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Charles DeCarli, Evan Fletcher, Vincent Ramey, Danielle Harvey and William J.
Relationships Between Periventricular WMH, Deep WMH, and Total WMH
Anatomical Mapping of White Matter Hyperintensities (WMH): Exploring the
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Anatomical Mapping of White Matter
Exploring the Relationships Between Periventricular WMH, Deep WMH,
and Total WMH Burden
Charles DeCarli, MD; Evan Fletcher, PhD; Vincent Ramey; Danielle Harvey, PhD; William J. Jagust, MD
Background and Purpose—MRI segmentation and mapping techniques were used to assess evidence in support of
categorical distinctions between periventricular white matter hyperintensities (PVWMH) and deep WMH (DWMH).
Qualitative MRI studies generally identify 2 categories of WMH on the basis of anatomical localization. Separate
pathophysiologies and behavioral consequences are often attributed to these 2 classes of WMH. However, evidence to
support these empirical distinctions has not been rigorously sought.
Methods—MRI analysis of 55 subjects included quantification of WMH volume, mapping onto a common anatomical
image, and spatial localization of each WMH voxel. WMH locations were then divided into PVWMH and DWMH on
the basis of distance from the lateral ventricles and correlations, with total WMH volume determined. Periventricular
distance histograms of WMH voxels were also calculated.
Results—PVWMH and DWMH were highly correlated with total WMH (R2?0.95) and with each other (R2?0.87).
Mapping of all WMH revealed smooth expansion from around central cerebrospinal fluid spaces into more distal
cerebral white matter with increasing WMH volume.
Conclusion—PVWMH, DWMH, and total WMH are highly correlated with each other. Moreover, spatial analysis
failed to identify distinct subpopulations for PVWMH and DWMH. These results suggest that categorical
distinctions between PVWMH and DWMH may be arbitrary, and conclusions regarding individual relationships
between causal factors or behavior for PVWMH and DWMH may more accurately reflect total WMH volume
relationships. (Stroke. 2005;36:50-55.)
Key Words: cerebrovascular disorders ? magnetic resonanace imaging ? white matter
categories: periventricular WMH (PVWMH), which abut the
cerebral ventricles, and deep WMH (DWMH), which are
patchy areas of WMH in subcortical white matter distinct
from the periventricular area.1–8Qualitative MRI studies
evaluating the impact of vascular risk factors on WMH
routinely distinguish PVWMH from DWMH.6,9–14Results
from these studies generally show age and vascular risk
factors as the strongest correlate of PVWMH, whereas
associations between vascular risk factors and DWMH are
much weaker. Similarly, studies examining the relationship
between PVWMH, DWMH, and cognitive performance
among nondemented elderly15generally find strong correla-
tions between PVWMH and cognitive measures15,16but not
DWMH. MRI pathological correlations of WMH also suggest
differences between PVWMH and DWMH.9,17–26However,
within both types of WMH lesions, there is vascular fibrosis
hite matter hyperintensities (WMH) are commonly
seen on T2-weighted MRI and are often divided into 2
and lipohyalinosis,9,22,23,25–27supporting a common ischemic
vascular pathological mechanism for WMH among older
individuals.17,24,25Therefore, whereas qualitative MRI studies
generally support distinctions between PVWMH and
DWMH, pathological studies suggest that both types of
WMH share the same ischemic etiology supporting patholog-
ical linkage. However, most previous MRI work has used
qualitative single-slice assessments that may not fully appre-
ciate the complex 3D anatomy of WMH. Thus, existing MRI
data cannot unequivocally support distinctions between
PVWMH and DWMH. This study sought to confirm these
anatomical distinctions using new image segmentation and
Subjects for this study consisted of the first 55 consecutive individ-
uals recruited through the University of California at Davis (UCD)
Received June 2, 2004; final revision received September 22, 2004; accepted October 6, 2004.
From the Department of Neurology (C.D., E.F., V.R., W.J.J.) and Imaging of Dementia and Aging (IDeA) Laboratory (C.D., E.F., V.R., W.J.J.), Center
for Neuroscience, and the Division of Biostatistics (D.H.), Department of Epidemiology and Preventive Medicine, University of California at Davis,
Correspondence to Dr Charles DeCarli, Department of Neurology, 4860 Y St, Suite 3700, Sacramento, CA 95817. E-mail firstname.lastname@example.org
© 2004 American Heart Association, Inc.
Stroke is available at http://www.strokeaha.orgDOI: 10.1161/01.STR.0000150668.58689.f2
Alzheimer’s Disease Center for whom research MRI was available
for analysis. As expected, these individuals had variable cognitive
abilities ranging from normal to cognitive impairment not demented
(CIND) to dementia as defined according to standard diagnostic
criteria.28,29Etiologies of cognitive impairment included Alzhei-
mer’s disease (AD) and cerebrovascular disease (CVD), including
symptomatic stroke, although individuals with cortical infarctions
were excluded. Subjects were recruited for participation through
advertisements, community screening, and physician referrals. Sub-
ject demographics according to cognitive syndrome are summarized
in the table. Informed consent was obtained for each patient at the
time of participation in the study according to UCD institutional
review board guidelines.
All brain imaging was obtained at the UCD MRI research center on
a 1.5T GE Signa Horizon LX Echospeed system. Two sequences
were used: a T1-weighted coronal 3D spoiled gradient recalled echo
acquisition and a fluid-attenuated inversion recovery (FLAIR) se-
quence designed to enhance WMH segmentation.30
An overview of image analysis is summarized in Figure 1. In brief,
image segmentation using previously described algorithms31,32was
applied to the FLAIR sequences to segment WMH (Figure 2). After
affine coregistration of the FLAIR image to the high-resolution T1
image, WMH voxels were used to correct intensity changes in the T1
image to reduce any adverse impact of the WMH voxel values on the
accuracy of the nonlinear warping algorithm. The details and
rationale for these processes are included in an appendix available
online only at http://www.strokeaha.org.
Nonlinear warping enables precise matching of anatomical regions
across subjects (see online appendix). We used this characteristic of
the method to determine the exact distance between each WMH
voxel and the ventricular ependymal surface for all subjects. To test
the hypothesis of the PVWMH versus DWMH distinction, we
measured distributions of WMH voxels in reference to the ependy-
mal surface of the target ventricular system in 2 ways. We first
created histograms of the average distance from the ventricular
surface for 5 quintiles of WMH burden. We hypothesized that if a
true distinction in WMH location (ie, PVWMH versus DWMH)
were present, we would see 2 peaks in the histograms related to the
separate WMH categories. Second, we created a standardized divi-
sion of WMH location into PVWMH and DWMH on the basis of
1-cm distance from the ventricular system. Volumes for PVWMH
and DWMH were then calculated for each individual subject. Linear
regression analysis was used to examine PVWMH and DWMH
volumes in relation to total WMH volume and each other.
There were no significant differences in age across the
groups, although subjects with dementia tended to be some-
what older (Table). Among the dementia subjects, 9 were
diagnosed as clinically probable AD, and 6 were diagnosed as
mixed dementia with AD and CVD combined. A total of 8
subjects had clinical stroke that presented as a lacunar
Figure 1. Schema for WMH segmentation and nonlinear trans-
formation for mapping. See text for details.
Subject Demographics, WMH, and Brain Volumes
*Expressed as percentage of total cranial volume. MMSE indicates mini
mental state examination.
Figure 2. Example of FLAIR segmenta-
DeCarli et al Anatomical Mapping of WMH
syndrome, predominantly with hemiparesis. Of the 8 with
clinical stroke, 6 were demented and 2 had CIND. No
subjects had cerebral hemorrhage.
WMH volumes were calculated for all subjects and ranged from
1.1 to 63 mL and divided into quintiles with ranges consisting of
first, 1.1 to 2.3 mL; second, 2.6 to 4.6 mL; third, 5.1 to 9.0 mL;
fourth, 9.7 to 16.0 mL; and fifth, 18.0 to 63 mL. There were no
significant differences in mean WMH volumes in association
with degree of cognitive impairment (Table), although subjects
with dementia had nearly twice the volume of WMH.
Mapping of subjects by quintile of total WMH volume
(Figure 3) revealed a continuous gradient of mapped WMH
voxels extending from around the cerebrospinal fluid (CSF)
ventricular system in direct relation to calculated WMH
volume. Evidence for the potential misclassification of
DWMH on the basis of 2D visualization is also illustrated
(Figure 3). When viewed axially, as is common in studies of
WMH,33,34DWMH appear present. However, the sagital and
coronal orientations show that these WMH are actually
contiguous with the ventricular lining.
Distance histograms of WMH voxels are shown in Figure
4. There is no clear sign of a bimodal distribution. Instead, the
peak of the WMH distribution widens continuously from the
lowest WMH quintile, where the median distance is
?3.5 mm, to highest quintile, where the median distance is
?6.0 mm. One exception to this general observation is at the
lowest quintile, where a small second peak occurs at ?30 mm
from the ventricular surface. Examination of the images in the
lowest quartile of WMH revealed the presence of multiple
punctate WMH scattered within the centrum semiovale.
In the second analysis, WMH were divided into PVWMH
and DWMH on the basis of a 1-cm distance from the
ventricular surface. The relationship between PVWMH,
DWMH, and total WMH burden is graphically illustrated in
Figure 5. PVWMH and DWMH volumes were closely
associated with WMH burden (R2?0.99 and 0.92, respec-
tively). PVWMH and DWMH volumes also were signifi-
cantly correlated (R2?0.87). The slope of PVWMH to total
WMH burden is ?2.5? that of the slope between DWMH
and total WMH burden, suggesting a preferential increase in
PVWMH with increasing total WMH burden.
Use of image segmentation, 3D anatomical mapping of
WMH voxels, and 2 separate analytical methods failed to find
distinctions between PVWMH and DWMH. Not only did
analyses of distance histograms fail to identify 2 separate
WMH voxel populations, but application of a standard
categorical definition for PVWMH versus DWMH across all
subjects found high correlations with total WMH burden as
well as with each other. These results suggest that categorical
distinctions between PVWMH and DWMH are likely to be
arbitrary, and conclusions regarding individual relationships
between causal factors or behavior for PVWMH and DWMH
may more accurately reflect total WMH volume relationships.
However, our data cannot speak to possible regional differences
that both phenomena are highly correlated with each other,
suggesting a common underlying mechanism.
Our results appear different from visual inspection (Figure
2) as well as published examples of WMH.8,33One obvious
explanation for this discrepancy is our use of 3D mapping
techniques that may avoid some of the limitations of 2D
qualitative MRI studies. For example, we show that WMH
typical of DWMH, when viewed axially, are in fact contig-
uous with ventricular WMH (Figure 3). This finding is not
specific to our method because a recently published Statisti-
cal Parametric Mapping study found similar results,35al-
though it did not specifically examine the question of
PVWMH versus DWMH. A second explanation for differing
results also may derive from our use of consistent measures
and anatomical definition of PVWMH versus DWMH. For
example, in the Rotterdam Scan Study, DWMH are measured
according to width and number as opposed to categorical
definitions for PVWMH,8making direct comparisons be-
tween the 2 types of WMH difficult. Different measures for
DWMH versus PVWMH may also explain differences in
Figure 3. Mean voxel distributions
according to quintile of WMH burden.
Black hatch marks indicate level of axial
image shown below and illustrate the
limitations of 2D viewing for determina-
tion of WMH location.
associations between causal factors, behavior, and DWMH
found with qualitative studies.15,16However, our conclusions
are not meant to suggest that islands of abnormal WMH
signal located in the centrum semiovale do not exist. Quite
the contrary; we believe that our data support the notion
proposed by Schmidt et al36that WMH burden increases
through the confluence of PVWMH with punctate WMH
located in the centrum semiovale, although our experiment
was not designed to address this particular question. How-
ever, our histogram data do support this hypothesis by
showing a second peak at the lowest quintile of WMH
volume, indicating more frequent DWMH initially that may
then converge with PVWH as the total WMH increases. We
further suggest that the strong correlations between causal
factors and behavior found with PVWMH in qualitative
studies likely reflect the steeper slope of change of PVWMH
with total WMH burden, as seen with our quantitative data
analysis (ie, the steeper slope suggests increased sensitivity to
detect differences across individuals).
Our results are also consistent with current concepts of
WMH pathology. Although some controversy remains,37
there is general consensus for a single vascular white matter
watershed area extending between 3 and 13 mm from the
ventricular surface,37–40remarkably similar to the distances
described by our quantitative MRI analysis (Figure 4).
However, some neuropathological evidence distinguishing
different types of WMH lesions does remain.25For example,
subependymal gliosis, irregularity of the ependymal lining,
adjacent myelin pallor,22,23,25,41,42or a normal fasciculus
subcallosus22are commonly found in postmortem samples
when WMH are limited to ventricular capping or a smooth
halo about the ventricles (eg, similar to WMH quintiles 1
through 3; Figure 3). Conversely, vascular hyalinization,
ischemic white matter injury, and microscopic infarction are
consistently found when the periventricular changes become
extensive.17,24,25In these cases, DWMH sharing features of
ischemic pathology commonly co-occur with PVWMH,25
suggesting a pathophysiology common to both.17,24Although
differences in pathological features argue strongly for sepa-
rate categories of WMH, we believe these categories are
different from designations of PVWMH or DWMH used for
qualitative MRI studies. That is, minor degrees of WMH
(rims and caps1) are most consistent with periventricular
edema or disturbed CSF transport25,41,42and most likely
accompany normal aging.32Conversely, more extensive
WMH likely have a vascular etiology independent of desig-
nations such as PVWMH or DWMH.17,24,25Quantitative MRI
studies support this distinction by showing strong associations
between vascular risk factors and vascular disease when WMH
volumes are extensive,32,43further supporting the notion that it is
the overall extent and not categorical distinctions that best
represent the underlying pathology of the WMH.
Because of a number of limiting factors, these results
should be interpreted cautiously. First, our data are based on
segmented WMH values and mathematical interpolation
methods, raising the possibility that measurement error might
reduce the sensitivity to identify a second population of
WMH voxels. That is, our segmentation method favors
selecting voxels of most extreme signal change, and our
interpolation method induced a small amount of image
smoothing. However, we do not believe that these errors were
substantial because our segmentation of WMH is based
strictly on voxel intensity parameters31,43and, therefore,
would tend to underestimate the continuity of these changes
Figure 4. Distance histogram distributions according to quintile of WMH burden.
DeCarli et al Anatomical Mapping of WMH
by selecting only voxels above a specified threshold, favoring
separate populations of WMH voxels. Minor degrees of
image smoothing from image interpolation during warping
are similarly unlikely to lead to substantial error in detection
of separate voxel populations because the distinctions be-
tween PVWMH and DWMH are defined in more macro-
scopic terms.15,34Subject selection may be a second weakness
of the study for 2 reasons. First, our population included
individuals referred to or recruited for our memory disorders
clinic and, therefore, is not representative of the general
population. However, the patterns of WMH seen with the 55
subjects studied do not differ from other studies reporting
various degrees of WMH severity.33,34Secondly, our sample
included individuals with a wide range of cognitive abilities
and concurrent CVD, although individuals with cortical
infarction were excluded from the analysis. Although it has
been suggested that individuals with cognitive impairment
are more likely to have larger PVWMH compared with
DWMH,44we would argue that this finding more closely
reflects total WMH burden, as seen with other studies.45,46
Therefore, although this is a sample of convenience that
included individuals with differing degrees of cognitive
impairment, the type and pattern of WMH seen were typical
of those noted by population studies and would not be
expected to alter the results found. However, our conclusions
may not be directly applicable to other diseases such as
late-life depression, in which frontal DWMH are significantly
more common,47,48an area worthy of further investigation
using these newer methods.
In conclusion, we believe the methods developed here
conclusively show that WMH extend smoothly from the
ventricular wall as the overall burden increases, offering no
clear evidence for distinguishing WMH subtypes. In fact,
these data support the notion of a single vascular watershed
area that extends from the CSF ventricular surface to the
central white matter, consistent with currently proposed
cerebral vascular anatomy.37,38These observations also sup-
port the notion of a common ischemic etiology among elderly
individuals when WMH burden is extensive.
This research was supported by National Institute of Aging grants
P30 AG10129 and R01 AG021028.
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