Impact of White Matter Changes on Clinical
Manifestation of Alzheimer’s Disease
A Quantitative Study
Nobutsugu Hirono, MD; Hajime Kitagaki, MD; Hiroaki Kazui, MD;
Mamoru Hashimoto, MD; Etsuro Mori, MD
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Background and Purpose—There have been conflicting results involving the clinical significance of white matter changes
in patients with Alzheimer’s disease (AD). We studied the association between the volume of white matter
hyperintensities (WMHs) on T2-weighted images and cognitive, neurological, and neuropsychiatric symptoms.
Methods—The subjects were 76 AD patients who had WMHs but no obvious cerebrovascular diseases. We quantified the
volume of WMHs by using fast-fluid–attenuated inversion recovery images and whole brain atrophy by using 3D
spoiled gradient-echo images. Effects of WMHs and brain atrophy on dementia severity, cognitive function,
neuropsychiatric disturbances, and neurological findings were examined.
Results—Whole brain atrophy was significantly associated with dementia severity and cognitive disturbances, as well as
with grasp reflex and some kinds of neuropsychiatric disturbances. After we controlled for the effects of brain atrophy,
duration of symptoms, and demographic factors, we found that WMH volume was not associated with global cognitive
disturbances or dementia severity but was significantly associated with urinary incontinence, grasp reflex, and aberrant
motor behaviors. Brain atrophy and WMH volume were not significantly correlated either before or after controlling for
age, sex, education, and duration of symptoms. WMH volume was associated with hypertension, but brain atrophy was
not positively correlated with any vascular risk factors.
Conclusions—Our results support the hypothesis that WMHs in AD patients are superimposed phenomena of vascular
origin. WMHs contribute to specific neurological and neuropsychiatric manifestations but not to global cognitive
impairment, which is more closely associated with brain atrophy. (Stroke. 2000;31:2182-2188.)
Key Words: Alzheimer disease 䡲 atrophy 䡲 neurological deficits 䡲 risk factors 䡲 white matter
W
hite matter (WM) changes are often found in patients
with vascular dementia, especially of small vessel/
subcortical subtypes, including Binswanger’s disease, and are
generally considered to be a consequence of chronic ischemia
associated with microangiopathy.1,2 However, it is also true
that the presence of WM changes is not exclusively linked to
vascular dementia, because WM changes on neuroimagings
are also common in patients with Alzheimer’s disease
(AD).3–10 Presently, there is a lack of agreement on the effect
of WM changes on the development of dementia.
Some studies have shown a significant relationship between WM changes and certain cognitive functions and/or
dementia severity,11–17 whereas others4,10,18 –32 have failed to
find any such relationships in patients with AD. Different
study populations and the heterogeneity of WM changes may
explain the inconsistency among these studies. Selection of
AD patients without vascular risk factors and/or neurological
signs may result in excluding severe WM changes and
ignoring the clinical significance of WM changes. Small
subcortical infarcts frequently accompanied by WM changes,1,2 which do not necessarily exclude the diagnosis of AD
by the clinical diagnostic criteria, obscure the independent
effects of WM changes by affecting the strategic sites causing
dementia.24 In some studies, periventricular changes in the
form of caps or smooth halos were included, with irregular
confluent changes in the deep and periventricular WM. The
former changes are apparently of nonischemic origin,
whereas the latter changes represent ischemic tissue
damage.33,34
In addition, the neuroimaging technique used is an important factor. MRI is more sensitive to WM changes than is
CT.35–38 Although WM changes have been semiquantified
with a visual rating scale in most studies, none of these scales
have been validated, and the concordance of different scales
is insufficient.39 Although MRI quantification of WM
changes is desirable, a manual outlining of WM changes12,25
Received May 5, 2000; final revision received June 16, 2000; accepted June 16, 2000.
From the Divisions of Clinical Neurosciences (N.H., H. Kazui, M.H., E.M.) and Neuroimaging Research (H. Kitagaki), Hyogo Institute for Aging Brain
and Cognitive Disorders, Himeji, Japan.
Correspondence to Dr Nobutsugu Hirono, Division of Clinical Neuroscience, Hyogo Institute for Aging Brain and Cognitive Disorders, 520 Saisho-ko,
Himeji 670-0981, Japan. E-mail hirono@hiabcd.go.jp
© 2000 American Heart Association, Inc.
Stroke is available at http://www.strokeaha.org
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Hirono et al
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is essentially dependent on visual inspection that may be
affected by an arbitrary gray scale used for display and
filming. Computer-based thresholding methods for voxel
intensities40,41 are preferable. Finally, the effect of brain
atrophy has rarely been considered,16,29 although some studies4,13,22 have analyzed the cerebrospinal fluid space. Because
diffuse brain atrophy, which is a main gross pathological
feature of AD, is an index of neuronal and synaptic loss, brain
atrophy should also be taken into consideration in analyzing
the impact of WM changes on cognitive function.
In the present study, we examined a purely selected cohort
of patients with AD who had WM changes but no obvious
cerebrovascular diseases so as to determine the effect of WM
changes on cognitive, neurological, and neuropsychiatric
symptoms. We quantified the volume of WM hyperintensities
(WMHs) and brain atrophy on MRI by means of computerbased techniques. We also tested the hypothesis that WM
changes are associated with vascular risk factors but not with
brain atrophy.
Subjects and Methods
The present study was conducted at the Hyogo Institute for Aging
Brain and Cognitive Disorders (HI-ABCD), a research-oriented
hospital for dementia
Subjects
All procedures of the present study strictly followed the 1993
Clinical Study Guidelines of the Ethics Committee of HI-ABCD and
were approved by the Internal Review Board. After a complete
description of all procedures of the present study, written informed
consent was obtained from patients or their relatives.
On the basis of the following inclusion/exclusion criteria, 76 AD
patients were selected from a consecutive series of 391 patients with
dementia who were given a short-term admission for examination to
the HI-ABCD infirmary between April 1997 and March 1999. All
patients were examined by both neurologists and psychiatrists with
standardized medical history inquiries, neurological examinations,
routine laboratory tests, electroencephalography, magnetic resonance (MR) images of the brain, and MR angiography of the head
and neck. The inclusion criteria were those identified by (1) the
Diagnostic and Statistical Manual of Mental Disorders, Third Edition, revised for dementia42; (2) the presence of WM changes, which
were defined as irregular periventricular, early confluent deep, or
confluent deep WMHs on T2-weighted MRI according to Fazekas
and colleagues34,43 and our previous report44; and (3) the National
Institute of Neurological and Communicative Disorders and Stroke,
Alzheimer’s Disease and Related Disorders Association, for AD45
when disregarding WM changes.
Although 234 patients fulfilled the inclusion criteria, 158 patients
were excluded from the present study in accordance with the
exclusion criteria. Excluded were patients (1) with medical illnesses
possibly causing cognitive impairment or WM lesions, including
demyelinating diseases, thyroid diseases, vitamin deficiencies, and
malignant diseases with or without antineoplastic agents (n⫽23);
(2) with focal brain lesions, including lacunar infarcts and hematoma
(n⫽84); (3) with complication of developmental abnormalities,
mental diseases, substance abuse, or significant neurological antecedents, such as brain trauma, brain tumor, epilepsy, and inflammatory disease (n⫽21); (4) with evidence of severe intracranial or
cervical arterial occlusive lesions on MR angiography (n⫽1); and
(5) whose informed consent was not obtained (n⫽29).
The subjects consisted of 64 women and 12 men; the mean⫾SD
age at examination was 75.6⫾7.1 years, and the mean educational
attainment was 8.8⫾2.0 years. The mean duration of symptoms,
determined through an interview with the primary caregiver and
defined as the time between the first appearance of symptoms of
Quantitative Study of WM Hyperintensities
2183
sufficient severity to interfere with social or occupational functioning
and the admission,46 was 30.8⫾19.4 months. The functional severity
was very mild in 7 patients, mild in 41 patients, moderate in 22
patients, and severe in 6 patients, as determined by the Clinical
Dementia Rating Scale (CDR).47 No patient had a history of stroke.
Assessment of Vascular Risk Factors and
Neurological Disturbances
Hypertension, diabetes mellitus, lipid disorder, smoking habit, drinking habit, and cardiac diseases were evaluated as vascular risk
factors. Hypertension was judged as present when either a systolic
pressure of ⬎160 mm Hg or a diastolic pressure of ⬎95 mm Hg was
demonstrated on repeated examinations or when a history of treatment for hypertension was present. Diagnosis of diabetes mellitus
was made when the fasting blood glucose level was ⬎7.770 mmol/L
(140 mg/dL) or when a history of treatment for diabetes mellitus was
present. Lipid disorder was judged as present when laboratory
examination of the serum at presentation showed a total cholesterol
level of ⬎5.698 mmol/L (220 mg/dL), a triglyceride level of
⬎1.695 mmol/L (150 mg/dL), or an HDL cholesterol level of
⬍1.036 mmol/L (40 mg/dL) or when a history of treatment was
present. Smoking habit was defined as ⱖ1 cigarette/d for ⱖ1 year,
and drinking habit was defined as ⱖ30 mL ethanol equivalent per
day for ⱖ1 year sometime in life. Cardiac diseases were assumed to
be present whenever there was a known history or clinical demonstration of any kind of heart disease, including myocardial infarction,
angina pectoris, and arrhythmia.
A careful neurological examination was given to document the
presence or absence of hemiparesis, sensory loss, visual field defects,
postural instability (gait disturbance and/or pulsion), pyramidal signs
(hyperreflexia, spasticity, and/or extensor plantar responses), extrapyramidal signs (resting tremor, bradykinesia, and/or rigidity),
pseudobulbar palsy, ataxia, grasp reflexes, and urinary incontinence.
No attempt was made to grade the severity of these risk factors or
neurological abnormalities.
Assessment of Cognitive Function and
Neuropsychiatric Status
We assessed the cognitive function of the patients with the MiniMental State Examination,48 Wechsler Adult Intelligence Scale–
Revised,49 and Alzheimer’s Disease Assessment Scale–Cognitive
Part.50 The 10-word list recall subtest of the Alzheimer’s Disease
Assessment Scale was also analyzed separately. The patients’ behavioral changes were assessed semiquantitatively during an interview with the caregiver by using the Neuropsychiatric Inventory
(NPI).51 In the NPI, the following 10 behavioral changes in dementia
were rated on the basis of the condition of the patients in the previous
month before the interview: delusions, hallucinations, depression
(dysphoria), anxiety, agitation and aggression, disinhibition, euphoria, irritability and lability, apathy, and aberrant motor activity.
According to the criterion-based rating scheme, the severity of each
manifestation was classified into 4 grades (from 0 to 3), and the
frequency of each manifestation was classified into 5 grades (from 0
to 4). The NPI score (severity⫻frequency) was calculated for each
manifestation (range of possible scores 0 to 12). All clinical
measures were taken with the investigators blinded to the inclusion
of subjects in the present study.
MR Acquisition
MR was performed on a 1.5-T superconducting magnet (Signa
Advantage, General Electric Medical Systems). Axial double-echo
fast-spin echo T2-weighted images (3000/105/2 [repetition time/
effective echo time/excitations]), spin-echo T1-weighted images
(550/15/2), and fast-fluid–attenuated inversion recovery (FLAIR)
images (9002/147/2200/1 [repetition time/effective echo time/inversion time/excitations]) were obtained for 14 locations parallel to the
anteroposterior commissure plane with a section thickness of 5 mm
and intersection gap of 2.5 mm covering the area from the base of the
cerebellum to the vertex. In all acquisitions, the field of view was
200⫻200 mm, and the matrix size was 256⫻256. All scans were
2184
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Measurement of volume of WMH areas.
Original images (top) and processed
images (bottom). The volume of WMH
areas was obtained by automatic count of
the number of voxels of values higher than
the threshold (shown in blue) within the
regions of interest determined by a manually driven mouse cursor (white line).
Downloaded from http://stroke.ahajournals.org/ by guest on October 2, 2016
reviewed by one neuroradiologist without knowledge of the patients’
clinical data. Lacunar infarcts were specified as lesions with diameters of ⱕ15 mm with (1) hyperintensity on T2-weighted images, (2)
distinct hypointensity on T1-weighted images, and (3) hyperintensity
with central hypointensity on FLAIR images. By use of these
criteria, lacunar infarcts can be distinguished from the état cribré or
punctuate hyperintensity form of WMHs.52 For measurements of
whole brain volume (WBV) and total intracranial volume (TIV), we
also obtained coronal, 3D, spoiled gradient-echo images (11.1/2/2
[repetition time/effective echo time/excitations]). The field of view
was 220⫻220 mm, the matrix size was 256⫻256, contiguous
sections were 124⫻1.5 mm, and the flip angle was 20°. The images
were generated perpendicular to the anteroposterior commissural
plane, which covers the whole calvarium.
Measurement of Volume of WMHs
We used FLAIR images for quantification of WMH volume. FLAIR
is a heavily T2-weighted inversion-recovery technique that nulls
fluid, such as cerebroventricular fluid. By use of this technique,
subtle periventricular WM lesions can be easily recognized, and their
extents can be assessed on a background of cerebrospinal fluid and
normal WM. This technique has been reported to be useful for
examining WM diseases, such as multiple sclerosis, cerebral infarction, and leukoaraiosis.53,54 The MR data sets of all images were
directly transmitted to a personal computer (Power Macintosh
8100/80, Apple) from the MR unit and analyzed by means of the
public-domain National Institutes of Health Image version 1.61
program (written by Wayne Rasband and available from the Internet
by anonymous ftp from zippy.nimh.nih.gov or on floppy disk from
NTIS, 5285 Port Royal Rd, Springfield, VA 22161, part No.
PB93-504868) with residential macro programs developed in our
institution. To fit a limitation of the software (8-bit voxel value), 12
bits of MR voxel data were converted to 8 bits at a scale factor of 0.5
by the minimum (voxel value 0) and maximum (voxel value 510)
levels. Basically, we used a semiautomatic segmentation technique
through intensity thresholding, thereby avoiding the observer’s bias.
The segmentation thresholding for WMHs was a priori determined to
be 3.5 SDs in voxel intensity levels of the normal WM. The outline
of WMHs with the surrounding normal WM, gray matter, and
cerebroventricular fluid was first traced with a manually driven
mouse cursor (Figure). The volume of WMHs was obtained by
automatically counting the number of voxels that showed signal
intensities higher than the threshold within this outline and then by
multiplying the number by the voxel size [(200/256)2⫻7.5⫽
4.58 mm3]. Because some normal gray matter demonstrated signal
intensities higher than the threshold, we carefully excluded these
high–signal-intensity gray matter structures as far as possible.
Measurements were performed by another investigator blinded to the
clinical information. The test-retest reliability for this method was
examined with 20 patients, and a high intraclass correlation coefficient was obtained (r⫽0.969).
Measurement of WBV and TIV
The detailed MRI procedure for obtaining WBV and TIV is
described elsewhere.55 In brief, the data sets of all spoiled gradientecho images were directly transmitted to a graphic workstation
(INDIGO II HighImpact, Silicon Graphics) from the MRI unit and
were analyzed with 3D MRI-analyzing software developed by
Yamato et al.56 The software makes use of a combination of a
gray-scale algorithm (a 3D expansion of the region-growing method), an edge-detection algorithm (a 3D expansion of Sobel filtering),
and some a priori knowledge. With this software, the whole brain
was segmented by detecting the boundary between the cerebrospinal
fluid and gray matter, and the calvarium was extracted by detecting
the outer surface of the dura mater. WBV and TIV were calculated
by multiplying the number of voxels in the extracted regions by the
voxel size. The caudal end of the whole brain and calvarium was
manually set at the plane intersecting the occipitoatloid junction,
which is the only supervised operation required. The appropriateness
of the extraction of the whole brain and calvarium was assessed by
2 reviewers who were blinded to the clinical data and who examined
on-site 3D reconstruction displays of elective view points and 2D
slice images of selected sections. The reliability and validity of this
method have been established and are described elsewhere.55,57 The
measurements were performed by the same neuroradiologist who
reviewed the MR images. To adjust for premorbid brain volume
variability, WBV was normalized by dividing it by TIV. A smaller
normalized WBV (nWBV) indicates a greater brain atrophy.
Statistical Analysis
We used nonparametric statistics because many variables were not
normally distributed. Computation was performed with the SAS
program package, release 6.12 (Statistical Analysis Systems). The
relationship between vascular risk factors and WMH volume was
examined by using partial Spearman rank correlation coefficients,
where the effects of age, sex, education, duration of symptoms, and
brain atrophy (nWBV) were controlled. Similarly, the relationship
between vascular risk factors and brain atrophy (nWBV) were
examined while controlling the effects of the confounding variables
and WMH volume. The effects of WMH volume and nWBV on
Hirono et al
TABLE 1. Risk Factors, Neurological Signs, and Scores of
Cognitive Tests and NPI
Frequency,
n (%)
Mean⫾SD
Score
Score
30 (39.5)
Diabetes
MMSE
19.1⫾4.3
7 (9.2)
ADAS total
23.8⫾9.7
Lipid disorder
35 (46.1)
ADAS recall
3.5⫾1.3
Smoking
12 (15.8)
WAIS VIQ
78.4⫾11.5
Alcohol
Cardiac disease
12 (15.8)
WAIS PIQ
10 (13.2)
75.7⫾13.5
WAIS FIQ
75.4⫾12.1
Neurological disturbances: NPI scores
Pyramidal sign
12 (15.8)
Delusions
2.5⫾3.5
Extrapyramidal sign
10 (13.2)
Hallucinations
0.2⫾0.9
7 (9.2)
Agitation
2.2⫾3.5
19 (25)
Grasp reflex
Postural instability
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Dysphoria
1.2⫾2.5
Pseudobulbar palsy
1 (1.3)
Anxiety
1.1⫾2.5
Urinary incontinence
18 (23.7)
Euphoria
0.1⫾0.5
Apathy
4.6⫾3.1
Disinhibition
1.4⫾3
Irritability
1.8⫾3.3
Aberrant motor
behavior
2.4⫾2.4
MMSE indicates Mini-Mental State Examination; ADAS, Alzheimer’s Disease
Assessment Scale–Cognitive Part; WAIS, Wechsler Adult Intelligence Scale–
Revised; VIQ, Verbal Intelligence Quotient; PIQ, Performance Intelligence
Quotient; and FIQ, Full Scale Intelligence Quotient.
neurological signs, cognitive functions, CDR, and the NPI scores
were also tested by using partial Spearman rank correlation coefficients; age, sex, duration of symptoms, and education level were
entered into the models. The effect of nWBV or WMH volume was
also controlled in each analysis. For all analyses, the statistical ␣
level was set at 0.05.
Results
The mean⫾SD WMH volume was 38.4⫾23.3 cm3. The mean
WBV was 1009⫾96 cm3, TIV was 1394⫾103 cm3, and
nWBV was 0.725⫾0.048. No significant correlation was
noted between the volume of WMHs and nWBV before
(r⫽⫺0.002, P⫽0.99) or after (r⫽0.12, P⫽0.31) controlling
for the effects of age, sex, education, and duration of
symptoms. The frequencies of risk factors and neurological
disturbances and the mean scores of the cognitive tests and
TABLE 2. Association of Volume of WMHs and nWBV With
Demographic Factors
WMH Volume
rs
nWBV
P
rs
P
Age
0.20
0.088
⫺0.34
⬍0.001
Sex (female/male)
0.13
0.28
⫺0.12
0.30
⫺0.05
0.66
⫺0.06
0.59
0.11
0.34
⫺0.23
0.052
Education
Duration
2185
TABLE 3. Association of Volume of WMHs and nWBV With
Risk Factors
Risk factors: cognitive test scores
Hypertension
Quantitative Study of WM Hyperintensities
Values are P values or partial Spearman rank correlation coefficients (rs) after
controlling for effects of the remaining demographic factors and either nWBV
or volume of WMHs.
WMH Volume
nWBV
Risk Factors
rs
P
rs
P
Hypertension
0.26
0.029
⫺0.07
0.55
Diabetes
0.12
0.32
0.12
0.31
Lipid disorder
0.092
0.44
0.09
0.44
Smoking
⫺0.029
0.81
0.24
0.048
Alcohol
⫺0.15
0.22
0.04
0.76
Cardiac disease
⫺0.063
0.60
⫺0.11
0.35
Values are P values or partial Spearman rank correlation coefficients (rs) after
controlling for effects of age, sex, education, duration of symptoms, and either
nWBV or volume of WMHs.
NPI are summarized in Table 1. None of the patients showed
hemiparesis, sensory loss, ataxia, or visual field defects.
Because pseudobulbar palsy was seen in only 1 patient, no
further analysis of this sign was undertaken.
There was a highly significant negative correlation between
age and nWBV even after controlling for the effects of the
confounding variables and volume of WMHs (Table 2). Although the relationship between volume of WMHs and age was
significant in a univariate analysis, it did not remain significant
after controlling for the effect of the confounding variables and
nWBV. Similarly, the relationship between nWBV and the
duration of symptoms was significant only before controlling for
the effects of the confounding variables and volume of WMHs.
Table 3 describes partial Spearman rank correlation coefficients
between the volume of WMHs and the risk factors and between
nWBV and the risk factors. WMH volume was positively
correlated with hypertension. nWBV was positively correlated
with smoking (nWBV was larger in smokers). Table 4 summarizes the partial Spearman rank correlation coefficients of volume of WMHs and nWBV with neurological disturbances,
CDR, cognitive test scores, and NPI scores after controlling for
the effects of the confounding variables. The volume of WMHs
was significantly correlated with incontinence and grasp reflex
and with the NPI aberrant motor behavior scores but not with
cognitive test scores or CDR. On the other hand, nWBV was
significantly correlated with all cognitive test scores and CDR,
with grasp reflex, and with NPI disinhibition and aberrant motor
behavior scores.
Discussion
WM changes, when defined as irregular periventricular hyperintensities, early confluent deep WMHs, and confluent
deep WMHs, had a significant relationship with hypertension, as expected. An association between WM changes and
high blood pressure or hypertension and/or the other vascular
risk factors has been demonstrated in a number of clinical
studies,7,13,18,44 and the relationship between these WM
changes and ischemic vascular changes has been documented
in pathological studies.33,34 On the other hand, we found no
significant correlation between brain atrophy and vascular
risk factors. Furthermore, brain atrophy was not correlated
with WMH volume either with or without controlling for age,
sex, education, and the duration of symptoms. Using quanti-
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September 2000
TABLE 4. Association of volume of WMHs and nWBV With
Neurological, Cognitive, and Neurobehavioral Symptoms
WMH Volume
rs
P
nWBV
rs
P
Neurological findings
Pyramidal sign
⫺0.005
0.97
0.19
Extrapyramidal sign
⫺0.20
0.10
⫺0.21
0.077
0.11
Incontinence
0.26
0.027
⫺0.16
0.20
Grasp
0.32
0.007
⫺0.27
0.025
⫺0.019
0.88
⫺0.15
0.20
Postural instability
Cognitive tests
0.022
0.86
0.42
⬍0.001
⫺0.001
0.99
⫺0.45
⬍0.001
ADAS recall
0.11
0.35
0.30
0.011
WAIS VIQ
0.025
0.84
0.35
0.003
MMSE
ADAS
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WAIS PIQ
⫺0.11
0.38
0.48
⬍0.001
WAIS FIQ
⫺0.039
0.75
0.47
⬍0.001
0.11
0.36
⫺0.26
0.028
0.42
CDR
NPI scores
Delusion
⫺0.005
0.97
⫺0.10
Hallucination
⫺0.069
0.57
0.05
0.69
Aggression
0.11
0.37
⫺0.05
0.68
Depression
0.032
0.79
⫺0.13
0.29
Anxiety
0.16
0.18
⫺0.17
0.17
Euphoria
0.063
0.60
⫺0.09
0.45
Apathy
0.14
0.23
⫺0.17
0.17
Disinhibition
0.14
0.25
⫺0.25
0.036
Irritability
0.056
0.64
⫺0.14
0.26
Aberrant motor behavior
0.28
0.019
⫺0.27
0.023
Values are P values or partial Spearman rank correlation coefficients (rs) after
controlling for effects of age, sex, education, duration of symptoms, and either
nWBV or volume of WMHs.
tative MRI, DeCarli et al29 also demonstrated no significant
differences either in brain or cerebrospinal fluid volumes
between AD patients with and without WMHs. These findings suggest that WM changes and brain atrophy were
independent phenomena. Recent studies have demonstrated
that AD itself is not involved in forming WMHs.20,58,59
Although WMHs are sometimes reported as being significantly more frequent in AD patients than in control subjects,
the increase is attributed to mild periventricular changes of
probably nonischemic origin, including caps, halos, and thin
lining.5,7,9,10 Moreover, Scheltens et al6 reported that compared with WMH in control subjects, WMH was more intense
in patients with senile onset AD but not in patients with
presenile onset AD and suggested that additional microvascular factors are involved in elderly patients with senile onset
AD. Together with those findings in the recent studies, our
results suggests that WM changes in AD patients, when they
are defined as irregular periventricular or confluent deep
WMHs, are superimposed phenomena of ischemic origin. It
is also interesting that our results demonstrated that a smoking habit had a modest but significant protective effect
against brain atrophy. Smoking has been reported to prevent
AD,60 and our findings might support this hypothesis.
In the present study, WMH volume was not correlated with
the severity of dementia, global cognitive impairment, or memory impairment. This finding is compatible with the findings of
our previous study,61 in which we demonstrated that WMHs
were associated with decreased cerebral blood flow but not with
decreased oxygen metabolism in patients with AD. Brain atrophy, but not reduced cerebral blood flow, was significantly
associated with cognitive impairments. These findings suggest
that the cognitive impairment in our patients is not attributable to
WM changes but to brain atrophy, although WM changes are
reported to impair some cognitive functions that were not
evaluated in the present study.11 WMHs associated with more
severe small-vessel diseases might affect cognitive functions in
patients with AD. Snowdon et al62 reported that in subjects with
pathological evidence of AD, those lacunar infarcts in the basal
ganglia, thalamus, or deep white matter had poorer cognitive
function and a higher prevalence of dementia than those without
infarcts. However, even in patients who were diagnosed as
having vascular dementia, the association between WM changes
and global cognitive impairment is unconvincing.10,11,22,25,63– 66
Although Binswanger’s disease reportedly causes dementia
without a cortical degenerative process, the pathological features
of this disorder include not only WM changes but also lacunar
infarcts in the basal ganglia and thalamus.1,2 Coexistent lacunar
infarcts may affect the strategic sites, causing dementia.67
Inzitari et al68 pointed out that a strong association between WM
changes and dementia was an epiphenomenon that could be
explained by a history of stroke. In a longitudinal study of
patients with lacunar infarcts, Loeb et al69 found that the
development of dementia was significantly associated with
cerebral atrophy and new focal cerebrovascular episodes but not
with WM changes. These findings, together with those in the
present study, suggest that cognitive impairment, both in AD and
vascular dementia, is not principally attributable to WM changes.
On the other hand, the present study clearly demonstrated that
WM changes and brain atrophy were independently associated
with certain neurological and neurobehavioral signs. Urinary
incontinence, grasp reflexes, and aberrant motor behaviors were
significantly correlated with WMH volume even after controlling for brain atrophy, although the latter 2 were also correlated
with brain atrophy after controlling for WMH volume. Urinary
incontinence is considered to be one of the central clinical
features of Binswanger’s disease,1,2 and an involvement of WM
changes in its development has been shown in previous studies.23,24 Primitive reflexes have also been reported to be associated with WM changes in elderly people70,71 and in patients with
dementia.72 Positive associations between WM changes and
psychiatric symptoms have been reported in subjects without
dementia.73,74 Although previous studies have failed to find a
relationship between WM changes and neurobehavioral signs in
patients with dementia,14,21,28,30 the present study clearly demonstrated that WM changes were involved in the development of
aberrant motor behaviors. Aberrant motor behaviors, including
wandering, pacing, and rummaging, belong to repetitive and
excessive behaviors, which are likely to be caused by frontal
lobe dysfunction. Our findings indicate that WM changes would
Hirono et al
at least add frontal lobe–related neurological and neurobehavioral features as manifestations of dementia.
In conclusion, WM changes in AD patients without any
obvious cerebrovascular diseases are related to hypertensive
microangiopathy and are independent of brain atrophy that
would be attributable to a degenerative process. WM changes
contribute to the development of some frontal lobe–related
neurological and neurobehavioral signs but not to the development of a global cognitive impairment, which is more closely
associated with brain atrophy. Further studies are needed to
generalize our findings to include AD patients with more severe
vascular disease.
Acknowledgments
Downloaded from http://stroke.ahajournals.org/ by guest on October 2, 2016
We thank Satoshi Tanimukai, MD, Tokiji Hanihara, MD, Toru
Imamura, MD (Division of Clinical Neurosciences), Mieko Matsui,
MD, Setsu Sakamoto, MD, and Kazunari Ishii, MD (Division of
Neuroimaging Research) for their help with various parts of
the study.
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Impact of White Matter Changes on Clinical Manifestation of Alzheimer's Disease: A
Quantitative Study
Nobutsugu Hirono, Hajime Kitagaki, Hiroaki Kazui, Mamoru Hashimoto and Etsuro Mori
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Stroke. 2000;31:2182-2188
doi: 10.1161/01.STR.31.9.2182
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