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Frontal Lobe Hypometabolism Predicts Cognitive Decline in Patients With Lacunar Infarcts
Bruce R. Reed, PhD;
Jamie L. Eberling, PhD;
Dan Mungas, PhD;
Michael Weiner, MD;
William J. Jagust, MD
Arch Neurol. 2001;58:493-497.
ABSTRACT
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Background A proportion of patients with subcortical lacunes will suffer progressive
cognitive dysfunction, but the basis for this decline is controversial and
little is known about predicting cognitive decline in these patients. Studies
of Alzheimer disease have shown that imaging measures of temporal and parietal
metabolism and blood flow predict disease course.
Objective To determine whether regional cerebral glucose metabolism predicts cognitive
decline by testing 2 opposing hypotheses: (1) temporoparietal activity predicts
decline (based on the idea that concomitant Alzheimer disease causes decline)
vs (2) frontal hypometabolism predicts decline (based on evidence that subcortical
frontal circuits are especially vulnerable to small vessel ischemia).
Design Prospective cohort study.
Setting University outpatient dementia center.
Patients A convenience sample of 26 patients with radiologically defined lacunes
and baseline cognitive function ranging from normal to moderately demented.
Main Outcome Measures Regional cerebral metabolism was quantitated in the form of atrophy-corrected
positron emission tomographic activity ratios in cortical regions that were
defined a priori. Patients were followed up at a mean of 1.8 years, and the
dependent variable was rate of change in the Mini-Mental State Examination
score.
Results Bilateral and right hemisphere dorsolateral frontal metabolism significantly
predicted cognitive decline, with right dorsolateral frontal metabolism explaining
19% of the variance. No other positron emission tomographic region was a significant
predictor, nor were demographic variables or baseline Mini-Mental State Examination
scores significant predictors.
Conclusion Cognitive decline in patients with lacunes may result in part from progressive
vascular compromise in subcortical frontal circuits.
INTRODUCTION
PATIENTS WITH lacunar infarcts may be cognitively normal or may have
cognitive impairments ranging in severity from mild deficits to severe dementia.1 The trajectory of cognitive function following lacunar
infarction is also variable: stable in some cases, improving in others, but
deteriorating in a significant proportion of cases.2, 3, 4
Although cognitive status is an important determinant of patient quality of
life and need for care, there is currently no effective means of establishing
a prognosis for these patients.
Different models of the mechanisms of cognitive failure with lacunar
infarction lead to different ideas about how the prognosis might be established.
One view, supported by autopsy series that find cases with lacunes and dementia
also overwhelmingly have amyloid plaques and neurofibrillary tangles,5, 6 is that coexisting Alzheimer disease
(AD) is the primary cause of progressive mental deterioration in these patients.
From this perspective, predicting decline amounts to diagnosing AD. Although
the diagnosis of AD is problematic in patients with strokes or patients without
dementia, a diagnostic marker for AD that could be detected in patients with
lacunes might serve as a prognostic sign. One such candidate is temporoparietal
hypometabolism or hypoperfusion. Positron emission tomography (PET) and single
photon emission computed tomography studies have consistently found that reduced
activity in these neocortical association areas is strongly associated with
AD7, 8, 9 and worsens
as the illness progresses.10, 11, 12, 13
Also, there is evidence that metabolic abnormalities may precede symptoms
in AD.14 Temporoparietal hypoactivity is especially
interesting because of studies that have shown that it predicts both cognitive
decline15, 16 and survival17, 18 in AD several years into the future.
However, other autopsy studies19, 20
suggest that in at least some proportion of cases cerebrovascular disease
alone is responsible for cognitive impairment, even in the absence of large
cortical strokes. Indeed, some studies of risk factors for dementia following
stroke have identified small vessel strokes as conferring an especially high
risk of dementia.21 Why this is so is uncertain,
but a plausible account is that subcortical infarcts, because of characteristics
of the cerebral vasculature, tend to occur within cortico-striato-thalamo
cortical anatomic loops that support the functioning of prefrontal cortex.
Dementia may follow in part because of the generalized impact of the loss
of cognitive regulatory functions of the frontal lobes.22
This model is supported by empirical descriptions of the cerebral vasculature23 by studies showing that lacunes predominantly occur
in thalamus, basal ganglia, and the frontal white matter,24, 25
all of which are important components of frontal-subcortical circuits,26 and by neuropsychological studies of vascular dementia
that have shown a predominance of symptoms associated with frontal lobe dysfunction.22, 27, 28, 29 If
dysfunction of the frontal lobes is indeed especially important in small vessel
vascular dementia, then one might hypothesize that a marker of frontal dysfunction
could prove to have prognostic value.
Functional imaging studies of ischemic vascular dementia (IVD) are considerably
less uniform in their findings than are the studies on AD,30, 31, 32
but as more recent studies have used better defined diagnostic criteria to
select more homogeneous groups, more consistent findings have emerged. These
studies support the idea that subcortical infarcts impair cognition through
their effects in cortex33, 34, 35
and suggest that basal ganglia29, 34
and prefrontal cortex29, 33 are
especially hypoactive in small vessel IVD.
Thus, 2 opposing hypotheses regarding the prognostic value of metabolic
imaging in IVD may be proposed. One is that temporoparietal hypometabolism
will predict cognitive decline and the other is that frontal hypometabolism
will predict cognitive decline. This study was done to test these hypotheses.
SUBJECTS AND METHODS
SUBJECTS
Twenty-six patients were recruited through university dementia and neurology
clinics, with subcortical (but not cortical) stroke identified on a magnetic
resonance imaging (MRI) scan performed as part of the study protocol. All
subjects had supratentorial lacunes, and most had multiple lacunes (mean number
of lacunes, 4.3). The lacunes were widely distributed throughout the white
matter, basal ganglia, and thalamus, without any hemispheric predominance
to the distribution for the group as a whole. In all but 3 cases, caudate,
thalamus, or both had lacunes; of the 3 cases that had single lacunes, 2 had
thalamic involvement. Evaluation consisted of a general medical history and
physical examination, neurological examination, laboratory evaluation of serum
chemistry, blood cell count, vitamin B12 level, thyroid functions,
and neuropsychological testing. Informed consent was obtained from each subject
in compliance with local institutional review board policies. Data from most
of these patients have been previously reported in a comparison of metabolic
activity between stroke and control patients.33
Subjects were excluded if they were younger than 55 years, did not speak
English, had severe dementia (Clinical Dementia Rating Scale36
score of >2), had a history of alcohol or other substance abuse within 5 years
of the onset of cognitive change (last 5 years for cognitively normal subjects),
had a history of head trauma with loss of consciousness lasting longer than
15 minutes, had other significant neurological or psychiatric disorders, were
taking medications that affected cognitive function, or had a serious unstable
medical illness. In addition, subjects were excluded if the study MRI showed
evidence of cortical infarction or structural brain disease other than cerebral
atrophy, lacunar infarction, or white matter changes.
The subjects, 16 men and 10 women, had a mean ± SD age of 75.8
± 8.7 years and education of 12.5 ± 3.6 years. Mini-Mental State
Examination (MMSE)37 score at baseline ranged
from 17 to 30, with a mean ± SD of 25.8 ± 4.8. Subjects were
categorized according to overall level of cognitive function on the basis
of clinical history and neuropsychological testing. There were 10 cases classified
as unimpaired (mean MMSE score, 29.1), 11 classified as demented (mean MMSE
score, 21.3), and 5 as having cognitive impairment not meeting criteria for
dementia (CIND) (mean MMSE score, 27.6). Dementia was diagnosed according
to the criteria used in the National Institutes of Neurological Disorders
and Stroke–Alzheimer's Disease and Related Disorders Association guidelines
for the diagnosis of AD.38 CIND was diagnosed
when neuropsychological testing revealed either circumscribed cognitive deficit
or very mild deficits, which, according to informant report, did not affect
daily function.
Clinical follow-up was obtained on average 1.8 years after the initial
evaluation (range, 0.9-3.5 years). The MMSE, which was also obtained at baseline,
was readministered at follow-up. When multiple follow-ups were available,
the last score was used.
MAGNETIC RESONANCE IMAGING
All subjects received a research MRI scan on a 1.5-T system. Double
spin-echo sequences (repetition time, 5000 milliseconds; echo time, 20 milliseconds,
80 milliseconds; slice thickness, 3 mm) were used for radiological interpretation
of lacunes, and T1-weighted volumetric data sets (repetition time, 9.7 milliseconds;
echo time, 4 milliseconds; slice thickness, 1.5 mm) were acquired for use
in region identification for PET data analysis. T1-, T2-, and proton density–weighted
images were used by a single neuroradiologist to identify the presence of
lacunes using criteria that incorporated size and location features of lesions
in addition to their signal intensity. Lacunes were defined as lesions 3 to
15 mm in diameter. Such lesions in the basal ganglia or thalamus that were
hyperintense to cerebrospinal fluid on the proton density–weighted images
were classified as lacunes, as were discrete low-signal-intensity lesions
on T1-weighted images in the basal ganglia, thalamus, or white matter. Because
perivascular spaces are most common at the level of the anterior commissure,
low-signal-intensity lesions of any size in the region of the anterior commissure
were categorized as perivascular spaces. Outside that region, we used the
arbitrary cutoff of less than 3 mm for perivascular spaces. Lesions representing
exceptions to these criteria were classified according to the neuroradiologist's
judgment.
PET IMAGING
PET studies were performed on a PET scanner (ECAT EXACT HR PET scanner;
CTI/Siemens, Knoxville, Tenn) using the glucose metabolic tracer fludeoxyglucose
F 18 within 3 months of clinical evaluation and MRI, using methods that have
been detailed previously.39 All subjects performed
a continuous verbal recognition memory task during the tracer uptake phase
of the PET scan. The data were reconstructed using standard 2-dimensional
filtered back-projection techniques. Metabolic activity was quantitated by
defining regions of interest and normalizing atrophy-corrected regional activity
to atrophy-corrected whole brain counts. The regions were outlined using procedures
developed locally for the analysis of whole brain PET data sets that are described
in detail elsewhere.40 The approach uses a
coregistered T1-weighted 3-dimensional MRI data set for anatomic region specification
and analysis of the PET data. Two-dimensional regions of interest are operator-drawn,
and their surfaces are tiled together into a closed triangular mesh polyhedral
surface model, defining a 3-dimensional region or volume of interest (VOI).
The VOI metabolic rates or activity values are adjusted for the effects
of partial volume caused by cerebral atrophy by using the segmented MRI data
set as prior information, in a manner described by Meltzer et al.41 The MRIs were smoothed to the resolution of the PET
scanner by first reorienting the magnetic resonance volume into the registered
voxel space of the PET data, then smoothing via a nonuniform gaussian convolutional
kernel. The nonuniform smoothing was implemented by computing at each voxel
location an appropriate 3-dimensional gaussian kernel based on the known axial,
radial, and tangential blurring characteristics with respect to the tomograph
center.39 Then the proportion of each VOI containing
brain and cerebrospinal fluid is determined using the convolved MRI data set,
and this proportion is applied to the calculated metabolic rate to correct
the VOI for atrophy.
All VOIs were drawn on the 3-dimensional T1-weighted MRI by either of
2 operators blind to patient classification, using a set of rules for guiding
the boundaries of the VOIs. The VOIs were drawn using detailed rules to guide
the region boundaries. Interoperator reliability of region drawing in our
laboratory is high, with differences in regional cerebral metabolic rate for
glucose (rCMRglc) in regions drawn by different operators averaging less than
5%.40
Volumes of interest were selected a priori based on our hypotheses with
the goal of sampling neocortical association areas of high importance to memory
and a variety of other cognitive functions. The regions drawn were dorsolateral
frontal cortex (DLF), orbitofrontal cortex, temporal lobe (middle and posterior
neocortex), inferior parietal lobe, calcarine cortex, and hippocampus. Hippocampus
was selected because of its obvious importance to memory, and calcarine cortex,
which is sensory cortex, was chosen as a control region against which to test
the specificity of findings in regions of association cortex. Hippocampus
included the subiculum, horn of Ammon, and associated white matter tracts.
Whole brain counts were determined by drawing a VOI that encompassed the whole
brain, including both cerebral hemispheres, the posterior fossa, and subcortical
structures, and then applying calculations outlined above, including atrophy
correction. Thus, the PET data were analyzed in the form of count ratios,
with atrophy-corrected VOI counts normalized to atrophy-corrected whole brain
counts.
RESULTS
As a primary test of both hypotheses, a multivariate model regressed
rate of change in the MMSE score on the bihemispheric ratios for DLF, orbitofrontal
cortex, temporal lobe, inferior parietal lobe, calcarine cortex, and hippocampus.
The only significant term was that for DLF (F1,25 = 12.42, P<.01). All other terms had P
values greater than .14. Figure 1
shows the effect plot for DLF derived from this model and illustrates that
subsequent rate of decline is greater when baseline DLF metabolism is lower.
As follow-up analyses to this finding, we performed simple regressions of
the rate of change in MMSE score on right and left DLF. Right DLF was significant
(R2 = 0.19, F1,25 = 4.8, P = .04) but left DLF was not (P>.12).
Inspection of the scatterplots revealed one possible high-influence outlier
with a rate of change approximately 9 points per year. Excluding that case
yielded very similar results: right DLF was significant (P = .03), and left was not significant (P>.10).
To ensure that the negative findings for temporal and parietal VOIs were not
a consequence of dividing variance shared between VOIs, we performed simple
regressions using the bihemispheric and unilateral ratios for temporal lobe,
inferior parietal lobe, and hippocampus as single predictors of MMSE score
change. No model was significant (P>.15 for all).
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Regression of annualized change in Mini-Mental State Examination
(MMSE) score on metabolic activity in dorsolateral frontal cortex (DLF). Plot
shows the effect for DLF with all other metabolic regions in the model.
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Because the scans were done using a behavioral task during tracer uptake,
we examined the relation of task performance to the key variables of this
study. An overall measure of accuracy on this task, corrected for false-positive
responses, is Pr.42 Bilateral and right DLF
are the PET regions that predicted MMSE score decline, and both were significantly
related to Pr (R2 = 0.22, P < .02 for bilateral and R2
= 0.31, P < .01 for right). However, Pr did not
predict MMSE score decline. Although an initial analysis suggested a trend
(P = .06), the scatterplot showed one high-influence
outlier and removal of that single case made the relation clearly nonsignificant
(P = .55).
We also examined the predictive power of demographic and baseline characteristics
to see if they independently predict decline or if they modify the relation
of MMSE score change with DLF. No significant univariate (or multivariate)
relation with MMSE score change was found for baseline MMSE score, education,
age, or sex (each P>.30), nor did inclusion of these
variables in a multiple regression analysis significantly alter the ß
weight for right or bihemispheric DLF. Number of lacunes did not predict decline
(R2 = 0.0). Another potential predictor
of decline is presence of dementia. However, although demented patients declined
more on average (-1.5 points per year) than did those with CIND (-1.0
per year) or unimpaired subjects (0 per year), the amount of change varied
considerably within groups and differences between the mean values did not
approach significance (P>.30).
The hypothesis that cerebrovascular disease disrupts prefrontal function
implies that baseline, as well as subsequent cognitive function, will be correlated
with frontal metabolism. We tested this by performing a series of simple regressions
of baseline MMSE score on each PET ratio. Significant positive associations
were found for multiple prefrontal regions, specifically, bilateral (R2 = 0.30, P<.01),
right (R2 = 0.38, P<.001), and left DLF (R2 = 0.15, P<.05) and bilateral (R2 = 0.21, P<.02) and right (R2 = 0.23, P<.02) orbitofrontal
cortex. Left orbitofrontal cortex was close to significance (P = .054). No other regional PET measure approached significance (P>.20 for all).
COMMENT
Hypometabolism of dorsolateral prefrontal cortex proved to be a relatively
strong predictor of global cognitive decline in patients with lacunes, explaining
about 19% of the variance in rate of MMSE score change. Dorsolateral prefrontal
metabolism predicted change substantially better than baseline MMSE score,
the presence of dementia, or basic demographic characteristics of subjects.
In contrast, we found no evidence that temporal or parietal metabolism is
predictive of cognitive trajectory in these patients. Thus, these findings
form a double dissociation with previous findings regarding functional imaging
as a predictor of global cognitive decline; temporoparietal activity predicts
decline in AD but not in subcortical stroke, whereas frontal activity predicts
decline in subcortical stroke but not in AD.
There are at least 2 prior reports that functional measures may prognosticate
cognitive change in cerebrovascular disease. Rogers et al43
reported in 1986 that cerebral blood flow, as measured by xenon computed tomography
methods, was abnormally low 2 years before the onset of symptoms in patients
who developed vascular dementia. In addition, Gur et al44
reported that abnormal electroencephalograms in nondemented patients with
ischemic stroke predicted the incidence of dementia in the subsequent 2 years.
These studies suggest that measures of brain function can reveal early evidence
of pathology that either directly cause cognitive decline or increase the
risk of decline.
Other studies support the idea that defining the risk of cognitive decline
subsequent to stroke is not a simple matter of predicting risk of cerebral
ischemia. For example, in a study of stroke patients who were initially nondemented,
Tatemichi et al45 studied numerous variables,
including risk factors for stroke, but found only age, prior stroke, and cortical
atrophy predicted the emergence of dementia. Greater ischemic risk indeed
seems to increase the likelihood that a patient having stroke will be demented,21 but age,45, 46
education,46 and other comorbid medical conditions21 also increase this likelihood.
Our finding that right but not left DFL metabolism predicts decline
may be an example of type II error and could reflect sampling vagaries or
the effects of the verbal memory task used during the uptake period. However,
it is worth considering that there may be a more substantive basis for the
lateralized association. Interestingly, prior reports17, 47
on prognostic imaging markers in AD have found a stronger relation between
right hemisphere pathology and decline. Also, our previous analysis comparing
patients with lacunes at different levels of cognitive function with controls
showed between-group differences in right but not left DLF.33
A number of cautions ought to be noted regarding these findings. First,
we do not have histopathological data, and the likelihood is that some of
these patients have AD. Two factors argue that most do not. Temporoparietal
hypometabolism typical of AD was not found in a prior analysis that included
most of these cases.33 Also, the rate of decline
in the demented cases, -1.5 MMSE points per year, is less than what
is generally reported for patients with AD. Second, we also do not have much
evidence about what caused the progression of symptoms in those patients who
declined, since neither PET nor MRI studies were done at follow-up. Third,
IVD is a heterogeneous disorder. We deliberately focused on patients with
lacunes and no cortical strokes, but the applicability of these findings to
cases with different types of strokes is uncertain.
The significance of these findings lies not so much in the application
of functional imaging to the clinical prediction of decline, but rather in
the implications that they carry for the pathogenesis of cognitive impairment
with lacunes. Other PET studies have shown that metabolic abnormalities can
be measured before cognitive symptoms are apparent,14, 48, 49
which suggests either that metabolic measurements can be more sensitive than
cognitive measurements or that physiologic compensation can permit normal
function in the face of disease. In either case, metabolic prediction implies
that the underlying pathologic condition is progressive. Our hypothesis that
frontal metabolism predicts cognitive course was derived from a conceptual
model of subcortical IVD that emphasizes the important role of damage to subcortical
frontal circuits in causing dementia. It may be that diminished frontal metabolism
reflects progressive compromise of small arteries that supply the subcortical
nuclei and white matter tracts of these frontal circuits. The present data,
however, only show a relation between frontal metabolism and global decline,
since the MMSE measures "frontal" cognitive function indirectly at best. Evidence
of a direct link between frontal hypometabolism, progressive loss of executive
function, and global decline would be more compelling evidence of this mechanism.
AUTHOR INFORMATION
Accepted for publication November 10, 2000.
This work was supported by grants AG10129 and AG12435 from the National
Institute on Aging, the California Department of Health Services Alzheimer's
Disease Program, and the Northern California Veterans Affairs Health Care
System.
We thank David Norman, MD, for reading the MRI scans.
From the Department of Neurology, University of California, Davis (Drs
Reed, Eberling, Mungas, and Jagust); Department of Veterans Affairs, Northern
California Health Care System, Martinez (Drs Reed, Mungas, and Jagust); Center
for Functional Imaging, Lawrence Berkeley Laboratory, Berkeley, Calif (Dr
Eberling); and Departments of Medicine, Radiology, Psychiatry, and Neurology,
University of California, San Francisco, and Department of Veterans Affairs
Medical Center, San Francisco (Dr Weiner).
Corresponding author and reprints: Bruce R. Reed, PhD, UCD-Alzheimer's
Disease Center, 150 Muir Rd (127A), Martinez, CA 94553 (e-mail: BRReed{at}ucdavis.edu).
REFERENCES
| |
1. Fisher CM. Lacunar strokes and infarcts: a review. Neurology. 1982;32:871-876.
FREE FULL TEXT
2. Desmond D, Moroney J, Sano M, Stern Y. Recovery of cognitive function after stroke. Stroke. 1996;27:1798-1803.
FREE FULL TEXT
3. Schmidt R, Mechtler L, Kinkel P, Fazekas F, Kinkel W, Freidl W. Cognitive impairment after acute supratentorial stroke: a 6-month follow-up
clinical and computed tomographic study. Eur Arch Psychiatry Clin Neurosci. 1993;243:11-15.
FULL TEXT
|
ISI
| PUBMED
4. Downhill JJ, Robinson R. Longitudinal assessment of depression and cognitive impairment following
stroke. J Nerv Ment Disord. 1994;182:425-431.
ISI
| PUBMED
5. Hulette C, Nochlin D, McKeel D, et al. Clinical-neuropathologic findings in multi-infarct dementia: a report
of six autopsied cases. Neurology. 1997;48:668-672.
FREE FULL TEXT
6. Nolan KA, Lino MM, Seligmann AW, Blass JP. Absence of vascular dementia in an autopsy series from a dementia clinic. J Am Geriatr Soc. 1998;46:597-604.
ISI
| PUBMED
7. Friedland RP, Brun A, Budinger TF. Pathological and positron emission tomographic correlations in Alzheimer's
disease. Lancet. 1985;1:228.
8. Duara R, Grady C, Haxby J, et al. Positron emission tomography in Alzheimer's disease. Neurology. 1986;36:879-887.
FREE FULL TEXT
9. Foster NL, Chase TN, Fedio P, Patronas NJ, Brooks RA, DiChiro G. Alzheimer's disease. Neurology. 1983;33:961-965.
FREE FULL TEXT
10. Grady CL, Haxby JV, Horwitz B, et al. Longitudinal study of the early neuropsychological and cerebral metabolic
changes in dementia of the Alzheimer type. J Clin Exp Neuropsychol. 1988;10:576-596.
ISI
| PUBMED
11. Smith GS, de Leon MJ, George AE, et al. Topography of cross sectional and longitudinal glucose metabolic deficits
in Alzheimer's disease: pathophysiologic implications. Arch Neurol. 1992;49:1142-1150.
ABSTRACT
12. DeKosky ST, Shih W-J, Schmitt FA, Coupal J, Kirkpatrick C. Assessing utility of single photon emission computed tomography (SPECT)
scan in Alzheimer disease: correlation with cognitive severity. Alzheimer Dis Assoc Disord. 1990;4:14-23.
PUBMED
13. Jagust WJ, Reed BR, Seab JP, Budinger TF. Alzheimer's disease: relationships between SPECT findings and clinical
features. J Nucl Med. 1988;29:743.
14. Haxby JV, Grady CL, Duara R, Schlageter N, Berg G, Rapoport SI. Neocortical metabolic abnormalities precede nonmemory cognitive defects
in early Alzheimer's-type dementia. Arch Neurol. 1986;43:882-885.
ABSTRACT
15. Jagust WJ, Haan MN, Eberling JL, Wolfe N, Reed BR. Functional imaging predicts cognitive decline in Alzheimer's disease. J Neuroimaging. 1996;6:156-160.
ISI
| PUBMED
16. Wolfe N, Reed BR, Eberling JL, Jagust WJ. Temporal lobe perfusion on single photon emission computed tomography
predicts the rate of cognitive decline in Alzheimer's disease. Arch Neurol. 1995;52:257-262.
ABSTRACT
17. Jagust WJ, Haan MN, Reed BR, Eberling JL. Brain perfusion imaging predicts survival in Alzheimer's disease. Neurology. 1998;51:1009-1013.
FREE FULL TEXT
18. Claus JJ, Walstra GJM, Hijdra A, Van Royen EA, Verbeeten B, van Gool WA. Measurement of temporal regional cerebral perfusion with single photon
emission computed tomography predicts rate of decline in language function
and survival in early Alzheimer's disease. Eur J Nucl Med. 1999;26:265-271.
FULL TEXT
|
ISI
| PUBMED
19. Pantoni L, Garcia JH, Brown GG. Vascular pathology in three cases of progressive cognitive deterioration. J Neurol Sci. 1996;135:131-139.
FULL TEXT
|
ISI
| PUBMED
20. Katz DL, Alexander MP, Mandell AM. Dementia following strokes in the mesencephalon and diencephalon. Arch Neurol. 1987;44:1127-1133.
ABSTRACT
21. Tatemichi TK, Desmond DW, Paik M, et al. Clinical determinants of dementia related to stroke. Ann Neurol. 1993;33:568-575.
FULL TEXT
|
ISI
| PUBMED
22. Reed BR, Eberling JL, Mungas D, Weiner MW, Jagust WJ. Memory failure has different mechanisms in subcortical stroke and Alzheimer's
disease. Ann Neurol. 2000;48:275-285.
FULL TEXT
|
ISI
| PUBMED
23. Moody DM, Bell MA, Challa VR. Features of the cerebral vascular pattern that predict vulnerability
to perfusion or oxygenation deficiency: an anatomic study. AJNR Am J Neuroradiol. 1990;11:431-439.
ABSTRACT
24. Ishii N, Nishihara Y, Imamura T. Why do frontal lobe symptoms predominate in vascular dementia with
lacunes? Neurology. 1986;36:340-345.
FREE FULL TEXT
25. Fukuda H, Kobayashi S, Okada K, Tsunematsu T. Frontal white matter lesions and dementia in lacunar infarction. Stroke. 1990;21:1143-1149.
FREE FULL TEXT
26. Cummings JL. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993;50:873-880.
ABSTRACT
27. Kertesz A, Clydesdale S. Neuropsychological deficits in vascular dementia vs Alzheimer's disease. Arch Neurol. 1994;51:1226-1231.
ABSTRACT
28. Lafosse JM, Reed BR, Mungas D, Sterling SB, Wahbeh H, Jagust WJ. Fluency and memory differences between ischemic vascular dementia and
Alzheimer's disease. Neuropsychology. 1997;11:514-522.
FULL TEXT
|
ISI
| PUBMED
29. Starkstein SE, Sabe L, Vazquez S, et al. Neuropsychological, psychiatric, and cerebral blood flow findings in
vascular dementia and Alzheimer's disease. Stroke. 1996;27:408-414.
FREE FULL TEXT
30. Benson DF, Kuhl DE, Hawkins RA, Phelps ME, Cummings JL, Tsai SY. The fluorodeoxyglucose 18F scan in Alzheimer's disease and multi-infarct
dementia. Arch Neurol. 1983;40:711-714.
ABSTRACT
31. Eberling JL, Jagust WJ, Reed BR, Kwo-on-Yuen PF, Martin EM. Single-photon emission computed tomography studies of regional cerebral
blood flow in multiple infarct dementia. J Neuroimaging. 1992;2:79-85.
32. Frackowiak RSJ. PET scanning and the pathophysiology of vascular and multi-infarct
dementia. In: Meyer JS, Marshall J, Lechner H, Toole JF, eds. Vascular and Multi-infarct Dementia. New York, NY: Futura; 1988:149-156.
33. Kwan LT, Reed BR, Eberling JL, et al. Effects of subcortical cerebral infarction on cortical glucose metabolism
and cognitive function. Arch Neurol. 1999;56:809-814.
FREE FULL TEXT
34. Mielke R, Herholz K, Grond M, Kessler J, Heiss W-D. Severity of vascular dementia is related to volume of metabolically
impaired tissue. Arch Neurol. 1992;49:909-913.
ABSTRACT
35. Tohgi H, Yonezawa H, Takahashi S, et al. Cerebral blood flow and oxygen metabolism in senile dementia of Alzheimer's
type and vascular dementia with deep white matter changes. Neuroradiology. 1998;40:131-137.
FULL TEXT
|
ISI
| PUBMED
36. Morris JC. The Clinical Dementia Rating (CDR). Neurology. 1993;43:2412-2414.
37. Folstein MF, Folstein SE, McHugh PR. Mini Mental State. J Psychiatr Res. 1975;12:189-198.
FULL TEXT
|
ISI
| PUBMED
38. McKhann G, Drachman D, Folstein M, et al. Clinical diagnosis of Alzheimer's disease. Neurology. 1984;34:939-944.
FREE FULL TEXT
39. Wienhard K, Dahlbom M, Eriksson L, et al. The ECAT EXACT HR. J Comput Assist Tomogr. 1994;18:110-118.
ISI
| PUBMED
40. Klein GJ, Teng X, Jagust WJ, et al. A methodology for specifying PET VOIs using multi-modality techniques. IEEE Trans Med Imaging. 1997;16:405-415.
FULL TEXT
|
ISI
| PUBMED
41. Meltzer CC, Leal JP, Mayberry HS, Wagner HN, Frost JJ. Correction of PET data for partial volume effects in human cerebral
cortex by MR imaging. J Comput Assist Tomogr. 1990;14:561-570.
ISI
| PUBMED
42. Corwin J. On measuring discrimination and response bias. Neuropsychology. 1994;8:110-117.
FULL TEXT
43. Rogers RL, Meyer JS, Mortel KF, Mahurin RK, Judd BW. Decreased cerebral blood flow precedes multi-infarct dementia, but
follows senile dementia of Alzheimer type. Neurology. 1986;36:1-6.
FREE FULL TEXT
44. Gur AY, Neufeld MY, Treves TA, Aronovich BD, Bornstein NM, Korczyn AD. EEG as predictor of dementia following first ischemic stroke. Acta Neurol Scand. 1994;90:263-265.
ISI
| PUBMED
45. Tatemichi TK, Foulkes MA, Mohr JP, et al. Dementia in stroke survivors in the stroke data bank cohort. Stroke. 1990;21:858-866.
FREE FULL TEXT
46. Gorelick P, Brody J, Cohen D, Freels S, Levy P, Dollear W. Risk factors for dementia associated with multiple cerebral infarct:
a case-control analysis in predominantly African-American hospital-based patients. Arch Neurol. 1993;50:714-720.
ABSTRACT
47. Naguib M, Levy R. Prediction of outcome in senile dementia: a computed tomography study. Br J Psychiatry. 1982;140:263-267.
FREE FULL TEXT
48. Mazziotta JC, Phelps ME, Pahl JJ, et al. Reduced cerebral glucose metabolism in asymptomatic subjects at risk
for Huntington's disease. N Engl J Med. 1987;316:357-362.
ABSTRACT
49. Small GW, Mazziotta JC, Collins MT, et al. Apolipoprotein E type 4 allele and cerebral glucose metabolism in relatives
at risk for familial Alzheimer disease. JAMA. 1995;273:942-947.
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