood, 1958Go). This may partly account for the higher shrinkage in the anterior vermis, but cannot be the only factor, because the hemisphere shows only minor changes with age.

Functional Factors

Brain atrophy can also occur due to a decrease in afferent or efferent signals (Baudrimont et al., 1983Go; Chung, 1985Go; Jessell, 1991Go). Defined by their connectivity, three zones in the cerebellar cortex can be distinguished (vermis, intermediate and lateral zone) each projecting to a specific cerebellar nucleus (Jansen and Brodal, 1940Go; Ito, 1984Go). This rough sagittal organization is further refined by the distribution of corticofugal efferents (Voogd, 1964Go) and climbing fibre afferents (Brodal and Kawamura, 1980Go). Dow described the distribution of mossy fibre afferents, which separates the anterior (V1/3, mH1; mostly spinal input) from the posterior lobe (V2, mH2/3, lH; predominantly cortical input) (Dow, 1942Go). The latter compartmentalization correlates with the phylogenetic divisions of the cerebellum (Larsell, 1967Go). Associating the distributions of these fibre systems with our results may be doubtful, because they were described in animals and are too fine to be compared with our gross subdivision. However, the distribution of mossy fibre afferents can be roughly matched with the vulnerability pattern observed in the vermis, although it does not fit the lateromedial gradient. The hemispheres should behave like the posterior vermis, but the contrary was observed. The lateromedial gradient is better compared with the sagittal banding of corticofugal efferents and climbing fibre afferents.

Functional mapping of the cerebellum supports the sagittal division of the cerebellar cortex. While the vermis and medial cerebellum are involved in motor control and coordination [with truncal movement being represented medially and distal movement laterally (Snider and Stowell, 1944Go; Luft et al., 1998aGo)], the lateral cerebellum is thought to be involved in perceptual/sensory and cognitive processing (Botez et al., 1989Go; Leiner et al., 1991Go; Schmahmann, 1991Go; Daum and Ackermann, 1995Go; Schmahmann, 1996Go; Gao et al., 1996Go; Paradiso et al., 1997Go). Evidence for the latter comes from studies demonstrating connections between the lateral cerebellum/dentate nucleus and parietal or frontal association areas in the contralateral cerebral cortex (Crosby, 1969Go; Middleton and Strick, 1994Go).

One may speculate that the volume loss in the vermis is related to a lack of usage with decreasing mobility in the elderly, especially of the truncal musculature. Non-motor functions of the lateral hemisphere remain less impaired or used more frequently. This interpretation is supported by regional volumetry of the cerebral hemispheres. Precentral areas shrink with age, whereas the parietal sensory cortex and association cortex remain relatively unchanged (Haug, 1997Go). However, this hypothesis is tempered by the fact that the dentate nucleus degenerates (Sturrock, 1989bGo; Grandi and Arcari, 1997Go). This nucleus is part of the lateral hemisphere–parietal cortex system.

Developmental/Molecular Factors

Herrup and Kuemerle recently reviewed the growing amount of studies describing regional gene expression in the cerebellum (Herrup and Kuemerle, 1997Go). They identified two orthogonal axes of compartmentalization. The first axis runs horizontally from the vermis to the lateral hemisphere. A sagittal axis separates the anterior from the posterior and the flocculonodular lobe. One may notice the similarity of these divisions to the maps of afferent and efferent connections. The authors emphasize that these congruencies strengthen the compartmentalization as such.

The proteins Zebrin I (a 120 kDa protein of unknown function) and Zebrin II (aldolase C) are distributed in seven sagittal stripes in each hemicerebellum. At certain developmental stages the expression of genes follows a somewhat coarser sagittal distribution (3–5 zones. e.g. the PC-specific protein L7 and regulatory genes En-1 and En-2). These markers are concentrated in the vermis and decrease laterally. In anteroposterior direction, markers including En-2 and L7 exhibit gradients that recognize the primary and the posterolateral fissure as a boundary.

Although none of these markers is directly linked to agerelated atrophy in humans, evidence exists for their role in cerebellar development in mice. Until now, these regional molecular gradients in the cerebellum cannot explain our findings, but they are a promising approach towards an appropriate model.

Regional Differences in the Brainstem

Our findings in the brainstem are similar to previous results of other authors. Most studies did not report effects of age on the total brainstem volume (Raz, 1996Go). Age-related shrinkage of the midbrain has been observed previously (Doraiswamy et al., 1992Go; Weis et al., 1993Go). This was attributed to shrinkage of the substantia nigra (Raz, 1996Go). The major part of the substantia nigra was excluded from our midbrain region. Age-related volume loss was nevertheless observed. The different embryogenesis of the midbrain as compared to metencephalon and medulla may be related to this finding, as well as its high content of fibres. Interestingly, the cells in the anterior cerebellum (including the anterior vermis) migrate from the mesencephalon, while the more posterior cells are of rhombencephalic origin (Herrup and Kuemerle, 1997Go).

Conclusion

Our data demonstrate regional differences in the degree of age-related atrophy in cerebellum and brainstem. The brainstem remains stable with age. In the cerebellum, the vermis is affected most, followed by the medial hemisphere. The volume of the lateral hemisphere is not affected by age. This pattern of atrophy is similar to alcoholic cerebellar atrophy, suggesting a common, possibly nutritional aetiology. Inherited factors may explain why some regions are more vulnerable than others. This is suggested by similarities between the patterns of atrophy and compartments defined by the distribution of genes and proteins. Functional causes may also be involved, because similarities exist between the pattern of atrophy and the maps of afferent and efferent connections and concomitant atrophy of interconnected areas in the cerebral cortex. CSF dynamics or structural components (regional differences in grey–white matter ratio) cannot be ruled out, whereas vascular factors could not be linked to our results.


    Notes
 
We thank Dr James Steiger (Department of Medical Statistics, University of Vancouver) for his statistical expertise and Dr Naftali Raz (Department of Psychology, University of Memphis, TN), Dr Philip Boyer, and Dr Jean-Paul Vonsattel (Department of Neuropathology, Massachusetts General Hospital, Boston, MA) for their comments and suggestions. Part of this work was presented at the 1997 meeting of the Society for Neuroscience, New Orleans (October 1997).

Address correspondence to Andreas R. Luft, Division of Neurosciences Critical Care, Johns Hopkins University, 600 N. Wolfe Street, Meyer 8–140, Baltimore, MD 21287–7840, USA. Email: arluft{at}t-online.de.


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 Materials and Methods
 Results
 Discussion
 References
 
1 Present address: Division of Neurosciences Critical Care, Department of Neurology, Johns Hopkins University, 600 N. Wolfe Street, Meyer 8-140, Baltimore, MD 21287-7840, USA Back


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 Materials and Methods
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 Discussion
 References
 
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