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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Brain. Author manuscript; available in PMC Aug 24, 2009.
Published in final edited form as:
PMCID: PMC2730778
NIHMSID: NIHMS137103
Focal atrophy in Dementia with Lewy Bodies on MRI: a distinct pattern from Alzheimer's disease
Jennifer L Whitwell, PhD,1 Stephen D Weigand, MS,3 Maria M Shiung, BA,1 Bradley F Boeve, MD,2 Tanis J Ferman, PhD,5 Glenn E Smith, PhD,4 David S Knopman, MD,2 Ronald C Petersen, MD,2 Eduardo E Benarroch,2 Keith A Josephs, MST MD,2 and Clifford R Jack, Jr, MD1
1Department of Radiology, Mayo Clinic Rochester, MN
2Department of Neurology (Behavioral Neurology), Mayo Clinic Rochester, MN
3Department of Biostatistics, Mayo Clinic Rochester, MN
4Department of Psychiatry and Psychology, Mayo Clinic Rochester, MN
5Department of Psychiatry and Psychology, Mayo Clinic Jacksonville, FL
Corresponding author: Clifford R. Jack Jr, MD Department of Radiology Mayo Clinic 200 1st St SW Rochester MN 55905 Tel: 507 774 8548 Fax: 507 284 9778 ; jack.clifford/at/mayo.edu
Dementia with Lewy Bodies (DLB) is the second most common cause of degenerative dementia after Alzheimer's disease (AD). However, unlike in AD the patterns of cerebral atrophy associated with DLB have not been well established. The aim of this study was to identify a signature pattern of cerebral atrophy in DLB and to compare it to the pattern found in AD. Seventy-two patients that fulfilled clinical criteria for probable DLB were age and gender-matched to 72 patients with probable AD and 72 controls. Voxel-based morphometry (VBM) was used to assess patterns of grey matter atrophy in the DLB and AD groups, relative to controls, after correction for multiple comparisons (p<0.05). Study specific templates and prior probability maps were used to avoid normalization and segmentation bias. Region-of-interest (ROI) analyses were also performed comparing loss of the midbrain, substantia innominata (SI), temporoparietal cortex and hippocampus between the groups. The DLB group showed very little cortical involvement on VBM with regional grey matter loss observed primarily in the dorsal midbrain, SI and hypothalamus. In comparison, the AD group showed a widespread pattern of grey matter loss involving the temporoparietal association cortices and the medial temporal lobes. The SI and dorsal midbrain were involved in AD however they were not identified as a cluster of loss discrete from uninvolved surrounding areas, as observed in the DLB group. On direct comparison between the two groups, the AD group showed greater loss in the medial temporal lobe and inferior temporal regions than the DLB group. The ROI analysis showed reduced SI and midbrain grey matter in both the AD and DLB groups. The SI grey matter was reduced more in AD than DLB, yet the midbrain was reduced more in DLB than AD. The hippocampus and temporoparietal cortex showed significantly greater loss in the AD group compared to the DLB group. A pattern of relatively focused atrophy of the midbrain, hypothalamus and SI, with a relative sparing of the hippocampus and temporoparietal cortex, is therefore suggestive of DLB and may aid in the differentiation of DLB from AD. These findings support recent pathological studies showing an ascending pattern of Lewy Body progression from brainstem to basal areas of the brain. Damage to this network of structures in DLB may affect a number of different neurotransmitter systems which in turn may contribute to a number of the core clinical features of DLB.
Keywords: Dementia with Lewy Bodies, Alzheimer's disease, voxel-based morphometry, magnetic resonance imaging, neurotransmitter systems
Dementia with Lewy Bodies (DLB) accounts for up to 30% of all cases of dementia (Zaccai et al., 2005). In contrast to AD, which is associated with early deficits in memory, the core clinical features of DLB include fluctuating cognitive impairment, recurrent visual hallucinations, and features of parkinsonism such as bradykinesia, rigidity, resting tremor and postural instability (McKeith et al., 2004). Rapid eye movement (REM) sleep behavior disorder (RBD) has also recently been recognized as an important early feature of DLB (Boeve and Saper, 2006) and has been included in the revised diagnostic criteria for DLB (McKeith et al., 2005). The structural correlates of these clinical features are however unclear. The differential diagnosis of DLB and AD can be challenging given clinical overlap between the disorders. Clinically defined DLB cases may also have AD-type pathological changes as well as the characteristic Lewy bodies (Dickson et al., 1987; Josephs et al., 2004). The clinical diagnostic accuracy for DLB is especially low in cases with high Braak stages of neurofibrillary tangle distribution (Merdes et al., 2003). The differential diagnosis is however particularly important given that patients with DLB respond well to cholinesterase inhibitors but show sensitivity to the side-effects of neuroleptic drugs (McKeith et al., 2004) and there are some reports of side-effects to memantine and NMDA-antagonists (Menendez-Gonzalez et al., 2005; Ridha et al., 2005; Sabbagh et al., 2005).
Volumetric MRI has been extensively used to characterize the patterns of cerebral atrophy in AD, demonstrating involvement of the medial temporal lobe and temporoparietal association cortices (Fox et al., 2001; Fox et al., 1996; Jack et al., 1992; Jack et al., 1997). However, relatively less is known about the patterns of atrophy in DLB. Studies have consistently shown that patients with DLB have less atrophy of the medial temporal lobe than patients with AD (Ballmaier et al., 2004; Barber et al., 2000; Barber et al., 1999; Barber et al., 2001; Burton et al., 2002; Hashimoto et al., 1998; Tam et al., 2005), but have not explicitly identified a signature pattern specific to DLB. There are inconsistencies across studies, with some showing patterns of atrophy that overlap with AD (Almeida et al., 2003; Ballmaier et al., 2004; Barber et al., 2002; Bozzali et al., 2005; Brenneis et al., 2004; Burton et al., 2002; Cousins et al., 2003), and others showing greater atrophy in specific subcortical regions, such as the putamen (Cousins et al., 2003) and basal forebrain (Brenneis et al., 2004; Hanyu et al., 2006; Hanyu et al., 2005). The explanation for such inconsistencies and failure to replicate findings is likely due to variability in the clinical cohorts (e.g. dementia severity, symptom presentation), variability in the definition of the core clinical features (e.g. particularly fluctuating cognition), and small sample sizes.
Methodological issues regarding volumetric assessment may also account for the mixed findings in the literature. A number of the previous studies have used region-of-interest (ROI) based analyses in which only a few selected structures are assessed. These measurements are useful but can only assess those regions selected in advance. Automated techniques which look throughout the whole brain without the need for any a priori decisions concerning which structures to assess are now available. One such widely used technique is voxel-based morphometry (VBM) which performs a voxel-level analysis of tissue volume between groups of subjects. VBM has been widely used in studies of AD but only a few studies have applied it to DLB and have found contradictory results (Brenneis et al., 2004; Burton et al., 2002; Burton et al., 2004).
The aim of this study was to identify the characteristic pattern of atrophy in patients with DLB that may aid in the differential diagnosis of DLB and AD, and may shed light on the structural correlates of the core clinical features of DLB. Voxel-based morphometry was used to assess the patterns of grey matter atrophy in a large group of prospectively studied patients clinically diagnosed with DLB relative to normal elderly controls, and to compare these patterns to those found in a large group of patients with AD. In addition, ROI analyses were performed in order to validate the findings of the VBM analysis and to investigate the severity of regional differences between DLB and AD.
Subjects and diagnosis
All subjects that fulfilled recently revised clinical criteria for probable DLB (McKeith et al., 2005) and had a volumetric MRI within four months of the diagnosis were identified from the Mayo Clinic Alzheimer's Disease Research Center (ADRC) and Alzheimer's Disease Patient Registry (ADPR) datasets.
Clinical evaluations were carried out prospectively and the participants underwent detailed physical, neurological and neurocognitive examinations. The clinical diagnosis was based solely on clinical features without reference to imaging results. The diagnosis of DLB required the presence of at least two of the following features: visual hallucinations, fluctuations in alertness or cognition, spontaneous features of parkinsonism, or RBD (McKeith et al., 2005). Visual hallucinations had to be fully formed, occurring on more than one occasion and not attributable to medical factors (e.g., infection, post-operative confusion), medications or advanced dementia. Fluctuations were considered present if the patient scored 3 or 4 on the Mayo Fluctuations Questionnaire (Ferman et al., 2004). This requires “yes” responses from caregivers to structured questions about the presence of daytime drowsiness and lethargy, sleeping during the day, staring into space for long periods of time, and episodes of disorganized thought. The presence of parkinsonism required at least two of the four cardinal extrapyramidal signs based on neurologic examination (i.e., bradykinesia, rigidity, tremor and postural instability). Patients were considered to have probable RBD if they had a history of recurrent nocturnal dream enactment behavior (i.e. met the International Classification of Sleep Disorders diagnostic criteria B for RBD, defined as abnormal, wild flailing movements occurring during sleep, with sleep-related injuries, potentially injurious behaviors or disruptions of sleep by history (AASM, 2005)).
Each subject with DLB was matched by age and gender to a cognitively normal control subject and a subject that fulfilled clinical criteria for probable AD (McKhann et al., 1984). The date/year that the scans were performed were also matched in an attempt to control for any temporal fluctuations associated with different scanner platform versions, although this possibility was minimized by quality control measures mentioned below. All subjects were prospectively recruited into the Mayo Clinic ADRC, or the ADPR, and were identified from the ADRC/ADPR database. Control subjects were cognitively normal individuals that had been seen in internal medicine for routine physical examinations and asked to enroll in the ADRC and ADPR. All subjects were then evaluated by a Neurologist to verify the normal diagnosis. Controls were identified as individuals who a) were independently functioning community dwellers, b) did not have active neurologic or psychiatric conditions, c) had no cognitive complaints, d) had a normal neurological and neurocognitive examination, and e) were not taking any psychoactive medications in doses that would affect cognition. Subjects that fulfilled clinical criteria for AD were excluded if they had any evidence of parkinsonism. Cognitive ability across groups was assessed using the Mini-Mental Status Examination (MMSE) (Folstein et al., 1975) and the Clinical Dementia Rating scale (CDR) (Hughes et al., 1982).
Seventy-two subjects that fulfilled clinical criteria for DLB (McKeith et al., 2005), 72 subjects with AD, and 72 healthy controls were identified. Twelve subjects with DLB and 16 subjects with AD have since come to autopsy; 92% (11/12) of the clinically diagnosed DLB subjects had diffuse neocortical Lewy Bodies on pathology, and 81% (13/16) of the AD subjects had AD-type pathology.
Image analysis
Imaging parameters
T1-weighted three-dimensional volumetric spoiled gradient echo (SPGR) sequences with 124 contiguous partitions and 1.6mm slice thickness (22×16.5cm FOV, 25° flip angle) were performed and used for analysis. An identical scan acquisition protocol was used for all scans. A T1-weighted sagittal sequence with 5mm contiguous sections was also acquired and used for the measurement of total intracranial volume (TIV). Different scanners were used, but all were GE Signa 1.5T with body resonance module gradient sets and transmit-receive single channel head coils. All scanners undergo a standardized quality control calibration procedure daily, which monitors geometric fidelity over a 200mm volume along all three cardinal axes, signal-to-noise, and transmit gain, and maintains the scanner within a tight calibration range.
Voxel-based morphometry
An optimized method of VBM was applied, implemented using SPM2 (http://www.fil.ion.ucl.ac.uk/spm) (Ashburner and Friston, 2000; Senjem et al., 2005). In order to reduce any potential normalization bias across the disease groups' customized templates and prior probability maps were created from all subjects in the study. To create the customized template and priors all images were registered to the MNI template using a 12 degrees of freedom (dof) affine transformation and segmented into grey matter (GM), white matter (WM) and CSF using MNI priors. GM images were normalized to the MNI GM prior using a nonlinear discrete cosine transformation (DCT). The normalization parameters were applied to the original whole head and the images were segmented using the MNI priors. Average images were created of whole head, GM, WM and CSF, and smoothed using 8mm full-width at half-maximum (FWHM) smoothing kernel. All images were then registered to the customized whole brain template using a 12dof affine transformation and segmented using the customized priors. The GM images were normalized to the custom GM prior using a nonlinear DCT. The normalization parameters were then applied to the original whole head and the images were segmented once again using the customized priors. All images were modulated and smoothed with an 8mm FWHM smoothing kernel. In addition, a re-initialization routine was implemented. This uses the parameters from the initial normalization to the MNI template (performed to generate the customized template) to initialize the normalization to the custom template (Senjem et al., 2005).
Grey matter differences between the DLB group and the control group, between the AD group and controls, and between the DLB and AD groups were assessed using the general linear model on a voxel basis after correction for multiple comparisons over the whole brain volume (p<0.05). Age, gender, and TIV were included in the model as nuisance variables. Total intracranial volume was measured on each subjects MRI using the method described below.
VBM-based ROI analysis
Mean grey matter density was also calculated within a number of selected ROIs using the modulated grey matter images generated from the VBM analysis. Previous studies have suggested that the basal forebrain, particularly the substantia innominata (SI), is a region that is particularly involved in DLB (Brenneis et al., 2004; Hanyu et al., 2005), even more so than in AD (Brenneis et al., 2004; Hanyu et al., 2005; Mesulam and Geula, 1988; Teipel et al., 2005). We therefore sought to assess the differences in mean grey matter volumes in this region in the DLB and AD subjects. A ROI was drawn around the SI on the slice where the anterior commissure was fully visible on the unsmoothed customized template (Figure 1A). This location was chosen because the SI reaches its greatest mass under the anterior commissure (Mesulam and Geula, 1988; Teipel et al., 2005), and the measurement protocol has been previously defined (Hanyu et al., 2005). The lateral boundary was positioned 20mm from the midline to the left or right side of the brain, the superior margin was defined by the edge of the anterior commissure, and the gray matter/CSF interface was used as the inferior boundary (Hanyu et al., 2005). Another ROI was placed in the dorsal midbrain since this region was identified as being involved in the DLB group in the VBM analysis (see results) and is a region that contains a number of central neurotransmitter nuclei that have been implicated in DLB (see discussion). A sphere with a diameter of 5mm was placed in the dorsal midbrain centered on the voxel showing maximum loss in the VBM analysis (x = 1; y = −30; z = −20) (Figure 1b). Reference spherical ROIs were also then placed in the left temporoparietal cortex (Diameter = 5mm, x = −59, y = −36, z = −2, Figure 4C) and the left sensorimotor cortex (Diameter = 5mm, x = −31, y = −27, z = 59, Figure 4D). We hypothesize that the AD group would show greater grey matter loss in the temporoparietal cortex than the DLB group, whereas there would be no difference between the groups in the sensorimotor cortex.
Figure 1
Figure 1
Coronal slices of the unsmoothed customized template image showing the location of the four regions of interest used in the VBM-based ROI analysis.
Figure 4
Figure 4
Regions showing significantly greater grey matter loss in the AD group than the DLB group (corrected for multiple comparisons, p<0.05), overlaid on the unsmoothed customized template.
The intensity of each voxel in the modulated grey matter image represents the proportion of grey matter present at that voxel. The median proportion of grey matter in each voxel was then calculated from the modulated grey matter images over each of the ROIs in each subject. A similar method has been applied to assess regional differences in a previous VBM study (Teipel et al., 2004).
Volumetric-based ROI analysis
In addition, in order to validate the VBM-based ROI analysis a standard volumetric ROI analysis was performed. Regions of interest were drawn around the SI, and also around the hippocampus since the VBM analysis highlighted greater involvement of the hippocampus in AD than DLB. This procedure counts the number of voxels present within a region manually traced on the raw volumetric MRI scan. This contrasts with the VBM-based ROI analysis described above which estimates the proportion of grey matter present within a defined region on the modulated grey matter image. While these ROI methods use different images they both provide an estimate of volume over a defined region. All image processing steps were performed by the same research associate who was blinded to all clinical information.
The SI measurements were performed on volumetric images that had been aligned along the anterior-posterior commissure. The contrast among the SI, globus pallidus and CSF was automatically optimized. The SI was measured on the slice where the anterior commissure was fully visible and all boundaries were defined using the protocol described in the VBM-based ROI analysis section. Hippocampal measurements were performed after several image-preprocessing steps had been performed (Jack, 1994). The borders of the left and right hippocampi were traced sequentially from posterior to anterior. In-plane hippocampal anatomic boundaries were defined to include the CA1 through CA4 sectors of the hippocampus proper, the dentate gyrus, and subiculum. The posterior boundary was determined by the oblique coronal anatomic section on which the crura of the fornices were identified in full profile. The hippocampal head is defined to encompass those imaging slices extending from the intralimbic gyrus forward to the anterior termination of the hippocampal formation. Disarticulation of the hippocampal head from the amygdalae and uncinate gyrus on the anterior sections is aided by recognizing the undulating contour of the pes digitations and also by the fact that the alveus provides a high signal intensity marker defining the superior border of the head of the hippocampus formation where it directly abuts the overlying amygalae. The inferior boundary of the hippocampus is determined by the grey-white interface formed by the subiculum and underlying parahippocampal gyrus. Test re-test reproducibility expressed as co-efficient of variation (CV) for hippocampal volume measurements has been previously measured as 0.28% (Jack et al., 1998). In order to assess test re-test reproducibility for SI volume ROI measures, 21 (7 subjects from each of the three clinical groups) of the 73 cases were re-measured several weeks apart by the MRI analysis technician who was blinded to results of the original volume measurements. The CV was 0.53%. Total intracranial volume was determined by tracing the margins of the inner table of the skull on contiguous images of the T1-weighted spin echo sagittal MR scan.
Statistical analysis
Due to skewness in numeric clinical measurements, we compared average values among and between groups using non-parametric methods. To confirm the effectiveness of our age matching, we compared average age across the three groups using the nonparametric Kruskal-Wallis test. Differences in years of education among the three groups were also tested with a Kruskal-Wallis test. A chi square test was used to assess whether the rate of APOE ε4 carriers differed between DLB and AD patients. MMSE and CDR sum of boxes scores were compared between DLB and AD groups using two-sided Wilcoxon rank sum tests.
For group comparisons of VBM- and volumetric-based ROI data, we used linear regression models in which the ROI value was the response, group was a three-level predictor, and age, sex and TIV were included as covariates. In order to reduce skewness we log-transformed the VBM-based midbrain values, and square-root-transformed the VBM-based sensorimotor values. P-values for these ROIs are from the regression model and are based on a two-group contrast. For the hippocampal volume analysis we calculated age, sex, and TIV adjusted W-scores since reference values for this ROI were available. W-scores can be interpreted as covariate-adjusted Z-scores indicating hippocampal atrophy in terms of standard deviations from normal (Jack et al., 1997). For this ROI we performed two-sample t-tests using the W-scores. Since ROI differences between the DLB and control group, and the DLB and AD group were of distinct (albeit related) interest, we did not adjust the P-values for multiple comparisons. Raw, uncorrected data, has however been shown in the figures and tables to improve the ease of interpretation and comparison. The association between VBM-based ROI measurements and cognitive scores was assessed using partial correlation. Specifically, we calculated the partial correlation between the rank of the ROI measurement and the rank of the cognitive measurement, adjusting for age, sex, and TIV. The effect of cholinesterase treatment was assessed by comparing the ROI volumes of subjects that were on cholinesterase inhibitors to those that were not, adjusting for age, sex and TIV using methods similar to those described above. The SI and hippocampal ROI volumes were calculated as total volumes (left plus right). All statistical analyses were performed using R version 2.2.1(RdevelopmentCoreTeam, 2005).
Subjects
Seventy-two subjects that fulfilled clinical criteria for DLB (McKeith et al., 2005), 72 subjects with AD, and 72 healthy controls were identified. The participant characteristics are shown in Table 1. By design there was no difference in age or gender distribution across all three subject groups. There was also no difference in education across the groups. The MMSE score was lower in the AD group than the DLB group reflecting the relative overweighting of memory and language items compared to visuospatial and attention items on the MMSE, although there was no difference in overall functional impairment using the CDR. Table 2 shows the frequency of each of the core clinical features in the DLB cohort. Parkinsonism was the most common feature, present in 94% of the subjects. Features of RBD were present in 76% of subjects; 32/72 (44%) of these subjects underwent polysomnography (PSG) and had RBD confirmed according to the PSG criteria for the diagnosis of RBD (AASM, 2005). The least common feature was fluctuations which were only present in 46% of the subjects.
Table 1
Table 1
Patient characteristics at the time of the MRI scan
Table 2
Table 2
Frequency of core diagnostic features in the patients with DLB
Image analysis
Voxel-based morphometry
Very little cortical grey matter loss was observed in the DLB group (Figure 2). The grey matter loss was instead focused on the dorsal midbrain and a region of the SI (Figure 3). Small regions of loss were identified in the posterior hippocampus, insula, and in the frontal and parietal lobes (corrected for multiple comparisons, p<0.05). Grey matter loss was also identified in a region surrounding the third ventricle.
Figure 2
Figure 2
Three-dimensional surface renders showing the patterns of cortical grey matter loss in the DLB, and AD groups, compared to controls (corrected for multiple comparisons, p<0.05). The AD group shows widespread cortical loss particularly involving (more ...)
Figure 3
Figure 3
Patterns of grey matter loss in the DLB and AD groups compared to controls (corrected for multiple comparisons, p<0.05), overlaid on the unsmoothed customized template. This shows that the grey matter loss in DLB is focused on the substantia innominata, (more ...)
The AD group showed a widespread pattern of grey matter loss particularly affecting the medial temporal lobes and temporoparietal association neocortex (corrected, p<0.05, Figure 2). The pattern was bilateral but showed a slight left-sided predominance. Grey matter loss was identified throughout the temporal lobes, in the posterior cingulate, insula and the inferior and middle frontal gyri. The SI and dorsal midbrain were also involved in the AD group although they were not identified as discrete clusters of loss, as observed in the DLB group, but rather as part of a widespread pattern of loss (Figure 3). Regions of grey matter loss were also identified around the lateral and third ventricles.
Direct comparisons between the DLB and AD groups were also performed. No regions showed greater grey matter loss in the DLB group than the AD group at the corrected threshold of p<0.05. However, the AD group showed greater grey matter loss in the medial temporal lobe bilaterally, the left inferior, middle and superior temporal gyri, and in the left parietal lobe than the DLB group (corrected, p<0.05) (Figure 4).
VBM-based ROI analysis
The VBM-based ROI results are shown in Table 3 and as box-plots in Figure 5. The average grey matter density in the dorsal midbrain and SI ROI were significantly lower in both the AD and DLB groups compared to controls. The grey matter density in the dorsal midbrain was significantly lower in the DLB group than the AD group (p<0.0001). There was no difference between the groups in the SI density, although there was a trend for lower values in the AD group (p=0.06). The AD group had significantly more grey matter loss in the temporoparietal cortex ROI than both the DLB and control groups (p<0.0001 in both), with no significant difference observed between the DLB group and controls (p=0.25). In addition, there were no significant differences in average grey matter density between any of the three groups in the sensorimotor cortex. The grey matter densities within each of the four ROIs did not differ between the subjects that were taking cholinesterase inhibitors and those that were not within either the AD or DLB groups.
Table 3
Table 3
Region-of-interest analysis results
Figure 5
Figure 5
Box-plots of each patient's average VBM-based modulated gray matter density per voxel for four regions of interest. The horizontal lines of the boxes represent the 25th, 50th (median), and 75th percentiles of the distributions. The vertical lines extending (more ...)
The p-values in Table 4 illustrate the relationships between the average grey matter loss in each VBM-based ROI and the cognitive scores. The MMSE and CDR scores correlated with the VBM-based ROI grey matter loss of the SI in the AD subjects, and there was a trend for a correlation between CDR and the SI measurements in the DLB subjects. The temporoparietal cortex grey matter densities correlated to both the MMSE and CDR in the DLB subjects, but only to the MMSE in the AD subjects. The midbrain grey matter densities correlated to both the MMSE and CDR in the DLB subjects, but not in the AD subjects. The grey matter densities in the sensorimotor ROI did not correlate to either cognitive measure.
Table 4
Table 4
Pairwise Spearman rank-order correlation (p value) between VBM-based ROI volumes and cognitive tests
Volumetric-based ROI analysis
The volumetric-based ROI results are shown in Table 3 and as box-plots in Figure 6. Both the DLB and AD groups showed significantly smaller volumes of the SI and hippocampus than the control subjects (both p<0.05). The AD group showed significantly smaller volumes of the hippocampus than the DLB group (p<0.0001) and a trend for smaller volumes of the SI (p=0.06). The relative degree of reduction of the hippocampus and SI in AD compared to DLB differed. Compared to the DLB group, the hippocampal volumes were 16% smaller in the AD group, whereas the SI volumes were only 4% smaller in the AD group. The volumes of the SI and hippocampus did not differ between the subjects that were taking cholinesterase inhibitors and those that were not within either the AD or DLB groups.
Figure 6
Figure 6
Box-plots showing the hippocampal and substantia innominata volumes measured from the volumetric MRI for each subject. The horizontal lines of the boxes represent the 25th, 50th (median), and 75th percentiles of the distributions. The vertical lines extending (more ...)
This study identifies a unique pattern of grey matter atrophy in patients with DLB that differentiates it from AD. The DLB patients showed very little cortical involvement with the grey matter loss instead focused on the dorsal midbrain, hypothalamus, and the substantia innominata. While these structures were also involved in the AD group they formed part of a more widespread pattern of grey matter loss involving the medial temporal lobes and the temporoparietal association cortices. The regions identified in the DLB patients may contribute to the relatively specific clinical features of DLB.
Grey matter loss in the substantia innominata was identified both on the VBM analysis and in the ROI measurements. The substantia innominata contains the nucleus basalis of Meynert (NBM) which forms a major component of the cholinergic neurotransmitter system (Mesulam et al., 1983) (Figure 7). Pathology is present in the NBM in DLB (Jellinger, 2004; Lippa et al., 1999; Tsuboi and Dickson, 2005) and previous MRI studies have demonstrated atrophy of this region in DLB (Brenneis et al., 2004; Hanyu et al., 2005). Deficits in the cholinergic system have traditionally been associated with both AD and DLB, although profound cholinergic loss and severely depleted choline acetyltransferase levels occur earlier in the disease course in DLB than AD (Davis et al., 1999; Perry et al., 1994; Tiraboschi et al., 2002). There is also some evidence that DLB patients exhibit a greater therapeutic response to cholinesterase inhibitors on average than AD patients, suggesting that cholinergic deficiency is more central to symptomology in DLB than AD (Ellis, 2005; McKeith et al., 2000). Atrophy of the substantia innominata was however also present in the AD group. In fact, both the VBM and volumetric-based ROI analyses showed a trend towards greater involvement of the substantia innominata in AD than DLB. This result is somewhat surprising given evidence that suggests earlier and more severe depletion of the cholinergic system in DLB than AD, and previous MRI studies which have shown greater involvement of the NBM in DLB than AD (Brenneis et al., 2004; Hanyu et al., 2005). Differences across studies may reflect variability in the clinical cohorts and inclusion criteria. Alternatively, the extent of cholinergic deficits observed in DLB may occur as a result of additional damage to some of the other major cholinergic nuclei.
Figure 7
Figure 7
Schematic diagram showing the location of cholinergic, serotonergic and noradrenergic neurons in the brainstem and basal forebrain. The inset shows the patterns of grey matter loss identified in the DLB group (see Figure 3), and illustrates how the location (more ...)
Two other major centers of the cholinergic system are the laterodorsal and pedunculopontine tegmental nuclei located in the dorsal midbrain (Benarroch, 2006), and the hypothalamus (Figure 7). This study demonstrated grey matter volume loss in the dorsal midbrain that was greater in DLB than AD. Grey matter loss in this region correlated to worse performance on MMSE and CDR in the DLB subjects but not in the AD subjects. While the midbrain has not previously been implicated in MRI studies of patients with DLB, autopsy studies have shown the midbrain to be severely involved pathologically early in the DLB disease course (Braak et al., 2004; Dickson et al., 1987; Jellinger, 2004). Regions of grey matter loss were also identified around the third ventricle in the DLB group. This most likely reflects grey matter loss in the surrounding regions, particularly the hypothalamus which is affected in DLB (Fujishiro et al., 2006). Classification errors during segmentation commonly occur around enlarged ventricles in VBM producing an artificial rim of periventricular grey matter. In this case the loss was relatively focused around just the third ventricle suggesting more localized expansion. Similar findings have been interpreted as involvement of the hypothalamus in another recent study that assessed atrophy of the cholinergic nuclei in AD (Teipel et al., 2005). Damage to the dorsal midbrain in DLB may also affect a number of other neurotransmitter systems (Figure 7). The noradrenergic system may be affected due to damage of the locus coeruleus which extends into the inferior midbrain region, and the serotonergic system system may be affected via damage to the dorsal raphe nuclei (Benarroch, 2006). Both these nuclei are affected pathologically in DLB and AD (German et al., 1992; Szot et al., 2006) (Langlais et al., 1993), although there some evidence that they are more affected in DLB than AD (Szot et al., 2006) (Jellinger, 1990).
The fact that the substantia innominata, midbrain and hypothalamus show greatest loss in DLB suggests that these regions are involved early in the disease course. These regions fit well with the proposed pathological progression in Parkinson's disease (PD), in which Lewy bodies have been shown to move up the brainstem into the midbrain and then to the forebrain before spreading into the cortex (Braak et al., 2004). A similar pattern of progression has been suggested to occur in DLB (Jellinger, 2004). It has also been suggested that the neurons in the basal forebrain may be the most vulnerable to Lewy Body pathology, and therefore the basal forebrain may be one of the earliest brain regions to be affected in DLB (Tsuboi and Dickson, 2005). These regions are involved much later in the AD disease course (Braak and Braak, 1996; Kobayashi et al., 1991).
Other scattered regions of grey matter loss were identified on VBM in the hippocampus, parietal lobes and frontal lobes in DLB. This fits with results from previous studies that have shown more widespread patterns of loss similar to those found in AD (Ballmaier et al., 2004; Burton et al., 2002). Involvement of these structures could reflect underlying concurrent AD pathology. The volumetric measurements of the hippocampus confirmed that hippocampal atrophy was present in the DLB group; however the degree of hippocampal atrophy was much less than that observed in the AD group. The VBM analysis also showed that the medial temporal lobes were significantly more affected in the AD patients than the DLB patients. These results concord with a number of previous MRI (Ballmaier et al., 2004; Barber et al., 2000; Barber et al., 1999; Barber et al., 2001; Burton et al., 2002; Hashimoto et al., 1998; Tam et al., 2005) and pathological studies (Lippa et al., 1998), and nicely reflect the fact that episodic/declarative memory impairment is more of a prominent early feature in AD than DLB (Calderon et al., 2001; Ferman et al., 2006). The VBM-based ROI analysis also demonstrated significantly greater grey matter loss of the temporoparietal cortex in AD than DLB, while as expected there was no loss in the sensorimotor cortex in either DLB or AD. Loss of grey matter in the temporoparietal cortex correlated with the degree of clinical impairment in both the AD and DLB subjects. This is somewhat surprising since the DLB group did not have significantly reduced grey matter density of the temporoparietal cortex compared to controls. While the majority of the DLB subjects showed very little temporoparietal loss there was overlap in the two distributions with some subjects showing loss to a similar degree to that observed in AD. Again, it is likely that these may be the clinical DLB subjects that show mixed DLB and AD changes on pathology. The results of the VBM and ROI analyses therefore suggest that assessing the patterns of atrophy of a number of different structures may provide the best discrimination of subjects with DLB and AD. A pattern of substantia innominata, dorsal midbrain, and hypothalamic atrophy with relative sparing of the hippocampus and temporoparietal cortex, suggests a diagnosis of DLB. While AD subjects also show atrophy of the SI they show a relative sparing of the midbrain, and a characteristically more severe pattern of hippocampal and temporoparietal loss. It is important to stress however that this is a group study; a large degree of overlap exists between individual subjects in the AD and DLB groups.
Disruption of one or more of the neurotransmitter systems may contribute to a number of the core clinical features of DLB. Deficits in the cholinergic system have been suggested to represent the functional substrate of visual hallucinations (McKeith et al., 2000; Mori et al., 2006; O'Brien et al., 2005; Perry and Perry, 1995), although the serotonergic system, or an imbalance between the serotonergic and cholinergic systems, could also be involved (Cheng et al., 1991; Manford and Andermann, 1998; Perry et al., 1990). The specific structural locus of visual hallucinations is however unclear. Authors have suggested that deficits in the NBM (Josephs et al., 2006; Perry and Perry, 1995), or midbrain may be critical (Josephs et al., 2006; Manford and Andermann, 1998). Cortical regions have also been implicated (Harding et al., 2002; Imamura et al., 1999; Mori et al., 2006), although our study, and others, have failed to find widespread cortical atrophy in DLB (Middelkoop et al., 2001). Studies have shown that fluctuations in attention may also reflect impairments of the cholinergic system, particularly in the cholinergic inputs into the thalamus (Piggott et al., 2006; Pimlott et al., 2006) (O'Brien et al., 2005).
However, both the noradrenergic cells of the locus coeruleus and regions of the hypothamalus also play important roles in arousal and attention (Benarroch, 2006) and may contribute to fluctuations (Ferman et al., 2004).
The neuroanatomical and neurochemical basis of the features of parkinsonism that are observed in patients with DLB are less clear. The dopaminergic system, specifically involving the substantia nigra, is predominantly affected in Parkinson's disease (Braak et al., 2004) yet we observed no volume loss in the region of the substantia nigra in DLB. The probable reason that VBM does not pick up loss in the substantia nigra is due to iron deposition which results in decreased signal on the T2* sequence and prevents it from being detected as grey matter. It is however possible that dysfunction of the substantia nigra may result from neuronal depigmentation and gliosis without observable volume loss. Alternatively damage to other structures in the dorsal midbrain may be contributing to features of parkinsonism (Brooks, 1999; Grafton, 2004; Kassubek et al., 2002; Velasco et al., 1979). The neuroanatomical basis of RBD is also poorly understood but likely involves a network of structures located in the brainstem, particularly the mesopontine tegmentum and the sublaterodorsal nucleus (subcoeruleus area). These regions play crucial roles in the control of REM sleep and REM sleep atonia and it has recently been suggested that damage to the sublaterodorsal nucleus may contribute specifically to RBD (Lu et al., 2006). Regions in the lower brainstem and pons segment as white matter in the VBM processing and therefore would not have been picked up in our VBM analysis of grey matter differences.
The strength of this study is the large number of subjects in each of our subject groups and the accurate matching between groups. However, the limitations include the fact that the diagnoses were pathologically confirmed in the minority of subjects. We did however have autopsy data for 28 of our cases. The majority of the subjects clinically diagnosed with DLB had diffuse neocortical Lewy bodies on pathology (McKeith et al., 2005). The frequency of APOE 4 in our DLB cohort is typical of previous frequencies reported in clinical cohorts (Lamb et al., 1998; Singleton et al., 2002) yet was higher than one might expect from pathologically confirmed cases showing only diffuse neocortical Lewy Bodies suggesting that a number of our DLB subjects may have concomitant AD on post mortem (Josephs et al., 2004). In addition, the majority of the subjects clinically diagnosed with AD had AD-type pathology. The patterns of atrophy observed in this study may also reflect the clinical characteristics of our cohort. For example, the high proportion of parkinsonism may have contributed to the severe midbrain atrophy. There are also a number of limitations inherent to the techniques of normalization and segmentation within VBM, which can be a particular problem in the analysis of atrophic brains (Good et al., 2002). However, these issues are not specific to this study and apply to all VBM studies on atrophic brains. The ROI measurements largely confirmed the VBM findings.
In summary, this study has shown a pattern of atrophy on MRI involving the substantia innominata, dorsal midbrain, and hypothalamus in DLB. The prominent involvement of these structures differentiates it from AD at a group level which showed a more widespread cortical pattern of loss. It also supports previous studies that have demonstrated greater involvement of the medial temporal lobe in AD than DLB. Neurons in the substantia innominata, dorsal midbrain, and hypothalamus are major components of the cholinergic system suggesting a central role of cholinergic dysfunction in DLB. However, the pattern of loss also suggests involvement of serotonergic and noradrenergic neurons. It is therefore likely that the clinical features that characterize DLB result from dysfunction of multiple neurotransmitter systems.
ACKNOWLEDGEMENTS
This study was supported by grants P50 AG16574, U01 AG06786, R01 AG11378 and R01 AG15866 from the National Institute on Aging, Bethesda MD, the generous support of the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer s Disease Research Program of the Mayo Foundation, U.S.A, and by the NIH Roadmap Multidisciplinary Clinical Research Career Development Award Grant (K12/NICHD)-HD49078. DSK has been a consultant to GE HealthCare, GlaxoSmithKline and Myriad Pharmaceuticals, has served on a Data Safety Monitoring Board for Neurochem Pharmaceuticals, and is an investigator in a clinical trial sponsored by Elan Pharmaceuticals. RCP has been a consultant to GE Healthcare and an investigator in a clinical trial sponsored by Elan Pharmaceuticals. We would also like to acknowledge Dr Dennis Dickson and Dr Joseph Parisi for conducting the pathological analyses.
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