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Published online before print July 12, 2005, 10.1148/radiol.2363041454
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(Radiology 2005;236:990-995.)
© RSNA, 2005

Neuroradiology

Late-Onset Dementia: Structural Brain Damage and Total Cerebral Blood Flow1

Aart Spilt, MD, Annelies W. E. Weverling-Rijnsburger, MD, Huub A. M. Middelkoop, PhD, Wiesje M. van Der Flier, PhD, Jacobijn Gussekloo, MD, Anton J. M. de Craen, PhD, Eduard L. E. M. Bollen, MD, Gerard J. Blauw, MD, Mark A. van Buchem, MD and Rudi G. J. Westendorp, MD

1 From the Departments of Radiology (A.S., M.A.v.B.), General Internal Medicine (A.W.E.W., J.G., A.J.M.d.C., G.J.B., R.G.J.W.) and Neurology (H.A.M.M., W.M.v.d.F., E.L.E.M.B.), Leiden University Medical Center, Albinusdreef, C2-S, 2333 ZA Leiden, the Netherlands. Received August 23, 2004; revision requested October 29; revision received November 26; accepted January 5, 2005. Address correspondence to A.S. (e-mail: A.Spilt{at}LUMC.nl)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
PURPOSE: To prospectively compare indicators of structural brain damage and total cerebral blood flow in patients with late-onset dementia, subjects of the same age with optimal cognitive function, and young subjects.

MATERIALS AND METHODS: The institutional ethics committee approved the studies, and all participants (or their guardians) gave informed consent. The test group included 17 patients older than 75 years (four men, 13 women; median age, 83 years) and with a diagnosis of dementia according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. The control group included 16 subjects (four men, 12 women; median age, 87 years) with optimal cognitive function, who were selected from among 599 elderly subjects enrolled in a population-based follow-up study, and 15 young healthy subjects (seven men, eight women; median age, 29 years). Measurements of intracranial and total brain volumes, structural brain damage, and cerebral blood flow were obtained with magnetic resonance imaging. Mean values were compared with the t test; medians, with the Mann-Whitney U test.

RESULTS: Values for total brain volume were significantly smaller in elderly subjects (P < .001) but did not differ significantly between patients with dementia and subjects of the same age with optimal cognitive function (P = .69). Among the elderly, significantly higher scores for number and extent of white matter areas of signal hyperintensity (P = .028) and lower magnetization transfer ratios (P = .016) indicated greater structural brain damage in those with dementia. Cerebral blood flow was 246 mL/min lower (P < .001) in elderly subjects than in young subjects. In patients with dementia, cerebral blood flow was 108 mL/min lower than that in subjects of the same age with optimal cognitive function (551 vs 443 mL/min, P < .001).

CONCLUSION: The combined observations of more structural brain damage and lower cerebral blood flow in demented elderly individuals than in subjects of the same age with optimal cognitive function support the hypothesis that vascular factors contribute to dementia in old age.

© RSNA, 2005


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
The incidence of dementia and cerebrovascular disease rapidly increases after the age of 70 years. The association between the two disorders has been debated for more than 100 years. The notion that cerebrovascular disease might cause dementia goes back to Binswanger in the beginning of the 20th century (1). At that time, this notion competed with Alzheimer's interpretation of the cause of dementia (2). Amyloid plaques are now considered the cause of dementia in patients with a familial type of dementia. In most of these patients, a mutation in the presenilin gene is responsible for the occurrence of dementia before the age of 70 (3). It is assumed that these abnormal presenilin genes lead to amyloid depositions through an impaired {gamma}-secretase function (4). This familial type of early-onset dementia, however, accounts for the disease in less than 5% of the total number of patients with dementia. Therefore, this mechanism is unlikely to be the cause of disease in the majority of patients with late-onset dementia.

Autopsy in a consecutive series of patients with late-onset dementia from the general population showed that amyloid plaque was present in only one of three patients, according to current histopathologic definitions (5). Moreover, amyloid deposition was also present in the brains of elderly people with no clinical signs of dementia and was even sufficient to classify one of six such individuals as demented (5). The great majority of brains of patients with dementia also showed evidence of multiple pathologic cerebrovascular conditions. Atherosclerotic disease and congophilic angiopathy of the perforating arteries were far more prevalent in brains from demented patients than in those from control subjects. These data indicate that deposition of amyloid plaque is neither a necessary nor a sufficient cause of late-onset dementia. Dementia in old age is better explained as a multifactorial disorder for which several risk factors have been identified, including amyloid deposition and atherosclerotic disease (6).

We hypothesized that patients with late-onset dementia had more brain damage and lower cerebral blood flow than did subjects of the same age without dementia, or young subjects. Thus, the purpose of our study was to prospectively compare indicators of structural brain damage and total cerebral blood flow in patients with late-onset dementia, subjects of the same age with optimal cognitive function, and young subjects.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Participants
From the outpatient memory clinic of the Leiden University Medical Center, Leiden, the Netherlands, 25 consecutive patients with a diagnosis of dementia were selected (15 women and 10 men; median age, 83 years). More information about the memory clinic is available in a publication by Van der Flier et al (7). Note that the patients reported in that publication are not the same as the 25 in our current study. For all patients, a standardized dementia screening examination was completed. As part of the usual diagnostic work-up, each patient was examined by a geriatrician and a neurologist. Routine blood examination included measurement of the following parameters: erythrocyte sedimentation rate; complete blood cell count; electrolyte levels; liver, renal, and thyroid function; antibodies indicating the presence of syphilis; and levels of vitamins B1, B6, and B12 and folic acid. Full neuropsychological examination and magnetic resonance (MR) imaging of the brain were performed. Diagnosis of dementia was made by consensus according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. For the present analysis, subjects who received a diagnosis of dementia with a vascular cause were excluded. This means that our study included only subjects with Alzheimer or Parkinson disease. The criterion for late-onset dementia was the occurrence of dementia after the age of 75 years.

As a primary control group, we selected a series of subjects with optimal cognitive function from a large population sample of 85-year-olds in whom MR imaging was not routinely performed. (A detailed description of this study population is available in a publication by van Exel et al [8]). In short, all members of the 1912–1914 birth cohort living in Leiden, the Netherlands, were enrolled in the month of their 85th birthday. There were no selection criteria based on health or demographic characteristics. Those who were eligible for the study were visited yearly at their place of residence. To investigate the various domains of cognitive function, we used a battery of neuropsychological tests that is widely used in observational studies and that has proven clinical relevance (9,10). To be selected for inclusion in the control group with optimal cognitive function, subjects had to have test scores above the median values for all cognitive tests. The selected subjects had a Mini-Mental State Examination score of 27 or higher; attention span of less than 75 seconds with the abbreviated 40-item version of the Stroop Test; processing speed of more than 16 entries with the Letter-Digit Coding Test; and delayed recall of more than 10 words with the 12-Word Learning Test. On the basis of these four criteria, 19 subjects (14 women and five men; median age, 87 years) were selected from the original sample of 599. A secondary control group of 15 young healthy individuals (eight women and seven men; median age, 29 years) were recruited through advertisements among students of Leiden University.

The ethics committee of the Leiden University Medical Center approved the studies, and all participants gave informed consent. For subjects with severe cognitive impairment, informed consent was obtained from a guardian.

Imaging and Evaluation
All imaging was performed on a 1.5-T MR system (ACS-NT15; Philips Medical Systems, Best, the Netherlands) equipped with a gradient system (PowerTrak 6000; Philips Medical Systems). In 11 subjects, the quality of the MR images was insufficient because of movement artifacts or the subject's unwillingness to complete the imaging examination, and these images were therefore excluded from all analyses. The characteristics of the three final groups are summarized in Table 1. There was a significant difference between the three groups in age (P < .001) but not in the percentage of men (P = .21).


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  		TABLE 1. Clinical and Brain Volume Characteristics of Study Participants

 
To assess the extent of white matter regions of signal hyperintensity, we used a dual turbo spin-echo sequence (3000/27, 120 [repetition time msec/echo time msec]; echo train length, 10; section thickness, 3 mm; no intersection gap; matrix, 256 x 256; field of view, 220 mm; acquisition of 80%). A gradient-echo phase-contrast technique (16/9; flip angle, 7.5°; number of sections, one; section thickness, 5 mm; matrix, 256 x 256; field of view, 250 mm [75% rectangular]; acquisition of 60%) with velocity encoding of 100 cm/sec was used for flow measurements (11). The imaging plane was perpendicular to the basilar artery and both internal carotid arteries. Flow measurement sequences were applied with and without electrocardiographic triggering. The number of signals acquired was one for electrocardiographically triggered acquisitions and eight for nontriggered acquisitions. For electrocardiographically triggered acquisitions, gating was performed retrospectively with a peripheral pulse unit. Magnetization transfer imaging was performed by using a three-dimensional gradient-echo pulse sequence (106/6; flip angle, 12°; section thickness, 5 mm; matrix, 256 x 256; field of view, 220 mm; acquisition of 50%). Two consecutive sets of transverse images were acquired: The first set was acquired without a radiofrequency saturation pulse, and the second was acquired with a radiofrequency saturation pulse (sinc shaped for selective excitation at a frequency approximately 1100 Hz below the water resonance frequency) (12).

For assessing white matter lesion load, we used a modified version of the rating scale used by Scheltens et al (13) (hereafter, Scheltens rating scale). This rating scale is based on the size and number of white matter lesions in the periventricular areas, deep white matter, infratentorial regions, and basal ganglia. Each region was rated separately. The rating scale for the periventricular region was extended, and periventricular lesions larger than 10 mm were given a score of 3. For periventricular lesions, the maximum possible total score was 9; for deep white matter and infratentorial lesions, 24; and for basal ganglia lesions, 30. The images were assessed by an experienced neuroradiologist with more than 10 years of experience with the Scheltens rating scale (M.A.v.B.). This rater also assessed the images for the presence of cerebral infarct. Infarct was defined, on all images regardless of the pulse sequence used, as an area in the brain with a signal intensity that was identical to that of cerebrospinal fluid and that could not be attributed to perivascular space. The size of infarcts was measured by one of the authors (A.S.) with calipers and was defined as the largest diameter of the infarct on transverse T1-weighted images.

Total cerebral blood flow was analyzed by using a workstation (UltraSparc 10; Sun Microsystems, Santa Clara, Calif) with an internally developed proprietary software package (14). Total cerebral blood flow was defined as the sum of the flow in the basilar artery and both internal carotid arteries and was expressed in milliliters per minute. To correct for systematic difference between electrocardiographically triggered and nontriggered flow measurements, we added 40.4 mL/min to total cerebral blood flow measurements obtained without electrocardiographic triggering. The plane of imaging selected was the plane perpendicular to the basilar artery and both internal carotid arteries. More information about blood flow measurement, including a reproducibility study, can be found in a previous publication (15).

For semiautomated postprocessing of magnetization transfer images, software (3DVIEWNIX; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa) was used. A detailed description of this procedure has been published previously (16). Briefly, the following steps were performed: semiautomated segmentation of the intracranial volume from the three-dimensional magnetization transfer imaging study, automated calculation of the magnetization transfer ratio (MTR) for every intracranial voxel, and automated display of all voxels to represent the brain as an MTR histogram. The MTR was defined as the percentage of change in signal intensity between images acquired with the saturation pulse and those acquired without the saturation pulse, and it was obtained with the following equation: MTR = [(SI–sat – SI+sat)/SI–sat] · 100, where SI–sat and SI+sat are the signal intensity without and with the saturation pulse, respectively.

Voxels with an MTR of less than 20% were defined as cerebrospinal fluid (17). The following parameters were derived from the histogram: intracranial volume, derived as IV = Vic · VS, where Vic is the number of intracranial voxels and VS is the individual voxel size, 3.698 mL; parenchymal volume, derived as PV = Vpa · VS, where Vpa is the number of parenchymal voxels; and normalized peak MTR, derived as MTRnp = (MTRpeak/Vpa) · 1000, where MTRpeak is, in the MTR histogram, the number of voxels in the largest bin. Atrophy was defined as the percentage of intracranial voxels with an MTR of less than 20%.

Statistical Analysis
Results of a power analysis indicated that 17 subjects in each group were needed to demonstrate a difference of 80 mL/min ± 70 (standard deviation) in total cerebral blood flow ({alpha} = .05; power = 80%).

All data were calculated as the mean ± standard deviation or the median and range, depending on the underlying distribution of the data. Means were compared with the t test, and medians were compared with the Mann-Whitney U test. Multivariate logistic regression analysis was performed to simultaneously assess the data for independent association of total cerebral blood flow and of MTR with dementia.

P values of less than .05 were considered to indicate statistically significant differences. Data were analyzed by using statistical software (SPSS, version 11.0; SPSS, Chicago, Ill).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Cerebral Volume Measurements
Intracranial volume was comparable among the three groups of subjects (elderly patients with late-onset dementia, elderly subjects with optimal cognitive function, and young healthy subjects) (Table 1). Total brain volume, however, was significantly smaller among elderly subjects than among young subjects, whereas it was similar between elderly subjects with dementia and those with optimal cognitive function.

Brain Findings
As expected, MR images of the brain showed that young subjects, compared with elderly subjects, had fewer cerebral infarctions (P = .005) and a lesser load of white matter lesions (ie, less extensive area of signal hyperintensity) as assessed with the Scheltens score (P < .001) (Table 2). The median number of infarcts did not differ significantly between elderly subjects with dementia and those with optimal cognition, while the median Scheltens score was significantly higher in elderly subjects with dementia than in those with optimal cognitive function (P = .028).


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  		TABLE 2. Cerebral Infarction, White Matter Signal Hyperintensity, MTR, and Cerebral Blood Flow in Study Subjects

 
Compared with the elderly (whether those with dementia or with optimal cognition), young subjects had less structural damage of the brain (P < .001), as indicated by a higher normalized peak MTR (18). Moreover, elderly patients with dementia had significantly more structural brain damage than did elderly subjects with optimal cognitive function. The normalized peak MTR was 88 au in elderly subjects with dementia and 97 au in those with optimal cognition (P = .016). Figure 1 provides an illustration of the individual MTR estimates in the three study groups.



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  		Figure 1. Individual estimates of normalized peak MTR in young subjects, elderly subjects with optimal cognitive function, and elderly subjects with dementia. Horizontal lines indicate the group mean.

 
Cerebral Blood Flow
In healthy young subjects, we measured a mean total cerebral blood flow of 742 mL/min (Table 2). Mean total cerebral blood flow was 246 mL/min lower (P < .001) in elderly subjects (whether with dementia or with optimal cognition) than in young subjects. When elderly patients with dementia were compared with subjects of the same age with optimal cognitive function, mean cerebral blood flow was 108 mL/min lower (P < .001) in those with dementia. Figure 2 provides an illustration of the individual estimates of total cerebral blood flow in the three study groups.



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  		Figure 2. Individual estimates of total cerebral blood flow in young subjects, elderly subjects with optimal cognitive function, and elderly subjects with dementia. Horizontal lines indicate the group mean.

 
Logistic Regression Analysis
In a multivariate logistic regression model with total cerebral blood flow and MTR simultaneously entered as covariates, there was a significant association between dementia and total cerebral blood flow values (P = .018), while the MTR was not significantly associated with dementia (P = .17).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
As expected, we found that increasing age was clearly associated with cerebral volume loss, extent of white matter regions of signal hyperintensity, increase of structural brain damage as detected with magnetization transfer imaging (ie, MTR), and a decrease of total cerebral blood flow. However, the main findings of our study were greater brain damage and a lower total cerebral blood flow in patients with late-onset dementia than in subjects of the same age with optimal cognitive function. We found that both the MTR and the cerebral blood flow were lower in the group with dementia, even though patients with a clinical diagnosis of vascular dementia were excluded from the study.

Total blood flow through the brain is the net result of cardiac output, arterial caliber, and vasomotor tone, and it largely determines the extent to which glucose and oxygen are delivered to the brain and consumed (19). Old age is associated with a decrease in cerebral blood flow. Based on the data presented in this study, the estimated decrease is 4.4 mL/min per year, which is similar to values obtained in earlier studies on the topic (20).

An argument for a possible causal relation between lower cerebral blood flow and dementia is that patients who have Alzheimer disease and white matter regions of signal hyperintensity (21) and patients who have silent brain infarctions (22) not only have a reduced cerebral blood flow but also have an increased oxygen extraction fraction. In our opinion, these observations strongly suggest that decreased cerebral blood flow indeed causes brain damage. After all, if the reduced blood flow observed in patients with white matter regions of signal hyperintensity had been secondary to a reduced need for oxygen, we would expect the oxygen extraction fraction to have been unaltered and not increased. Moreover, in the multivariate analysis, the association found between dementia and decreased cerebral blood flow was statistically significant, while that between dementia and MTR was not. These results also indicate that cerebral blood flow is more important than MTR.

Possible causes of low cerebral blood flow include heart failure, atherosclerosis-related or amyloid plaque–related narrowing of cerebral arteries, and impaired endothelium-dependent vasodilation. It is likely that the difference between cerebral blood flow in patients with dementia and that in elderly subjects who are cognitively intact was underestimated in our study. In our sample of subjects with dementia, subjects with vascular dementia were excluded. This means that subjects with Alzheimer-type dementia and a heavy burden of ischemia were also excluded. In these subjects, a lower total cerebral blood flow could be expected, not only because of their ischemic burden but also because of the Alzheimer dementia.

Patients with typical clinical manifestations of vascular dementia were not included in this series. Therefore, we think it is not surprising that the prevalence of infarctions was not increased in our subjects with a clinical diagnosis of dementia according to the criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, compared with the prevalence among elderly subjects with good cognitive functioning.

Investigators in several other studies have shown associations between vascular risk factors and white matter regions of signal hyperintensity (23), vascular factors and cognitive impairment (24), and white matter regions of signal hyperintensity and cognitive impairment (25). In a previous study, we presented additional arguments for a generalized process when using magnetization transfer imaging, a quantitative MR imaging technique that depicts macroscopic and microscopic brain damage in patients with minimal cognitive impairment and dementia (26). Magnetization transfer imaging was used in patients with dementia to demonstrate structural changes in all parts of the brain, not only in the entorhinal area of cortex and the hippocampus. Moreover, the observation of these structural changes in patients with mild cognitive impairment and patients with dementia alike suggests that generalized brain damage is present before dementia is manifested.

There were limitations in our study. First, the elderly with dementia were somewhat younger than the elderly with good cognitive functioning. Since total cerebral blood flow and MTR diminish with increasing age, this age difference between the two groups dilutes the association. This means that the association we observed between total cerebral blood flow and MTR was actually underestimated. Second, both groups of elderly subjects were small. Despite these small numbers, however, the differences we found were significant.

In conclusion, the combined observations of greater structural brain damage and lower cerebral blood flow in elderly subjects with dementia than in subjects of the same age with optimal cognitive function support the hypothesis that vascular factors contribute to dementia in old age.


   FOOTNOTES
 

Abbreviations: MTR = magnetization transfer ratio

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, A.S., M.A.v.B.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, A.S., E.L.E.M.B., G.J.B., R.G.J.W.; clinical studies, A.S., A.W.E.W., H.A.M.M., J.G., E.L.E.M.B., G.J.B., R.G.J.W.; experimental studies, A.S., G.J.B.; statistical analysis, A.S., H.A.M.M., A.J.M.d.C., E.L.E.M.B., G.J.B., R.G.J.W.; manuscript editing, A.S., H.A.M.M., W.M.v.d.F., A.J.M.d.C., E.L.E.M.B., G.J.B., M.A.v.B., R.G.J.W.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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