1. The study examined the relationship between regional white matter lesions (WMLs) and neuropsychological performance in 70 older adults diagnosed with mild cognitive impairment (MCI). 2. Using volumetric analyses of magnetic resonance imaging data, the study differentiated between deep white matter lesions (DWMLs) and periventricular lesions (PVLs). 3. Regression analyses showed that increased total WML volume strongly predicted executive impairments, slowed processing speed, and visuospatial/construction difficulties. DWMLs, but not PVLs, were the main driver of these relationships and predicted overall poorer neuropsychological functioning after controlling for confounds.

1. Andrea Bozoki
Lisa Delano-Wood, Norm Abeles, Joshua M. Sacco, Christina E. Wierenga, Nikki R. Horne and
Lesion Type on Neuropsychological Functioning
Regional White Matter Pathology in Mild Cognitive Impairment : Differential Influence of
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2. Regional White Matter Pathology in Mild
Cognitive Impairment
Differential Influence of Lesion Type on Neuropsychological Functioning
Lisa Delano-Wood, PhD; Norm Abeles, PhD; Joshua M. Sacco, PhD; Christina E. Wierenga, PhD;
Nikki R. Horne, MS; Andrea Bozoki, MD
Background and Purpose—Associations between regional white matter lesion pathology and neuropsychological
performance across the aging spectrum are not well understood and, to date, research has been largely contradictory and
inconclusive. The current study set out to clarify some of the inconsistencies in the literature by relating volumetric
analyses of white matter lesions (deep white matter lesions and periventricular lesions) to neuropsychological
performance in a large clinical sample of older adults diagnosed with mild cognitive impairment.
Methods—Seventy older adults with mild cognitive impairment were administered a comprehensive neuropsychological
battery. White matter lesions identified on T2-weighted FLAIR images were quantified using a semi-automated
volumetric approach (pixel thresholding).
Results—Results showed that, in contrast to performance on memory and naming tasks, total white matter lesions strongly
predicted executive impairments, slowed processing speed, and visuospatial/construction difficulties. In addition,
separate regression analyses demonstrated that results were primarily accounted for by deep white matter lesions (but
not periventricular lesions), most likely due to frontal-subcortical circuitry disruption. Moreover, deep white matter
lesions, but not periventricular lesions, significantly predicted overall poorer neuropsychological functioning after
controlling for age, education, and level of depression.
Conclusions—Taken together, findings demonstrate a differential influence of lesion type on cognitive impairment in mild
cognitive impairment and implicate deep white matter lesions as being most detrimental in terms of neuropsychological
functioning. Clinical, theoretical, and methodological implications of these results are discussed. (Stroke. 2008;39:794-799.)
Key Words: cognition Ⅲ deep white matter lesions Ⅲ mild cognitive impairment
Ⅲ white matter hyperintensities Ⅲ white matter lesions
Considered to be rare before the advent of MRI, white
matter lesion (WML) pathology is now receiving con-
siderable attention with respect to the understanding of
neurobehavioral decline seen in aging and dementia.1 WMLs
appear to be related to chronic microvascular disease and
hypoperfusion,2 and they are known to increase with age.3 In
addition, WML pathology is commonly seen in Alzheimer
disease,4 and recent studies have shown that they appear to
increase the risk of developing dementia.5 However, the role
of WML in the pathology or disease severity of dementia has
not been clearly established and whether WMLs represent a
pathognomonic sign for brain disease remains a matter of
debate.6
Over the past several years, many studies have attempted to
elucidate the neuropsychological significance of WML in
healthy aging and dementia; however, for the most part,
results have been mixed and unclear. For example, some
recent studies have shown associations of WML and cogni-
tive impairment in normal aging, particularly on tests that
measure processing speed,7–9 whereas earlier studies have
demonstrated no relationship between WML and cogni-
tion.10,11 Similarly, results from some studies have found
significant correlations between WML and neuropsycholog-
ical functioning in Alzheimer disease12,13; however, other
reports suggest no relationship among WML, cognition, and
dementia severity.4,14 Thus, the association of WML with
cognitive impairment in normal aging and dementia is still
not fully elucidated. Discrepant findings may be due to
numerous factors, including the use of insensitive measures
of neuropsychological functioning, differential sensitivity of
MRI methodologies, and inclusion of samples with a broad
range of cognitive functioning.
Conflicting findings across the aging spectrum, particularly
in the earliest clinical stages of Alzheimer disease, highlight
Received August 22, 2007; final revision received October 9, 2007; accepted October 19, 2007.
From the Department of Psychiatry (L.D.-W., C.E.W., N.R.H.), University of California San Diego, School of Medicine, San Diego, Calif; and the
Departments of Psychology (N.A., J.M.S.) and Neurology and Radiology (A.B.), Michigan State University, School of Medicine, East Lansing, Mich.
Correspondence to Lisa Delano-Wood, PhD, University of California, San Diego, Division of Geriatric Psychiatry VASDHC (MC 1164-1), 3350 La
Jolla Village Dr, San Diego, CA 92161. E-mail ldelano@ucsd.edu
© 2008 American Heart Association, Inc.
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3. the critical need for better characterization of white matter
changes seen in mild cognitive impairment (MCI), a transi-
tional stage between normal and demented aging that is based
on a pathological model of cognitive change.15 However,
although studies of MCI have been increasingly prevalent in
the literature, few have examined WML within this popula-
tion. Recently, Kumar et al16 reported that WMLs were not
related to cognitive impairment in their MCI sample. How-
ever, the authors’ definition of MCI was restricted to memory
impairment, and the authors acknowledge that their sample
was younger and healthier than most MCI samples. Both of
these factors may have attenuated any associations between
WML and cognition. Mendonca et al17 also reported no
association between neuropsychological impairment and
WML in their MCI sample; however, authors of this study
also applied MCI diagnostic criteria based solely on memory
performance. In addition, WML was measured as a dichoto-
mous variable (either present or absent) on the basis of CT
scans, which are relatively insensitive to white matter anom-
alies. In a recent volumetric study, van der Flier et al18
reported a significant relationship of WML with cognition
(psychomotor speed, naming, and memory abilities); how-
ever, regional measures of WML (periventricular lesions
[PVLs] and deep white matter lesions [DWMLs]) were not
delineated, thus precluding any investigation of differential
neuropsychological associations between these 2 lesion types.
Overall, of the existing studies examining WML within MCI,
many suffer from low sample sizes16,18,19; a failure to distin-
guish between lesion types16,20; MCI criteria that require
specific memory impairment16–19; and the use of visual rating
scales versus more reliable volumetric methods in the quan-
tification of WML burden.16–19,21 Furthermore, many studies
of WML in MCI have not examined associations with
neuropsychological functioning.20,21
Bowler and Hachinski22 have argued that both types of
WML should be analyzed separately, and this appears to be
critically important given recent suggestions that PVL and
DWML may differ in their pathogenesis and clinical signif-
icance.21 Recently, an immunohistochemistry study by Simp-
son et al23 showed that the lesion types differ in their cellular
pathology. Specifically, the authors demonstrated PVLs ap-
pear to be primarily associated with cerebrospinal fluid
leakage into the white matter and subsequent loss of ependy-
mal lining in the ventricles. In contrast to PVLs, which appear
to be most strongly related to age,5,24 DWMLs seem to be
reflective of underlying vascular risk and the presence of
vascular disease.25,26 Despite a call for studies to investigate
both types of WML, few studies have made this differentia-
tion. Given this, coupled with the lack of studies that have
examined WML in MCI, the primary aims of this study were
to (1) explore the relationship between WML and cognition
in a heterogeneous MCI sample; and (2) characterize specific
patterns of cognitive impairment associated with both PVL
and DWML. It is expected that increased WML volumes will
be associated with greater overall cognitive impairment and,
in contrast to DWML, PVL will be more strongly related to
age. In addition, given that DWML may interrupt critical
neuropathways that facilitate complex neuropsychological
functioning, we hypothesized that DWML, but not PVL, will
be associated with greater overall neuropsychological impair-
ment, particularly in cognitive domains thought to be depen-
dent on frontal–striatal circuits (executive functioning, pro-
cessing speed, and visuospatial/construction).27
Materials and Methods
Participants
Data were obtained from a clinical sample of 70 older adults who
were recruited from the Michigan State University Geriatric Neurol-
ogy Clinic, an outpatient subspecialty of the Michigan State Univer-
sity Neurology and Radiology Department. Participants were ex-
cluded from the study if there was any evidence of current or past
diagnoses of neurological or psychiatric disorder, stroke, thyroid
disease, diabetes, known head injury, or any significant visual or
auditory impairment that precluded them from participating in
neuropsychological testing. All participants provided written in-
formed consent and the procedures in the present study were
approved by the Michigan State University Committee on Research
Involving Human Subjects.
Materials and Procedures
A diagnosis of MCI was based on the following criteria recently
defined by Petersen and Morris28: (1) subjective patient memory
complaints; (2) normal activities of daily living; (3) absence of
dementia; (4) Mini-Mental State Examination score of 24 or greater;
and (5) mild quantifiable cognitive impairment. Although no reliable
cutoff for defining impairment in MCI has yet been delineated,
Busse et al,29 demonstrated that a more liberal cutoff (ie, 1 SD below
the mean) is optimal because it offers higher sensitivity than the
traditional cutoff of 1.5 SD. Thus, given our aim to be broadly
inclusive, we attempted to compromise using a cutoff of 1.2. SD
(after applying norms adjusted for age, education, and gender),
signifying a level of performance worse than 88.5% of the popula-
tion (indicative of mild to moderate impairment). All neuropsycho-
logical scores were standardized with a z-score transformation on the
Figure. WML pathology identified on axial T2-weighted fluid-
attenuated inversion recovery.
Delano-Wood et al Regional White Matter Pathology and Cognition in MCI 795
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4. basis of the Consortium to Establish a Registry for Alzheimer’s
Disease (CERAD) or other normative data of the neuropsychological
tests.30 Scores that reflected number of errors or response times were
multiplied by Ϫ1 so that negative z-scores consistently reflected
poor performance. All participants were categorized into 4 sub-
groups (single-domain amnestic MCI, nϭ12; single-domain nonam-
nestic MCI, nϭ12); multiple-domain amnestic MCI, nϭ28); and
multiple-domain nonamnestic MCI, nϭ18) based on neuropsycho-
logical test scores.28
Neuropsychological Test Battery
Neuropsychological classification of MCI was conducted indepen-
dently of the WML quantitative analyses and all cognitive tasks were
administered to each participant individually by a technician who
was blind to the participant’s medical status and MRI results. In
addition to the Mini-Mental State Examination (MMSE)31, a general
measure of overall cognitive status, participants were administered
the CERAD30, a reliable, well-standardized battery that includes 7
individual tests designed to tap several domains, including memory,
praxis/visuoconstruction, and language. Specific CERAD tasks in-
cluded verbal fluency (animal fluency), object naming (15 items);
constructional praxis (figure copying), and memory tests (word list
learning, delayed free recall, and recognition). Delayed visual
memory was assessed by recall of geometric figure presented earlier.
The CERAD was designed to detect impairment in mildly impaired
populations, and recent studies have begun to use the battery in their
MCI diagnosis determination.32,33 To strengthen our diagnostic
scheme, the CERAD battery was supplemented by the inclusion of
additional neuropsychological tests to augment the assessment of
processing speed and executive functioning (Trails A and B34 and
Stroop Color Word Test35). To control for speed of processing, time
to complete Trail A was subtracted from time to complete Trail B.
Finally, we used the Geriatric Depression Scale36, a brief measure
widely used to assess depression in older adult samples.
MRI Protocol and Quantification of White
Matter Lesions
All MRI was performed on a 1.5-T Signa scanner (General Electric,
Milwaukee, Wis) and WML volumes were estimated from T2-
weighted axial fluid-attenuated inversion recovery (FLAIR) images
(field of viewϭ20ϫ20 cm; matrixϭ256ϫ256; flip angleϭ90°,
TEϭ142 ms, TRϭ10000 ms, 5-mm slice thickness with no interslice
gap). A semiautomated volumetric approach was used using the
FLAIR images, a methodology recently shown to be the most
reliable approach for the analysis of WML when compared with
other image types and traditional quantitative visual rating scales.37
WML volumes were obtained based on 17 to 21 axial images per
subject using GE’s Advantage Workstation software (version 4.2)
and WMLs were measured according to protocols established
elsewhere.1,3 Hyperintense regions, defined as circumscribed areas
of increased signal intensity within the white matter, were measured
on axial slices beginning at the most inferior slice on which the
inferior horn of the lateral ventricles could be seen. WMLs were
coded according to their presence, volume, and type (PVL and
DWML). If the largest diameter was adjacent to the ventricular
lining, WML were considered to be PVL; otherwise, they were
considered to be in the deep white matter. All questionable cases
were resolved by consulting an experienced neuroradiologist.
Because two operators completed the WML measurements, inter-
rater reliability coefficients were computed based on a random
sample of 5 traced brains. The intraclass correlation formula for 2
random raters38 was used, and the resulting reliability estimates for
all regions of interest and types of WML exceeded 0.85. As
described in detail by Raz et al,1 the total volume of each region in
cubic centimeters was calculated by multiplying the summed pixel
cross-sectional area in square centimeters by slice thickness in
centimeters, and WML volumes were normalized to the intracranial
cavity volume for each participant. See the Figure for an illustration
of WML measurement on an axial slice of a randomly chosen
participant’s FLAIR.
Statistical Analyses
Composite scores were computed for memory (CERAD word list
delayed and visual memory scores; rϭ0.38, PϽ0.05) as well as
executive functioning (Trail B and the Stroop Color–Word Test
Interference score; rϭ0.69, PϽ0.001). Significant correlations be-
tween the tests that comprise each composite score supported this
approach. Given a recent study that demonstrated that the CERAD
word list learning trials are associated with low sensitivity and
specificity in MCI,39 this particular subtest was omitted from our
memory composite score. In addition, a composite variable was
computed from the means of all neuropsychological tests to evaluate
the relationship between WML and overall neuropsychological
functioning (NP). Pearson product-moment correlations and multiple
hierarchical regressions were used to examine the role of WMLs on
cognitive functioning and to further characterize the independent role
of PVL and DWML on cognitive performance. Finally, when
appropriate, adjustments for multiple comparisons were made in the
reported analyses. All statistical analyses were performed using the
Statistical Package for the Social Sciences for Windows (SPSS,
2004).
Results
Table 1 provides descriptive statistics and intercorrelations
for demographic and clinical variables of interest for the
sample. As can be expected, higher age was associated with
lower scores on the MMSE (rϭϪ0.39, PϽ0.05) as well as
poorer overall neuropsychological functioning (NP; rϭϪ0.41,
PϽ0.05). In addition, as can be seen in Table 2, age was
positively associated with PVL (rϭ0.27, PϽ0.05) but was
not significantly related to DWML (rϭ0.15, PϾ0.05). For
the analyses presented subsequently, MCI subgroups were
collapsed across groups given that they did not differ signif-
icantly in terms of any WML index (total WML, DWML, or
PVL). However, qualitatively, the multiple-domain nonamnes-
tic group demonstrated the highest volumes of total WML
and DWML.
Table 1. Descriptives and Intercorrelations Among Demographic Variables for MCI Sample
Mean SD Age Sex Education Geriatric Depression Scale MMSE Overall NP
Age 75.5 7.5 1
Sex Females (53%) ⅐ ⅐ ⅐ 0.18 1
Education 13.3 3.4 Ϫ0.19 0.12 1
Geriatric Depression Scale 7.7 6.7 0.13 0.19 Ϫ0.34* 1
MMSE 26.3 1.6 Ϫ0.39* 0.06 0.24 Ϫ0.08 1
Overall NP 0 0.58 Ϫ0.41* 0.05 0.42* Ϫ0.43* 0.36* 1
Note: Mean for total NPϭ0 because this variable represents a composite score. Sex: females coded 1, males coded 0. Nϭ70.
*PϽ0.05.
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5. The sample as a whole demonstrated slightly higher levels
of DWML versus PVL (DWML: meanϭ6.3, SDϭ5.3; PVL:
meanϭ5.8, SDϭ3.4). Although no relationship was found
between total WML volume and MMSE performance
(rϭϪ0.03, PϾ0.05), poorer overall NP was significantly
related to total WML burden (rϭϪ0.36, PϽ0.05), PVL
volume (rϭϪ0.33, PϽ0.05), and DWML volume (rϭϪ0.44,
PϽ0.05). Linear regression analysis was performed to deter-
mine if total WML and specific lesion types (DWML and
PVL) accounted for a significant amount of variance in
neuropsychological test performance (eg, whether WML
volumes were predictive of cognition). Results showed that
total WML significantly predicted overall NP after control-
ling for age, education, gender, and depression (␤ϭϪ0.53;
⌬R2
ϭ0.22, PϽ0.02). After adjusting for multiple compari-
sons and important demographic variables (age, gender,
education, and depression), separate linear regression analy-
ses demonstrated that total WML was strongly predictive of
executive functioning (␤ϭϪ0.63; ⌬R2
ϭ0.22, PϽ0.001), pro-
cessing speed (␤ϭϪ0.54; ⌬R2
ϭ0.16, PϽ0.001), and visuo-
spatial/construction (␤ϭϪ0.44; ⌬R2
ϭ0.12, Pϭ0.004).
Given the high correlations between the predictors (PVL
and DWML; rϭ0.74, Pϭ0.001), separate regressions were
conducted on each lesion type. Age, education, gender, and
depression were entered into the first block and lesion type
was entered in block 2. Results showed that PVL volume did
not significantly add to the prediction of overall NP function-
ing (⌬R2
ϭ0.009, PϾ0.05). However, the prediction of overall
NP functioning incremental to that of the predictors in step 1
was significant for DWML (⌬R2
ϭ0.043, PϽ0.001). A series
of standard multiple regressions were performed with
DWML entered as the independent variable and each of the
neuropsychological variables as dependent variables after
controlling for age, education, gender, and depression. As
expected, DWML strongly predicted poorer executive func-
tioning (␤ϭϪ0.65; ⌬R2
ϭ0.25, PϽ0.001), Trail A
(␤ϭϪ0.56, ⌬R2
ϭ0.18, PϽ0.001), and visuospatial/construc-
tion (␤ϭϪ0.53, ⌬Rϭ0.17, PϽ0.001). However, DWML was
not found to significantly predict naming or memory (all
probability values Ͼ0.05). The same series of multiple
regressions were then performed with PVL entered as the
dependent variable. After controlling for age, education,
gender, and depression, PVL was not predictive of perfor-
mance on any neuropsychological variables.
Discussion
This study set out to examine the relationship between WML
and cognition in a heterogeneous group of older adults
diagnosed with MCI. Results demonstrate that DWML, but
not PVL, strongly predicted performance on tests of execu-
tive functioning, speed of processing, and visuospatial/con-
structional skills after adjusting for important demographic
variables. In contrast, DWML did not predict performance on
tests of verbal memory or language. Given these results,
coupled with the strong associations reported in the literature
among DWML, microangiopathy, and hypoperfusion,2 it may
be that early manifestations of vascular cognitive impairment
associated with DWML lead to dysexecutive deficits in
cognition thought to be dependent on the integrity of frontal–
subcortical circuits.40 Indeed, DWMLs are more frequently
identified at the level of the dorsolateral prefrontal cortex, and
concomitant cognitive deficits may be the result of small
vessel disease disrupting frontal–subcortical pathways.41 Our
results are consistent with other recent studies that have
shown that PVL, but not DWML, may represent an age-
related phenomenon.5,21 Overall, our results corroborate the
hypothesis that these 2 lesion types may differentially impact
clinical presentation, and additional research is needed to
more clearly elucidate whether WML subtypes arise from
dissociable paths of pathogenesis.
Our findings stand in contrast to some earlier studies exam-
ining the effect of lesion types on cognitive functioning in aging
populations.7,9,42 For example, although the majority of our
results are similar to those of Prins et al,7 we did not find a
relationship between PVL or DWML and memory performance.
Prins and colleagues administered a much more difficult test of
verbal memory (15-word verbal learning test), which may have
been more sensitive than the memory measure that we used. It is
thus plausible that our lack of findings in terms of memory
performance is linked to ceiling effects. In addition, in contrast
to our findings, other studies have demonstrated that PVLs, but
not DWMLs, are related to cognitive impairment, particularly
processing speed.7,42 Possible explanations for differing findings
between their results and ours include methodological differ-
ences in terms of sample selection, imaging techniques, and
WML quantification.
Similar to other studies that have not found associations
between WML and measures of global cognition,43,44 our
results indicated that total WML volume was not associated
with MMSE performance. It is known that the MMSE is not
sensitive to subtle neuropsychological impairment and the
measure largely taps verbal and memory functions, cognitive
domains that are not typically related to WML. These results
suggest that studies investigating the relationship between
Table 2. Bivariate Correlations Between WML and
Demographic, Clinical, and Neuropsychological Variables
Variables PVL DWML Total WML
Age 0.27* 0.15 0.21
Sex 0.05 0.18 0.10
Education Ϫ0.28* Ϫ0.35* Ϫ0.33*
Geriatric Depression Scale 0.51† 0.56† 0.52†
MMSE Ϫ0.08 0.07 Ϫ0.03
VS (figure copying) Ϫ0.28* Ϫ0.44† Ϫ0.36*
Trail A Ϫ0.20 Ϫ0.37* Ϫ0.29*
BNT Ϫ0.15 Ϫ0.05 Ϫ0.09
Fluency (animals) Ϫ0.21 Ϫ0.18 Ϫ0.20
Memory Ϫ0.12 Ϫ0.09 Ϫ0.11
EF Ϫ0.42† Ϫ0.56† Ϫ0.51†
Note: Nϭ70. Correlations between WML variables (PVL, DWML, and total
WML) and neuropsychological variables (MMSE, VS, Trail A, BNT, Fluency,
Memory, and EF) are adjusted for age.
VS indicates Visuospatial/Construction; BNT, Boston Naming Test; EF,
Executive Functioning (composite score).
*PϽ0.05.
†PϽ0.01.
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6. WML and cognition may fail to find a significant association
if only brief screening measures are used. Future studies
should select, a priori, neuropsychological measures that tap
domains known to be sensitive to white matter changes (eg,
speed of processing and executive function).
To our knowledge, the results presented here represent one
of the few existing studies to associate volumetric analyses of
WML subtypes and neuropsychological impairment in a
large, heterogeneous clinical sample of patients with MCI. It
should be noted, however, that some of the neuropsycholog-
ical measures used in this study were designed as screening
instruments in the assessment of cognitive deficits of aging
and disease. Thus, the range of neuropsychological perfor-
mance was likely restricted, and this may have resulted in less
sensitivity to detect brain–behavior relationships. In addition,
the current study used a volumetric methodology in the
measurement of WML burden and thus little can be inferred
regarding the extent and pattern of microstructural white
matter changes in the pathology of MCI. Moreover, we did
not use a measure of vascular burden or stroke risk, and future
studies are needed to further clarify the association between
vascular risk and WML subtypes. However, given that the
selection criteria restricted the range of white matter abnor-
malities observed in this study, these results may represent a
conservative estimate of the role of WML in MCI.
Summary
The results of this study indicate that, when controlling for
the effects of age, education, gender, and depression,
DWMLs (but not PVLs) appear to be associated with specific
neuropsychological deficits dependent on frontal–subcortical
circuitry, including executive functioning, processing speed,
and visuospatial/construction. Future studies should attempt
to disentangle the effects of vascular pathology, aging, and
early Alzheimer disease pathology on the relationship be-
tween WML and neuropsychological functioning. In addi-
tion, the use of newer techniques such as diffusion tensor
imaging should aid in better identification of white matter
pathology across the aging spectrum. Finally, given the
growing prevalence of cognitive disorders in late life (asso-
ciated with population increases of older adults) and advances
in health care, longitudinal studies following older adults
(with and without vascular risk factors and associated WML)
who transition from normal healthy aging to early stages of
cognitive impairment, and eventually to dementia, will be
important to further elucidate and possibly prevent early,
preclinical manifestations of cognitive impairment.
Disclosures
None.
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