Brain Advance Access originally published online on February 25, 2004
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Brain, Vol. 127, No. 4, 811-824, 2004
© 2004 Guarantors of Brain
doi: 10.1093/brain/awh101
Age-related cortical grey matter reductions in non-demented Down’s syndrome adults determined by MRI with voxel-based morphometry
1 Alzheimer Memorial Center and Geriatric Psychiatry Branch, Dementia and Neuroimaging Section, Department of Psychiatry, Ludwig-Maximilian University, Munich, Germany, 2 Neuroimage Analysis Laboratory, Department of Psychology, Arizona State University, Tempe, the Arizona Alzheimer’s Research Center, and the Arizona Alzheimer’s Disease Core Center, AZ, 3 Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH and 4 Brain Physiology and Metabolism Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
Correspondence to: Stefan J. Teipel, MD, Alzheimer Memorial Center and Geriatric Psychiatry Branch, Dementia and Neuroimaging Section, Department of Psychiatry, Ludwig-Maximilian University, Nussbaumstrasse 7, 80336 Munich, Germany E-mail: stefan.teipel{at}med.uni-muenchen.de
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Summary |
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Ageing in Down’s syndrome is accompanied by amyloid and neurofibrillary pathology the distribution of which replicates pathological features of Alzheimer’s disease. With advancing age, an increasing proportion of Down’s syndrome subjects >40 years old develop progressive cognitive impairment, resembling the cognitive profile of Alzheimer’s disease. Based on these findings, Down’s syndrome has been proposed as a model to study the predementia stages of Alzheimer’s disease. Using an interactive anatomical segmentation technique and volume-of-interest measurements of MRI, we showed recently that non-demented Down’s syndrome adults had significantly reduced hippocampus, entorhinal cortex and corpus callosum sizes with increasing age. In this study, we applied the automated and objective technique of voxel-based morphometry, implemented in SPM99, to the analysis of structural MRI from 27 non-demented Down’s syndrome adults (mean age 41.1 years, 15 female). Regional grey matter volume was decreased with advancing age in bilateral parietal cortex (mainly the precuneus and inferior parietal lobule), bilateral frontal cortex with left side predominance (mainly middle frontal gyrus), left occipital cortex (mainly lingual cortex), right precentral and left postcentral gyrus, left transverse temporal gyrus, and right parahippocampal gyrus. The reductions were unrelated to gender, intracranial volume or general cognitive function. Grey matter volume was relatively preserved in subcortical nuclei, periventricular regions, the basal surface of the brain (bilateral orbitofrontal and anterior temporal) and the anterior cingulate gyrus. Our findings suggest grey matter reductions in allocortex and association neocortex in the predementia stage of Down’s syndrome. The most likely substrate of these changes is alterations or loss of allocortical and neocortical neurons due to Alzheimer’s disease-type pathology.
Key Words: Alzheimer’s disease; Down’s syndrome; grey matter; neocortex; neurodegeneration
Abbreviations: DSMSE = Down Syndrome Mental Status Examination; FDR = false discovery rate; MNI = Montreal Neurological Institute; PPVT-R = Peabody Picture Vocabulary Test—Revised; ROI = region of interest; SPM = statistical parametric mapping; VBM = voxel-based morphometry
Received August 4, 2003. Revised November 24, 2003. Accepted December 8, 2003.
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Introduction |
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With advancing age, an increasing proportion of subjects with Down’s syndrome who are older than 40 years develop cognitive impairment with early memory involvement, followed by a linear decline in non-memory cognitive function (Lai and Williams, 1989
; Schapiro et al., 1992; Alexander et al., 1997). Cognitive impairment finally results in a dementia syndrome consistent with the features of Alzheimer’s disease (Lai and Williams, 1989; Evenhuis, 1990; Schapiro et al., 1992; Alexander et al., 1997). Prevalence of dementia has been estimated to reach 5–10% up to the fifth decade of life and 40–50% by the sixth decade of life in Down’s syndrome (Lai and Williams, 1989; Evenhuis, 1990). Post mortem studies show that at 40 years of age, virtually all Down’s syndrome subjects have neuropathological lesions that meet the pathological criteria for Alzheimer’s disease (Mann et al., 1984; Wisniewski et al., 1985a, b). An important factor for the development of the neuropathological and clinical phenotype of Alzheimer’s disease in elderly Down’s syndrome subjects is an increased amyloid burden due to an extra copy of the amyloid precursor protein gene on chromosome 21 (Rumble et al., 1989). On this basis, Down’s syndrome has been proposed as a model to study the predementia stages of Alzheimer’s disease (Mann, 1988).
In recent years, in vivo studies using MRI have investigated age-related brain atrophy in Down’s syndrome. Several studies reported significant volume reductions of hippocampus and adjacent medial temporal lobe structures with advancing age in non-demented Down’s syndrome adults. A consistent finding in non-demented Down’s syndrome subjects in cross-sectional studies was an increased ventricular volume with age (Kesslak et al., 1994; Ikeda and Arai, 2002). Age-related reductions of overall cerebral total and grey matter volumes, however, were not detectable before onset of dementia (Schapiro et al., 1989, 1992; Kesslak et al., 1994; Raz et al., 1995). These earlier studies used operator-dependent volumetric techniques. In recent years, voxel-based morphometry (VBM) has been developed to search the entire brain for morphological changes (Ashburner and Friston, 2000). VBM is observer-independent and automated, and therefore highly replicable. It allows processing of a high number of scans within a relatively short time, and can detect morphological changes throughout the entire brain. A recent study reported a distinct pattern of grey matter reductions in non-demented Down’s syndrome adults compared with age-matched controls, using MRI and VBM (White et al., 2003). The effects were independent of age, and most likely reflect the developmentally abnormal brain morphology in Down’s syndrome. However, effects of age on brain morphology that may be related to the onset of Alzheimer’s disease type pathology in Down’s syndrome have not yet been investigated using VBM.
In the present study, we determined the effects of age on cortical grey matter in a group of non-demented Down’s syndrome adults using VBM modified according to Good et al. (2001b). We hypothesized that increasing age would be associated with grey matter reductions in regions of parietal, temporal and frontal association cortex, similar to the pattern of structural and functional cortical alterations in mild sporadic Alzheimer’s disease (Baron et al., 2001; Ishii, 2002). We used group-specific normalization and segmentation templates, and performed analyses only within the Down’s syndrome group. Thus we reduced the effect of global shape differences that would have occurred when comparing the developmentally abnormal brains of Down’s syndrome subjects with those of a normal comparison group.
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Subjects and methods |
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Subjects
Thirty-four subjects (17 female, 17 male) with trisomy 21, as ascertained through karyotyping, underwent MRI scans with identical imaging parameters. The scans of seven subjects were discarded due to folding (three scans) or motion artefacts (four scans); therefore, only scans of 27 Down’s syndrome subjects remained in the study. The mean ages and gender distributions of the entire Down’s syndrome group and the selected subgroup were similar: 41.6 years (SD 9.1, range 25.3–62.5) compared with 41.1 years (SD 8.9, range 25.3–61.6), and 17:17 female:male compared with 15:12 female:male. Peabody Picture Vocabulary Test—Revised (PPVT-R) mean test age was higher in the selected subgroup than in the entire group [5.6 years (SD 3.1, range 2.0–13.3) compared with 4.3 years (SD 2.6, range 2.3–10.4)], although this difference was not significant (Mann–Whitney U-test = 54, P = 0.12).
Overall cognitive ability was assessed using the PPVT-R (Dunn and Dunn, 1981). Mean test age in the PPVT-R was 6.2 years (SD 3.0) in the young (<40 years of age) and 4.8 years (SD 3.4) in the older (>40 years of age) subjects. PPVT-R data were unavailable for five patients.
All of the Down’s syndrome subjects underwent medical, neurological and psychiatric evaluations according to published criteria (Duara et al., 1983). A clinical screening evaluation of the MRI, performed independently of the volumetric analyses, showed no evidence of stroke, tumour or mass effect.
Subjects with dementia were excluded from this study. The exclusion of dementia in Down’s syndrome was done using modified criteria from Diagnostic and Statistical Manual of Mental Disorders—Third Edition (DSM-III), which specified an acquired, progressive loss of intellectual function, such as loss of daily living and vocational skills, memory impairment, reduced speech and comprehension, and personality change. The diagnosis was based on interviews with caregivers, clinical examination and bedside mental status tests using standardized criteria (Schapiro et al., 1987). Diagnosis was made independently of the results of neuropsychological testing and MRI, and discussed to consensus by a team of neurologists, psychiatrists and neuropsychologists experienced in the diagnosis of dementia in Down’s syndrome. Inter-rater reliability for our method of diagnosis of dementia in Down’s syndrome has been established previously (Schapiro et al., 1989).
After complete description of the study to the holder of a durable power of attorney or legal guardian, written informed consent was obtained. Assent to participate in the study was also obtained from the Down’s syndrome subjects. The research was approved by the National Institute on Aging Institutional Review Board.
MRI
Volumetric T1-weighted scans were obtained in coronal orientation on a 1.5 T scanner (General Electric Signa II, Milwaukee, WI, USA), slice thickness 2 mm, in-plane resolution 0.94 x 0.94 mm. Total intracranial volume was measured from 6-mm thick contiguous coronal slices (repetition time/echo time, TR/TE = 2000/20 ms, flip angle = 45°, field of view = 25 cm, matrix = 256 x 160 mm) obtained perpendicular to the inferior orbitomeatal line on a 0.5 T scanner (Picker Instruments, Cleveland, OH, USA).
Cognitive assessment
Overall cognitive ability in the Down’s syndrome subjects was assessed using the PPVT-R (Dunn and Dunn, 1981). The PPVT-R is a test of word knowledge, in which the subject chooses a drawing that best depicts the meaning of a spoken word. It provides a measure of general intellectual ability (Stevenson, 1986) that does not rely on verbal expressive skills, which are often impaired in individuals with Down’s syndrome. It spans both very low age ranges and levels of mental ability and levels considerably above average adult ability (Lezak, 1995).
Additionally, the Down’s syndrome subjects underwent an extensive set of neuropsychological tests, including tests of general cognitive function, memory, attention, language and visuospatial construction. The entire set of tests has been described in a previous paper (Alexander et al., 1997). In order to limit the number of models, we selected the memory subtest of the Down Syndrome Mental Status Examination (DSMSE) (Haxby, 1989; Tyrrell et al., 2001) for investigations of correlations between specific cognitive performance and regional grey matter changes. This test includes immediate and delayed recall of the identity of three objects and the location of three hidden objects. The scale ranges between 0 and 12 points, with higher scores indicating better performance. We selected this test because episodic memory impairment is among the first cognitive changes in non-demented Down’s syndrome subjects over the age of 40 years (Lai and Williams, 1989; Schapiro et al., 1992; Alexander et al., 1997). Additionally, this test was sensitive to age in a group of 41 Down’s syndrome subjects (Alexander et al., 1997), and test performance was correlated with left hippocampus volume loss measured with a region of interest (ROI) technique in a group of 34 Down’s syndrome subjects (Krasuski et al., 2002). Complete cognitive assessment, including the PPVT-R and the DSMSE memory test, was available in 22 Down’s syndrome subjects.
Data processing for VBM
An optimized VBM protocol was followed for preprocessing and subsequent analysis of imaging data. This method, previously described in detail (Ashburner and Friston, 2000; Good et al., 2001a, b), was implemented within Matlab 5.3 (MathWorks, Natick, MA, USA) through statistical parametric mapping (SPM99; Wellcome Department of Imaging Neuroscience, London, UK; available online at http://www.fil.ion.ucl.ac.uk/spm) (Friston et al., 1995a, b). Preprocessing of structural data followed a number of defined steps, as follows.
Manual preprocessing
Seven scans with low quality due to movement or folding artefacts were excluded from the analyses (see section Subjects). The remaining scans were manually reoriented with the inter-hemispheric gap parallel to the vertical axis of the field of view and the anterior–posterior commissure line parallel to the horizontal axis. The origin was manually set on the anterior commissure. The reorientation matrix (six parameter rigid body transformation) was stored.
Creation of group-specific templates and priors
A group-specific template was created from the scans of the Down’s syndrome subjects. Each structural MRI was normalized to the standard Montreal Neurological Institute (MNI) T1 MRI template using a set of non-linear basis functions (Ashburner et al., 1997; Ashburner and Friston, 2000). Normalized scans then were smoothed (12-mm full-width at half maximum isotropic Gaussian kernel) and averaged to obtain a group specific T1 template. All structural MRIs in native space were then normalized to this template. The normalized MRIs were segmented into CSF, grey matter and white matter compartments using the SPM99 priors. The SPM segmentation employs a mixture model cluster analysis (after correcting for non-uniformity in image intensity) to identify voxel intensities that match particular tissue types combined with a priori probabilistic knowledge of the spatial distribution of tissues derived from grey and white matter, and CSF prior probability images (priors) (Ashburner and Friston, 1997). In order to further improve the segmentation, before segmentation the skull had been stripped from the normalized scans using MRIcro (implementing Brain Extraction Tool; FMRIB Image Analysis Group, Oxford, UK) (Smith, 2002). Next, CSF, grey matter and white matter images were smoothed with an 8-mm kernel and averaged to obtain group-specific CSF, grey matter and white matter priors for later segmentation of native MRI scans. In addition, grey matter images were smoothed with a 12-mm kernel and averaged to obtain a group-specific grey matter template.
Segmentation of native scans and derivation of optimized normalization parameters
The original MRI scans were segmented using the group-specific T1 template and grey matter, white matter and CSF priors. This segmentation step involves an affine transformation of each scan to the template with a subsequent back-projection into native space. Next, an automated brain extraction procedure that incorporated a segmentation step was used to remove non-brain tissue (Good et al., 2001b). The extracted grey matter images were then normalized to the group-specific grey matter template. Spatial normalization used residual sum of squared differences as the matching criterion and included affine transformations and linear combination of smooth basis functions modelling global non-linear shape differences (Ashburner et al., 1997; Ashburner and Friston, 2000).
Optimized normalization and segmentation
The normalization parameters were then applied to the original structural images in native space, thereby reducing any contribution from non-brain voxels and affording optimal spatial normalization of grey matter. These normalized images were resliced to a final voxel size of 1.0 mm3. The skull was stripped from the normalized scans using MRIcro (implementing Brain Extraction Tool) (Smith, 2002), and stripped scans then were segmented into grey and white matter and CSF partitions. After an automated brain extraction step, the partitioned grey matter images were modulated by the Jacobian determinants from spatial normalization to correct for volume changes introduced during the non-linear spatial transformations. Analysis of modulated data tests for regional differences in absolute amount (volume) of grey matter. In contrast, analysis of unmodulated data tests for differences in grey matter concentrations (Ashburner and Friston, 2000). Finally, all normalized, segmented unmodulated and modulated images were smoothed with a 12-mm full-width at half maximum isotropic Gaussian kernel.
Statistical analysis
For statistical analysis we employed the general linear model on a voxel basis. Prior to regression analysis, scans were proportionally scaled to the global mean and thresholded at 40% of global intensity to reduce the influence of any remaining non-brain tissue. Proportional scaling to the global mean allows detection of voxels with a relative accelerated loss or a relative preservation of grey matter (i.e. more or less than the global loss). We considered significant effects in the negative and positive direction. Results were thresholded at P < 0.05, corrected for multiple comparisons using false discovery rate (FDR), and an extent threshold of 50 contiguous voxels was applied. FDR correction ensures that on average not more than 5% of the significant voxels are false positives (Genovese et al., 2002). To assess changes in absolute grey matter volume and concentration, we re-analysed the scans without prior scaling to global grey matter signal.
Independent multiple regression models were calculated for the age effect with gender, intracranial volumes and the PPVT-R (Dunn and Dunn, 1981) as covariates. The PPVT-R was used as a measure of general cognitive function. The level of significance for the analysis with gender as covariate was thresholded at P < 0.05, corrected for multiple comparison using FDR. Because intracranial volume data were unavailable for one subject and PPVT-R data were unavailable for six subjects, the significance threshold was set to a less stringent level of P < 0.005, uncorrected for multiple comparisons, for these models.
We calculated a multiple regression model for the regression of the DSMSE memory subtest on grey matter volume, including age as a covariate to control for age effects on memory function, and PPVT-R as a covariate to control for general intellectual function. The significance threshold was set at P < 0.001, uncorrected, and an extent threshold of 50 contiguous voxels was applied.
Localization
Localization of peak correlations was based on the coordinates from the MNI template. We used a non-linear algorithm provided by Matthew Brett (MRC Cognition and Brain Sciences Unit, Cambridge, UK) (Brett et al., 2002), to transform MNI into Talairach coordinates. Peak correlations then were identified from the Talairach and Tournoux atlas (Talairach and Tournoux, 1988) based on these coordinates.
ROI analysis
In a recent study we found significant reductions of ROI-derived hippocampus, amygdala and parahippocampal gyrus volumes with increasing age in 34 Down’s syndrome subjects (Krasuski et al., 2002). The sample of the present study is a subgroup of these 34 subjects. To test for the age effect in medial temporal lobes we defined a cube-shaped ROI of side length 10 mm, centred at Talairach and Tournoux coordinates x = 22, y = –6 and z = –18 for right, and x = –22, y = –6 and z = –18 for left ROI, covering parts of the hippocampus head and the amygdala (see Fig. 1 for localization of ROIs). Intensities of voxels contained within these two ROIs were extracted from the modulated smoothed grey matter maps and averaged across voxels to obtain an estimate of medial temporal lobe intensity for each subject. We used Pearson’s correlation coefficient to assess correlations between averaged voxel intensity in medial temporal lobe and age.
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Results |
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Results of the analysis of the modulated data (proportionally scaled to global grey matter) are summarized in Table 1 and illustrated in Fig. 2. In decreasing order of statistical significance, clusters of relative accelerated reduction of grey matter volume with increasing age were located in bilateral parietal cortex (mainly the precuneus and inferior parietal lobule), bilateral frontal cortex with left side predominance (mainly middle frontal gyrus), left occipital cortex (mainly lingual cortex), right precentral and left postcentral gyrus, left transverse temporal gyrus, and right parahippocampal gyrus.
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50 contiguous voxels passing the significance threshold of P < 0.05, FDR corrected. (A) SPM(t) map in ‘glass brain’ projection. L = left hemisphere. (B) Colour-coded SPM(t) map projected on the normalized rendered brain surface from the MRI scan of a Down’s syndrome subject. This figure can be viewed in colour as supplementary material at Brain Online.Figure 3 shows grey matter volumes as a function of age at selected voxels to control for potential effects of extreme values driving the significant correlations.
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Results of the analysis of the unmodulated data were very similar to those obtained from the modulated data.
When we repeated our analyses without prior scaling to global grey matter intensity, the negative contrasts for modulated and unmodulated data showed significantly decreased absolute grey matter throughout the entire cortical grey matter, whereas the positive contrast showed no significant clusters.
When controlling for gender as a covariate, the results for the modulated and the unmodulated data remained unchanged (Fig. 4A). When controlling for total intracranial volume and PPVT-R scores, the results remained unchanged at an uncorrected significance threshold of P < 0.005 (Fig. 4B and C). For the same model at an uncorrected threshold of P < 0.001, there were reductions of grey matter with age in regions of the bilateral superior parietal and left prefrontal lobes. This finding supports the notion that differences in age-related patterns of grey matter loss with and without controlling for intracranial volume and PPVT-R scores are probably related to the loss of power due to unavailable data in some subjects. Figure 4 shows the findings from the covariate analyses.
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The average DSMSE memory score was 10.5 points (SD 2.3, range 4–12) in the 22 Down’s syndrome subjects for whom data were available. There was no significant effect of age on the DSMSE memory score (Spearman’s rho = –0.12, P = 0.6). After controlling for PPVT-R and age, the memory subscore of the DSMSE showed significant correlations with grey matter volume in left superior and middle temporal gyrus, bilateral precuneus, left hippocampus, right middle temporal gyrus, and right middle frontal gyrus (Fig. 5, Table 2).
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The correlations between right and left ROI-derived medial temporal lobe intensities, including hippocampus and amygdala, and age were –0.51 (P < 0.01) and –0.45 (P < 0.02), respectively. Figure 6 shows averaged grey matter intensities in left and right medial temporal lobe ROIs as a function of age.
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Positive contrasts for modulated and unmodulated data showed significant clusters of relative preserved grey matter volume and concentration in bilateral striatum, left cerebellum, pons, left thalamus, anterior cingulate, periventricular regions and at the basal surface of the brain (bilateral orbitofrontal and anterior temporal).
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Discussion |
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In the present study, we examined age-related cortical grey matter changes in non-demented adults with Down’s syndrome using MRI scans and VBM. There were reductions of absolute grey matter concentration and volume with increasing age across the entire brain. When proportionally scaling to global grey matter, we found reductions of grey matter volume and concentration in association neocortex, particularly in bilateral parietal, left prefrontal, left occipital and left temporal cortex, in primary sensorimotor areas and in right parahippocampal gyrus. The age effect was unrelated to gender, intracranial volume and general cognitive function. Additionally, we found that grey matter volume and concentration were relatively preserved in cerebellum, subcortical nuclei, and basal regions of frontal and temporal lobe. We suggest that regional reductions in grey matter volume and concentration reflect Alzheimer’s disease-type cortical pathological changes in the predementia stage of Down’s syndrome.
Post mortem studies demonstrate Alzheimer’s disease-type neurofibrillary and amyloid pathology (Mann and Esiri, 1989; Hof et al., 1995), as well as regional atrophy and decreased neuron density in the hippocampus (Ball and Nuttall, 1980), entorhinal cortex (Sadowski et al., 1999) and neocortical regions (Ross et al., 1984; Mann et al., 1987), in the brains of older Down’s syndrome subjects, even preceding clinical onset of dementia. MRI-derived measures of regional atrophy correlate with neuron density in clinicopathological studies in non-Down’s syndrome Alzheimer’s disease (Nagy et al., 1996; Bobinski et al., 2000). One post mortem study described reduced grey matter volume in posterior cortical areas correlated with neurofibrillary tangle and neuritic plaque load in Down’s syndrome subjects (de la Monte and Hedley-Whyte, 1990). These findings suggest that regional grey matter reductions in our Down’s syndrome subjects reflect reductions of neuron density due to Alzheimer’s disease-type pathological changes.
Our findings of reduced grey matter with increasing age in neocortical association regions are consistent with the results of a previous study on the same subjects (Teipel et al., 2003). We had found a significant reduction of posterior corpus callosum area with advancing age, suggesting loss of neocortical association neurons, particularly in posterior temporal, parietal and occipital association cortex in the predementia stage of Down’s syndrome (Schaltenbrand et al., 1970; De Lacoste et al., 1985; Pandya and Seltzer, 1986). Furthermore, the regional distribution of morphological changes with age in neocortical association cortex in our Down’s syndrome subjects is consistent with studies of cortical glucose metabolism using [18F]fluorodeoxyglucose (FDG) and PET in older Down’s syndrome subjects and in early stages of sporadic Alzheimer’s disease. Although one earlier longitudinal study suggested decline of resting cortical glucose metabolism only with onset of dementia in Down’s syndrome subjects using an ROI approach (Dani et al., 1996), a recent study reported decline of resting cortical metabolism even before onset of dementia (Rondal and Comblain, 2002). Additionally, a significantly reduced number of frontal-parietal metabolic correlations was observed in older non-demented Down’s syndrome subjects (Azari et al., 1994). Audiovisual stimulation with FDG-PET in older non-demented Down’s syndrome adults showed reduced metabolism in parietal and temporal association cortex (Pietrini et al., 1997). The regional distribution of resting and stimulation metabolic alterations in Down’s syndrome subjects resembles the pattern of metabolic impairment in patients with mild sporadic Alzheimer’s disease (Mielke and Heiss, 1998; Alexander et al., 2002), and suggests neocortical neuronal functional impairment with age in Down’s syndrome even before onset of dementia.
Volumetric studies with MRI described decreased total cortical grey matter volume with increasing age in Down’s syndrome subjects only after onset of dementia (Schapiro et al., 1989, 1992; Kesslak et al., 1994; Raz et al., 1995). VBM, however, may be more sensitive than ROI-based volumetric studies for demonstrating reductions of neocortical grey matter in the predementia stage of Down’s syndrome.
In the allo- and mesocortical regions of the medial temporal lobes, we found age-related reductions of grey matter volume only in the right parahippocampal gyrus. In contrast, several volumetric studies using MRI reported age-related hippocampus atrophy in Down’s syndrome (Kesslak et al., 1994; Raz et al., 1995; Aylward et al., 1999; Lawlor et al., 2001). Moreover, using a manual method we had found age-related reductions of hippocampus, amygdala and entorhinal cortex volumes in a larger Down’s syndrome group including the subjects of the present study (Krasuski et al., 2002). The ROI-based volumetric data are consistent with the early involvement of hippocampus and entorhinal cortex by Alzheimer’s disease-type pathological changes in Down’s syndrome and sporadic Alzheimer’s disease (Price et al., 1991, 2001; Hof et al., 1995). To compare directly results between the previous ROI-based volumetric studies and the present analysis we defined two ROIs in the right and left medial temporal lobes, encompassing a cubic volume of 1000 mm3, to derive grey matter intensities of a representative subregion of the medial temporal lobes, including portions of the hippocampus and amygdala. We found that age accounted for 
20–25% of variability in averaged medial temporal lobe grey matter intensity. This effect is comparable in size to that of previous ROI-based volumetric studies of medial temporal lobe structures, although it does not pass the single voxel significance threshold of P < 0.001. This finding suggests that VBM- and ROI-based volumetric analyses detect age effects in the medial temporal lobes with comparable sensitivity. The sensitivity of VBM in the medial temporal lobe, however, was inferior to the sensitivity of VBM in neocortical association regions. These findings are consistent with a study comparing VBM- and ROI-based measurements of temporal lobe atrophy in Alzheimer’s disease (Good et al., 2002). In this study, medial temporal lobe changes in VBM did not reach corrected significance threshold, but yielded effect sizes comparable to those of an ROI analysis. In contrast, effects in temporal lobe areas near the surface of the brain reached the corrected significance threshold. A potential reason for different sensitivity of analyses in medial temporal lobe and neocortical regions in this and the previous study are regional differences in the quality of the segmentation into grey and white matter. The segmentation works properly on the grey–white matter border of the cortical surface; however, it may be less accurate in the medial temporal lobes, particularly in structures like the hippocampus, where grey and white matter sheets are convoluted into each other. The quality of the segmentation was improved by using group-specific priors, but even then did not appear to reach the quality of the segmentation on the cortical surface. Therefore, the poorer performance of the segmentation algorithm in the medial temporal lobe may have reduced the power of the VBM analysis to detect age effects in these areas.
Using VBM, Baron et al. (2001) reported reduced grey matter in parietal and temporal association cortex and in medial temporal lobe in patients with sporadic Alzheimer’s disease in mild to moderate clinical stages of dementia compared with healthy subjects. When data were proportionally scaled to global intensity, the effect size in that study in medial temporal lobe regions was comparable to our study, but effects in neocortical regions were less pronounced. Chételat et al. (2002) investigated regional patterns of grey matter reductions in patients with mild cognitive impairment (MCI) (an at-risk group of Alzheimer’s disease) compared with healthy elderly subjects. They found grey matter reductions predominantly in bilateral hippocampus, cingulate gyrus and temporal neocortex. They did not, however, normalize their data to global grey matter intensity. When using the same approach in our data, we found significant reductions of grey matter volume and concentration throughout virtually the entire cortex, including medial temporal lobes. These two previous studies were based on normalization of Alzheimer’s disease or MCI brains to the standard MNI template, which is derived from young healthy subjects, and a comparison with a healthy control group. This approach may have rendered these two previous studies more sensitive to systematic misregistration effects (Bookstein, 2001), thereby reducing the overall power of the analyses. Using VBM in a group of mildly to moderately impaired Alzheimer’s disease patients compared with healthy subjects, Busatto et al. (2003) found reduced grey matter volumes in bilateral parietal and temporal cortices, lateral prefrontal, and sensorimotor cortices and precuneus. After taking into account global differences in grey matter, effects in medial temporal lobes were reduced to two small clusters of reduced grey matter volume in left parahippocampal and right entorhinal cortex (Busatto et al., 2003). In the medial temporal lobe, effect sizes were comparable between the Busatto et al. study and ours. This is consistent with the notion that VBM may be less sensitive to atrophy effects in the medial temporal lobes than in regions on the cortical surface.
Our Down’s syndrome subjects showed grey matter changes not only in association cortex, but also in primary sensorimotor cortex. At present, there is no clear interpretation of this finding. Volumetric changes in these areas have not been investigated before in Down’s syndrome. The only PET studies directly assessing metabolic changes in primary sensorimotor cortex showed a decrease of resting metabolism in these areas in one demented Down’s syndrome subject (Schapiro et al., 1988) and of metabolism during stimulation in a group of non-demented older Down’s syndrome subjects (Pietrini et al., 1997). In a clinical longitudinal study, Evenhuis (1990) reported a very high incidence of gait deterioration accompanied by muscle hypertonia in demented subjects with Down’s syndrome. This report agrees with a review of 15 Down’s syndrome cases, showing gait disturbances in 73% of demented Down’s syndrome subjects (Lott and Lai, 1982). One could hypothesize that neuronal alterations in primary sensorimotor areas occur relatively early in Down’s syndrome and may contribute to the reported clinical findings. Further longitudinal imaging studies are needed to test this hypothesis.
It is unlikely that the results of our study solely reflect the effect of normal aging. In a recent study using VBM, Good et al. (2001b) showed a regional pattern of reduced cortical grey matter in normal aging focused in anterior cortex, particularly anterior cingulate, anterior insula, pericentral cortex and planum temporale. This pattern is clearly distinct from our findings. However, it remains possible that some areas of reduction observed in our Down’s syndrome sample reflect a combination of the effects of early Alzheimer’s disease pathology and ageing. In addition, differences in global cognitive function (determined by the PPVT-R) (Dunn and Dunn, 1981), reflecting a priori differences in the degree of developmental abnormality across subjects, did not account for the age effects.
There are two caveats in the interpretation of our findings. First, Down’s syndrome is a developmental disorder and therefore any age-related changes are superimposed on developmental abnormalities. This is well illustrated by a recent study (White et al., 2003) demonstrating morphological differences between adult non-demented Down’s syndrome subjects and healthy age-matched controls using MRI and VBM. Down’s syndrome subjects had reduced grey matter volume in cerebellum, left medial frontal lobe, right superior and middle temporal lobe, and anterior and middle cingulate gyrus. This regional pattern of grey matter changes is clearly distinct from the age effects of our study. However, the possibility that age effects are masked by higher variability of brain morphology in brain areas that are severely affected by developmental abnormalities cannot be excluded. Secondly, the resemblance between Down’s syndrome and Alzheimer’s disease neuropathology is close, but not perfect. Plaque density was found to be higher in many Down’s syndrome subjects compared with Alzheimer’s disease patients, and more widely distributed throughout the cortex (Hof et al., 1995). Additionally, results of several studies suggest cytoskeletal abnormalities in Down’s syndrome (Mann et al., 1989), and overexpression of proteins other than amyloid that may contribute to neurodegeneration (De La Torre et al., 1996; Engidawork and Lubec, 2001). Therefore, the possibility cannot be excluded that age effects on cortical grey matter in non-demented Down’s syndrome subjects reflect, at least partially, Down’s syndrome-specific degenerative processes other than Alzheimer’s disease.
We further investigated specific correlations between episodic memory performance and cortical grey matter changes. After controlling for age and general cognitive function, reduced memory performance was associated with reduced grey matter in bilateral precuneus, left lateral temporal cortex, left hippocampus and right prefrontal cortex. Neuropsychological lesion studies and functional neuroimaging experiments suggest that these areas are involved in visual attention (precuneus) (Corbetta et al., 1995), episodic memory encoding (hippocampus) (Squire and Zola-Morgan, 1991; Greicius et al., 2003), episodic memory retrieval (right prefrontal cortex) (Lee et al., 2000; Rugg et al., 2002) and language (left temporal pole, left lateral temporal cortex, including Wernike’s area) (Benson, 1988; Cabeza and Nyberg, 2000). These findings indicate that impaired memory performance in the Down’s syndrome subjects is related to local morphological brain changes, most likely on the basis of developmental abnormalities or neurodegenerative changes, or both. They support the potential use of VBM to reveal morphological brain changes that are related to functional impairment. However, one has to take into account that only non-demented subjects were included, which led to a ceiling effect in the memory scores.
After proportional scaling, we found significant positive correlations between age and grey matter volume and concentration in some cortical and subcortical regions of the brain. However, the unscaled analysis showed no significant positive correlations between age and grey matter volume and concentration, suggesting that the positive age effect at least partially reflects a smaller than average reduction or a relative preservation of regional grey matter volume and concentration. Furthermore, we found a relative increase of signal with increasing age in periventricular regions. A decrease of T1 signal in periventricular white matter (‘periventricular rims’) is commonly found in elderly subjects (Fazekas et al., 1998), and may lead to misclassification of white into grey matter in these areas. Additionally, a partial volume effect between white matter and CSF may lead to misclassification as grey matter of voxels being located close to the ventricles. Therefore, the positive correlation with age in the periventricular region may not reflect true grey matter changes, but rather a systematic age effect in the spatial enlargement of the ventricles. Age-related signal changes may also contribute to the positive age effect in the basal ganglia. Age has been found to be associated with a decrease of T1 signal in the putamen, caudate and globus pallidus (Yamada et al., 2002), most likely related to increases in tissue iron stores with age (Connor et al., 1990).
When using VBM as an automated search algorithm across the entire cortex, one has to consider the possibility of false-positive findings. There are essentially two sources of false-positive findings: statistical and methodological. The first source of false-positive results was checked by using the FDR to control the experiment-wise type I error (Genovese et al., 2002). The chosen threshold establishes that on average not more than 5% of the voxels that are assigned as significant are false positives. The second source is related to the methodological framework of VBM itself. VBM is based on the analysis of local variations in grey matter concentration after global shape differences between individual scans have been removed (Ashburner and Friston, 2000). Therefore, the analysis is sensitive to local misregistrations independently of differences in local grey matter concentration (Bookstein, 2001). Accordingly, there is no straightforward interpretation of VBM results in terms of regional grey matter atrophy. We tried to minimize the potential influence of local misregistrations by analysing only effects within the Down’s syndrome group and by using a group-specific template for normalization. We also applied a modified VBM technique that has recently been proposed to attenuate some of these effects (Ashburner and Friston, 2001; Good et al., 2001b). Following this protocol, the normalization to standard stereotactic space was driven by grey matter only, thereby reducing the influence of non-grey matter tissue on the registration. Additionally, the resulting grey matter images were corrected for volumetric changes introduced by the non-linear normalization process (Ashburner and Friston, 2001; Good et al., 2001b).
In conclusion, our results provide evidence for grey matter reductions in allocortex and association neocortex in the predementia stage of Down’s syndrome. The most likely substrate of these changes is alterations or loss of allocortical and neocortical neurons. Since Down’s syndrome has been regarded as a model for preclinical Alzheimer’s disease, our findings raise the question of whether significant neocortical neuronal loss occurs parallel to allocortical neuronal loss in preclinical stages of sporadic Alzheimer’s disease as well. Onset and progression of neocortical neuronal degeneration have major implications for the treatment of dementia in Down’s syndrome and Alzheimer’s disease. Future longitudinal studies are needed to track the progression of MRI changes in grey matter over time.
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The authors wish to thank Ms Diane Teichberg and Ms Lisa Chang for assisting in the preparation of the MRI scans for analysis. Part of this work was supported by grants from the Medical Faculty of the Ludwig-Maximilian University (Munich, Germany) to S.J.T., the Hirnliga e. V. (Nürmbrecht, Germany) to S.J.T. and H.H., Eisai (Frankfurt, Germany) and Pfizer (Karlsruhe, Germany) to H.H. and S.J.T, and from the German Competency Network on Dementias (Kompetenznetz Demenzen), funded by the Bundesministerium für Bildung und Forschung (BMBF), Germany.
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