MRI correlates of vascular cerebral lesions
and cognitive impairment
E.C.W. van Straaten
© E.C.W. van Straaten, 2007, Amsterdam, the Netherlands. All rights reserved. ISBN: 978-90-9022597-5 The studies described in this thesis were carried out at the Alzheimer Center of the Department of Neurology at the VU University Medical Center, Amsterdam. The Alzheimer Center Vumc is supported by Stichting Vumc fonds and Stichting Alzheimer Nederland. Unrestricted funding is received through the Stichting Vumc fonds from: AEGON Nederland NV, Royal Numico Nederland, Kroonenberg Groep, Heineken Nederland NV, ING Private Banking, Krafft stichting, Stichting Dioraphte, VitaValley, Jansen Cilag BV, Novartis Nederland. The LADIS study was supported by the European Union (grant QLRT-2000-00446). Printed by: Printpartners Ipskamp BV, Enschede
VRIJE UNIVERSITEIT
MRI correlates of vascular cerebral lesions and cognitive impairment
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus
prof.dr. L.M. Bouter, in het openbaar te verdedigen
ten overstaan van de promotiecommissie van de faculteit der Geneeskunde
op vrijdag 11 januari 2008 om 15.45 uur in de aula van de universiteit,
De Boelelaan 1105
door
Elisabeth Catharina Wilhelmina van Straaten
geboren te ‘s-Gravenhage
promotoren: prof.dr. F. Barkhof prof.dr. Ph. Scheltens
Contents
Chapter 1 General introduction
1.1 Vascular dementia 9 1.2 White Matter Hyperintensities (WMH) 9 1.3 Aim of thesis 10 1.4 Outline of thesis 10
Chapter 2 Characteristics of vascular dementia on imaging
2.1 MRI and CT in the diagnosis of vascular dementia 15 2.2 Operational definitions for the NINDS-AIREN criteria
for vascular dementia 23 2.3 Thalamic lesions in vascular dementia 37
Chapter 3 Characteristics of White Matter Hyperintensities on imaging
3.1 Impact of White Matter Hyperintensities scoring method on correlations with clinical data: the LADIS study 51
3.2 Measuring progression of cerebral white matter lesions on MRI 65
Chapter 4 Clinical impact of cerebral vascular lesions
4.1 Periventricular White Matter Hyperintensities increase likelihood of conversion from amnestic Mild Cognitive Impairment to Alzheimer’s Disease 85
4.2 Risk factor profiles for different radiological expressions of cerebrovascular disease; findings from the LADIS study 99
Chapter 5 General discussion, conclusions and future directions 111 Chapter 6 Summary 121 Chapter 7 Dutch summary 127 List of abbreviations
Dankwoord
General introduction
7
Chapter 1
General introduction
Chapter 1
8
General introduction
9
1.1 Vascular dementia
Dementia is a decline in cognitive function to such extent that daily living is impaired.1
Vascular lesions in the cerebrum can lead to this syndrome (vascular dementia, VaD) and is
believed to be the most prevalent form after Alzheimer’s disease (AD).2 The incidence rates of
VaD are highly dependent on age and vary between 1.5 and 3.3 per 1000 person-years in elderly
populations.3-5 Typically, the clinical course is described by a stepwise progression. Cognitive
deficits vary according to the site of the lesions but memory dysfunction is not the most
prominent feature in most cases. Mental slowing and executive function disturbances are more
pronounced.6 Focal neurological deficits, such as motor and sensory disturbances, can
accompany the cognitive deficits. Ischemic vascular lesions leading to VaD are large vessel
infarctions, lacunar infarctions and white matter hyperintensities (WMH).
A. B. C. Figure 1. A. FLAIR image of a left medial temporal lobe infarction. B. T1-weighted image of lacunar
infarctions. C. FLAIR image of confluent WMH.
1.2 White Matter Hyperintensities
WMH are located in the subcortical white matter, where myelated fibers form
connecting tracts between cortical and subcortical gray matter structures. Subtotal ischemic
damage can lead to dysfunction of the fibers and cognitive decline. The exact mechanisms are
still unknown but disease of the small perforating vessels has been recognized as a causative
factor.7 Older age and hypertension are among the most reported risk factors for small vessel
disease.8-12 The dementia syndrome is of subcortical type and WMH can be visualized by
computed tomography (CT) and especially magnetic resonance imaging (MRI). The use of
cerebral MRI scanning has become more available, and has lead to more knowledge on the
Chapter 1
10
incidence, radiological features, risk factors, and clinical implications of WMH. Not much is
known at this time on the progression of the disease. However, longitudinal data are now
becoming available.13
1.3 Aim of thesis
The aim of this thesis was twofold. First, in the last decade of the previous century a
renewed interest in VaD rose among researchers. This was partly due to the fact that
neuroimaging became more widely available and vascular lesions were visualized with high
resolution. At first, relationship of the lesions with clinical features was hard to establish and
only advancing age and hypertension were consistently found to be correlated. Among the
reasons could be the heterogeneity of the lesions (cortical infarcts, lacunar infarcts, and WMH)
and the fact that the vascular lesions can occur in any part of the brain, leading to a variety of
(cognitive) symptoms. In general, dementia is considered a slowly progressive disease due to
degeneration of neuronal cells, with several possible causes. VaD is not necessary slowly
progressive (but can show a stepwise decline in cognitive function), and this might influence
outcome of studies to the relationship between vascular disease and dementia. In this thesis, we
set out to describe and discuss the different radiological appearances of vascular brain disease,
and their relationship to vascular dementia and general vascular risk factors.
Second, we focus on WMH, the subcortical subtotal vascular lesion type. They are a common
observation in the elderly. A review published in 1995 on this subject reported an association
with age, selective cognitive dysfunction and future stroke.14 Pathogenesis, pathological substrate
and clinical significance were not completely understood. Furthermore, data on progression of
the lesions over time were not yet available and prognostic value was unclear.15 Later, the profile
of the cognitive deficits became more clear with mainly attention, executive functions, and
visuospatial skills affected but again, longitudinal data were lacking.16 We set out to establish
some methodological issues of WMH assessment (the correlation of WMH, as assessed with
several methods, with clinical information and measurement of lesion change in time with
different visual scales and a volumetric method) and correlation of WMH with longitudinal data
(conversion from mild cognitive impairment (MCI) to AD).
1.4 Outline of thesis
Chapter 2.1 is an overview of the possible applications of neuroimaging in VaD. Also,
different lesions and underlying diseases are discussed. In chapter 2.2 the radiological part of the
NINDS-AIREN criteria and its interobserver variability are investigated. Use of different MRI
General introduction
11
sequences for the detection of lesions in the thalamus, known for their effect on cognition, is
evaluated in chapter 2.3.
Chapter 3 focuses on the radiological features of WMH. Impact of scoring method on
correlations with clinical characteristics (chapter 3.1) and on the detection of WMH progression
in time (chapter 3.2) is described.
In chapter 4, the radiologically assessed vascular lesions are correlated with vascular risk factors,
symptoms and signs. Chapter 4.1 describes the role of WMH in the conversion from amnestic
mild cognitive impairment (MCI) to Alzheimer’s disease and in chapter 4.2 risk factor profiles
for large- and small vessel disease are assessed. Interpretation and discussion of the results of this
thesis, as well as future directions, are presented in chapter 5. Chapter 6 consists of a brief
summary.
Chapter 1
12
References
1. American Psychiatric Association. American Psychiatric Association Committee on Nomenclature and Statistics. Diagnostic and Statistical Manual of Mental Disorders (DSM-III), Third Edition. 1980. Washington, DC.
2. Dubois MF, Hebert R. The incidence of vascular dementia in Canada: a comparison with Europe and East Asia. Neuroepidemiology 2001;20:179-87.
3. Di Carlo A, Baldereschi M, Amaducci L et al. Incidence of dementia, Alzheimer's disease, and vascular dementia in Italy. The ILSA Study. J Am Geriatr Soc 2002;50:41-8.
4. Hebert R, Lindsay J, Verreault R et al. Vascular dementia : incidence and risk factors in the Canadian study of health and aging. Stroke 2000;31:1487-93.
5. Ruitenberg A, Ott A, Van Swieten JC et al. Incidence of dementia: does gender make a difference? Neurobiol Aging 2001;22:575-80.
6. Cummings JL, Benson DF. Psychological dysfunction accompanying subcortical dementias. Annu Rev Med 1988;39:53-61.
7. Ovbiagele B, Saver JL. Cerebral white matter hyperintensities on MRI: Current concepts and therapeutic implications. Cerebrovasc Dis 2006;22:83-90.
8. Basile AM, Pantoni L, Pracucci G et al. Age, hypertension, and lacunar stroke are the major determinants of the severity of age-related white matter changes. The LADIS (Leukoaraiosis and Disability in the Elderly) Study. Cerebrovasc Dis 2006;21:315-22.
9. Breteler MM, Van Swieten JC, Bots ML et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994;44:1246-52.
10. Jeerakathil T, Wolf PA, Beiser A et al. Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke 2004;35:1857-61.
11. Longstreth WT, Jr., Manolio TA, Arnold A et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274-82.
12. Schmidt R, Fazekas F, Hayn M et al. Risk factors for microangiopathy-related cerebral damage in the Austrian stroke prevention study. J Neurol Sci 1997;152:15-21.
13. Schmidt R, Enzinger C, Ropele S et al. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study. Lancet 2003;361:2046-8.
14. Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger's report. A review. Stroke 1995;26:1293-301.
15. Kapeller P, Schmidt R. Concepts on the prognostic significance of white matter changes. J Neural Transm Suppl 1998;53:69-78.
16. Ferro JM, Madureira S. Age-related white matter changes and cognitive impairment. J Neurol Sci 2002;203-204:221-5.
Characteristics of vascular dementia on imaging
13
Chapter 2
Characteristics of vascular
dementia on imaging
Chapter 2
14
Characteristics of vascular dementia on imaging
15
Chapter 2.1
MRI and CT in the Diagnosis of Vascular
Dementia
E.C.W. van Straaten
Ph. Scheltens
F. Barkhof
Journal of the Neurological Sciences 2004;226:9 – 12
Chapter 2
16
Abstract
Neuroimaging is necessary to demonstrate cerebrovascular disease (CVD) and is therefore an
important examination in vascular dementia (VaD) and vascular cognitive impairment (VCI).
MRI is preferred over CT because multiple planes and sequences are needed to assess various
types of pathology in relevant regions. These protocols allow differentiation of VaD from other
forms of dementia and sometimes identify specific underlying disorders. Different diagnostic
criteria for VaD exist but the NINDS-AIREN criteria are widely used in controlled clinical trials
in VaD. These criteria have relatively low sensitivity but are highly specific and include
radiological requirements. The radiological criteria have poor interobserver agreement. In
general, the radiological portion of the diagnostic criteria for VaD needs revision and refinement
to include bone fide cases of VaD not currently accepted by imaging rules, and for the early
detection of patients with VCI.
Characteristics of vascular dementia on imaging
17
Introduction
Dementia is rapidly becoming a major health care problem. Despite progress in the
treatment of dementia, broader knowledge is needed for the development of more effective
agents. Significant effort has resulted in clinical trials to investigate possible therapeutic agents
on different groups of dementia patients.1-3 It is, therefore, important to be able to differentiate
well between the different causes of dementia.
In the past, brain imaging—computerized tomography (CT) and magnetic resonance imaging
(MRI)—was regarded as an optional examination in patients with cognitive decline. It was used
mainly to exclude surgically treatable causes of cognitive impairment, such as subdural
hematoma, hydrocephalus and mass lesions. Recently, the focus has shifted from its use to rule
out certain aetiologies, towards a supporting role for the clinical diagnosis with positive imaging
findings.4 For all of the above reasons, it now appears desirable to obtain a structural brain scan
at least once during the work-up of patients with cognitive decline.
CT or MRI
In the setting of a patient with cognitive decline, CT will generally suffice to rule out
surgically treatable disorders. However, MRI is preferred to demonstrate specific types of
pathologies, such as regionally specific atrophy, e.g. of the hippocampus in Alzheimer’s disease
(AD), and presence of relevant vascular lesions. Reasons include its ability to reveal more detail
and the greater capabilities to show subtle lesions in regions that are difficult to image with CT,
such as the temporal lobe, but also the possibility to scan in different directions (e.g. coronal and
sagittal).
MRI protocols
When the MRI scan is performed in a patient suspected of dementia, application of contrast
material is not routinely indicated. The scanning protocol should include a coronal (3D) T1-
weighted series for the evaluation of the medial temporal lobe (MTL) and other regional patterns
of atrophy. If cortical infarctions and lacunes are present, these can be seen using this sequence.
In addition, axial Fluid-Attenuated Inversion Recovery (FLAIR) or dual- echo Turbo Spin Echo
Chapter 2
18
(TSE) images will reveal cortical infarcts and hypoxic/ischemic pathology (white matter
hyperintensities or WMH).
A. B.
Figure 1. A. Infarction of the medial temporal lobe in the left hemisphere, FLAIR image.
B. infarction in the parieto-temporal association area, FLAIR image.
A. B.
Figure 2. A. Severe white matter hyperintensities in a patient with vascular dementia on
FLAIR image. B. Extensive widened perivascular spaces, seen on FLAIR image.
The use of FLAIR has the advantage of suppressing cerebrospinal fluid (CSF) signal,
allowing a simple distinction of lacunes and perivascular spaces from WMH, both of which are
Characteristics of vascular dementia on imaging
19
bright on standard T2-weighted (T)SE images. However, the reduced sensitivity of FLAIR in
infratentorial lesions appears to extent to the diencephalon, and FLAIR should not be used in
isolation since thalamic lesions may easily be missed.5 It may replace a proton-density type of
image. Finally, axial T2* gradient echo images are useful to detect hemorrhages (microbleeds)
and calcifications.
Vascular dementia
In vascular dementia (VaD), brain imaging can greatly add to the accuracy of the
diagnosis and is the only way to determine the vascular cause of the dementia with certainty in
vivo. Following the successful medication trials for AD, a number of controlled clinical trials
were also completed for patients with VaD. A renewed interest in assuring a diagnosis of
certainty has therefore developed. The most commonly used criteria for VaD in clinical trials
require demonstration of lesions of cerebrovascular disease (CVD) with brain imaging; MRI
being preferred over CT in patients with suspected VaD. It distinguishes better between ‘pure’
VaD and other forms of dementia, such as ‘mixed’ dementia (AD+CVD), as well as
distinguishing between the different causes of VaD (e.g. CADASIL). Subcortical vascular
lesions can be seen with higher sensitivity and therefore the severity of WMH can be better
assessed. MRI is also superior in detecting the presence of microbleeds.
Overview of diagnostic criteria for VaD
Numerous sets of criteria for VaD have been proposed. The most widely used criteria
are: DSM-IV, ADDTC, NINDS-AIREN, HIS and ICD-10.6-10 The DSM IV and the ICD-10
criteria do not require brain imaging, whereas the ADDTC and NINDS-AIREN do require such
direct evidence. The DSM-IV criteria are the most liberal, leading to high sensitivity but low
specificity. On the other hand, the NINDS-AIREN are most specific but are not as sensitive. The
ADDTC and HIS have intermediate sensitivity and specificity.11 The NINDS-AIREN criteria for
VaD are the most recent and are widely used in randomized clinical trial on VaD at this time.
Chapter 2
20
Characteristics of NINDS-AIREN criteria for vascular dementia
The NINDS-AIREN criteria for VaD have three main features: a patient must be
demented, have evidence of CVD on clinical examination and imaging, and fulfil a temporal
relationship between onset of dementia relevant CVD. In order to assess the presence of CVD on
the brain scan, a number of radiological features have been listed. In short, this radiological part
of the criteria prescribes that vascular lesions should fulfil criteria for topography as well as
severity. In case of large vessel stroke, the locations that meet criteria are: bilateral anterior
cerebral artery, paramedian thalamic, inferior medial temporal lobe, parietotemporal and
temporo-occipital association areas and angular gyrus, superior frontal and parietal watershed
areas, as long as they involve the dominant hemisphere. In case of small vessel disease, lesions
that fulfil criteria are: WMH more than 25% of the total white matter, multiple basal ganglia and
frontal white matter lacunes and bilateral thalamic lesions. However, further specifications are
missing and interobserver agreement is low, even after operationalization; further work is needed
to increase the applicability of these criteria.12
Territorial infarctions
In VaD, cognitive impairment may result from large or small vessel disease. Following
stroke localized in eloquent brain areas, dementia may emerge, especially when located in the
dominant hemisphere. The clinical characteristics may indicate the location, but MRI and CT
provide in vivo evidence for infarction, for example, in the medial temporal lobe. Criteria for
VaD require stroke(s) in specific areas that can be easily depicted using neuroimaging. In
selected cases, MR angiography (MRA) can be useful in the diagnosis in case of large vessel
stroke.
Findings in CADASIL
Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and
Leukoencephalopathy (CADASIL) can be diagnosed when a mutation in the NOTCH 3 gene on
chromosome 19 is demonstrated. The diagnosis can also be made with typical pathological
findings in skin or brain biopsies. When this information is not available, radiological criteria for
Characteristics of vascular dementia on imaging
21
probable CADASIL have been shown to be quite sensitive and accurate. Early MRI findings
include extensive symmetrical WMH involving the U-fibers at the vertex and the temporal pole;
in later stages, involvement of the corpus callosum, external capsule, and development of
multiple small lacunes and microbleeds may develop.13
Findings in amyloid angiopathy
Dementia is present is approximately 40% of cases of cerebral amyloid angiopathy
(CAA). The clinical presentation of CAA is often with a lobar hemorrhage. The radiological key
feature of CAA is the presence of microbleeds that can best be evaluated with gradient-echo
scanning. This sequence may show multiple residues of petechial hemorrhages throughout the
brain.14
Conclusions
Brain imaging is a crucial component in the evaluation of patients with VaD and VCI.
MRI has a number of advantages over CT and currently is the examination of choice. The
radiological portion of the NINDS-AIREN criteria has poor interobserver agreement and
excludes some cases. Therefore, these radiological criteria need revision and refinement to
include bone fide cases of VaD not currently accepted by imaging rules, and for the early
detection of cases (e.g. vascular cognitive impairment).
Chapter 2
22
References
1. Rogers SL, Farlow MR, Doody RS et al. A 24-week, double-blind, placebo-controlled
trial of donepezil in patients with Alzheimer's disease. Donepezil Study Group. Neurology 1998;50:136-45.
2. Rosler M, Anand R, Cicin-Sain A et al. Efficacy and safety of rivastigmine in patients with Alzheimer's disease: international randomised controlled trial. BMJ 1999;318:633-8.
3. Wilcock GK, Lilienfeld S, Gaens E. Efficacy and safety of galantamine in patients with mild to moderate Alzheimer's disease: multicentre randomised controlled trial. Galantamine International-1 Study Group. BMJ 2000;321:1445-9.
4. Scheltens P, Fox N, Barkhof F et al. Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. Lancet Neurol 2002;1:13-21.
5. Bastos Leite AJ, van Straaten EC, Scheltens P et al. Thalamic lesions in vascular dementia: low sensitivity of fluid-attenuated inversion recovery (FLAIR) imaging. Stroke 2004;35:415-9.
6. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM IV). Fourth Edition. 1994. Washington, DC.
7. Chui HC, Victoroff JI, Margolin D et al. Criteria for the diagnosis of ischemic vascular dementia proposed by the State of California Alzheimer's Disease Diagnostic and Treatment Centers. Neurology 1992;42:473-80.
8. Hachinski VC, Iliff LD, Zilhka E et al. Cerebral blood flow in dementia. Arch Neurol 1975;32:632-7.
9. Roman GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43:250-60.
10. World Health Organisation. The ICD-10 Classification of Mental and Behavioural Disorders. 1993. Geneva, Switserland.
11. Chui HC, Mack W, Jackson JE et al. Clinical criteria for the diagnosis of vascular dementia: a multicenter study of comparability and interrater reliability. Arch Neurol 2000;57:191-6.
12. van Straaten EC, Scheltens P, Knol DL et al. Operational definitions for the NINDS-AIREN criteria for vascular dementia: an interobserver study. Stroke 2003;34:1907-12.
13. Auer DP, Putz B, Gossl C et al. Differential lesion patterns in CADASIL and sporadic subcortical arteriosclerotic encephalopathy: MR imaging study with statistical parametric group comparison. Radiology 2001;218:443-51.
14. Fazekas F, Kleinert R, Roob G et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol 1999;20:637-42.
Characteristics of vascular dementia on imaging
23
Chapter 2.2
Operational Definitions for the NINDS-AIREN Criteria for Vascular Dementia An Interobserver Study
E.C.W. van Straaten P. Scheltens D.L. Knol M.A. van Buchem E.J. van Dijk P.A.M. Hofman G. Karas O. Kjartansson F-E. de Leeuw N.D. Prins R. Schmidt M.C. Visser H.C. Weinstein F. Barkhof
Stroke 2003;34:1907-1912
Chapter 2
24
Abstract
Vascular dementia (VaD) is thought to be the most common cause of dementia after Alzheimer’s
disease. The commonly used International Workshop of the National Institute of Neurological
Disorders and Stroke (NINDS) and the Association Internationale pour la Recherche et
l’Enseignement en Neurosciences (AIREN) criteria for VaD necessitate evidence of vascular
disease on CT or MRI of the brain. The purposes of our study were to operationalize the
radiological part of the NINDS-AIREN criteria and to assess the effect of this operationalization
on interobserver agreement.
Six experienced and 4 inexperienced observers rated a set of 40 MRI studies of patients with
clinically suspected VaD twice using the NINDS-AIREN set of radiological criteria. After the
first reading session, operational definitions were conceived which were subsequently used in the
second reading session. Interobserver reproducibility was measured by Cohen’s κ.
Overall agreement at the first reading session was poor (κ=0.29) and improved slightly after
application of the additional definitions (κ=0.38). Raters in the experienced group improved their
agreement from almost moderate (κ=0.39) to good (0.62). The inexperienced group started out
with poor agreement (κ=0.17) and did not improve (κ=0.18). The experienced group improved in
both the large- and small-vessel categories, whereas the inexperienced group improved generally
in the extensive white matter hyperintensities categories.
Considerable interobserver variability exists for the assessment of the radiological part of the
NINDSAIREN criteria. Use of operational definitions improves agreement but only for already
experienced observers.
Characteristics of vascular dementia on imaging
25
Introduction
Vascular dementia (VaD) is thought to be the most common cause of dementia after
Alzheimer’s disease. The reported incidence rates of VaD vary between 1.5 and 3.3 per 1000
person-years in elderly populations.1-3 Incidence rates are highly dependent on age. The
prevalence of VaD ranges from 1.0% in a population cohort ≥55 years of age to 4.2% in a cohort
of subjects ≥71 years of age.4,5 Differences in diagnostic criteria may partly explain this
variability. In 1993, the International Workshop of the National Institute of Neurological
Disorders and Stroke (NINDS) and the Association Internationale pour la Recherche et
l’Enseignement en Neurosciences (AIREN) reported diagnostic criteria for the diagnosis of VaD
for research studies.6 Criteria were formulated for the different parts of the diagnostic process
(history and physical, radiological, and pathological examination) to classify patients as having
possible, probable, and definite VaD. The NINDS-AIREN criteria state that the diagnosis of
probable VaD cannot be made without some form of radiological assessment. Consequently, a
list of lesions associated with VaD was included in the NINDSAIREN criteria. Recently, a vast
interest in clinical trials on the efficacy of cholinesterase inhibitors and other drugs for VaD has
emerged, and the NINDS-AIREN criteria with their radiological definitions are being used on a
large scale in these trials. However, clear operational definitions on how to use and interpret the
radiological criteria are lacking. Only a few interobserver studies of the NINDS-AIREN criteria
have been published. In 2 of these studies, both clinical and radiological diagnoses were studied
together.7,8 The agreement between raters was moderate to good (κ=0.42 in the first study
mentioned, 0.46<κ<0.72 in the second study). It was suggested that a cause of the disappointing
results could have been the difference in interpretation of the radiological criteria by the different
raters.8 In this study, we examined the interobserver agreement of the radiological part of the
NINDS-AIREN criteria and the effect of subsequently formulated operational definitions on the
level of agreement in patients with clinical signs of VaD. Second, we investigated whether
experienced and inexperienced raters would benefit equally from such definitions.
Methods
MRI Studies
For this study, we selected MRI studies of patients with dementia and clinical signs of
cerebrovascular disease. The selection was done to get 10 cases of large-vessel disease and 30
Chapter 2
26
cases of small-vessel disease, reflecting the distribution in a recently completed trial on VaD.9
Two authors who did not participate in the interobserver studies (E.C.W. van S., F.B.) performed
the selection by applying the NINDS-AIREN criteria to the MRI scans. Based on the experience
of an ~50% rejection rate in the above-mentioned trial and to have a balanced distribution in the
study sample, MRI studies were selected in a way that we expected half of the scans to be rated
as having sufficient abnormalities to fulfil the NINDS-AIREN criteria. It should be noted that the
percentage of cases fulfilling such criteria is not reflective of the general population of patients
clinically suspected of having VaD. In addition to the 40 scans, we selected 10 scans to be scored
during the first assessment and to be used for consensus reading and formulation of definitions.
All MRI studies consisted of axial T2, axial fluid-attenuated inversion recovery, and axial and
coronal T1 series using 5-mm slices and 1x1-mm pixel size.
Study Design
Ten raters with different levels of experience evaluated the 40 selected MRI studies in
2 consecutive reading sessions. The decision to use the same data set twice (rather than having 2
independent data sets) was based on the expectation that this would preclude variability to be
introduced by unbalanced matching in the distribution of cases over the various subcategories of
the NINDS-AIREN criteria. On the other hand, we expected no bias from a learning effect when
the same samples were rated twice because the second rating was done with a set of operational
criteria developed from the additional training set of 10 scans; if any, this design would tend to
maintain rather than to reduce interobserver variability and therefore is slightly conservative. The
team of raters consisted of 10 physicians (2 radiologists, 4 neurologists, 3 research fellows, 1
neurology resident). Six had extensive experience in the evaluation of vascular lesions on MRI
scans in clinical settings or in population-based studies on aging and dementia. The other 4 had
experience in assessing MRI scans of the brain, but they had never assessed vascular lesions
systematically on a large scale. The raters were blinded to all clinical and personal information.
During the first reading session, all raters individually assessed the scans
Characteristics of vascular dementia on imaging
27
Table 1. Scoring form of brain imaging lesions associated with VaD
1. TOPOGRAPHY Radiologic lesions associated with dementia include ANY of the following or combinations thereof: A. Large vessel strokes in the following territories: left right both none Anterior cerebral artery Posterior cerebral artery, including: Paramedian thalamic infarctions
Inferior medial temporal lobe lesions
Association areas: Parietotemporal
Temporo-occipital Angular gyrus
Watershed carotid territories: Superior frontal
Parietal region
B. Small vessel disease: yes no Multiple basal ganglia and frontal white matter lacunae Extensive periventricular white matter lesions Bilateral thalamic lesions
2. SEVERITY In addition to the above, relevant radiologic lesions associated with dementia include:
yes
no
Large vessel lesions of the dominant hemisphere Bilateral large-vessel hemispheric strokes Leukoencephalopathy involving at least ¼ of the total white
matter
Do the radiological findings fulfil the radiological criteria for VaD? yes no
Chapter 2
28
Table 2. Operational definitions of the imaging guidelines of the NINDS-AIREN criteria for Vascular Dementia
TOPOGRAPHY
Large vessel stroke: Large vessel stroke is an infarction, defined as a parenchymal defect in an arterial territory, involving the cortical gray matter 1) Anterior cerebral artery (ACA). Only bilateral ACA infarcts are sufficient to meet the
NINDS-AIREN criteria 2) Posterior cerebral artery (PCA). Infarcts in the PCA territory can only be included
when they involve the following regions: a. Paramedian thalamic infarction: the infarct includes the cortical gray matter of
the temporal/occipital lobe AND extends into the paramedian part (defined as extending to the third ventricle) of the thalamus; the extension may be limited to the gliotic rim of the infarct that surrounds the parenchymal defect
b. Inferior medial temporal lobe lesions 3) Association areas: a medial cerebral artery (MCA) infarction needs to involve the
following regions: a. Parietotemporal: the infarct involves both the parietal and temporal lobe (e.g.
angular gyrus) b. Temporo-occipital: the infarct involves both the temporal and occipital lobe
4) Watershed carotid territories: A watershed infarction is defined as an infarct in the watershed area between MCA and PCA or between MCA and ACA, in the following regions:
a. Superior frontal region b. Parietal region
Small vessel disease: Ischemic pathology resulting from occlusion of small perforating arteries may manifest itself as lacunes or white matter lesions. A lacune is defined as a lesion with CSF-like intensity on all sequences on MRI (water density on CT), surrounded by white matter or subcortical gray matter, larger than 2 mm. Care should be taken not to include Virchow-Robin spaces, which typically occur at the vertex and around the anterior commisure near the substantia perforata. Ischemic white matter lesions are defined as circumscribed abnormalities with high signal on T2-weighted images not following CSF signal (mildly hypodens compared to surrounding tissue on CT), with a minimum diameter of 2 mm.
1) Multiple basal ganglia and frontal white matter lacunes: The criteria are met when at least two lacunes in the basal ganglia region (including thalamus and internal capsule) AND at least two lacunes in the frontal white matter are present.
2) Extensive periventricular white matter lesions: lesions in the white matter, abutting the ventricles and extending irregularly into the deep white matter, or deep/subcortical white matter lesions. Smooth caps and bands by themselves are not sufficient. Gliotic areas surrounding large vessel strokes should not be included here.
3) Bilateral thalamic lesions: To meet the criteria, at least one lesion in each thalamus should be present.
Characteristics of vascular dementia on imaging
29
SEVERITY 1) Large vessel disease of the dominant hemisphere: If there is a large vessel infarct as
defined above, to meet the criteria it has to be in the dominant hemisphere. In the absence of clinical information, the left hemisphere is considered the dominant one.
2) Bilateral large vessel hemispheric strokes: One of the infarcts should involve an area listed under topography but is in the non-dominant hemisphere, while the one in the dominant hemisphere does not meet the topography criteria.
3) Leukencephalopathy involving at least ¼ of the total white matter: Extensive white matter lesions are considered to involve ¼ of the total white matter when they are confluent (grade 3 in the ARWMC scale) in at least two regions of the ARWMC scale and beginning confluent (grade 2 in the ARWMC scale) in two other regions. Note: a lesion is considered confluent when larger than 20 mm or consists of two or more smaller lesions that are fused by more than connecting bridges.
FULFILMENT OF RADIOLOGICAL CRITERIA FOR PROBABLE VaD
1) Large vessel disease: both the “topography” and “severity” criteria should be met (a lesion must be scored in at least one subsection of both “topography” and “severity” category).
2) Small vessel disease: for white matter lesions, both the topography and severity criteria should be met (a lesion must be scored in at least one subsection of both “topography” and “severity” category); for multiple lacunes and bilateral thalamic lesions, only the topography criterion is sufficient.
in random order with only the aid of the table of radiological findings of the NINDS-AIREN
criteria for VaD as stated in the original article.6 All images were presented to the readers on
identical personal computers using a digital viewing program, allowing window and level
adjustment. The readers were able to browse through the scans as often as they wanted; no time
limits were set. Scoring consisted of 2 stages. First, lesions had to be identified and classified
topographically on a scoring form (Table 1), divided into a section on large-vessel disease
(strategic infarcts in certain anterior, middle, or posterior cerebral artery territories) and a section
on small-vessel disease (lacunes, white matter hyperintensities, bilateral thalamic lesions).
Second, the topographical information had to be combined with severity criteria to decide
whether the scan met the radiological criteria for VaD (final diagnosis). Subsequently, a joint
consensus reading of the additional 10 scans was held, and operational definitions for scoring
vascular lesions according to the NINDS-AIREN criteria were discussed. After consensus on a
set of definitions was reached, a second reading of the 40 scans was performed the next day,
again in random order, according to the newly formulated operational definitions (Table 2).
Statistical Analysis
We determined agreement between raters for the 2 reading sessions separately by
Cohen’s κ for >2 raters.10,11 The weighted κ was not used because most scorings were
Chapter 2
30
dichotomous and the different categories were not ordered. We did this using AGREE software
(ProGAMMA), which also calculated standard error values. We determined κ for presence of
radiological evidence for probable VaD, presence of large-vessel disease, and presence of small-
vessel disease. To test whether agreement between the first and second readings differed
statistically, we determined z values for the difference in κ and used the corresponding
probability value for testing. All scores were calculated for 3 groups: the whole group of raters
(n=10), the group of experienced raters (n=6), and the group of inexperienced raters (n=4). A κ
between 0 and 0.2 refers to poor agreement; 0.2 to 0.4, fair agreement; 0.41 to 0.60, moderate
agreement; 0.61 to 0.80, good agreement; and 0.81 to 1.00, very good agreement.12
Results
Table 3 shows the results of the baseline readings. In 35.8% of all cases, a large-vessel
infarction was scored; in 60.3% of cases, small-vessel disease was scored. This distribution was
roughly as we expected. The percentage of cases in which the raters found vascular lesions that
met the radiological criteria of the NINDS-AIREN was 41.3%, which again is in line with what
we had anticipated. In Table 4, κ is given for the various sections of the scoring separately.
At the first reading session, agreement in the group of inexperienced raters was
generally less than the agreement in the group of experienced raters. This is also true for the
assessment of the final diagnosis (Table 5). At the first reading session, mean κ for the final
diagnosis for all raters signifies fair agreement. After the first scoring, operational definitions
were formulated in consensus (Table 2). During this consensus meeting, we identified the
problems that had risen with the interpretation of the criteria. The meaning, exact location, and
borders of a paramedian thalamic infarction were uncertain in our opinion. We had trouble
interpreting the term “multiple basal ganglia and frontal white matter lacunes.” Questions that
arose included, Are lacunes needed in both areas to meet the criteria? How many lacunes is
“multiple” exactly? How big should an extensive periventricular white matter lesion be, and is a
lesion considered only when directly abutting the ventricles? Should strokes in any area be
considered in the bilateral large-vessel hemispheric strokes category, or only those strokes that
are scored previously in the topography section? How can we approximate one fourth of the total
white matter? We tried to address these questions in the operational criteria, leaving the original
set of criteria fundamentally intact. Definitions were laid out for the different radiological types
of vascular pathology, different regions of relevant strokes were defined, and for small-vessel
Characteristics of vascular dementia on imaging
31
Table 3. Average number of times a lesion was scored by region (baseline scoring)
Mean number of times
a lesion was scored
SD % of all scans
ACA infarction 1.3 1.3 3.3
PCA infarction 7.3 7.9 18.3 MCA association area infarction 7.6 2.0 19.0 WSH infarction 5.8 2.9 14.5
Total large vessel stroke 14.3 6.9 35.8*
SVD1 16.6 11.0 41.5 SVD2 18.4 7.2 46.0 SVD3 3.9 2.9 9.8
Total small vessel disease 24.1 10.1 60.3*
SEV1 6.7 4.0 16.8 SEV2 0.4 0.5 1.0 SEV3 8.6 7.1 21.5
Final diagnosis 16.5 8.5 41.3* ACA: anterior cerebral artery, PCA: posterior cerebral artery, MCA: medial cerebral artery, WSH: watershed region, SVD1: multiple basal ganglia and frontal white matter lacunes, SVD2: extensive periventricular white matter lesions, SVD3: bilateral thalamic lesions, SEV1: large vessel lesion in dominant hemisphere, SEV2: bilateral large vessel strokes, SEV3: leukencephalopathy involving at least ¼ of the total white matter *: Due to overlap in scores this figure is not just a summation of the sub-categories above
disease, numeric definitions were adopted. With respect to the leukencephalopathy, we agreed
on quantification with the use of the age-related white matter changes (ARWMC) rating
scale.13 In the severity section, we discussed dominance of hemispheres, and for practical
reasons, the left hemisphere was considered dominant. In addition to describing the different
parts of the diagnostic criteria, rules on how to combine these parts were added because we
noticed differences in opinion during consensus reading. We agreed that a scan would meet the
final diagnosis of VaD if both severity and topography criteria were met, with the exception of
the bilateral thalamic lesions and multiple lacunes subcategories, which have no related
severity criterion.
Table 4 shows that agreement generally increases, especially in the small-vessel
category. To calculate significance of change in κ, z values were calculated. For appreciation of
Chapter 2
32
Table 4. Mean κ scores for first and second reading session by sub-category
Reading session
κ of all raters (n = 10)
κ of experienced raters (n = 6)
κ of inexperienced raters (n = 4)
ACA 1 0.43 0.29 0.57 2 0.24 0.19 0.21 PCA 1 0.11 0.35 0.06 2 0.08 0.69 0.06 MCA 1 0.28 0.40 0.31 2 0.42 0.53 0.31 WSH 1 0.39 0.44 0.23 2 0.38 0.47 0.15
Mean large 1 0.30 0.37 0.29 vessel stroke 2 0.28 0.47 0.18
SVD1 1 0.13 0.30 0.03 2 0.21 0.46 0.04 SVD2 1 0.47 0.51 0.39 2 0.67 0.64 0.74 SVD3 1 0.38 0.56 0.10 2 0.25 0.56 0.08
Mean small 1 0.33 0.46 0.17 vessel disease 2 0.38 0.55 0.29
SEV1 1 0.48 0.48 0.51 2 0.37 0.68 0.13 SEV2 1 0.00 1.00 0.00 2 0.12 0.59 0.08 SEV3 1 0.36 0.53 0.22 2 0.62 0.69 0.55 ACA: anterior cerebral artery, PCA: posterior cerebral artery, MCA: medial cerebral artery, WSH: watershed region, SVD: small vessel disease, SVD1: multiple basal ganglia and frontal white matter lacunes, SVD2: extensive periventricular white matter lesions, SVD3: bilateral thalamic lesions, SEV1: large vessel lesion in dominant hemisphere, SEV2: bilateral large vessel strokes, SEV3: leukencephalopathy involving at least ¼ of the total white matter
Characteristics of vascular dementia on imaging
33
Table 5. Mean κ (standard error) and differences in mean κ of the final diagnosis
All raters Experienced
raters
Inexperienced raters
Mean ĸ reading session 1 (SE) 0.29 (0.05) 0.39 (0.05) 0.17 (0.06)
Mean ĸ reading session 2 (SE) 0.38 (0.05) 0.62 (0.07) 0.18 (0.05)
Difference κ reading
sessions 1 and 2
0.09 0.22
0.01
z-value 1.27 2.51 0.04
p-value 0.20 0.01 0.97
SE=standard error
large-vessel disease, z values indicated that none of these differences are statistically significant.
For small-vessel disease, only the difference in scoring of the inexperienced raters in the small-
vessel category showed statistically significant improvement (p=0.04). The mean κ for the final
diagnosis for all raters at the second reading was slightly greater than at the first reading session
(Table 5). For the experienced group, agreement rose to κ=0.62, but in the inexperienced group,
it remained low. Only in the experienced group of raters did agreement improve significantly.
Discussion
We examined the interobserver agreement for the radiological assessment of the NINDS-AIREN
criteria and quantified the added value of operational definitions. We found that overall
agreement on the final diagnosis of VaD was only fair without guidelines, especially for
inexperienced raters; this was true for the agreement in both the large- and small-vessel
pathology categories. The large variability we found is in agreement with earlier studies on the
Chapter 2
34
total set of criteria for VaD of the NINDS-AIREN and is in part the result of a lack of operational
definitions for the radiological criteria. Already at the first scoring, several problems with the
interpretation of those criteria arose. The weakest parts of the radiological criteria are those
related to small-vessel disease, for which the original publication provides no details. This is
especially unfortunate because these are the most prevalent types of pathology in patients with
VaD. The raters also experienced difficulties in combining the individual parts of the criteria to
make the final decision of VaD or not. Confusingly, the original criteria by the NINDS-AIREN
list some of the topography characteristics in the severity section and vice versa. Additional
definitions will not solve this problem because they do not actually change the original criteria.
Taking this into consideration, we can explain low agreement. Our results suggest that a revision
of the original criteria might be needed in this respect. After the application of operational
definitions, agreement on the final diagnosis of VaD improved. However, stratified analysis
showed that this improvement in agreement was confined to the group of experienced raters with
a κ of 0.62, indicating good agreement. This was due to improvements in both the large- and
small-vessel categories. In the group of inexperienced raters, agreement worsened in the large-
vessel category but improved in the small-vessel category. The latter was due mainly to an
increase in κ by 0.35 to good agreement in the extensive white matter lesions subcategory.
The design of this study has some limitations. We did not have a gold standard. The
operational definitions were not validated against pathology or clinical findings but had the sole
purpose of being practical, usable, and able to improve standardization. In addition, the raters did
not have clinical information that could have contributed to the final diagnosis. In large clinical
trials in which the MRI scans are rated centrally, this information is also not available, but
agreement can be expected to improve in a clinical setting because previous studies show higher
κ when this information is accessible by the readers. Another limitation of the study might have
been the use of κ. In some cases, expected agreement was high because of the very low
prevalence of some lesions, especially some stroke types (e.g., anterior cerebral artery,
paramedian thalamic infarctions). This results in low κ even when agreement is high. Finally, the
operational definitions formulated are, of course, arbitrary and may be subject to further
amendments. However, the raters who formulated the criteria were the same raters who were
going to apply them in the second reading session. It can therefore be expected that they were
optimal for use in this interobserver study.
In conclusion, we found that the radiological criteria for the NINDS-AIREN criteria for
VaD are very complex. This makes these criteria less suitable for inexperienced raters and not
appropriate for routine diagnosis on the basis of a standard radiological report only. The
radiological criteria for the NINDS-AIREN criteria for VaD have suboptimal reproducibility.
Characteristics of vascular dementia on imaging
35
Use of operational criteria improves agreement to acceptable levels, but only in experienced
readers. Because operational definitions essentially do not change the original criteria, a critical
reappraisal of the NINDS-AIREN radiological criteria seems to be needed to further improve the
quality of the criteria and interobserver agreement. We hope that our results set the stage for such
an endeavour.
Chapter 2
36
References
1. Di Carlo A, Baldereschi M, Amaducci L et al. Incidence of dementia, Alzheimer's disease, and vascular dementia in Italy. The ILSA Study. J Am Geriatr Soc 2002;50:41-8.
2. Hebert R, Lindsay J, Verreault R et al. Vascular dementia : incidence and risk factors in the Canadian study of health and aging. Stroke 2000;31:1487-93.
3. Ruitenberg A, Ott A, Van Swieten JC et al. Incidence of dementia: does gender make a difference? Neurobiol Aging 2001;22:575-80.
4. Ott A, Breteler MM, van Harskamp F et al. Prevalence of Alzheimer's disease and vascular dementia: association with education. The Rotterdam study. BMJ 1995;310:970-3.
5. White L, Petrovitch H, Ross GW et al. Prevalence of dementia in older Japanese-American men in Hawaii: The Honolulu-Asia Aging Study. JAMA 1996;276:955-60.
6. Roman GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43:250-60.
7. Chui HC, Mack W, Jackson JE et al. Clinical criteria for the diagnosis of vascular dementia: a multicenter study of comparability and interrater reliability. Arch Neurol 2000;57:191-6.
8. Lopez OL, Larumbe MR, Becker JT et al. Reliability of NINDS-AIREN clinical criteria for the diagnosis of vascular dementia. Neurology 1994;44:1240-5.
9. Scheltens P, Kittner B. Preliminary results from an MRI/CT-based database for vascular dementia and Alzheimer's disease. Ann N Y Acad Sci 2000;903:542-6.
10. Cohen J. A coefficient of agreement for nominal scales. Educ Psycholog Measure 1960;20:37-46.
11. Hubert LJ. Nominal scale response agreement as a generalized correlation. Br J Math Stat Psychol 1977;30:98-103.
12. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-74.
13. Wahlund LO, Barkhof F, Fazekas F et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001;32:1318-22.
Characteristics of vascular dementia on imaging
37
Chapter 2.3
Thalamic Lesions in Vascular Dementia Low Sensitivity of Fluid-Attenuated Inversion Recovery (FLAIR)
Imaging
A.J. Bastos Leite
E.C.W. van Straaten
P. Scheltens
G. Lycklama
F. Barkhof
Stroke 2004;35:415-419
Chapter 2
38
Abstract
The criteria of the National Institute of Neurological Disorders and Stroke (NINDS)–Association
Internationale pour la Recherche et l’Enseignement en Neurosciences (AIREN) include thalamic
lesions for the diagnosis of vascular dementia (VaD). Although studies concerning VaD and
brain aging advocate the use of fluid-attenuated inversion recovery (FLAIR) or T2-weighted
images (T2-WI) to detect ischemic lesions, none compared the sensitivity of these sequences to
depict thalamic lesions.
We performed a blinded review of T2-WI and FLAIR images in 73 patients fulfilling the
radiological part of the NINDS-AIREN criteria (mean age, 71 years; range, 49 to 83 years). This
sample was drawn from a large multicenter trial on VaD and was expected to have a high
prevalence of thalamic lesions. In a side-by-side review, including T1-weighted images as well,
lesions were classified according to presumed underlying pathology.
The total number of thalamic lesions was 214. Two hundred eight (97%) were detected on T2-
WI, but only 117 (55%) were detected on FLAIR (χ2=5.1; P<0.05). Although the mean size of
lesions detected on T2-WI and not on FLAIR (4.4 mm) was significantly lower than the mean
size of lesions detected on both sequences (6.7 mm) (P<0.001), 5 of the 29 lesions >10 mm on
T2-WI were not visible on FLAIR. FLAIR detected only 81 (51%) of the 158 probable ischemic
lesions and 30 (60%) of the 50 probable microbleeds.
FLAIR should not be used as the only T2-weighted sequence to detect thalamic lesions in
patients suspected of having VaD.
Characteristics of vascular dementia on imaging
39
Introduction
In 1937, Papez1 described an anatomic circuit beginning and ending in the
hippocampal formation possibly related to emotional experience. The projections of the Papez
circuit involve the fornix, mammillary bodies, mamillothalamic tracts, anterior thalami, cingulate
cortex, and cingulate bundles. The early notion that the Papez circuit subserves emotion has been
abandoned and replaced by the proposal it is primarily involved in mnemonic functions. Lesions
of the major components of this circuit have been shown to disrupt memory in humans,
particularly those localized in the anterior group of thalamic nuclei.2-6 However, lesions affecting
other thalamic components or connections not considered in the circuit, such as the mediodorsal
(dorsomedial), intralaminar, and pulvinar nuclei or the thalamofrontal networks, may also cause
cognitive deficits and marked behavioral changes.2,7-11 MRI and CT are crucial for the diagnosis
of cerebrovascular diseases. The first studies using CT for the evaluation of brain lesions in
patients with ischemic stroke confirmed the importance of thalamic infarctions as a cause of
dementia.12,13 Therefore, the criteria of the National Institute of Neurological Disorders and
Stroke (NINDS)–Association Internationale pour la Recherche et l’ Enseignement en
Neurosciences (AIREN) include radiological evidence of thalamic lesions for the diagnosis of
probable vascular dementia (VaD).14 Moreover, a single thalamic infarction may induce VaD.15
MRI studies concerning VaD and brain aging advocate the use of fluid-attenuated inversion
recovery (FLAIR) or T2-weighted images (T2-WI) to detect and characterize brain
abnormalities.16-18 However, to our knowledge no comparative study was performed to assess
which MRI sequence yields the highest sensitivity for thalamic lesions. In this study we sought to
compare the sensitivity of each of these sequences to depict thalamic lesions in patients with
VaD.
Subjects and Methods
Patients
The subjects were derived from cases belonging to the VantagE study, a multicenter,
phase III, prospective, randomized, double-blind clinical trial on the effects of rivastigmine in
patients with VaD. For the present study we selected a sample of 73 patients (mean age, 71
years; range, 49 to 83 years) fulfilling the radiological part of the NINDS-AIREN criteria.14 On
the basis of earlier central reading of the images for trial inclusion, we knew that approximately
Chapter 2
40
75% of the current sample might be expected to have either unilateral or bilateral focal thalamic
lesions. To avoid any clinical bias, we were blinded to all clinical and center data of the patients.
MRI Technique
The patients were scanned on different scanners operating from 0.5 to 1.5 T. Axial T2
spin-echo weighted images (echo time [TE] 80 to 120 ms, repetition time [TR] 3000 to 4000 ms,
slice thickness 5 mm); axial FLAIR (TE 110 to 150 ms, TR 9000 to 10000 ms, inversion time
2000 to 2200 ms, slice thickness 5 mm); and axial, sagittal, and coronal T1 spin-echo weighted
images (TE 11 to 20 ms, TR 500 to 700 ms, slice thickness 5 mm) were acquired. To maintain
blinding, we were restricted from access to information about the type of the scanner used for
each particular patient as well as the location of the imaging center.
Image Assessment
The initial assessment was performed in a blinded way, in which the T2-WI and
FLAIR images were evaluated in pseudorandom order, with the use of 16-bit digital imaging
files. All lesions were marked and numbered with digital overlays. We included only focal
thalamic abnormalities >1 mm and excluded those suggestive of perivascular spaces.
Perivascular spaces were defined as sharply demarcated areas with a signal isointensity relative
to cerebrospinal fluid (CSF), following the course of a perforating vessel on sagittal or coronal
images.19 Care was also taken to avoid the inclusion of pulsation artifacts, recognizable by linear
patterns of signal banding due to phase misregistration. For further subtyping and analysis, T2-
WI, FLAIR, and T1-weighted images (T1-WI) were evaluated side by side. The greatest
dimension of each focal abnormality was measured, and all were classified on each of the 3
imaging sequences in the following categories: hyperintense, hypointense, predominantly
hypointense (hypointense with a small hyperintense component), and hypointense with a
peripheral rim of hyperintensity.
Statistical Evaluation
Statistical analysis was performed with the use of SPSS 11.0. We used χ2 statistics to
compare categorical data, such as proportions of lesions detected by each sequence. For
comparisons of continuous variables, Student’s t test was applied because the data were normally
distributed.
Characteristics of vascular dementia on imaging
41
Results
The total number of focal thalamic lesions detected was 214. One hundred twenty-four
(58%) of the 214 measured <5 mm, 61 (29%) between 5 and 10 mm, and 29 (14%) >10 mm.
One hundred nine (51%) were localized in the right thalamus and 105 (49%) on the left. Two
hundred eight (97%) of the 214 lesions were identified on T2-WI, but only 117 (55%) were
detected on FLAIR (χ2=5.1; P<0.05). Almost half (47%) of the lesions found on T2-WI were not
detected on FLAIR (Table 1).
Although the mean size of lesions detected on T2-WI and not on FLAIR (4.4 mm) was
significantly lower than the mean size of lesions detected on both sequences (6.7 mm) (P<0.001),
5 of the 29 lesions >10 mm on T2-WI were not visible on FLAIR (Table 2).
One hundred eight (50%) of the lesions were hyperintense on T2-WI and hypointense
on T1-WI and probably correspond to infarctions. Fifty lesions (23%) were hyperintense on T2-
WI and isointense on T1-WI and may correspond to areas of myelin pallor. Fifty lesions (23%)
were hypointense on T2-WI and T1-WI and probably represent microbleeds (hemorrhagic
lacunae).
FLAIR detected 61 (56%) of the 108 probable infarctions, 30 (60%) of the 50 probable
microbleeds, and 20 (40%) of the 50 probable areas of myelin pallor. Thirty-two of the probable
infarctions were hyperintense on FLAIR (incomplete or noncystic infarctions), and 29 were
totally or partially hypointense (cystic and partially cystic infarctions). The vast majority (79%)
of the 97 lesions not detected on FLAIR were hyperintense on T2-WI (Table 3).
Discussion
Our study shows that FLAIR imaging is not very sensitive in detecting focal thalamic
lesions and is therefore not well suited as a stand-alone sequence in the evaluation of patients
suspected of VaD. FLAIR sequences employ a long inversion time that suppresses the signal
from CSF and a long TE that provides heavy T2 weighting.
Chapter 2
42
Table 1. Lesions on T2-WI and FLAIR
Signal on T2-WI
Not
detected
Hyperintense Hypointense Total
Signal on FLAIR
Not Detected 0 77 20 97
Hyperintense 3 49 0 52
Hypointense 3 14 30 47
Predominantly hypointense 0 5 0 5
Hypointense with
hyperintense rim
0 13 0 13
Total 6 158 50 214
Table 2. Detection on FLAIR and Distribution by Size of Focal Lesions on T2-WI
Detection on FLAIR
Not Detected Detected Total
Size on T2-WI
1–5 mm 66 53 119
5–10 mm 26 34 60
>10 mm 5 24 29
Table 3. Detection on FLAIR and Signal on T2 and T1-WI
Detected on FLAIR Not Detected on FLAIR Hyperintense
on T2-WI Hypointense on T2-WI
Hyperintense on T2-WI
Hypointense on T2-WI
Isointense on T1-WI
20 10 30 13 Χ=0.079 P=0.779
Hypointense on T1-WI
61 20 47 7 Χ=2.785 P=0.095
Total 81 30 77 20 Χ=1.164 P=0.281
Characteristics of vascular dementia on imaging
43
Therefore, the major interest of FLAIR is to detect and characterize brain lesions around CSF
spaces.20,21 Most studies advocate superiority of FLAIR over conventional spin-echo imaging in
a wide range of pathologies.22-32 FLAIR images also have the advantage of easily identifying
CSF-like lesions.33 Some studies showed that FLAIR was more often associated with image
artifacts or could not corroborate the aforementioned superiority of FLAIR.31,34,35 Disadvantages
of FLAIR include a reduced sensitivity to detect infratentorial or spinal cord lesions.35-38 The
reason for this is unknown but most likely reflects different relaxation characteristics in those
regions, both in normal-appearing tissue and in lesions. For example, T1 and T2 relaxation times
of infratentorial lesions in patients with multiple sclerosis are closer to the relaxation times of
local normal-appearing white matter than those of supratentorial lesions, resulting in reduced
contrast between posterior fossa lesions and the background.39 Age-related increases in T1
relaxation times of human brain also have been shown, particularly in the thalami,40 and may
serve to explain the lack of sensitivity of FLAIR for thalamic lesions in elderly patients with
VaD. Alternatively, the occurrence of cystic changes in lacunar infarctions41 will lead to a
prolongation of T1 relaxation time, and the signal from these lesions may be suppressed, as in
CSF spaces. The same may occur with multiple sclerosis lesions severely hypointense on T1-
WI.42 MRI-pathological correlation studies performed to determine the background of age-
related subcortical gray and white matter hyperintensities on T2-WI found different types of
pathology: infarctions, gliosis, myelin and axonal loss, breakdown of the ependymal lining, and
enlarged perivascular spaces.17,43-47 Areas of myelin pallor can be hyperintense on T2-WI but
isointense on T1-WI,17,46 and it seems possible that differences in type of pathology can also
influence detection on FLAIR. Although the proposed neuropathological classification of
lacunae includes both ischemic (type I) and hemorrhagic (type II) vascular abnormalities and
enlarged perivascular spaces (type III),41 in VaD it is important to differentiate the vascular
lesions. MRI-pathological correlation studies found that the great majority of enlarged
perivascular (Virchow- Robin) spaces normally surround perforating arteries that enter the
striatum in the anterior perforated substance, just above the internal carotid artery bifurcation and
lateral to the anterior commissure. They are responsible for the so-called état criblé of the basal
ganglia19,48-52 and are much less frequently located in the thalami.52 Therefore, it is unlikely that
those lesions classified as cystic infarctions on the basis of MRI are in fact Virchow-Robin
spaces or could account for the greater number of lesions detected on T2-WI. Actually, FLAIR
performed more poorly for all types of presumed pathology. A limitation of our study is that we
used images acquired on a wide range of scanners and sequences, not all of which may be
optimally tuned. On the other hand, this reflects the normal variability of vendor-supported
Chapter 2
44
sequences, and given the more complex contrast mechanisms in FLAIR, these may be less stable
than for T2-WI. For the detection of type II hemorrhagic lacunae,53,54 both spin-echo and FLAIR
are insensitive compared with T2*-WI gradient-echo sequences,55,56 but these were not available
in the context of this trial. Nevertheless, we detected a fair amount of probable microbleeds. In
conclusion, the accuracy of T2-WI for the detection of thalamic lesions in patients with probable
VaD is far superior to FLAIR. Given the great clinical importance of these lesions, FLAIR
should not be used as the only T2-weighted sequence in patients suspected of having VaD. In
addition to the posterior fossa and spinal cord, the diencephalon seems to represent another
region not suitable for evaluation by FLAIR MRI.
Characteristics of vascular dementia on imaging
45
References
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Chapter 2
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21. Hajnal JV, Bryant DJ, Kasuboski L et al. Use of fluid attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain. J Comput Assist Tomogr 1992;16:841-4.
22. Alexander JA, Sheppard S, Davis PC et al. Adult cerebrovascular disease: role of modified rapid fluid-attenuated inversion-recovery sequences. AJNR Am J Neuroradiol 1996;17:1507-13.
23. Aprile I, Iaiza F, Lavaroni A et al. Analysis of cystic intracranial lesions performed with fluid-attenuated inversion recovery MR imaging. AJNR Am J Neuroradiol 1999;20:1259-67.
24. Arakia Y, Ashikaga R, Fujii K et al. MR fluid-attenuated inversion recovery imaging as routine brain T2-weighted imaging. Eur J Radiol 1999;32:136-43.
25. Bynevelt M, Britton J, Seymour H et al. FLAIR imaging in the follow-up of low-grade gliomas: time to dispense with the dual-echo? Neuroradiology 2001;43:129-33.
26. Essig M, Metzner R, Bonsanto M et al. Postoperative fluid-attenuated inversion recovery MR imaging of cerebral gliomas: initial results. Eur Radiol 2001;11:2004-10.
27. Filippi M, Yousry T, Baratti C et al. Quantitative assessment of MRI lesion load in multiple sclerosis. A comparison of conventional spin-echo with fast fluid-attenuated inversion recovery. Brain 1996;119 ( Pt 4):1349-55.
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29. Herskovits EH, Itoh R, Melhem ER. Accuracy for detection of simulated lesions: comparison of fluid-attenuated inversion-recovery, proton density--weighted, and T2-weighted synthetic brain MR imaging. AJR Am J Roentgenol 2001;176:1313-8.
30. Jager HR, Albrecht T, Curati-Alasonatti WL et al. MRI in neuro-Behcet's syndrome: comparison of conventional spin-echo and FLAIR pulse sequences. Neuroradiology 1999;41:750-8.
31. Rydberg JN, Hammond CA, Grimm RC et al. Initial clinical experience in MR imaging of the brain with a fast fluid-attenuated inversion-recovery pulse sequence. Radiology 1994;193:173-80.
32. Thurnher MM, Thurnher SA, Fleischmann D et al. Comparison of T2-weighted and fluid-attenuated inversion-recovery fast spin-echo MR sequences in intracerebral AIDS-associated disease. AJNR Am J Neuroradiol 1997;18:1601-9.
33. Barkhof F, Scheltens P. Imaging of white matter lesions. Cerebrovasc Dis 2002;13 Suppl 2:21-30.
34. Baratti C, Barkhof F, Hoogenraad F et al. Partially saturated fluid attenuated inversion recovery (FLAIR) sequences in multiple sclerosis: comparison with fully relaxed FLAIR and conventional spin-echo. Magn Reson Imaging 1995;13:513-21.
35. Okuda T, Korogi Y, Shigematsu Y et al. Brain lesions: when should fluid-attenuated inversion-recovery sequences be used in MR evaluation? Radiology 1999;212:793-8.
36. Keiper MD, Grossman RI, Brunson JC et al. The low sensitivity of fluid-attenuated inversion-recovery MR in the detection of multiple sclerosis of the spinal cord. AJNR Am J Neuroradiol 1997;18:1035-9.
37. Stevenson VL, Gawne-Cain ML, Barker GJ et al. Imaging of the spinal cord and brain in multiple sclerosis: a comparative study between fast FLAIR and fast spin echo. J Neurol 1997;244:119-24.
38. Tubridy N, Barker GJ, MacManus DG et al. Optimisation of unenhanced MRI for detection of lesions in multiple sclerosis: a comparison of five pulse sequences with variable slice thickness. Neuroradiology 1998;40:293-7.
39. Stevenson VL, Parker GJ, Barker GJ et al. Variations in T1 and T2 relaxation times of normal appearing white matter and lesions in multiple sclerosis. J Neurol Sci 2000;178:81-7.
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40. Steen RG, Gronemeyer SA, Taylor JS. Age-related changes in proton T1 values of normal human brain. J Magn Reson Imaging 1995;5:43-8.
41. Poirier J, Derouesne C. Cerebral lacunae. A proposed new classification. Clin Neuropathol 1984;3:266.
42. van Waesberghe JH, Castelijns JA, Weerts JG et al. Disappearance of multiple sclerosis lesions with severely prolonged T1 on images obtained by a FLAIR pulse sequence. Magn Reson Imaging 1996;14:209-13.
43. Awad IA, Johnson PC, Spetzler RF et al. Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. II. Postmortem pathological correlations. Stroke 1986;17:1090-7.
44. Braffman BH, Zimmerman RA, Trojanowski JQ et al. Brain MR: pathologic correlation with gross and histopathology. 2. Hyperintense white-matter foci in the elderly. AJR Am J Roentgenol 1988;151:559-66.
45. Chimowitz MI, Estes ML, Furlan AJ et al. Further observations on the pathology of subcortical lesions identified on magnetic resonance imaging. Arch Neurol 1992;49:747-52.
46. Fazekas F, Kleinert R, Offenbacher H et al. The morphologic correlate of incidental punctate white matter hyperintensities on MR images. AJNR Am J Neuroradiol 1991;12:915-21.
47. Scheltens P, Barkhof F, Leys D et al. Histopathologic correlates of white matter changes on MRI in Alzheimer's disease and normal aging. Neurology 1995;45:883-8.
48. Adachi M, Hosoya T, Haku T et al. Dilated Virchow-Robin spaces: MRI pathological study. Neuroradiology 1998;40:27-31.
49. Bokura H, Kobayashi S, Yamaguchi S. Distinguishing silent lacunar infarction from enlarged Virchow-Robin spaces: a magnetic resonance imaging and pathological study. J Neurol 1998;245:116-22.
50. Braffman BH, Zimmerman RA, Trojanowski JQ et al. Brain MR: pathologic correlation with gross and histopathology. 1. Lacunar infarction and Virchow-Robin spaces. AJR Am J Roentgenol 1988;151:551-8.
51. Pullicino PM, Miller LL, Alexandrov AV et al. Infraputaminal 'lacunes'. Clinical and pathological correlations. Stroke 1995;26:1598-602.
52. Takao M, Koto A, Tanahashi N et al. Pathologic findings of silent, small hyperintense foci in the basal ganglia and thalamus on MRI. Neurology 1999;52:666-8.
53. Challa VR, Moody DM. The value of magnetic resonance imaging in the detection of type II hemorrhagic lacunes. Stroke 1989;20:822-5.
54. Fazekas F, Kleinert R, Roob G et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol 1999;20:637-42.
55. Kim DE, Bae HJ, Lee SH et al. Gradient echo magnetic resonance imaging in the prediction of hemorrhagic vs ischemic stroke: a need for the consideration of the extent of leukoariosis. Arch Neurol 2002;59:425-9.
56. Ripoll MA, Siosteen B, Hartman M et al. MR detectability and appearance of small experimental intracranial hematomas at 1.5 T and 0.5 T. A 6-7-month follow-up study. Acta Radiol 2003;44:199-205.
Chapter 2
48
Characteristics of WMH on imaging
49
Chapter 3
Characteristics of White Matter
Hyperintensities on imaging
Chapter 3
50
Characteristics of WMH on imaging
51
Chapter 3.1
Impact of White Matter Hyperintensities Scoring
Method on Correlations With Clinical Data The LADIS Study
E.C.W. van Straaten F. Fazekas E. Rostrup P. Scheltens R. Schmidt L. Pantoni D. Inzitari G. Waldemar T. Erkinjuntti R. Mäntylä L-O. Wahlund F. Barkhof on behalf of the LADIS Group Stroke 2006;37:836-40
Chapter 3
52
Abstract
White matter hyperintensities (WMH) are associated with decline in cognition, gait, mood, and
urinary continence. Associations may depend on the method used for measuring WMH. We
investigated the ability of different WMH scoring methods to detect differences in WMH load
between groups with and without symptoms.
We used data of 618 independently living elderly with WMH collected in the Leukoaraiosis And
DISability (LADIS) study. Subjects with and without symptoms of depression, gait disturbances,
urinary incontinence, and memory decline were compared with respect to WMH load measured
qualitatively using 3 widely used visual rating scales (Fazekas, Scheltens, and Age-Related
White Matter Changes scales) and quantitatively with a semiautomated volumetric technique and
an automatic lesion count. Statistical significance between groups was assessed with the χ2 and
Mann–Whitney tests. In addition, the punctate and confluent lesion type with comparable WMH
volume were compared with respect to the clinical data using Student t test and χ2 test. Direct
comparison of visual ratings with volumetry was done using curve fitting.
Visual and volumetric assessment detected differences in WMH between groups with respect to
gait disturbances and age. WMH volume measurement was more sensitive than visual scores
with respect to memory symptoms. Number of lesions nor lesion type correlated with any of the
clinical data. For all rating scales, a clear but nonlinear relationship was established with WMH
volume.
Visual rating scales display ceiling effects and poor discrimination of absolute lesion volumes.
Consequently, they may be less sensitive in differentiating clinical groups.
Characteristics of WMH on imaging
53
Introduction
White matter hyperintensities (WMH) on MRI are associated with cognitive
dysfunction, gait abnormalities, falls, and depression and contribute to disability in the elderly
population.1-5 Lesion load on MRI may serve as surrogate marker of disease burden and may
ultimately guide treatment. For the measurement of WMH extent, different methods can be used,
ranging from visual rating to fully computerized techniques. Visual rating of WMH is easy, and
several scales are available with good reproducibility.6 The visual scales often do not detail size
and location, and most are not linear. Scores from different rating scales are not directly
comparable.7 Most volumetric studies use supervised semiautomated methods that may provide
more information on location and size, as well as continuous data, but are time consuming.8,9
Both methods have been used to correlate WMH with clinical data and have rendered varying
results.10,11 Subjective memory symptoms, although difficult to define, are associated with higher
risk of dementia and WMH and may be used for the early detection of subjects at risk.12 Number
of lesions and lesion pattern (punctate versus confluent) may also be correlated to clinical data.
WMH burden might be caused by a large number of punctate lesions or few confluent lesions,
possibly leading to different clinical signs.
In this study, we aimed to establish cross-sectionally the sensitivity of several visual
WMH scales, volumetric WMH measurement, as well as WMH lesion count and pattern, to
symptoms of cognitive decline, gait abnormalities, urinary incontinence, and depression. The
relationship between visual and volumetric methods was characterized by establishing the
mathematical function that best fitted the data. The study group consisted of elderly
independently living individuals recruited on the basis of WMH and stratified by lesion severity
into 3 groups.
Materials and Methods
Subjects
Data were drawn from the multinational multicenter longitudinal Leukoaraiosis And
DISability (LADIS) study among 639 elderly, described previously.13 Inclusion criteria were 65
to 85 years of age and no or mild disability in everyday life (as established with the Instrumental
Activities of Daily Living scale).14 Subjects were required to have at least some degree of WMH,
demonstrated on MRI. Participants presented for evaluation in various settings: stroke unit,
memory clinic, neurological or geriatric wards/clinic, population studies on aging, controls in
Chapter 3
54
other studies. The study was approved by the local ethics committees, and all subjects gave
informed consent. At baseline, subjects underwent a standardized evaluation (including global
functioning, cognitive, motor, and psychiatric assessment), and, together with their informants,
filled in questionnaires on medical history. The data used in this study are age, gender, presence
of depression requiring therapy, symptoms of urinary incontinence, gait disturbances, and
memory problems, as expressed by the participants or their informants.
MRI Scans
All subjects underwent magnetic resonance scanning following a standard protocol,
during which 0.5-T or 1.5-T scanners were used and series included axial T2-weighted images
(echo time [TE] 100 to 120 ms; repetition time [TR] 4000 to 6000 ms; voxel size 1x1x5 to 7.5
mm3; 19 to 24 slices), axial fluid-attenuated inversion recovery (FLAIR) images (TE 100 to 140
ms; TR 6000 to 10 000 ms; inversion time 2000 to 2400 ms; voxel size 1x1x5 to 7.5 mm3; 19 to
24 slices), and coronal or sagittal 3D T1 sequence (TE 4 to 7 ms; TR 10 to 25 ms; flip angle 15 to
30°; voxel size 1x1x1 to 1.5 mm3). All scans were checked and stored at the Image Analysis
Center of the VU Medical Center, Amsterdam, the Netherlands. Postprocessing and data analysis
for this study was performed in Amsterdam and Copenhagen. Of the 639 scans, 21 could not be
used because of insufficient quality for the volumetric assessment.
Visual Rating
On the FLAIR images, we applied the visual rating scales of Fazekas (range 0 to 3),
Scheltens (range 0 to 84), and the Age-Related White Matter Changes (ARWMC) scale (range 0
to 30).15-17 All ratings were performed by an experienced rater (E.vS.) blind to the clinical data.
Volumetric Assessment
Volumetric analysis of WMH was performed by a single rater on the same axial
FLAIR images, including the infratentorial region, using a Sparc 5 workstation (SUN). Lesions
were marked and borders were set using local thresholding (home-developed software
Show_Images, version 3.6.1) on each slice. No distinction was made between subcortical and
periventricular hyperintensities. Areas of hyperintensity on T2-weighted images around
infarctions and lacunes were disregarded.
Characteristics of WMH on imaging
55
Lesion Count
An automated assessment of the number of lesions was performed by defining each
lesion that was generated with the volumetric method, as a cluster of 3D connected voxels, and
counting the number of such clusters (26-connectivity).
Reliability Assessment
To test reproducibility of the different methods, 18 scans, with a mean volume (SD) of
26.3 (19.0) mL, were assessed twice with an interval of >2 months.
Statistical Analysis
Sensitivity of WMH measurements to detect clinical group differences was tested with χ2 for
trend (Fazekas scale) and Mann–Whitney test (Scheltens scale, ARWMC scale, volumetric
measurement, and lesion count). Nonparametric testing was used for the WMH volumes because
of the nonnormal distribution. Differences in lesion volume and number between Fazekas groups
were tested using ANOVA with Bonferroni correction. To test the hypothesis that lesion type
(punctate, defined as Fazekas score 1, versus confluent, defined as Fazekas score 3, with
comparable lesion volumes) was associated with clinical characteristics, we selected all subjects
with WMH volume between 15 and 30 mL. In this volume range, both Fazekas scores 1 and 3
were represented. These groups were compared with respect to the clinical data, using the
Student t test and χ2 test. Visual rating scales were correlated with the volumetric method using
Spearman rank correlation method. To test the hypothesis of a nonlinear relationship between the
visual methods and the volumetric method, we fitted a linear and a quadratic function to the plot
using a linear regression with a correction factor based on the local variance.18
Results
WMH Load and Clinical Data
Table 1 shows mean WMH volumes and scores for different subject groups. Mean
WMH volumes, but not visual ratings, were significantly greater in men than in women. Both
visual and volumetric assessments showed group differences in WMH load between the older
and younger subjects. No significant differences in WMH load could be found between the
groups with and without a history of depression or symptoms of urinary incontinence. The mean
WMH load of subjects with symptoms of gait disturbance was only significantly larger when
measured volumetrically or with the ARWMC and Scheltens scales. With the volumetric
Tab
le 1
. Mea
n W
MH
scor
es
†: M
ann-
Whi
tney
test
‡: χ
2 fo
r tre
nd
*: p
< 0
.05
**:
P <
0.0
1
N
Mea
n W
MH
vo
lum
e (S
D)
†
Mea
n Fa
zeka
s sc
ore
‡ M
ean
AR
WM
C
scor
e (S
D) †
Mea
n Sc
helte
ns
scor
e (S
D) †
M
ean
num
ber o
f le
sion
s †
Gen
der
Mal
e 27
8 25
.0 (2
6.6)
1.
9 (0
.8)
9.9
(5.9
) 18
.4 (9
.4)
43.0
(23.
1)
Fe
mal
e 34
0 18
.1 (1
8.2)
**
1.7
(0.8
) 9.
6 (5
.5)
18.0
(8.9
) 44
.5 (2
4.5)
A
ge g
roup
s 65
-74
year
s 31
5 17
.6 (1
9.4)
1.
7 (0
.8)
9.1
(5.5
) 17
.0 (9
.0)
42.8
(23.
8)
75
-85
year
s 30
3 24
.9 (2
5.1)
**
1.9
(0.8
)**
10.5
(5.8
)**
19.3
(9.1
)**
44
.8 (2
3.9)
H
isto
ry o
f Y
es
170
21.5
(22.
8)
1.8
(0.8
) 10
.1 (5
.7)
18.7
(9.7
) 45
.8 (2
6.3)
de
pres
sion
N
o 44
8 21
.1 (2
2.6)
1.
8 (0
.8)
9.6
(5.6
) 18
.0 (8
.9)
43.0
(22.
8)
Com
plai
nts o
f urin
e
inco
ntin
ence
Y
es
124
23.1
(23.
0)
1.8
(0.8
) 10
.4 (5
.7)
19.0
(9.2
) 45
.4 (2
4.9)
N
o 49
4 20
.7 (2
2.6)
1.
8 (0
.8)
9.6
(5.7
) 18
.0 (9
.1)
43.4
(23.
6)
Com
plai
nts o
f
Yes
25
5 24
.7 (2
5.2)
1.
9 (0
.8)
10.6
(5.9
) 19
.7 (9
.5)
46.5
(26.
3)
gait
dist
urba
nces
N
o 35
5 18
.6 (2
0.4)
**
1.7
(0.8
) 9.
1 (5
.5)*
* 17
.0 (8
.7)*
* 41
.9 (2
1.8)
not a
pplic
able
8
21.7
(17.
5)
1.8
(0.8
) 9.
8 (4
.5)
18.1
(8.4
) 42
.3 (2
6.3)
M
emor
y
Yes
39
3 22
.1 (2
3.0)
1.
8 (0
.8)
9.9
(5.7
) 18
.4 (9
.1)
45.4
(24.
7)
com
plai
nts
No
225
19.5
(21.
9)*
1.8
(0.8
) 9.
6 (5
.7)
17.8
(9.1
) 41
.1 (2
3.9)
Characteristics of WMH on imaging
57
assessment only, we were able to establish a significant difference in lesion load between
subjects with and without memory symptoms.
Mean Lesion Volume and Lesion Count
Fazekas score 1 corresponded to a mean lesion volume of 0.20 mL, score 2 to 0.45 mL
per lesion and score 3 to 1.26 mL per lesion (Table 2). The differences were statistically
significant between all groups. Table 2 also shows mean total WMH for each Fazekas category,
which was also statistically significant between the groups. Subjects in the Fazekas score 2
category tended to have most lesions. Number of lesions did not discriminate significantly
between groups with and without symptoms (Table 1).
Table 2. Lesion characteristics for each Fazekas category
Fazekas 1 Fazekas 2 Fazekas 3
Mean volume per lesion, ml (SD)† 0.20 (0.1) 0.45 (0.4) 1.26 (1.0)
Mean WMH volume, ml (SD)† 6.49 (4.7) 18.83 (7.7) 51.35 (26.1)
Mean number of lesions (SD)† 33.2 (18) 53.6 (25) 51.0 (24)
Table 3. Differences in a group of subjects with WMH volume 15 - 30 ml
Fazekas 1 Fazekas 3 p-value
Total subjects 17 29
Mean Age (years) 74.4 73.2 0.47*
% Male 58.8 37.9 0.2†
% History of depression 29.4 27.6 0.9 †
% Urinary incontinence
complaints
29.4 20.7 0.5 †
% Gait disturbance
complaints
41.2 62.1 0.1 †
% Memory complaints 82.4 58.6 0.1 †
*: Student’s t-test
†:Chi-square
Chapter 3
58
Lesion Pattern
We found no significant differences in clinical features between the groups with
punctate (Fazekas 1) and confluent (Fazekas 3) lesions (Table 3).
Correlation Between Visual Rating Scales and Volumetric Measurement
Scatter plots for the Fazekas and ARWMC scales with WMH volume are shown in figures 1 and
2. The scatter and shape of the plot for the Scheltens score was similar to the scatter plot of the
ARWMC score (data not shown). Increasing volume correlated with higher visual scores
(Spearman ρ 0.86), and scatter increased with higher WMH visual scores. The relationship with
WMH volume was better described by a quadratic than a linear model, indicated by higher R2
(Table 4). When corrected for difference in variance, the difference between the linear and
quadratic model was statistically significant (P<0.01).
Figure 1.
Characteristics of WMH on imaging
59
Figure 2.
Intraobserver Reliability
Intrarater agreement for the scales was good with a κ for Fazekas score of 0.84.
Intraclass correlation coefficients were 0.93 (ARWMC scale), 0.92 (Scheltens scale), and 0.99
(volume measurement). The mean difference between the 2 measurements was not statistically
significant when tested against 0 using a 1-sample t test.
Table 4. Mathematical models of the relationship between visual rating scales and WMH volume
Model R2
Fazekas scale linear 0.58 quadratic 0.62*
ARWMC scale linear 0.67 quadratic 0.71*
Scheltens scale linear 0.60 quadratic 0.63*
*: difference significant at the 0.01 level
Chapter 3
60
Discussion
The results indicate that volumetry may be more sensitive to detect small group
differences. This is in line with previous research, correlating WMH measurement methods with
cognitive performance.19 Subjective and objective memory symptoms are not interchangeable for
the assessment of cognition, but both seem to be related with WMH. The best method for the
measurement of WMH with respect to objective cognitive measures is still to be established. The
ARWMC and Scheltens rating scales have a greater range than the Fazekas scale and were found
to differentiate better between groups. This finding corresponds with a review on the relationship
between WMH and cognition.20 The Fazekas scale seems most appropriate for defining different
WMH groups. No group differences were detected for symptoms of urinary incontinence and
depression. One of the reasons could be that only WMH in certain areas correlate with these
symptoms. Frontal WMH has been associated with mood disorders, cognitive functions, and gait
problems. On the other hand, it was shown that WMH in different regions are highly correlated
and that their influence on clinical signs may therefore not be limited to certain areas of the
brain.21 To our knowledge, this is the first report on lesion count as a measure of WMH severity.
We found that it was not sensitive to detect associations with clinical signs, possibly because
lesion count does not take into account lesion size. In progressing disease, lesions can merge,
leading to a smaller total number of lesions. Few lesions could therefore indicate either mild or
severe disease. This lack of correlation between number of lesions and clinical findings was also
found in individuals with multiple sclerosis (MS). This caused studies in MS to focus on total T2
lesion volume and T1 gadolinium enhancing lesions instead of number of lesions.22,23 We
compared subjects with punctiforme and confluent lesion patterns who had comparable WMH
volumes. We found no differences in symptoms between the groups. Although these subanalyses
limited the number of subjects studied, it illustrates the arbitrary nature of the qualitative scoring
system. We confirmed the good correlations between all three visual rating scales and the WMH
volume, but the current study shows that the variability in WMHvolume is large in the patient
groups with high visual scores.24 Subject group with higher visual scores contains subjects with
different degrees of WMH burden, leading to decreased correlation with clinical data. When
progression of WMH is measured, this ceiling effect can be even greater. A previous study on the
detection of WMH progression with conventional visual rating scales showed lack of sensitivity
compared with WMH volume measurement.25 This effect is especially of interest because WMH
progression seems to occur fastest in patients with a high lesion load.26 The WMH volume in this
study was significantly higher than reported in previous studies.27,28 The LADIS study was
Characteristics of WMH on imaging
61
designed to enroll a large population of subjects with WMH, and participants were stratified into
three categories of WMH severity. This approach is different from population-based studies and
resulted in a relatively large group of subjects with a high WMH lesion load. Results are
therefore not directly applicable to the healthy elderly population. The advantage of this design is
the possibility of studying a broad spectrum of lesion loads. WMH burden can be presented as
volume or as a proportion of the total white matter or intracranial volume, depending on the
focus of the study. This makes comparison between studies complicated. We did not correct for
intracranial volume or white matter volume because we wanted to compare the raw volumes with
visual scales that are also uncorrected. Wen and Sachdev investigated uncorrected WMH volume
and found no differences in WMH volumes between men and women, whereas in our study, men
had a larger mean WMH volume than women.29 The subjects in their study were younger than
our study participants (60 to 64 years versus 65 to 85 years), which could partly explain this
difference. We did not control for risk factors for WMH such as hypertension. In addition,
objective measures for cognition, gait, depression, and urinary incontinence were also not
included here. This was done because the focus of this study was not to establish a causal
relationship of WMH with clinical data but a comparison between scoring methods in their
association with symptoms, which is clearly of clinical relevance for clinicians dealing with these
patients.
Chapter 3
62
Appendix Participating Centers and Personnel Helsinki, Finland (Memory Research Unit, Department of Clinical Neurosciences, Helsinki University): Timo Erkinjuntti, MD, PhD, Tarja Pohjasvaara, MD, PhD, Pia Pihanen, MD, Raija Ylikoski, PhD, Hanna Jokinen, LPsych, Meija-Marjut Somerkoski, MPsych; Graz, Austria (Department of Neurology and MRI Institute, Karl-Franzens University): Franz Fazekas, MD, Reinhold Schmidt, MD, Stefan Ropele, PhD, Brigitte Rous, MD, Katja Petrovic, Ulrike Garmehi; Lisboa, Portugal (Servic¸o de Neurologia, Centro de Estudos Egas Moniz, Hospital de Santa Maria): Jose´ M. Ferro, MD, PhD, Ana Verdelho, MD, Sofia Madureira, PsyD; Amsterdam, The Netherlands (Department of Neurology, VU Medical Center): Philip Scheltens, MD, PhD, Ilse van Straaten, MD, Wiesje van de Flier, PhD, Frederik Barkhof, MD, PhD; Goteborg, Sweden (Institute of Clinical Neuroscience, Goteborg University): Anders Wallin, MD, PhD, Michael Jonsson, MD, Karin Lind, MD, Arto Nordlund, PsyD, Sindre Rolstad, PsyD, Kerstin Gustavsson, RN; Huddinge, Sweden (Karolinska Institute, Department of Clinical Neuroscience and Family Medicine, Huddinge University Hospital): Lars-Olof Wahlund, MD, PhD, Militta Crisby, MD, PhD, Anna Pettersson, physiotherapist, Kaarina Amberla, PsyD; Paris, France (Department of Neurology, Hopital Lariboisiere): Hugues Chabriat, MD, PhD, Ludovic Benoit, MD, Karen Hernandez, Solene Pointeau, Annie Kurtz, Daniel Reizine, MD; Mannheim, Germany (Department of Neurology, University of Heidelberg, Klinikum Mannheim): Michael Hennerici, MD, Christian Blahak, MD, Hansjorg Baezner, MD, Martin Wiarda, PsyD, Susanne Seip, RN; Copenhagen, Denmark (Memory Disorders Research Unit, Department of Neurology, Rigshospitalet and Danish Research Center for Magnetic Resonance, Hvidovre Hospital, Copenhagen University Hospital): Gunhild Waldemar, MD, DMSc, Egill Rostrup, MD, MSc, Charlotte Ryberg, MSc; Tim Dyrby; Newcastle-on-Tyne, UK (Institute for Ageing and Health, University of Newcastle): John O’Brien, DM, Sanjeet Pakrasi, MRCPsych, Thais Minnet, PhD, Michael Firbank, PhD, Jenny Dean, PhD, Pascale Harrison, BSc, Philip English, DCR. The coordinating center is in Florence, Italy (Department of Neurological and Psychiatric Sciences, University of Florence): Domenico Inzitari, MD (Study Coordinator); Leonardo Pantoni, MD, PhD, Anna Maria Basile, MD, Michela Simoni, MD, Giovanni Pracucci, MD, Monica Martini, MD, Luciano Bartolini, PhD, Emilia Salvadori, PhD, Marco Moretti, MD, Mario Mascalchi, MD, PhD. The LADIS Steering Committee is formed by Domenico Inzitari, MD (study coordinator), Timo Erkinjuntti, MD, PhD, Philip Scheltens, MD, PhD, Marieke Visser, MD, PhD, and Kjell Asplund, MD, PhD.
Acknowledgments
The authors thank Ronald van Schijndel for his support with the volumetric measurements and
Dirk Knol for his help with the curve fitting.
Characteristics of WMH on imaging
63
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1. Breteler MM, van Swieten JC, Bots ML et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994;44:1246-52.
2. Camicioli R, Moore MM, Sexton G et al. Age-related brain changes associated with motor function in healthy older people. J Am Geriatr Soc 1999;47:330-4.
3. Longstreth WT, Jr., Manolio TA, Arnold A et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274-82.
4. Schmidt R, Fazekas F, Offenbacher H et al. Neuropsychologic correlates of MRI white matter hyperintensities: a study of 150 normal volunteers. Neurology 1993;43:2490-4.
5. Thomas AJ, Kalaria RN, O'Brien JT. Depression and vascular disease: what is the relationship? J Affect Disord 2004;79:81-95.
6. Scheltens P, Erkinjunti T, Leys D et al. White matter changes on CT and MRI: an overview of visual rating scales. European Task Force on Age-Related White Matter Changes. Eur Neurol 1998;39:80-9.
7. Pantoni L, Simoni M, Pracucci G et al. Visual rating scales for age-related white matter changes (leukoaraiosis): can the heterogeneity be reduced? Stroke 2002;33:2827-33.
8. Anbeek P, Vincken KL, van Osch MJ et al. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004;21:1037-44.
9. Jack CR, Jr., O'Brien PC, Rettman DW et al. FLAIR histogram segmentation for measurement of leukoaraiosis volume. J Magn Reson Imaging 2001;14:668-76.
10. Davis Garrett K, Cohen RA, Paul RH et al. Computer-mediated measurement and subjective ratings of white matter hyperintensities in vascular dementia: relationships to neuropsychological performance. Clin Neuropsychol 2004;18:50-62.
11. Sachdev P, Cathcart S, Shnier R et al. Reliability and validity of ratings of signal hyperintensities on MRI by visual inspection and computerised measurement. Psychiatry Res 1999;92:103-15.
12. Dufouil C, Fuhrer R, Alperovitch A. Subjective cognitive complaints and cognitive decline: consequence or predictor? The epidemiology of vascular aging study. J Am Geriatr Soc 2005;53:616-21.
13. Pantoni L, Basile AM, Pracucci G et al. Impact of age-related cerebral white matter changes on the transition to disability -- the LADIS study: rationale, design and methodology. Neuroepidemiology 2005;24:51-62.
14. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9:179-86.
15. Fazekas F, Chawluk JB, Alavi A et al. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-6.
16. Scheltens P, Barkhof F, Leys D et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993;114:7-12.
17. Wahlund LO, Barkhof F, Fazekas F et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001;32:1318-22.
18. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied Linear Statistical Models. Irwin: Burr Ridge, 1996;III.
19. Davis Garrett K, Cohen RA, Paul RH et al. Computer-mediated measurement and subjective ratings of white matter hyperintensities in vascular dementia: relationships to neuropsychological performance. Clin Neuropsychol 2004;18:50-62.
20. Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology 2000;14:224-32.
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21. Tullberg M, Fletcher E, DeCarli C et al. White matter lesions impair frontal lobe function regardless of their location. Neurology 2004;63:246-53.
22. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15:239-45.
23. Iannucci G, Minicucci L, Rodegher M et al. Correlations between clinical and MRI involvement in multiple sclerosis: assessment using T(1), T(2) and MT histograms. J Neurol Sci 1999;171:121-9.
24. Kapeller P, Barber R, Vermeulen RJ et al. Visual rating of age-related white matter changes on magnetic resonance imaging: scale comparison, interrater agreement, and correlations with quantitative measurements. Stroke 2003;34:441-5.
25. Prins ND, van Straaten EC, van Dijk EJ et al. Measuring progression of cerebral white matter lesions on MRI: visual rating and volumetrics. Neurology 2004;62:1533-9.
26. Schmidt R, Enzinger C, Ropele S et al. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study. Lancet 2003;361:2046-8.
27. Atwood LD, Wolf PA, Heard-Costa NL et al. Genetic variation in white matter hyperintensity volume in the Framingham Study. Stroke 2004;35:1609-13.
28. Wen W, Sachdev P. The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage 2004;22:144-54.
29. Wen W, Sachdev P. The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage 2004;22:144-54.
Characteristics of WMH on imaging
65
Chapter 3.2
Measuring Progression of Cerebral White Matter Lesions on MRI Visual rating and volumetrics
N.D. Prins E.C.W. van Straaten E.J. van Dijk M. Simoni R.A. van Schijndel H.A. Vrooman P.J. Koudstaal P. Scheltens M.M.B. Breteler F. Barkhof
Neurology 2004;62:1533-1639
Chapter 3
66
Abstract
To evaluate the concordance of a volumetric method for measuring white matter lesion (WML)
change with visual rating scales t
he authors selected a stratified sample of 20 elderly people (mean age 72 years, range 61 to 88
years) with an MRI examination at baseline and at 3-year follow-up from the community-based
Rotterdam Scan Study (RSS). Four raters assessed WML change with four different visual rating
scales: the Fazekas scale, the Scheltens scale, the RSS scale, and a new visual rating scale that
was designed to measure change in WML. The authors assessed concordance with a volumetric
method with scatter plots and correlations, and interobserver agreement with intraclass
correlation coefficients.
For assessment of change in WML, the Fazekas, Scheltens, and periventricular part of the RSS
scale showed little correlation with volumetrics, and low interobserver agreement. The authors’
new WML change scale and the subcortical part of the RSS scale showed good correlation with
volumetrics. After additional training, the new WML change scale showed good interobserver
agreement for measuring WML change.
Commonly used visual rating scales are not well suited for measuring change in white matter
lesion severity. The authors’ new white matter lesion change scale is more accurate and precise,
and may be of use in studies focusing on progression of white matter lesions.
Characteristics of WMH on imaging
67
Introduction
Cerebral white matter lesions (WML) are thought to result from small vessel disease,
and their presence and severity increase with age and the presence of arterial hypertension.1-3
Although the clinical significance of these lesions remains to be fully understood, WML have
been associated with dementia, depression, and stroke.4-6 In healthy elderly people, WML are
associated with adverse cognitive function and the presence of depressive symptoms.7-9 Patients
with Alzheimer disease (AD), vascular dementia, and depression have more severe WML than
controls.4,6 It has been suggested that WML progress gradually over time, and may ultimately
lead to subcortical vascular dementia and vascular depression or contribute to the clinical
expression of AD.10 Studies that have determined progression of WML over time are limited, and
comparison of their findings is difficult due to the use of different visual rating scales for the
assessment of WML progression.11-13 Evaluation of WML progression is of clinical importance,
since it is needed to determine the natural course of these lesions, and to study the effect of
intervention studies. Visual rating scales have proven their value in cross-sectional studies, but
very little is known about the sensitivity and reliability of these scales for measuring change in
WML over time. Volumetric methods may provide the most objective assessment method, but
are often time consuming, and therefore not always feasible in large studies. The objective of the
present study was to evaluate three commonly used visual rating scales—the Fazekas scale,14 the
Rotterdam Scan Study (RSS) scale,1 and the Scheltens scale15—in terms of accuracy and
precision in measuring change of WML in a defined population. We compared the degree of
concordance with a volumetric method, and the reproducibility of these scales. In addition, we
introduce a simple visual rating scale that was designed to measure change in WML over time.
We compared the performance of this WML change scale with the other three visual rating
scales, and with the volumetric method.
Methods
Subjects
The scan material used in the present study originates from subjects participating in the
RSS, a population-based study that was designed to study causes and consequences of age-
related brain changes in elderly people.7 In 1995 through 1996, 1,077 nondemented elderly
people aged 60 to 90 years underwent a baseline examination that included a cranial MRI scan.
In 1999 through 2000, 787 of the 973 participants who were alive and eligible (not
Chapter 3
68
institutionalized, not moved abroad) were re-examined (response rate 81%). Of these
participants, 668 underwent a second MRI (response rate 69%). We selected scan pairs from 10
participants, in a nonrandom manner, to serve as a training set. Additionally, we randomly
selected 20 participants who had a baseline and follow-up scan, in three strata of baseline
subcortical WML severity, as assessed with the RSS scale.1 We selected seven participants from
the first tertile of subcortical WML severity, seven from the second tertile, and six from the third
tertile to cover the whole range of the WML distribution. The mean age of participants was 72
years (range 61 to 88 years), 10 (50%) were women, and 8 (40%) had hypertension. The mean
time between the first and second MRI was 3.3 years (range 2.9 to 4.0 years).
MRI scanning and white matter lesions
Axial T1, T2, and proton density weighted cerebral MR scans were made on a 1.5-
Tesla scanner (Siemens, Erlangen, Germany). The following pulse sequences were applied: T1
(700 msec/14 msec/2 [repetition time/ echo time/excitations]), T2 (2,200 msec/80 msec), and
proton density (2200 msec/20 msec). Slice thickness was 5 mm, with an interslice gap of 1 mm,
and a matrix size of 192 x 256 pixels. MRI protocols were identical at baseline and at follow-up.
We defined WML as hyperintense lesions, located in the cerebral white matter, that are visible
on both T2- and proton density-weighted images, and do not have a hypointense center on proton
density weighted images (as in lacunes). Lesions were considered periventricular in location
when directly adjacent to the ventricles; otherwise we considered them as subcortical. If
periventricular lesions extended 10 mm perpendicularly from the ventricular border, the
extending part was per definition scored as a subcortical lesion.
Rating scales
We assessed WML severity at baseline and WML progression with four different
visual rating scales. The Fazekas scale rates WML both in the periventricular and subcortical
region on a 0 to 3 scale.14 The Scheltens scale rates WML in the periventricular region on a 0 to
6 scale, and in the subcortical region on a 0 to 24 scale, on the basis of the size and number of the
lesions.15 It also includes ratings for basal ganglia and infratentorial areas, which were not used
in this report. The RSS scale rates WML in the periventricular region on a 0 to 9 scale, and for
subcortical WML a lesion volume is approximated based on number and size of the lesions.1 In
addition, we designed and used a new simple scale to measure WML change: the WML change
scale. In this scale change in WML (-1 decrease, 0 no change, +1 increase) is scored in three
periventricular locations (frontal caps, lateral bands, occipital caps) resulting in a periventricular
score of -3 to +3, and in four subcortical locations (frontal, parietal, temporal, and occipital),
Characteristics of WMH on imaging
69
resulting in a subcortical score of -4 to +4. Increase is defined as the occurrence of a new focal
lesion or the enlargement of a previously visible lesion; decrease is defined as the reverse (i.e.,
disappearance or shrinkage).
Visual rating system
All ratings were performed at the VU medical center. The MRI studies were in digital
format. Four raters (N.D.P., E.C.W.v.S., E.J.v.D., M.S.) analyzed WML on baseline and follow-
up images, using the four different visual rating scales. Raters were blinded to clinical
information, but not to name, age, and scan year. WML were rated on proton density and T2-
weighted images, by direct scan comparison on a personal computer, using the viewing program
Radworks (version 5.1, Applicare, Zeist, the Netherlands). To optimize the comparability of the
baseline and follow-up scans, images had been registered and resliced using the software
package Mirit, which uses mutual information as optimization criteria.16 After a training session
in which the four raters in couples assessed 10 scan pairs of the training set, a consensus meeting
was held among the authors to identify and resolve any possible differences in application of the
various scales. Following this training stage, each rater then individually scored the 20 series of
baseline and follow-up MRI studies. The rating scales were always applied in the same order:
first the Fazekas scale, second the RSS scale, third the Scheltens scale, and finally our WML
change scale. Raters were aware which was the baseline and follow-up scan, and this may lead to
bias toward finding a positive change in WML severity. In order to estimate this potential
systematic measurement error, two raters reassessed the 20 series with the WML change scale in
the native domain, first blinded to scan date, and 2 weeks later not blinded to scan date.
Volumetric assessments
We used proton density images for the volumetric quantification of WML volume on a
workstation (Sparc 5; SUN, Palo Alto, CA). One reader identified lesions on the registered
images, and then determined the areas of the lesions using home-developed software
(Show_Images, version 3.6.1) in the native domain to avoid artificial enlargement of lesion areas
due to reslicing. We used a seed growing method to determine WML areas on each slice for
periventricular WML (frontal caps, lateral bands, occipital caps), and subcortical WML (frontal,
parietal, temporal, and occipital).17 WML areas were not recorded separately for the right and left
hemisphere. By summing the areas of each slice multiplied by the interslice distance, we
calculated total WML volumes for the different regions. The volumetric assessments were
performed twice, with an interval of 6 months, and the mean value of the two assessments was
Chapter 3
70
used in the analyses. The intraclass correlation coefficients reflecting the intrarater agreement for
the baseline assessment were 0.84 for periventricular and 0.97 for subcortical WML volume,
with a SD of the difference between the two ratings of 1.4 mL for periventricular and 0.86 mL
for subcortical WML.18
Data analysis
For the volumetric assessment, change in WML volume was calculated by subtracting the
baseline WML volume from the follow-up volume.19 Pearson’s correlation coefficient was used
to assess the relation between baseline WML severity and WML change in the periventricular
and subcortical region. For the visual rating scales (Fazekas scale, Scheltens scale, RSS scale),
change in WML score was calculated by subtracting the baseline from the follow-up rating for
each rater separately. Progression on the visual rating scales was defined as an increase of 1 point
or more on the scale. We made scatter plots to visualize the relation between the change in WML
assessed with the volumetric method (in mL), and the visual rating scales. Furthermore, we
assessed concordance between visual scales and volumetrics by the nonparametric Spearman’s
rho. Spearman’s rho values of 0 were considered no relationship between the variables; values
equal to 1 were considered to reflect perfect correlation. We quantified the interobserver
agreement on the visual rating scales with intraclass correlation coefficients. The intraclass
correlation coefficient is the biologic variation between participants divided by the sum of the
variation between participants and the rater variation. We estimated the possible bias in the
visual ratings that may have been introduced by being aware which were the baseline and follow-
up images. Bias was expressed as the mean difference in scores on the WML change scale
between not-blinded ratings and blinded ratings.18 Furthermore, we assessed the 95% limits of
agreement between the not-blinded and blinded method.
Results
WML severity at baseline and change
The median WML volumes as assessed with the volumetric method were 3.3 mL
(range 1.6 to 10.4) at baseline and 0.7 mL (range-2.1 to 6.7) increase for the periventricular
region, and 0.2 mL at baseline (range 0 to 15.2) and 0.1 mL (range -0.4 to 3.5) increase for the
subcortical region. Mean increase in the periventricular region was 1.4 (SD 2.2) and in the
subcortical region 0.5 (SD 0.9). This corresponds to a mean WML increase at a rate of 0.42 mL
per year in the periventricular region and 0.15 mL per year in the subcortical region. Figure 1
Characteristics of WMH on imaging
71
A.
B.
Figure 1. White matter lesions (WML) progression in an 88-year-old woman who participated in our study. A. a slice from the baseline study. B. a corresponding slice from the follow-up study. After 3 1/2 years, WML progression has occurred (arrows) in the left and right occipital cap (periventricular region), extending into the parietal subcortical regions.
Tab
le 1
. WM
L se
verit
y at
bas
elin
e, c
hang
e, a
nd n
umbe
r of p
artic
ipan
ts w
ith p
rogr
essi
on fo
r the
four
vis
ual r
atin
g sc
ales
and
for t
he fo
ur ra
ters
R
atin
g sc
ale
Rat
er 1
R
ater
2
Rat
er 3
R
ater
4
Periv
entri
cula
r
Faze
kas s
cale
, 0 to
3
Bas
elin
e 2
(2-3
) 2
(1-3
) 2
(1-3
) 2
(2-3
)
Cha
nge
0 (0
-1)
0 (0
-1)
0 (0
) 0
(0)
Prog
ress
ion
2 (1
0%)
3 (1
5%)
0 (0
%)
0 (0
)
RSS
scal
e, 0
to 9
Bas
elin
e 6
(4-9
) 5
(3-9
) 5
(3-9
) 5.
5 (3
.3-9
)
Cha
nge
0 (0
-1)
0 (0
-2)
0 (0
) 0
(0-1
)
Prog
ress
ion
6 (3
0%)
6 (3
0%)
0 (0
%)
6 (3
0%)
Sche
ltens
scal
e, 0
to 6
Bas
elin
e 4
(3-6
) 3
(3-6
) 3
(2-6
) 3
(2-6
)
Cha
nge
0 (0
-2)
0 (0
-3)
0 (0
-1)
0 (0
-1)
Prog
ress
ion
2 (1
0%)
3 (1
5%)
1 (5
%)
3 (1
5%)
WM
L ch
ange
scal
e, -3
to 3
Cha
nge
1 (0
-3)
1 (-
1-3)
0
(0-3
) 0
(0-3
)
Prog
ress
ion
11 (5
5%)
11 (5
5%)
5 (2
5%)
8 (4
0%)
Subc
ortic
al
Faze
kas s
cale
, 0 to
3
Bas
elin
e 1
(0-3
) 1
(0-3
) 1
(0-3
) 1
(0-3
)
Cha
nge
0 (0
-1)
0 (0
-1)
0 (0
-1)
0 (0
)
Prog
ress
ion
3 (1
5%)
3 (1
5%)
2 (1
0%)
0 (0
%)
RSS
scal
e, m
L
Bas
elin
e 0.
2 (0
-9.8
) 0.
4 (0
-6.4
) 0.
7 (0
-9.9
) 0.
6 (0
-7.0
)
Cha
nge
0.2
(0-1
.4)
0.2
(-0.
1-3.
8)
0 (-
0.2-
2.2)
0.
6 (-
0.1-
3.4)
Prog
ress
ion
2 (1
0%)
5 (2
5%)
1 (5
%)
6 (3
0%)
Sche
ltens
scal
e, 0
to 2
4
Bas
elin
e 5.
5 (0
-23)
6
(0-2
0)
4 (0
-23)
7
(1-2
1)
Cha
nge
1 (0
-3)
1.5
(-2-
4)
0 (-
2-6)
2
(-1-
12)
Prog
ress
ion
11 (5
5%)
11 (5
5%)
5 (2
5%)
18 (9
0%)
WM
L ch
ange
scal
e, -4
to 4
Cha
nge
1 (0
-3)
2 (-
1-4)
0
(-1-
3)
1 (0
-3)
Prog
ress
ion
16 (8
0%)
17 (8
5%)
9 (4
5%)
17 (8
5%)
Val
ues a
re m
edia
ns (r
ange
) or a
bsol
ute
num
bers
(per
cent
ages
). Pr
ogre
ssio
n on
the
visu
al ra
ting
scal
es, i
nclu
ding
the
WM
L ch
ange
scal
e, w
as d
efin
ed a
s a p
ositi
ve
chan
ge o
f 1 p
oint
or m
ore
on th
e sc
ale.
WM
L= w
hite
mat
ter l
esio
n; R
SS=R
otte
rdam
Sca
n St
udy.
A
B
C
D
E
F
G
H
Fi
gure
2. S
catte
r plo
ts sh
owin
g th
e re
latio
n be
twee
n ch
ange
in w
hite
mat
ter l
esio
ns (W
ML)
mea
sure
d w
ith th
e vo
lum
etric
m
etho
d (x
-axi
s) a
nd th
e di
ffer
ent v
isua
l rat
ing
scal
es (y
-axi
s) in
the
periv
entri
cula
r reg
ion:
(A) F
azek
as sc
ale,
(B) R
otte
rdam
Sc
an S
tudy
(RSS
) sca
le, (
C) S
chel
tens
scal
e, (D
) WM
L ch
ange
scal
e; a
nd in
the
subc
ortic
al re
gion
: (E)
Faz
ekas
scal
e, (F
) RSS
scal
e, (G
) Sch
elte
ns sc
ale,
(H) W
ML
chan
ge sc
ale.
Chapter 3
76
Table 2. Correlation between volumetrics and visual rating of WML change assessed by the nonparametric Spearman rho test
Spearman rho
WML Rater 1 Rater 2 Rater 3 Rater 4 Mean four
raters
Periventricular
Fazekas scale 0.29 0.30 NA NA 0.37
Rotterdam Scan Study scale 0.42 0.24 NA 0.13 0.27
Scheltens scale 0.29 0.40 0.18 0.11 0.39
WML change scale 0.55* 0.70† 0.38 0.13 0.62†
Subcortical
Fazekas scale 0.28 0.13 0.43 NA 0.40
Rotterdam Scan Study scale 0.62† 0.59† 0.19 0.50* 0.54*
Scheltens scale 0.12 0.28 0.25 -0.18 0.27
WML change scale 0.65† 0.61† 0.60† 0.35 0.79† * p < 0.05, † p < 0.01 WML = white matter lesion; NA = not assessed because none of the participants showed change on the visual scale.
shows an example of WML progression in the periventricular and subcortical region. WML
volumes at baseline were positively correlated with WML change (Pearson correlation
coefficient 0.70, p = 0.001 for periventricular WML; 0.90, p < 0.001 for subcortical WML).
Table 1 gives the WML severity at baseline and WML change as well as the number of
participants with WML progression, as assessed with the different visual rating scales for the
four raters. Several methods showed on average an increase in WML in both the periventricular
and subcortical region, but the number of participants showing progression varied largely
between the different methods applied.
Correlation between volumetric assessment and visual rating scales
We evaluated the concordance between the volumetric WML change and the change
assessed with the visual rating scales. This was done separately for the four raters, and after
averaging the visual rating of the four raters in order to reduce noise due to interobserver
disagreement. Figure 2 shows the scatter plots of the relationship between WML change
measured with the volumetric assessment and the visual rating scales (average of four raters) in
the periventricular and subcortical region. Visual inspection of the scatter plots shows
comparatively good agreement between the WML change scale and the volumetric method,
Characteristics of WMH on imaging
77
although the WML change scale tends to overestimate lesion change in the subcortical region
(figure 2D), and may systematically underestimate lesion change in the periventricular region as
volume change gets larger (figure 2H). Table 2 gives the nonparametric Spearman’s rho between
the volumetric method and the four visual rating scales on WML change. Only the subcortical
part of the RSS scale and the WML change scale showed significant correlation with the
volumetric method for rater 1, 2, and 3, and for the average of the four raters (see table 2).
Interobserver agreement
The intraclass correlation coefficients for the interobserver agreement on baseline
WML severity and change for the visual rating scales including our new WML change scale are
presented in table 3. Values <0.20 were considered to reflect poor agreement, 0.21 to 0.40 fair
agreement, 0.41 to 0.60 moderate agreement, 0.61 to 0.80 good agreement, and 0.81 to 1.00 very
good agreement.18 The interobserver agreement for the baseline ratings on the Fazekas,
Scheltens, and RSS scales was fair to good for the periventricular region and good to very good
for the subcortical region (see table 3). However, agreement on change was poor for the Fazekas
and Scheltens scale, fair for the WML change scale and the periventricular part of the RSS scale,
and moderate for the subcortical part of the RSS scale. The raters had been using the existing
rating scales by Fazekas, Scheltens, and the RSS previously and thus were better acquainted with
them. In a post hoc study that was performed using the new WML change scale after additional
training, two of the raters rated 200 additional pairs of scans. In this second sample, the
interobserver agreement was 0.73 for the periventricular region, and 0.72 for the subcortical
region, indicating good agreement.
Effect of blinding to the scan date
Mean difference in score on the WML change scale between the not-blinded and
blinded method were +0.075 (SD 0.40) points in the periventricular region, and -0.025 (SD 0.44)
in the subcortical region. This indicates there is no substantial bias toward higher progression
when images are scored with knowledge of which are the baseline and which are the follow-up
images. The 95% limits of agreement between the not-blinded and the blinded method were (-
0.71 to +0.86) for the periventricular region, and (-0.84 to +0.89) for the subcortical region,
which suggests that for an individual the not-blinded and blinded method are unlikely to disagree
more than one point on the WML change scale.
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Table 3. Interobserver agreement for the visual rating scales for baseline and change measurements
Baseline Change
Periventricular WML
Fazekas scale 0.37 0.08
Rotterdam Scan Study scale 0.64 0.23
Scheltens scale 0.56 0.18
WML change scale 0.39
Subcortical WML
Fazekas scale 0.84 0.18
Rotterdam Scan Study scale 0.90 0.47
Scheltens scale 0.84 0.003
WML change scale 0.24 Numbers are intraclass correlation coefficients. WML = white matter lesions.
Discussion
We evaluated three commonly used visual rating scales, and one new simple visual rating scale
in terms of their ability to measure change in WML severity on MRI. We assessed the
concordance of the visual assessments with volumetric change, and quantified the reproducibility
of the scales for measuring WML change. In a stratified sample from a defined population,
during a 3-year time period, both the volumetric method and the visual rating scales showed, on
average, an increase in WML. We found significant correlations with volumetric change for the
subcortical part of the RSS scale and for the new WML change scale. The interobserver
agreement was moderate for change on the subcortical part of the RSS scale, and fair for the
WML change scale. In a post hoc study, the interobserver agreement for the WML change scale
improved to good agreement after observers had become familiarized with the scale. The
Fazekas, Scheltens, and periventricular part of the RSS scale showed poor correlation with
volumetric change, and poor to fair interobserver agreement on the change measurements.
Several methodologic issues need to be addressed. First, there is currently no gold standard for
the assessment of WML change, and our volumetric method cannot be interpreted as such.
Characteristics of WMH on imaging
79
Second, the comparison between volumetric WML change and WML change measured with
different visual scales is complicated by differences in type of data (continuous versus
categorical) obtained with the different methods. We used rank correlation to evaluate the
relationship between the visual scales and volumetrics. Unlike agreement, correlation is not
affected by the scale of measurement, but does depend on the range of the quantity of the
sample.19 Therefore, the presented correlations cannot be interpreted as agreement between the
visual scales and volumetrics, although they do allow for comparison between the visual scales.
Third, visual rating was performed side by side, and with knowledge of the time sequence of
scans, which may have led to some bias toward a higher progression rating. However, we
evaluated this possible bias in a post hoc analysis, and found that this effect was very small.
Fourth, registration of the follow-up scans on the baseline scans may have caused blurring effect,
and although this effect was judged to be small, it may have contributed to higher progression
rating with the visual rating scales. The Fazekas, Scheltens, and RSS scales were designed for
cross-sectional assessments of WML. When applied in a cross-sectional fashion these scales
show both good intra- and interobserver agreement, which largely corresponds to our findings of
fair to very good interobserver agreement for the baseline assessments.1,15,20 A previous study
reported that visual rating with the Fazekas and Scheltens scales shows significant correlation
with quantitative volumetric assessments.20 However, visual assessment of WML change with
these scales is problematic. There are several explanations for the disappointing performance of
these scales in measuring WML change. As shown in our and other data sets, baseline WML
severity is positively correlated with WML change.11 WML that were already rated in the highest
category at baseline (and which are most likely to progress) cannot contribute to progression on
these scales due to a ceiling effect. Furthermore, new lesions may develop or lesions may grow
without crossing the limits of the categories of the scales, and thereby remain below detection on
the visual scales. The subcortical part of the RSS scale performed better in capturing change,
most likely because it incorporates the number and size of lesions in more detail, thus avoiding a
ceiling effect. However, the subcortical part of this scale is elaborate and time consuming. Our
new WML change scale that was designed to measure WML change not only seemed to be valid,
but also takes less time to apply. Although agreement on progression initially was fair, it
approved to good agreement after additional training. We found that during a 3-year period
WML volume increased at a mean rate of 0.42 mL per year in the periventricular region and 0.15
mL per year in the subcortical region. These figures on rate of change do not directly reflect the
rate of change in the population at large because of the way we constructed our sample for this
validation study. The NHLBI Twin Study reported a mean WML volume increase of 0.38 mL
per year in 168 individual male twins with a mean age of 72 years,21 which is comparable to the
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range of our findings on rate of change. When we compare the interobserver agreement on the
visual scales for the baseline assessments in the present study to those reported in the literature,
interobserver agreement was comparable for periventricular WML on the Fazekas scale (0.37
versus 0.35 to 0.74) and for subcortical WML on the RSS scale (0.90 versus 0.88), higher for
subcortical WML on the Fazekas scale (0.84 versus 0.34 to 0.78) and Scheltens scale (0.84
versus 0.69), but lower for periventricular WML on the Scheltens scale (0.56 versus 0.71) and
RSS scale (0.64 versus 0.79 to 0.90).1,15,20
Acknowledgment
The authors thank Dirk Knol for statistical advice.
Characteristics of WMH on imaging
81
References
1. de Leeuw FE, de Groot JC, Achten E et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001;70:9-14.
2. Longstreth WT, Jr., Manolio TA, Arnold A et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274-82.
3. Pantoni L, Garcia JH. Pathogenesis of leukoaraiosis: a review. Stroke 1997;28:652-9. 4. Barber R, Scheltens P, Gholkar A et al. White matter lesions on magnetic resonance
imaging in dementia with Lewy bodies, Alzheimer's disease, vascular dementia, and normal aging. J Neurol Neurosurg Psychiatry 1999;67:66-72.
5. Leys D, Englund E, del Ser T et al. White matter changes in stroke patients. Relationship with stroke subtype and outcome. Eur Neurol 1999;42:67-75.
6. O'Brien J, Desmond P, Ames D et al. A magnetic resonance imaging study of white matter lesions in depression and Alzheimer's disease. Br J Psychiatry 1996;168:477-85.
7. de Groot JC, de Leeuw FE, Oudkerk M et al. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol 2000;47:145-51.
8. DeCarli C, Murphy DG, Tranh M et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology 1995;45:2077-84.
9. Steffens DC, Krishnan KR, Crump C et al. Cerebrovascular disease and evolution of depressive symptoms in the cardiovascular health study. Stroke 2002;33:1636-44.
10. Awad IA, Johnson PC, Spetzler RF et al. Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. II. Postmortem pathological correlations. Stroke 1986;17:1090-7.
11. Schmidt R, Fazekas F, Kapeller P et al. MRI white matter hyperintensities: three-year follow-up of the Austrian Stroke Prevention Study. Neurology 1999;53:132-9.
12. Veldink JH, Scheltens P, Jonker C et al. Progression of cerebral white matter hyperintensities on MRI is related to diastolic blood pressure. Neurology 1998;51:319-20.
13. Wahlund LO, Almkvist O, Basun H et al. MRI in successful aging, a 5-year follow-up study from the eighth to ninth decade of life. Magn Reson Imaging 1996;14:601-8.
14. Fazekas F, Chawluk JB, Alavi A et al. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-6.
15. Scheltens P, Barkhof F, Leys D et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993;114:7-12.
16. Maes F, Collignon A, Vandermeulen D et al. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 1997;16:187-98.
17. van Walderveen MA, Barkhof F, Hommes OR et al. Correlating MRI and clinical disease activity in multiple sclerosis: relevance of hypointense lesions on short-TR/short-TE (T1-weighted) spin-echo images. Neurology 1995;45:1684-90.
18. Altman DG. Practical Statistics for Medical Research. Boca Raton: Chapman & Hall, 1999.
19. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.
20. Kapeller P, Barber R, Vermeulen RJ et al. Visual rating of age-related white matter changes on magnetic resonance imaging: scale comparison, interrater agreement, and correlations with quantitative measurements. Stroke 2003;34:441-5.
21. DeCarli C, Haxby JV, Gillette JA et al. Longitudinal changes in lateral ventricular volume in patients with dementia of the Alzheimer type. Neurology 1992;42:2029-36.
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Clinical impact of cerebral vascular lesions
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Chapter 4
Clinical impact of cerebral vascular lesions
Chapter 4
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Clinical impact of cerebral vascular lesions
85
Chapter 4.1
Vascular Lesions increase likelihood of progressing from Mild Cognitive Impairment to Dementia
E.C.W. van Straaten D. Harvey P. Scheltens F. Barkhof R.C. Petersen L.J. Thal C.R. Jack Jr. C. DeCarli for members of the Alzheimer’s Disease Cooperative Study Group* *Members of the Alzheimer’s Disease Cooperative Study who participated in this MRI study are presented in the appendix at the end of the chapter
Submitted
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Abstract
White matter hyperintensities (WMH) have an effect on cognition and are increased in severity
among individuals with amnestic mild cognitive impairment (aMCI). The influence of WMH on
progression of aMCI to Alzheimer’s disease (AD) is less clear. Data were drawn from a three-
year prospective, double blind, placebo controlled clinical trial that examined the effect of
donepezil or vitamin E on progression from aMCI to AD. WMH from multiple white matter
regions were scored on MR images obtained at entry into the trial from a subset of 152 study
participants using a standardized visual rating scale. Cox proportional hazards models adjusting
for age, education and treatment arm were used to investigate the role of WMH on time to
progression from aMCI to AD. 55 of the 152 (36.2%) aMCI subjects converted to AD. Only
periventricular hyperintensities (PVH) were related to an increased risk of AD within three years
(HR=1.59, 95% CI = 1.24 – 2.05, p-value<0.001). Correcting for medial temporal lobe atrophy
or the presence of lacunes did not change this relation. PVH are associated with an increased risk
of progression from aMCI to AD. This suggests that PVH, an MRI finding thought to represent
cerebrovascular damage, contributes to AD onset in vulnerable individuals independent of
Alzheimer pathology.
Clinical impact of cerebral vascular lesions
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Introduction
Recent data suggest that older individuals who have considerable, but circumscribed
cognitive impairment may be in a transition phase between normal aging and dementia that is
often denoted as mild cognitive impairment (MCI).1-3 MCI may be divided into multiple
subtypes.4 Individuals with the amnestic subtype of MCI (aMCI) have memory impairment as
the primary cognitive deficit and a subsequently high likelihood of progressing to clinically
probable Alzheimer’s disease (AD).5-8 While it has been suggested that most individuals with
aMCI are in the earliest stages of AD, cerebrovascular disease (CVD) is also associated with this
clinical syndrome.9-11 Amongst subjects with extensive white matter hyperintensities (WMH),
clinically relevant episodic memory impairment may result from dysfunction of working
memory and of executive control processes.12 Additional studies support this notion by showing
that WMH are associated with reduced frontal glucose metabolism.13,14 Although substantial
work has been done to study the role of the AD process on progression from aMCI to dementia,
studies examining the impact of CVD markers on progression to dementia are more limited and
have resulted in opposing results.15-17 We therefore examined the impact of WMH on a random
subgroup of 152 individuals with aMCI. We hypothesized that increased WMH would be
associated with an increased risk of progressing to AD.
Methods
Subjects
Subjects were drawn from the prospective, double-blind placebo controlled study to
test the efficacy of donepezil and vitamin E on the progression of aMCI to dementia.18 The
details of study rationale, design and subject characteristics for the parent study and the MRI sub-
study have been previously described, including description of previous qualitative estimates of
medial temporal atrophy measures that were used as part of this study.18-20 In brief, 769
participants were recruited from 69 Alzheimer’s Disease Cooperative Study (ADCS) centers in
the United States and Canada. Inclusion was based on criteria for amnestic MCI and modified to
utilize the Logical Memory II subtest of the Wechsler Memory Scale-Revised adjusted for
education.2,21 Additional requirements included a Clinical Dementia Rating (CDR) scale score of
0.5 and insufficient impairment to meet National Institute of Neurological and Communicative
Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria for AD
(NINCDS-ADRDA).22,23 The study was conducted according to Good Clinical Practice
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guidelines, the Declaration of Helsinki, and the U.S. Code of Federal Regulations title 21 Part 50
(Protection of Human Subjects) and title 21 Part 56 (Institutional Review Boards). Written
informed consent was obtained from all participants and study partners who had knowledge of
the participants’ functional activities. A data and safety monitoring board reviewed the blinded
safety data every three months during the trial. Subjects were followed-up for three years and
time to progression to dementia was recorded. AD was the clinically determined etiology for
dementia in 99% of the subjects. There was no significant treatment effect in the parent study.
A subset of 195 individuals received a research brain MRI examination at entry to the study as
part of an ancillary study.24,25 These individuals were selected based solely on their willingness
to undergo a research MRI and the availability of suitable MRI machinery at clinical sites that
participated in the parent trial. No other criteria were used to select these subjects and subjects
from 24 separate sites of the parent study were enrolled into this sub-study. Participants of the
MRI sub-study closely represented those of the parent study.24
MRI studies
The imaging protocol included a 3D T1-weighted gradient echo sequence, with 124
contiguous, 1.6 mm thick coronal slices and 2D proton density (PD) and T2-weighted spin-echo
sequences with 24 transverse slices, slice thickness 5 mm. MRI data were sent from the
participating centers to a central location at the Mayo Clinic in Rochester, Minnesota for quality
check, storage and analysis. For this study, images were stripped from identity data and
transferred to the Imaging of Dementia and Aging (IDeA) laboratory at the University of
California at Davis. Of the original 195 scans, WMH from 43 MRIs could not be read due to
image artefact or incomplete image acquisition of the T2-weighted series, leaving 152 subjects
with MRI for analysis.
MRI visual rating
One independent rater (EvS), who was blinded to all demographic and treatment
related data, applied a semi-quantitative visual rating scale for the analysis of WMH.26 Using this
scale, deep subcortical WMH were assessed on a 0 – 6 scale in different brain regions, where
score 0 reflects no WMH, and score 6 confluent lesions. The regions assessed were the frontal,
parietal, occipital and temporal lobes, basal ganglia and infratentorial regions. A total deep
WMH score (D-WMH) was composed by summing up the scores of the frontal, parietal,
occipital and temporal regions (range 0 – 36). In addition, periventricular hyperintensities (PVH)
were assessed on a scale ranging from 0 –2 scale in three regions (frontal and occipital caps and
bands). A total periventricular score was composed of the scores of these three regions (range 0 –
Clinical impact of cerebral vascular lesions
89
6). A total WMH score (T-WMH) was created by summing up the scores for D-WMH and PVH.
Intra-observer variability was good with an intraclass correlation coefficient (ICC) of 0.92. The
number of lacunes was assessed, where a lacune was defined as a T1-hypointense and T2-
hyperintense CSF-like lesion surrounded by white matter or subcortical gray matter with a
minimum diameter of 2 mm and was not located in areas with a high prevalence of widened
perivascular spaces (vertex, anterior commissure). Medial temporal lobe atrophy (MTA) was
assessed on this data set in an earlier study by one independent rater (PS) on coronal T1 images,
using a qualitative visual rating scale. 19,27 This scale ranges from 0 – 4 for both left and right
medial temporal lobe region with higher scores indicative of increased atrophy. The scores for
the left and right medial temporal lobe region were averaged and used as a general measure of
medial temporal atrophy.
Statistical analyses
The primary outcome of interest was time of progression to AD, according to the
NINCDS-ARDA criteria. Those that had not converted were considered censored at their last
assessment. Cox proportional hazards models were used to assess the association of qualitative
white matter ratings with progression to AD. Models were adjusted for age and education. In a
second step, we added MTA score, treatment arm and number of lacunes to the model to correct
for the presumed influence of the AD process, treatment effect of donepezil or vitamin E, and
vascular subcortical changes other than WMH. Kaplan-Meier curves were generated to illustrate
the findings, by comparing those in the highest 25th percentile of white matter ratings to the
remainder of the subjects. Mean differences in T-WMH, D-WMH, and PVH scores between
individuals who converted to AD and those who did not were assessed using Mann-Whitney
tests. All assumptions of the models were checked both graphically and numerically and were
met by the data.
Results
Demographics and WMH scores of the total group of 152 subjects, converters (subjects who
progressed to dementia, n = 55) and non-converters (subjects who did not progress to dementia, n
= 97) are presented in Table 1. The demographics of this MRI subgroup were similar to parent
study. The mean age was 72.4 + 6.6 years, the mean educational achievement was 15.0 + 3.0
years and women made up 55.3% of the sample. Mean baseline MMSE scores (SD) were nearly
identical between the parent study (27.3 + 1.8) and the current study (27.5 + 1.8). Randomization
by treatment arm was also well balanced in this study with 30% randomized to donepezil, 32% to
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vitamin E and 38% to placebo. A total of 55 subjects (36%) converted to dementia over the 3-
year study period. Subjects progressing to dementia were slightly older and included a higher
percentage of women, but the differences were not significant. Converters, however, performed
worse on baseline MMSE testing (26.8 + 1.9) than did non-converters (27.9 + 1.6), p < 0.001).
Mean WMH scores amongst those who converted to dementia also tended to be higher. These
differences were small and non-significant, although a trend was found for the PVH ratings
Table 1. Demographic characteristics of the study group
Total group
(n = 152)
Converters
(n = 55)
Non-converters
(n = 97)
Mean age in years, (SD) 72.5 (6.6) 73.4 (6.6) 72.0 (6.7)
Mean years of education (SD) 15 (3) 15 (3) 15 (3)
% male 54.2 48.1 57.6
MMSE (SD) 27.9 (1.8) 26.9 (1.9) 27.9 (1.7) **
Mean T-WMH score (SD) 13.4 (8.4) 13.7 (9.7) 13.1 (7.7)
Mean PVH score (SD) 3.6 (1.2) 3.9 (1.4) 3.5 (1.1)
Mean D-WMH score (SD) 7.3 (5.4) 7.4 (6.2) 7.2 (5.0)
Mean basal ganglia score (SD) 1.5 (2.2) 1.5 (2.2) 1.6 (2.3)
Mean infratentorial score (SD) 0.9 (1.7) 0.9 (1.8) 0.9 (1.7)
Number of subjects with lacunes 13 7 6
MTA score (SD) 1.2 (0.8) 1.3 (0.9) 1.1 (0.7)*
D-WMH: Deep White Matter Hyperintensities, PVH: Periventricular Hyperintensities, T-WMH: Total White
Matter Hyperintensities. *: p < 0.05, **: p < 0.001
(p= 0.057). Lacunes were seen in 13 subjects, of which seven converted. Mean T-WMH, D-
WMH and PVH scores were higher in subjects with lacunes (19.4 ml (SD 12.2), 10.6 ml (SD
7.6), and 4.5 (SD 1.4) respectively against 12.8 ml (SD 7.8), 7.0 (SD 5.1), and 3.5 (SD 1.2) in
subjects without lacunes). Mean MTA ratings were significantly higher in converters than non-
converters.
Table 2 shows the additional risk of progression to dementia with each one-point
increase on the WMH rating scale. Only PVH was significantly associated with an increased risk
of progression after correcting for age and education. A one-point increase in the rating was
Clinical impact of cerebral vascular lesions
91
associated with a 59% increased hazard of progression. This is also illustrated by figure 1, which
shows the relationship between the highest quartile of PVH (scores>4) as compared to lower
scores (scores ≤ 4) and progression to AD over time. Correcting for MTA and number of lacunes
did not change the significance of this association, even though total PVH and MTA ratings were
significantly correlated (r = 0.31).
Table 2. Hazard ratios (95% confidence intervals) of increase of 1 point WMH
HR (95% CI)
Model 1
HR (95% CI)
Model 2
HR (95% CI)
Model 3
PVH 1.59 (1.24 – 2.05)** 1.49 (1.15 – 1.93)* 1.42 (1.08-1.87)*
D-WMH 1.02 (0.97 – 1.08) 0.99 (0.94 – 1.05) 0.97 (0.92-1.03)
Basal ganglia
hyperintensities
1.06 (0.94-1.20) 1.05 (0.92-1.20) 1.01 (0.88-1.16)
Infratentorial
hyperintensities
1.08 (0.90-1.28) 1.06 (0.89-1.27) 1.01 (0.84-1.21)
T-WMH 1.03 (0.99-1.06) 1.01 (0.97-1.05) 1.00 (0.96-1.04)
*: p < 0.05, **: p < 0.001
Model 1: Age and education included in the model
Model 2: Age, education, MTA, and treatment arm included in the model
Model 3: Age, education, MTA, treatment arm, and presence of lacunes included in the model
In order to compare the relative risk for progression to dementia related to PVH and
MTA, which were rated on different scales, we also fitted the Model 2 after z-score
transformation. One standard deviation increase in the total PVH rating was associated with a
64% increased hazard (β=0.49, SE=0.17, p-value=0.003, HR=1.64, 95% CI=1.18-2.27), while
one standard deviation increase in MTA rating was associated with a 42% increased hazard
(β=0.35, SE=0.15, p=0.04, HR=1.42, 95% CI= 1.01-1.99).
Since ApoE genotype was a powerful predictor of progression from MCI to dementia in the
parent study, we also assessed the relationship between WMH, MTA, and ApoE genotype. Mean
PVH, D-WMH, T-WMH and MTA scores were significantly higher in ApoE4 allele carriers as
compared to the non-carriers (p<0.01 for the WMH scores and p = 0.046 for MTA).
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Figure 1. Relationship between high total Periventricular Hyperintensities score (scores > 4; upper
25th percentile of scores) and low total Periventricular Hyperintensities score (scores <=4) and
progression to dementia.
Discussion
Our results indicate that PVH, and not deep subcortical WMH, increase the likelihood
of progressing from amnestic MCI to AD. This is in line with previous cross-sectional studies 28.
Earlier studies showed influence of PVH on decline in cognition in non-demented elderly and
risk of dementia29,30. Moreover, the effect of PVH on progression to dementia was unchanged
after correction for atrophy of the medial temporal lobe suggesting that WMH lesions may have
effects independent of a presumed AD process and may increase the likelihood of clinically
evident AD through an additive mechanism. This adds to the growing body of evidence that
vascular factors increase lifetime risk of AD.15,31-33
Quantitative measures of WMH and medial temporal lobe atrophy differ notably
between AD and healthy controls.34 If we assume that most individuals with amnestic MCI have
Clinical impact of cerebral vascular lesions
93
at least some AD pathology, WMH would be expected to increase the likelihood of progression
to dementia as a second mechanism for brain injury similar to studies of stroke and AD.35-37
Hypertension is a known risk factor for WMH and can contribute also to cortical atrophy,
including in the medial temporal lobe. Moreover, since WMH can lead to deficits in cognitive
areas other than memory, such as executive function or attention, WMH related brain injury may
result in additional cognitive deficits that would contribute to dementia diagnostic criteria.38 The
above mentioned studies, however, did not differentiate between deep WMH and PVH, a notion
that creates confusion and needs clarification for future studies.39
In our study, subcortical WMH did not substantially add to the risk of progression to
dementia. There are at least two possible explanations for this finding. First, it may be that
qualitative estimates of PVH closely estimate total WMH volume.40 Second, it is possible that
the periventricular region includes functionally important (cholinergic) neural pathways.41,42 Of
course, the combined effect of total volume and location may be important. Finally, the
significance of the PVH finding may be a limitation to the qualitative scoring method as
neuropathological research suggests that the substrate of larger deep and periventricular lesions
(a likely co-occurrence when PVH scores are high) is similar.43 Irrespective of potential etiology,
qualitative estimates of PVH seem to reflect the cognitive effect of subcortical subtotal vascular
disease better than the deep WMH and can therefore serve as a better surrogate marker for
disease in this population of amnestic MCI subjects.
ApoE4 genotype has been found to increase the risk of developing AD, presumably by
increasing amyloid beta protein (Aβ) deposition and enhancing the vulnerability of neurons for
Aβ.44 We found an association between ApoE4 and increased MTA and WMH scores. ApoE is
also important to cholesterol metabolism and the ApoE4 genotype is associated with increased
risk for cardiovascular and possibly cerebrovascular disease.45 The association of ApoE4 with
both MTA and WMH may, therefore, reflect a role in the development of vascular lesions as well
as AD pathology and contribute to the apparent association between AD pathology, vascular
factors and dementia.
One limitation of this study could be the use of the visual scale for the assessment of
WMH. Visual scales are not always linear and the effect measured could be limited due to
ceiling effects.46 Our findings, however, showed a significant relationship of PVH with clinical
data, indicating that the PVH assessment was sensitive enough in this population. An effect of
WMH in general on progression from MCI to dementia, however, has not consistently been
found. Small subject groups and limited number of individuals, who convert to dementia during
the period of observation, may account for these discrepancies. In addition, we used the MTA
scale, a qualitative rating scale. Visual MTA scores may not reflect hippocampal atrophy as
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94
precise as volumetric measurement of this structure on MRI, although differences between visual
and volumetric scoring methods seem small with respect to clinical and cognitive
characteristics.47
Results of double-blind, placebo-controlled, clinical trials to test efficacy and safety of
drugs proven to be efficacious in AD, such as cholinesterase inhibitors and vitamin E, are being
carried out in MCI populations and the first results are becoming available.18,48,49 In animal
models, permanent oxidative stress is a major contributor to neurodegeneration, leading to the
investigation of protective effect of vitamin E as anti-oxidative agent.50 However, intervention
studies have not been able to demonstrate an effect in humans.51 Cholinesterase-inhibitors reduce
cognitive deficits in AD and VaD, but no effect has been demonstrated in prevention of AD.52
Whatever the exact mechanism by which PVH exert their influence on progression
from MCI to AD, these findings could have implications for therapeutic strategies. Effect of
medication in subjects with neurodegenerative dementia could vary depending on coexisting
vascular burden.Given the results of this study and others, prevention of vascular lesions such as
PVH through control of vascular risk factors may prove helpful in reducing the likelihood of
dementia for at risk populations.17,53,54
Clinical impact of cerebral vascular lesions
95
Appendix Members of the Alzheimer’s Disease Cooperative Study who participated in this MRI study include: John Adair, University of New Mexico, Albuquerque Geoffrey Ahern, University of Arizona, Tucson Bradley Boeve, David Knopman, Mayo Clinic, Rochester Sandra Black, Sunnybrook Health Sciences, Toronto Jeffrey Cummings, University of California, Los Angeles Sultan Darvesh, Queen Elizabeth II Health Sciences Centre, Halifax Charles DeCarli, Grisel J. Lopez, Kansas University, Kansas City Steven DeKosky, University of Pittsburgh Ranjan Duara, Wien Center, Miami Beach Charles Echols, Barrow Neurology Group, Phoenix Howard Feldman, U.B.C. Clinic for Alzheimer’s Disease, Vancouver Steven Ferris, Mony deLeon, New York University Medical Center Serge Gauthier, McGill Centre for Studies in Aging, Verdun, PQ Neill Graff-Radford, Mayo Clinic, Jacksonville, FL Danilo Guzman, E. Bruyere Memory Disorder Research, Ottawa Jeffrey Kaye, Oregon Health and Science University, Portland Alan Lerner, University Hospitals Health System, Cleveland Richard Margolin, Vanderbilt University, Nashville Marsel Mesulam, Northwestern University, Chicago Richard Mohs, Mt. Sinai School of Medicine, Bronx, NY John Olichney, University of California, San Diego Brian Ott, Memorial Hospital of Rhode Island, Pawtucket Elaine Peskind, University of Washington, Seattle Nunzio Pomara, Nathan Kline Institute for Psychiatric Research, Orangeburg Christopher van Dyck, Yale University School of Medicine, New Haven Myron Weiner, University of Texas Southwestern Medical Center, Dallas Kristine Yaffe, University of California, San Francisco
Chapter 4
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References
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2. Petersen RC, Smith GE, Waring SC et al. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999;56:303-8.
3. Price JL, Morris JC. Tangles and plaques in nondemented aging and "preclinical" Alzheimer's disease. Ann Neurol 1999;45:358-68.
4. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256:183-94.
5. Bennett DA, Wilson RS, Schneider JA et al. Natural history of mild cognitive impairment in older persons. Neurology 2002;59:198-205.
6. Ganguli M, Dodge HH, Shen C et al. Mild cognitive impairment, amnestic type: an epidemiologic study. Neurology 2004;63:115-21.
7. Morris JC, Storandt M, Miller JP et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol 2001;58:397-405.
8. Petersen RC. Mild cognitive impairment: transition between aging and Alzheimer's disease. Neurologia 2000;15:93-101.
9. DeCarli C, Miller BL, Swan GE et al. Cerebrovascular and brain morphologic correlates of mild cognitive impairment in the National Heart, Lung, and Blood Institute Twin Study. Arch Neurol 2001;58:643-7.
10. Lopez OL, Jagust WJ, Dulberg C et al. Risk factors for mild cognitive impairment in the Cardiovascular Health Study Cognition Study: part 2. Arch Neurol 2003;60:1394-9.
11. Riley KP, Snowdon DA, Markesbery WR. Alzheimer's neurofibrillary pathology and the spectrum of cognitive function: findings from the Nun Study. Ann Neurol 2002;51:567-77.
12. Nordahl CW, Ranganath C, Yonelinas AP et al. Different mechanisms of episodic memory failure in mild cognitive impairment. Neuropsychologia 2005;43:1688-97.
13. DeCarli C, Murphy DG, Tranh M et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology 1995;45:2077-84.
14. Tullberg M, Fletcher E, DeCarli C et al. White matter lesions impair frontal lobe function regardless of their location. Neurology 2004;63:246-53.
15. DeCarli C. The role of cerebrovascular disease in dementia. Neurologist 2003;9:123-36.
16. DeCarli C, Mungas D, Harvey D et al. Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology 2004;63:220-7.
17. Wolf H, Ecke GM, Bettin S et al. Do white matter changes contribute to the subsequent development of dementia in patients with mild cognitive impairment? A longitudinal study. Int J Geriatr Psychiatry 2000;15:803-12.
18. Petersen RC, Thomas RG, Grundman M et al. Vitamin E and donepezil for the treatment of mild cognitive impairment. N Engl J Med 2005;352:2379-88.
19. DeCarli C, Frisoni GB, Clark CM et al. Qualitative estimates of medial temporal atrophy as a predictor of progression from mild cognitive impairment to dementia. Arch Neurol 2007;64:108-15.
20. Grundman M, Petersen RC, Ferris SH et al. Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol 2004;61:59-66.
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21. Wechsler D. WMS-R Wechsler Memory Scale - Revised Manual. 1987. New York, Harcourt Brace Jovanovich Inc.
22. Hughes CP, Berg L, Danziger WL et al. A new clinical scale for the staging of dementia. Br J Psychiatry 1982;140:566-72.
23. McKhann G, Drachman D, Folstein M et al. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34:939-44.
24. Grundman M, Sencakova D, Jack CR, Jr. et al. Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial. J Mol Neurosci 2002;19:23-7.
25. Grundman M, Jack CR, Jr., Petersen RC et al. Hippocampal volume is associated with memory but not monmemory cognitive performance in patients with mild cognitive impairment. J Mol Neurosci 2003;20:241-8.
26. Scheltens P, Barkhof F, Leys D et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993;114:7-12.
27. Scheltens P, Leys D, Barkhof F et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992;55:967-72.
28. Burns JM, Church JA, Johnson DK et al. White matter lesions are prevalent but differentially related with cognition in aging and early Alzheimer disease. Arch Neurol 2005;62:1870-6.
29. de Groot JC, de Leeuw FE, Oudkerk M et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol 2002;52:335-41.
30. Prins ND, van Dijk EJ, den Heijer T et al. Cerebral white matter lesions and the risk of dementia. Arch Neurol 2004;61:1531-4.
31. de la Torre JC. Vascular basis of Alzheimer's pathogenesis. Ann N Y Acad Sci 2002;977:196-215.
32. Launer LJ. Demonstrating the case that AD is a vascular disease: epidemiologic evidence. Ageing Res Rev 2002;1:61-77.
33. Pansari K, Gupta A, Thomas P. Alzheimer's disease and vascular factors: facts and theories. Int J Clin Pract 2002;56:197-203.
34. van der Flier WM, Middelkoop HA, Weverling-Rijnsburger AW et al. Interaction of medial temporal lobe atrophy and white matter hyperintensities in AD. Neurology 2004;62:1862-4.
35. Schneider JA, Wilson RS, Bienias JL et al. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology 2004;62:1148-55.
36. Snowdon DA, Greiner LH, Mortimer JA et al. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. JAMA 1997;277:813-7.
37. Vermeer SE, Prins ND, den Heijer T et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med 2003;348:1215-22.
38. American Psychiatry Association. Diagnostic and statistical manual of mental disorders, 4th ed. 1994. Washington, American Psychiatry Association.
39. Barkhof F, Scheltens P. Is the whole brain periventricular? J Neurol Neurosurg Psychiatry 2006;77:143-4.
40. DeCarli C, Fletcher E, Ramey V et al. Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke 2005;36:50-5.
41. Desmond DW. The neuropsychology of vascular cognitive impairment: is there a specific cognitive deficit? J Neurol Sci 2004;226:3-7.
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42. Selden NR, Gitelman DR, Salamon-Murayama N et al. Trajectories of cholinergic pathways within the cerebral hemispheres of the human brain. Brain 1998;121 ( Pt 12):2249-57.
43. Fazekas F, Kleinert R, Offenbacher H et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43:1683-9.
44. Olichney JM, Hansen LA, Lee JH et al. Relationship between severe amyloid angiopathy, apolipoprotein E genotype, and vascular lesions in Alzheimer's disease. Ann N Y Acad Sci 2000;903:138-43.
45. DeCarli C, Reed T, Miller BL et al. Impact of apolipoprotein E epsilon4 and vascular disease on brain morphology in men from the NHLBI twin study. Stroke 1999;30:1548-53.
46. van Straaten EC, Fazekas F, Rostrup E et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006;37:836-40.
47. Visser PJ, Verhey FR, Hofman PA et al. Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry 2002;72:491-7.
48. Salloway S, Ferris S, Kluger A et al. Efficacy of donepezil in mild cognitive impairment: a randomized placebo-controlled trial. Neurology 2004;63:651-7.
49. Thal LJ, Ferris SH, Kirby L et al. A randomized, double-blind, study of rofecoxib in patients with mild cognitive impairment. Neuropsychopharmacology 2005;30:1204-15.
50. Ancelin ML, Christen Y, Ritchie K. Is antioxidant therapy a viable alternative for mild cognitive impairment? Examination of the evidence. Dement Geriatr Cogn Disord 2007;24:1-19.
51. Hull M, Berger M, Heneka M. Disease-modifying therapies in Alzheimer's disease: how far have we come? Drugs 2006;66:2075-93.
52. Burns A, O'Brien J, Auriacombe S et al. Clinical practice with anti-dementia drugs: a consensus statement from British Association for Psychopharmacology. J Psychopharmacol 2006;20:732-55.
53. Dufouil C, Chalmers J, Coskun O et al. Effects of blood pressure lowering on cerebral white matter hyperintensities in patients with stroke: the PROGRESS (Perindopril Protection Against Recurrent Stroke Study) Magnetic Resonance Imaging Substudy. Circulation 2005;112:1644-50.
54. Hanon O, Forette F. Prevention of dementia: lessons from SYST-EUR and PROGRESS. J Neurol Sci 2004;226:71-4.
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Chapter 4.2
Risk factor profiles for different radiological expressions of cerebrovascular disease; Findings from the LADIS study
E.C.W. van Straaten
W.M. van der Flier
P. Scheltens
F. Fazekas
R. Schmidt
L. Pantoni
D. Inzitari
G. Waldemar
T. Erkinjuntti
M. Crisby
F. Barkhof on behalf of the LADIS study group
Submitted
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100
Abstract
Ischemic cerebrovascular disease (CVD) is expressed on MRI as white matter hyperintensities
(WMH), lacunes and large vessel infarcts. This study was conducted to investigate specific
profiles of vascular risk factors associated with each of these CVD types in independently living
elderly subjects.
Baseline data of 618 subjects from the multicenter prospective Leukoaraiosis And DISability
(LADIS) study were used. Mean WMH volume was 21.2 ml (SD 22.6), 48 % of study subjects
had one or more lacunes, 9 % had one or more clinically silent or minor large vessel infarct. We
assessed associations between vascular risk factors and CVD with uni- and multivariate linear
regression (WMH) and logistic regression (lacunes, large vessel infarcts) using the following risk
factors: age, gender, smoking, history of high cholesterol level, diabetes, peripheral vascular
disease, hypertension, and heart disease (defined as heart failure, atrial fibrillation, cardiac valve
disease, and/or myocardial infarction).
The CVD types were only slightly correlated with each other (0.08 < r <0.23, p < 0.01). Male
gender was associated with all CVD types, older age and hypertension with WMH volume.
Diabetes was associated with large vessel infarcts.
Specific vascular risk profiles can be identified for large and small vessel CVD. These data may
be used to focus research in future preventive strategies.
Clinical impact of cerebral vascular lesions
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Introduction
Ischemic cerebrovascular disease (CVD) is expressed on magnetic resonance imaging
(MRI) as white matter hyperintensities (WMH), lacunes and large vessel infarcts.1 WMH and
lacunes are thought to reflect small vessel disease, while large vessel infarcts are due to
obstruction of large cerebral or carotid arteries, thromboembolic or by local arteriopathy.2,3
WMH seem indicative of incomplete infarction or ischemia, whereas lacunes and large vessel
infarcts are caused by complete infarction.4
CVD may give rise to a variety of clinical symptoms and signs, including cognitive
decline and dementia.5-7 The cognitive profile of patients with WMH and lacunes is typically
subcortical with impaired executive function and reduced mental speed, whereas large vessel
infarcts can lead to cortical deficits, including aphasia, alexia, agnosia and memory loss.8-12
Symptoms of lacunes and large vessel infarcts most often present acutely, whereas deficits due to
WMH are of more gradual onset.
Risk factors for individual types of CVD have been studied before. Risk factors of
WMH that have been repeatedly reported are age and hypertension.13-17 Results with respect to
other risk factors, including smoking, hypercholesterolemia, diabetes mellitus, and cardiac
disease have been inconsistent. Hypertension and diabetes mellitus are among the most
consistently found risk factors for lacunes.18 Older age, smoking, and carotid and cardiac disease
have also been reported.19,20 Finally, large vessel infarcts have been found to be associated with
hypertension, heart disease, atrial fibrillation, diabetes mellitus, smoking and hyperlipidemia.21
It is conceivable that each of these expressions of CVD is associated with a specific
profile of vascular risk factors. Some vascular risk factors may be associated with several
expressions of CVD, whereas others are more specific. However, former studies remain
inconclusive, as no study investigated the associations between vascular risk factors and the three
CVD types at the same time in one sample. In the current study we assessed the associations
between vascular risk factors and WMH, lacunes and clinically minor or silent large vessel
infarcts, respectively, in a large sample of independently living elderly.
Materials and Methods
Subjects
Baseline clinical and radiological data of the Leukoaraiosis And DISability study
(LADIS) were used. This prospective multicenter multinational project aims to assess the role of
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WMH on the transition to disability in every day life of elderly subjects who were functioning
independently at time of enrollment and who had different degrees of WMH. Details on purpose
and methods of this study were reported previously.22 Since subjects were independent in
instrumental activities of daily living, the cerebral vascular lesions studied here were not largely
disabling. At baseline, 639 participants were included and underwent extensive assessment
including standardized questionnaires on vascular risk factors, neuropsychological tests and
cerebral MR imaging.
Vascular risk factors
In the present study we examined the following risk factors: age (dichotomized in 65 –
74 and 75 – 85 years), gender, smoking (defined as positive in case of current or past smoking),
history or presence of high blood cholesterol (total cholesterol >200, LDL >130, HDL <35
mg/dL, and/or hypertriglyceridemia based on serum triglyceride >200 mg/dL, on at least two
occasions)23, diabetes mellitus (treatment with antidiabetic medications, or at least 8-hour fasting
plasma glucose ≥ 7.0 mmol/L or 126 mg/dL)24, peripheral vascular disease (symptomatic disease
presenting as intermittent claudication and/or critical leg ischemia)25, hypertension (subjects
receiving antihypertensive treatment or with values ≥140/90 mmHg, based on measurements
taken on several separate occasions)26, heart disease, defined as presence of at least one of the
following: symptoms of heart failure, objective evidence of cardiac dysfunction and response to
treatment directed toward heart failure27, atrial fibrillation (based on history and/or available
clinical records as ECG characterized by the presence of rapid, irregular, fibrillatory waves that
vary in size, shape and timing, usually associated with an irregular ventricular response)28,
cardiac valve disease (presence of one of more of the following: aortic stenosis, chronic aortic
regurgitation, mitral stenosis, mitral regurgitation, multiple valve disease, prosthetic heart
valves), or myocardial infarction (documented by history, ECG or cardiac enzymes)29. Data on
high cholesterol were missing in 20 subjects, on smoking in 1, on diabetes in 4, on peripheral
vascular disease in 6, on hypertension in 2, and on heart disease in 6.
MRI
MRI studies consisted of a sagittal or coronal T1-weighted magnetization prepared
rapid acquisition gradient echo (MPRAGE) sequence with 1-1.5 mm slices and axial T2 and
Fluid Attenuated Inversion Recovery (FLAIR) images of 5 mm thickness. WMH were measured
semi-automatically by a single rater (ECWvS) on the FLAIR images using a home-developed
seed-growing program (Show_Images, version 3.6.1) on a Sparc 5 workstation (SUN, Palo Alto,
Clinical impact of cerebral vascular lesions
103
Calif.).30 Briefly, lesions were marked manually and automatically outlined based on the locally
set threshold for upper and lower intensity values, rendering a WMH volume. WMH volume
measurements were not available for 21 subjects due to insufficient scan quality. Total number of
lacunes was assessed on the MPRAGE, a lacune being defined as a hypointense focus of at least
3 mm in the largest diameter, surrounded by white matter or subcortical gray matter, and not
located in areas with a high prevalence of widened perivascular spaces (vertex, anterior
commissure). Large vessel infarct was defined as a parenchymal defect in an arterial territory
involving the cortical gray matter.31
Statistical analyses
Associations among different types of CVD were assessed using Spearman’s rank
correlation analysis. Univariate and multivariate linear (WMH) and logistic (lacunes, large vessel
infarcts) regression analyses were performed with dichotomized vascular risk factors as
independent variables and WMH, lacunes and large vessel infarcts as the dependent variables.
WMH volumes (ml) were used and the number of lacunes and large vessel infarcts was
dichotomized in zero, or at least one. First, we performed univariate models for each risk factor
separately (Model 1). Second, all risk factors were entered simultaneously (Model 2), Finally, the
two other CVD types were entered as additional covariates in the multivariate model (Model 3).
Results
In the present study, 618 subjects were analysed, of which 278 (45%) males and 270
(44%) in the older age group. Mean WMH volume was 21.2 ml (SD 22.6, range 0.7 – 156.1). 48
% of study subjects had one or more lacunes, 9 % had one or more large vessel infarct. Location
of the infarcts was frontal in 12 subjects, parietal in 21, occipital in 16, temporal in 11,
infratentorial in 13, and in the basal ganglia in 4. The three CVD types were only slightly
correlated (0.08 < r <0.23, p < 0.01). The prevalence of risk factors in the study sample is shown
in figure 1. Hypertension was very common with a frequency of 70%. In addition, there was a
relatively large group of subjects with a positive history of smoking and hypercholesterolemia.
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104
Figure 1. Number of subjects in each risk factor category.
PVD: peripheral vascular disease
Univariate linear regression analyses showed that WMH volume was associated with older age,
male gender, and smoking (table 1, model 1). In the multivariate analysis, older age, male gender
and hypertension were significant predictors (model 2). When lacunes and large vessel infarcts
were additionally corrected for, age and gender remained statistically significant (model 3).
Univariate logistic regression analyses showed positive associations for older age and male
gender with the presence of lacunes (table 2, model 1). Male gender was a significant predictor in
the multivariate analyses (model 2 and 3) and together with older age when corrected for other
CVD types (model 3).
The presence of large vessel infarct was related to several risk factors in univariate
logistic regression analyses (table 3, model 1). In the multivariate analyses, the associations with
male gender and diabetes remained significant (model 2 and 3).
Clinical impact of cerebral vascular lesions
105
Table 1. Associations between vascular risk factors and WMH
risk factor Mean WMH
volume (SD)
Model 1 Model 2 Model 3
Age (years) 65 – 74 18.0 (19.7)
75 – 85 25.2 (25.5) 7.2 (1.8)** 7.8 (1.8)** 7.9 (1.8)**
Gender female 18.1 (18.2)
male 25.0 (26.6) 6.9 (1.8)** 6.6 (1.9)** 4.4 (1.9)*
Smoking no 19.5 (21.4)
yes 23.2 (24.0) 3.7 (1.8)* 1.9 (1.9) 2.1 (1.8)
Hyper- no 22.0 (24.9)
cholesterolemia yes 20.3 (20.5) -1.7 (1.9) -0.8 (1.8) -1.6 (1.8)
Diabetes no 20.8 (22.7)
yes 22.7 (22.5) 1.9 (2.6) 1.5 (2.6) -0.1 (2.6)
Peripheral no 20.8 (22.5)
vascular disease yes 20.7 (17.6) -0.1 (3.5) -2.2 (3.6) -3.0 (3.5)
Hypertension no 18.7 (22.5)
yes 22.1 (22.6) 3.4 (2.0) 4.1 (2.0)* 3.1 (2.0)
Heart disease no 20.4 (22.6)
yes 24.1 (23.0) 3.7 (2.2) 1.5 (2.2) 0.7 (2.2)
Data are presented as β (se). Model 1 represents univariate linear regression analyses. Model 2 represents
multivariate analysis with mentioned risk factors only. Model 3 represents multivariate linear regression
analysis including mentioned risk factors, lacunes and infarcts included in the model. *: p ≤ 0.05, **: p ≤
0.001
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106
Table 2. Associations between vascular risk factors and lacunes
risk factors % lacunes
Model 1 Model 2 Model 3
Age (years) 65 – 74 51 0.7 0.8 0.7
75 – 85 43 (0.5 – 1.0)* (0.5 – 1.1) (0.5 – 0.9)*
Gender female 40 2.0 1.8 1.6
male 57 (1.4 – 2.7)** (1.3 – 2.6)** (1.1 – 2.4)**
Smoking no 45 1.3 1.0 1.0
yes 51 (0.9 – 1.8) (0.7 – 1.5) (0.7 – 1.4)
Hyper- no 45 1.2 1.2 1.2
cholesterolemia yes 50 (0.9 – 1.7) (0.9 – 1.7) (0.8 – 1.8)
Diabetes no 47 1.3 1.1 1.0
yes 53 (0.8 – 2.0) (0.7 – 1.8) (0.6 – 1.7)
Peripheral no 47 1.7 1.4 1.4
vascular disease yes 61 (0.9 – 3.3) (0.7 – 2.8) (0.7 – 2.8)
Hypertension no 42 1.4 1.4 1.3
yes 50 (1.0 – 1.9) ( 0.9 – 2.0) (0.9 – 1.9)
Heart disease no 46 1.3 1.1 1.0
yes 63 (0.9 – 1.9) (0.7 – 1.6) (0.7 – 1.6)
Data for models 1 – 3 are presented as OR (95% CI). Model 1 respresents univariate logistic regression.
Model 2 represents multivariate logistic regression with mentioned risk factors only. Model 3 represents
multivariate logistic regression with mentioned risk factors, WMH, and infarcts.
*: p ≤ 0.05 , **: p ≤ 0.001
Clinical impact of cerebral vascular lesions
107
Table 3. Associations between vascular risk factors and large vessel infarcts
risk factors % infarcts
Model 1 Model 2 Model 3
Age (years) 65 – 75 8 1.3 1.7 1.5 75 – 85 11 (0.8 – 2.3) (0.9 – 3.1) (0.8 – 2.7)
Gender female 5 3.3 3.5 2.9 male 15 (1.8 – 6.0)** (1.8 – 6.7)** (1.5 – 5.7)**
Smoking no 9 1.1 0.8 0.7 yes 10 (0.6 – 1.8) (0.4 – 1.4) (0.4 – 1.3)
Hyper- no 8 1.5 1.6 1.7 cholesterolemia yes 11 (0.8 – 2.5) (0.9 – 1.9) (0.9 – 3.2)
Diabetes no 7 3.5 2.8 2.8 yes 21 (1.6 – 6.4)** (2.5 – 5.5)** (1.4 – 5.4)**
Peripheral no 9 1.7 1.4 1.4 vascular disease yes 14 (0.7 – 4.2) (0.5 – 3.8) (0.5 – 4.0)
Hypertension no 5 2.2 1.7 1.6 yes 11 (1.1 – 4.4)* (0.8 – 3.7) (0.7 – 3.4)
Heart disease no 8 2.2 1.7 1.8 yes 16 (1.2 – 3.9)** (0.9 – 3.2) (1.0 – 3.5)
The presence of infarcts is presented as percentage. Data for models 1 – 3 are presented as OR (95% CI).
Model 1 respresents univariate logistic regression. Model 2 represents multivariate logistic regression with
mentioned risk factors only. Model 3 represents multivariate logistic regression with mentioned risk factors,
WMH, and lacunes. *: p ≤ 0.05, **: p ≤ 0.001
Discussion
In the current study, risk factors for different types of CVD were sought.
Concommitant cerebrovascular disease was into account in the analyses and results were
comparable with those of previous population-based studies in which risk factors for WMH,
lacunes and large vessel infarcts were studied separately with respect to the fact that age and
hypertension were associated with WMH volume and diabetes mellitus with large vessel
infarcts14,16,20,32-35. We found older age, male gender, and hypertension to be independent risk
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factors for WMH, male gender for lacunes, and male gender and diabetes mellitus for clinically
minor or silent large vessel infarcts. In addition, our data suggest that male gender is a general
risk factor for CVD, which is in congruence with former studies on risk factors for
cardiovascular disease as well as cerebrovascular disease.36,37 Gender differences could be partly
due to differences in prevalence of risk factors. However, in the multivariate analyses we
corrected for the prevalence of other risk factors. Alternatively, men may be more susceptible to
the effect of vascular risk factors.38 Finally, a cardioprotective role of estrogens until menopause
and a later onset of vascular disease in women could also partly explain the differences between
males and female.39,40
The design of this study has the advantage that a large number of subjects were
included, examined and scanned in an identical fashion, information on risk factors was collected
uniformly and contributions of the risk factors could be compared directly. One of the limitations
of the study may be the subject selection. The stratification by WMH may prevent
generalizability of the results to a general population of elderly. However, the LADIS sample
likely suits with the patient population with WMH encountered in clinical practice because of the
broad range of reasons for referral. In addition, subjects who were not independently living could
not be included. Cerebrovascular disease leading to major deficits was therefore not present in
the LADIS study population. The large vessel infarcts analysed here were clinically silent or
minor.
This study shows different vascular risk profiles for the large and small vessel CVD
categories. Except the administration of thrombolytic medication in the first hours after ischemic
stroke, therapeutic strategies for ischemic disease are focused on prevention of recurrence of
ischemic events. This includes strict control of blood pressure and correction of hyperlipidemia.
Preventive treatment can be potentially more focused when risk factor profiles are known.
Clinical impact of cerebral vascular lesions
109
References
1. Valk J, Barkhof F, Scheltens P. Magnetic Resonance Imaging in Dementia. Heidelberg: Springer, 2002.
2. Kalaria RN, Kenny RA, Ballard CG et al. Towards defining the neuropathological substrates of vascular dementia. J Neurol Sci 2004;226:75-80.
3. Roman GC, Erkinjuntti T, Wallin A et al. Subcortical ischaemic vascular dementia. Lancet Neurol 2002;1:426-36.
4. Fernando MS, Simpson JE, Matthews F et al. White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury. Stroke 2006;37:1391-8.
5. Esiri MM, Wilcock GK, Morris JH. Neuropathological assessment of the lesions of significance in vascular dementia. J Neurol Neurosurg Psychiatry 1997;63:749-53.
6. Fisher CM. Binswanger's encephalopathy: a review. J Neurol 1989;236:65-79. 7. Hachinski VC, Lassen NA, Marshall J. Multi-infarct dementia. A cause of mental
deterioration in the elderly. Lancet 1974;2:207-10. 8. Ferro JM. Hyperacute cognitive stroke syndromes. J Neurol 2001;248:841-9. 9. Kramer JH, Reed BR, Mungas D et al. Executive dysfunction in subcortical ischaemic
vascular disease. J Neurol Neurosurg Psychiatry 2002;72:217-20. 10. Reed BR, Eberling JL, Mungas D et al. Effects of white matter lesions and lacunes on
cortical function. Arch Neurol 2004;61:1545-50. 11. Schmidt R, Fazekas F, Offenbacher H et al. Neuropsychologic correlates of MRI white
matter hyperintensities: a study of 150 normal volunteers. Neurology 1993;43:2490-4. 12. Wolfe N, Linn R, Babikian VL et al. Frontal systems impairment following multiple
lacunar infarcts. Arch Neurol 1990;47:129-32. 13. Basile AM, Pantoni L, Pracucci G et al. Age, hypertension, and lacunar stroke are the
major determinants of the severity of age-related white matter changes. The LADIS (Leukoaraiosis and Disability in the Elderly) Study. Cerebrovasc Dis 2006;21:315-22.
14. Breteler MM, Van Swieten JC, Bots ML et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994;44:1246-52.
15. Jeerakathil T, Wolf PA, Beiser A et al. Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke 2004;35:1857-61.
16. Longstreth WT, Jr., Manolio TA, Arnold A et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274-82.
17. Schmidt R, Fazekas F, Hayn M et al. Risk factors for microangiopathy-related cerebral damage in the Austrian stroke prevention study. J Neurol Sci 1997;152:15-21.
18. Sacco SE, Whisnant JP, Broderick JP et al. Epidemiological characteristics of lacunar infarcts in a population. Stroke 1991;22:1236-41.
19. Horowitz DR, Tuhrim S, Weinberger JM et al. Mechanisms in lacunar infarction. Stroke 1992;23:325-7.
20. Longstreth WT, Jr., Bernick C, Manolio TA et al. Lacunar infarcts defined by magnetic resonance imaging of 3660 elderly people: the Cardiovascular Health Study. Arch Neurol 1998;55:1217-25.
21. Adams RD, Victor M, Ropper AH. Cerebrovascular Disease.Principles of Neurology. New York City: McGraw-Hill Professional Book Group, 2007;Sixth Edition:780-1.
22. Pantoni L, Basile AM, Pracucci G et al. Impact of age-related cerebral white matter changes on the transition to disability -- the LADIS study: rationale, design and methodology. Neuroepidemiology 2005;24:51-62.
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23. National Cholesterol Education Program. Second Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II). Circulation 1994;89:1333-445.
24. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 1997;20:1183-97.
25. Weitz JI, Byrne J, Clagett GP et al. Diagnosis and treatment of chronic arterial insufficiency of the lower extremities: a critical review. Circulation 1996;94:3026-49.
26. 1999 World Health Organization-International Society of Hypertension Guidelines for the Management of Hypertension. Guidelines Subcommittee. J Hypertens 1999;17:151-83.
27. Guidelines for the diagnosis of heart failure. The Task Force on Heart Failure of the European Society of Cardiology. Eur Heart J 1995;16:741-51.
28. Falk RH. Atrial fibrillation. N Engl J Med 2001;344:1067-78. 29. Alpert JS, Thygesen K, Antman E et al. Myocardial infarction redefined--a consensus
document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000;36:959-69.
30. van Straaten EC, Fazekas F, Rostrup E et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006;37:836-40.
31. van Straaten EC, Scheltens P, Knol DL et al. Operational definitions for the NINDS-AIREN criteria for vascular dementia: an interobserver study. Stroke 2003;34:1907-12.
32. Vermeer SE, den Heijer T, Koudstaal PJ et al. Incidence and risk factors of silent brain infarcts in the population-based Rotterdam Scan Study. Stroke 2003;34:392-6.
33. Di Carlo A, Lamassa M, Baldereschi M et al. Risk factors and outcome of subtypes of ischemic stroke. Data from a multicenter multinational hospital-based registry. The European Community Stroke Project. J Neurol Sci 2006;244:143-50.
34. Manolio TA, Kronmal RA, Burke GL et al. Magnetic resonance abnormalities and cardiovascular disease in older adults. The Cardiovascular Health Study. Stroke 1994;25:318-27.
35. Zhang XF, Attia J, D'Este C et al. Prevalence and magnitude of classical risk factors for stroke in a cohort of 5092 Chinese steelworkers over 13.5 years of follow-up. Stroke 2004;35:1052-6.
36. Bujo H, Takahashi K, Saito Y et al. Clinical features of familial hypercholesterolemia in Japan in a database from 1996-1998 by the research committee of the ministry of health, labour and welfare of Japan. J Atheroscler Thromb 2004;11:146-51.
37. Meissner I, Khandheria BK, Sheps SG et al. Atherosclerosis of the aorta: risk factor, risk marker, or innocent bystander? A prospective population-based transesophageal echocardiography study. J Am Coll Cardiol 2004;44:1018-24.
38. Verhave JC, Hillege HL, Burgerhof JG et al. Cardiovascular risk factors are differently associated with urinary albumin excretion in men and women. J Am Soc Nephrol 2003;14:1330-5.
39. Knopp, R. H. Estrogen, female gender, and heart disease. 196. 1998. Lippincott-Raven.
40. Verhoef P. Hyperhomocysteinemia and risk of vascular disease in women. Semin Thromb Hemost 2000;26:325-34.
General discussion
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Chapter 5
General discussion, conclusions
and future directions
Chapter 5
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General discussion
113
General discussion
Vascular lesions on MRI
The effect of compromised vascularisation of the brain can be visualized on MRI and
has several appearances. The different T1- and T2-weighted sequences have their own qualities
and when combined give complementary information on the characteristics and probable cause
of the ischaemic pathology. This may be important to fulfill diagnostic criteria, as mentioned
below, and changes the role of neuroimaging from excluding possibly treatable causes of
cognitive decline to a more diagnostic approach. T1-weighted images reveal lacunes and cortical
infarcts, FLAIR is best suited for the assessment of WMH and we found that conventional T2-
weighted images are preferred in the assessment of WMH in the thalamus and infratentorial
regions. In addition, T2* sequences (e.g. gradient-echo) can be used for the detection of
microbleeds. An imaging protocol using T1, T2, T2* and FLAIR images may therefore
optimizes diagnostic capabilities of MRI for VaD (table 1).
Diagnosing VaD
For diagnosing VaD the NINDS-AIREN criteria are the most recent and widely used criteria. To
fulfill a diagnosis of VAD according to those criteria, a subject is required to be a) demented, b)
have clinical and radiological signs of vascular disease and c) have an onset of symptoms within
a couple of months after the vascular ictus.1 The criteria were originally designed for research
purposes in 1993, but are now also widely used in patient care. Already in 1993, the authors
recognized the added value of neuroimaging and some form (CT or MRI) was required for the
diagnosis. The criteria include a radiological part, which includes a list of vascular lesions
thought to be leading to cognitive deficits. This list is complex; only lesions in specific areas of
the cerebrum and of certain severity are regarded sufficient to lead to VaD. We demonstrated that
the radiological part of the NINDS-AIREN criteria has poor agreement between raters and we
hypothesized that this may at least be partly due to lack of description of the areas and severity of
the lesions. Operationalization (using a manual on how to apply the criteria and used to create
uniformity between raters) only improved agreement in raters that were already experienced in
the assessment of vascular lesions on MRI. Operationalization does not change criteria and
therefore can not address all problems related to diagnosing VaD. Further work is certainly
needed to elaborate on the radiological evidence needed to demonstrate VaD.
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Table 1. Example of imaging protocol for the detection of cerebral vascular lesions
Coronal 3D T1 gradient echo TR 15 ms
TE 7 ms
TI 500 ms
Slice thickness 1 mm
Flip-angle 15-30 degrees
Axial T2 Turbo Spin Echo TR 4600 ms
TE 119 ms
Slice thickness 3-5 mm
Axial FLAIR TR 9000 ms
TE 105 ms
TI 2200 ms
Slice thickness 3-5 mm
Axial T2* gradient-echo TR 650 ms
TE 15 ms
Slice thickness 3-5 mm
Flip-angle >20 degrees TR = repetition time, TE = echo time, TI = inversion time
Based on observations of patients with multiple infarctions and dementia, the stepwise
progression of cognitive decline and the presence of other focal neurological deficits were
adopted in the criteria. Extensive WMH as a cause of VaD only gained general recognition since
the 1980’s with the availability of cerebral CT and MR imaging. In the years after 1993, when
imaging became available on a wider scale and could be used for patient care as well as research,
it was more obvious that VaD due to WMH is in fact the most common form of VaD2,3, and that
the requirements of a stepwise progression and other focal neurological deficits are typically not
applicable to this group of VaD subjects.
General discussion
115
Radiological considerations of WMH
Pathology / etiology
WMH are considered to be an expression of small vessel disease. Arteriolopathy of the
small vessels penetrating the white matter may compromise the supply of oxygen, leading to
subtotal ischemia and partial demyelination. The ensuing alterations in the biochemical
properties of the tissue lead to altered relaxation behavior facilitating the visibility of WMH on
MRI.4 Neuropathological studies showed that WMH, as seen on MRI, correspond with
rarefaction of myelin, gliosis, and axonal loss to a varying degree.4-6 Further work is needed to
improve the possibilities to differentiate WMH into more benign and more severe types of tissue
damage, and thereby hopefully improving the clinico-radiological associations.
Assessment of lesion distribution and severity
Several methods exist to measure severity of WMH. Visual rating scales are quick,
easily applicable and interobserver reliability is good for most used scales. On the other hand, the
scales do usually not provide details on size and location of the lesions and most are not linear.
To obtain lesion volume, a computerized method can be used. Most of these techniques are semi-
automated, meaning that some operator interference is necessary, leading to time-consuming
measurements. In addition, these volumetric methods require a designated computerized
environment, which has limited availability. We demonstrated in chapter 3.1 that correlations
with clinical data are dependent of the method used, and the volumetric methods seem more
sensitive. Especially in small study samples this seems therefore the preferred technique. In
addition, computerized volume measurements can be used in several applications: by combining
computerized lesion outlines with an anatomic map (which can be registered to the subject
space), it is possible to generate regional lesion loads. When intracranial volumes are assessed,
these can be included in the analyses. Further work is needed to automate lesion detection using
computer algorithms, which provides plausible results in some labs, but typically are difficult to
apply to images generated by other MR scanners.7,8
Clinical considerations of WMH
Research has shown that WMH can lead to memory problems, mood disorders, gait
disturbances and bladder control dysfunction.9-13 It was hypothesized that this could be due to the
disruption of axons, running through the white matter regions, and hereby change connections
between cortical regions and other parts of the CNS.14,15 On the other hand, ischemia of basal
forebrain cholinergic nuclei, basal ganglia or white matter that have extensive cholinergic
cortical projections may result in cholinergic deficits and clinical symptoms.16,17 The severity of
Chapter 5
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the WMH correlates with clinical symptoms, but there are individuals with extensive and
confluent lesions without significant clinical correlates, although they are are at greater risk for
becoming impaired in daily life.18-20 The recruitment of other cortical areas than originally
designed for the task can possibly mask the deficits.21 WMH can lead to cognitive deficits to
such extend that a vascular dementia syndrome becomes present. There are, however, also
indications that these lesions contribute to the development of AD.22 The exact mechanisms are
not completely understood and a number of possible causes have been postulated. WMH might
lead to deficits in cognitive areas other than memory, such as executive function or attention, and
can therefore contribute to AD in an earlier stage than by AD pathology alone.23 Another
explanation could be that vascular lesions can lead to enhanced neuronal degeneration. Recently,
it was shown that oxidative stress was one of the earliest pathological changes in AD, possibly
leading to cell dysfunction and degenerative changes.24,25
Future Directions
Diagnosing VaD
We were able to demonstrate that operationalization of the criteria for VaD improves
interobserver reliability in experienced observers to a clinically acceptable level. Further work
however, is needed to refine the radiological criteria, and make them more widely applicable in a
reliable fashion. Since good interobserver reliability is needed for criteria to clinically diagnose a
disease, we suggest the current criteria to be revised with the incorporation of exact imaging
criteria. Second, the knowledge that the subcortical subtype is more prevalent than previously
believed and the clinical course and signs that are applicable to the cortical subtype do not apply
to the subcortical form should in our opinion be incorporated in the definition of VaD26.
Imaging and assessment of WMH
We showed that the method used for the assessment of WMH can increase statistical power in
studies describing the relationship of WMH with clinical parameters. It can be hypothesized that
this is also true for the evaluation of risk factors for WMH. If automated quantitative
measurements will be further developed, precision might be improved, and operator assistance
and time of assessment can be reduced. This offers the possibility of monitoring disease
General discussion
117
progression and effects of therapeutic interventions. Other imaging techniques, such as diffusion
fiber tracking techniques, may show the anatomical connections of different cortical regions
through white matter tracts and provide more information on the (degree of) disruption of
specific fiber tracts affected by WMH and the loss of function that follows.
Treatment of VaD
Symptomatic treatment has been focused on the cholinergic deficits that have been found in
VaD. Acetylcholine can induce vasodilation and studies on the effect of cholinesterase inhibitors,
approved for the treatment of AD, have shown beneficial effects on cognition and daily
functioning in subjects with VaD as well, although the effects were not as large as in individuals
with AD.17,27-29 Memantine, a N-methyl-d-aspartate receptor antagonist possibly preventing
neurodegeneration by reducing the cells’ pathologically increased sensitivity to glutamate, leads
to modest improvement of cognition and behavior in individuals with mild to moderate VaD.30,31
A more causal therapy for VaD would be the prevention of (further) vascular damage to the
brain. Several risk factors have been identified, some of which are amenable to treatment. A
reduction in the incidence of dementia by treating hypertension has been reported.32 Potential
secondary preventive agents such as NSAID's are also subject of study. In two retrospective
studies, subjects with VaD using antiplatelet or anticoagulant medication had a longer life
expectancy that those without.33,34 Caution should be taken not to generalize the results to the
entire VaD population, since subjects with microbleeds possibly are at greater risk of developing
hemorrhagic strokes and have not been studied separately. The first study only included
individuals with ischemic VaD and the second study included 79 VaD patients with the number
of subjects with microbleeds not mentioned. In longitudinal designs, the effect of prevention of
progressing dementia by changing vascular risk factors still needs to be studied.
Chapter 5
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References
1. Roman GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43:250-60.
2. Chen CP. Transcultural expression of subcortical vascular disease. J Neurol Sci 2004;226:45-7.
3. Jellinger KA. Pathology and pathophysiology of vascular cognitive impairment. A critical update. Panminerva Med 2004;46:217-26.
4. Englund E. Neuropathology of white matter lesions in vascular cognitive impairment. Cerebrovasc Dis 2002;13 Suppl 2:11-5.
5. Kalaria RN, Kenny RA, Ballard CG et al. Towards defining the neuropathological substrates of vascular dementia. J Neurol Sci 2004;226:75-80.
6. Olsson Y, Brun A, Englund E. Fundamental pathological lesions in vascular dementia. Acta Neurol Scand Suppl 1996;168:31-8.
7. Anbeek P, Vincken KL, van Osch MJ et al. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004;21:1037-44.
8. Jack CR, Jr., O'Brien PC, Rettman DW et al. FLAIR histogram segmentation for measurement of leukoaraiosis volume. J Magn Reson Imaging 2001;14:668-76.
9. Breteler MM, Van Swieten JC, Bots ML et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994;44:1246-52.
10. Camicioli R, Moore MM, Sexton G et al. Age-related brain changes associated with motor function in healthy older people. J Am Geriatr Soc 1999;47:330-4.
11. Longstreth WT, Jr., Manolio TA, Arnold A et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274-82.
12. Schmidt R, Fazekas F, Offenbacher H et al. Neuropsychologic correlates of MRI white matter hyperintensities: a study of 150 normal volunteers. Neurology 1993;43:2490-4.
13. Thomas AJ, Kalaria RN, O'Brien JT. Depression and vascular disease: what is the relationship? J Affect Disord 2004;79:81-95.
14. Mori E. Impact of subcortical ischemic lesions on behavior and cognition. Ann N Y Acad Sci 2002;977:141-8.
15. O'Sullivan M, Morris RG, Huckstep B et al. Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic leukoaraiosis. J Neurol Neurosurg Psychiatry 2004;75:441-7.
16. Roman GC. Cholinergic dysfunction in vascular dementia. Curr Psychiatry Rep 2005;7:18-26.
17. Behl P, Bocti C, Swartz RH et al. Strategic subcortical hyperintensities in cholinergic pathways and executive function decline in treated Alzheimer patients. Arch Neurol 2007;64:266-72.
18. Inzitari D, Simoni M, Pracucci G et al. Risk of rapid global functional decline in elderly patients with severe cerebral age-related white matter changes: the LADIS study. Arch Intern Med 2007;167:81-8.
19. Sachdev PS, Brodaty H, Valenzuela MJ et al. The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology 2004;62:912-9.
20. Scheid R, Preul C, Lincke T et al. Correlation of cognitive status, MRI- and SPECT-imaging in CADASIL patients. Eur J Neurol 2006;13:363-70.
21. Gavazzi C, Borsini W, Guerrini L et al. Subcortical damage and cortical functional changes in men and women with Fabry disease: a multifaceted MR study. Radiology 2006;241:492-500.
General discussion
119
22. DeCarli C. The role of cerebrovascular disease in dementia. Neurologist 2003;9:123-36.
23. Snowdon DA, Greiner LH, Mortimer JA et al. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. JAMA 1997;277:813-7.
24. Kalaria RN. Vascular factors in Alzheimer's disease. Int Psychogeriatr 2003;15 Suppl 1:47-52.
25. Zhu X, Smith MA, Honda K et al. Vascular oxidative stress in Alzheimer disease. J Neurol Sci 2007.
26. Erkinjuntti T, Inzitari D, Pantoni L et al. Research criteria for subcortical vascular dementia in clinical trials. J Neural Transm Suppl 2000;59:23-30.
27. Black S, Roman GC, Geldmacher DS et al. Efficacy and tolerability of donepezil in vascular dementia: positive results of a 24-week, multicenter, international, randomized, placebo-controlled clinical trial. Stroke 2003;34:2323-30.
28. Erkinjuntti T, Kurz A, Gauthier S et al. Efficacy of galantamine in probable vascular dementia and Alzheimer's disease combined with cerebrovascular disease: a randomised trial. Lancet 2002;359:1283-90.
29. Wilkinson D, Doody R, Helme R et al. Donepezil in vascular dementia: a randomized, placebo-controlled study. Neurology 2003;61:479-86.
30. Orgogozo JM, Rigaud AS, Stoffler A et al. Efficacy and safety of memantine in patients with mild to moderate vascular dementia: a randomized, placebo-controlled trial (MMM 300). Stroke 2002;33:1834-9.
31. Wilcock G, Mobius HJ, Stoffler A. A double-blind, placebo-controlled multicentre study of memantine in mild to moderate vascular dementia (MMM500). Int Clin Psychopharmacol 2002;17:297-305.
32. Forette F, Seux ML, Staessen JA et al. Prevention of dementia in randomised double-blind placebo-controlled Systolic Hypertension in Europe (Syst-Eur) trial. Lancet 1998;352:1347-51.
33. Devine ME, Rands G. Does aspirin affect outcome in vascular dementia? A retrospective case-notes analysis. Int J Geriatr Psychiatry 2003;18:425-31.
34. Freels S, Nyenhuis DL, Gorelick PB. Predictors of survival in African American patients with AD, VaD, or stroke without dementia. Neurology 2002;59:1146-53.
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Summary
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Chapter 6
Summary
Chapter 6
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Summary
123
Summary
The incidence of dementia is increasing to the stunning number of 30.000 new cases each
year in the Netherlands. Despite progress in knowledge of the several forms of dementia, causal
therapies are yet not available at this moment. However, several therapeutic agents are being
investigated in different groups of dementia and it is therefore important to be able to
differentiate between different causes of dementia. This especially holds for the distinction
between Alzheimer’s disease versus vascular dementia.
In the past, brain imaging was regarded as optional, mainly to exclude possibly surgically
treatable causes of dementia, such as mass lesions. Today imaging can contribute to specifically
diagnose the illness underlying a dementia syndrome, for example by demonstrating medial
temporal lobe atrophy in Alzheimer’s disease. MRI is very sensitive to vascular lesions. With
T1-weighted series, cortical infarcts and lacunes can be visualized. T2-weighted sequences, like
Fluid-Attenuated Inversion Recovery (FLAIR) and dual-echo Turbo Spin Echo (TSE) are used
to show subcortical lesions; FLAIR is able to distinguish between WMH on one hand and
lacunes and perivascular spaces on the other, while TSE is preferred for the basal ganglia (and
thalamus) and infratentorial regions.
VaD is thought to be the second most common type of dementia. The NINDS-AIREN
criteria for VaD are the most recent and widely used. These criteria contain a radiological part,
necessitating evidence of vascular disease on brain imaging. In chapter 2.2 we showed that
overall interobserver agreement of these radiological criteria is poor (Cohen’s κ 0.29), and that
operationalization only improves agreement between already experienced raters of vascular
lesions on MRI (κ = 0.62), but that it does not affect agreement between raters who are not
trained in scoring vascular lesions (from κ = 0.17 to κ = 0.18 after operationalization).
According to the NINDS-AIREN radiological criteria for VaD, bilateral vascular lesions in
the thalamus can account for a VaD syndrome. In Chapter 2.3, axial TSE imaging is compared
to axial FLAIR with respect to these thalamic lesions. In a blinded review of MRIs of 73
subjects, meeting the NINDS-AIREN radiological criteria of VaD, TSE was found to be more
sensitive. Using FLAIR, only 55% of the probable vascular lesions are detected. For TSE, this is
97%. Those results signify that, when applying the NINDS-AIREN criteria, FLAIR should not
be the only T2-weighted sequence.
WMH can be assessed qualitatively, using a visual scale, or quantitatively, with volumetric
methods. Chapter 3.1 shows the characteristics of several visual scales for WMH in comparison
with a semiautomated volumetric assessment. The visual scales appear to have ceiling effects
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and their relationship with increase in WMH volume is not linear, possibly compromising their
ability to detect differences between groups. Several visual scales for the assessment of WMH
were investigated with respect to their power of detecting WMH changes over time in chapter
3.2. It is reported that on 3-year interval MRIs of subjects with varying degree of WMH, the
rating scales that are developed for the assessment of WMH on cross-sectionally, are largely
unable to pick up changes in WMH burden over time within subjects. Volumetric assessment or
the use of a visual rating scale, specifically designed to detect change over time, are more
sensitive to progression of WMH, and are therefore preferred in longitudinal studies.
Vascular lesions can lead to VaD, but there is a growing body of evidence that they also
might play a role in the etiology of AD. Vascular lesions are more common in subjects with AD
compared to healthy controls of the same age. We investigated the presence of WMH in a
sample of subjects with amnestic MCI, which is regarded by some as a transition phase between
normal aging and AD, and who were followed up over a period of three years. Using a visual
rating scale, WMH was assessed in several brain regions: subcortical, periventricular, in the
basal ganglia, and infratentorial. We found that WMH located in the periventricular regions was
significantly more prevalent in the individuals who later progressed to AD. This may indicate
that vascular factors influence the development of degenerative dementia such as AD.
Ischemic vascular brain lesions can be divided into cortical (large vessel) infarcts, lacunar
(subcortical) infarcts and WMH (subtotal ischemia). In chapter 4.2 we investigated risk factor
profiles of these three types of CVD and found that male gender is a risk factor for all of these
CVD types.
Summary
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Chapter 7
126
Summary
127
Chapter 7
Nederlandse samenvatting
Chapter 7
128
Summary
129
Samenvatting
De incidentie van dementie neemt toe tot 30.000 nieuwe gevallen per jaar in Nederland.
Ondanks toename van kennis over de verschillende vormen van dementie is een causale
behandeling nog niet beschikbaar op dit moment. Omdat diverse medicijnen worden onderzocht
op werkzaamheid, is het belangrijk om onderscheid te kunnen maken tussen de verschillende
vormen van dementie, met name tussen de ziekte van Alzheimer (AD) en vasculaire dementie
(VaD).
Voor het stellen van de diagnose dementie was het verrichten van beeldvorming van de
hersenen in het verleden optioneel en met name gericht op het uitsluiten van mogelijk
chirurgisch behandelbare oorzaken, zoals ruimte-innemende processen. Tegenwoordig kan
beeldvorming bijdragen om de onderliggende ziekte bij dementie te diagnoticeren, zoals het
aantonen van atrofie van de mediale temporaal kwab bij AD. Met MRI kunnen vasculaire lesies
zeer goed worden aangetoond. Met T1-gewogen opnamen kunnen corticale en lacunaire
infarcten zichtbaar worden gemaakt. T2-gewogen series, zoals Fluid-Attenuated Inversion
Recovery (FLAIR) en dual-echo Turbo Spin Echo (TSE) kunnen worden gebruikt om
subcorticale lesies in beeld te brengen; met FLAIR kan onderscheid worden gemaakt tussen
witte stof hyperintensiteiten (WMH) enerzijds en lacunaire infarcten en perivasculaire ruimten
anderzijs, terwijl TSE het meest geschikt is voor de beoordeling van de basale kernen (en
thalamus) en infratentoriële gebieden.
Er wordt aangenomen dat VaD de op één na meest voorkomende oorzaak is van dementie.
De International Workshop of the National Institute of Neurological Disorders and Stroke
(NINDS) and the Association Internationale pour la Recherche et l’Enseignement en
Neurosciences (AIREN) criteria zijn de meeste recente en meest gebruikte criteria voor VaD. Ze
bevatten een radiologisch deel, waardoor beeldvorming nodig is om aan deze criteria te kunnen
voldoen. In dit proefschrift wordt beschreven dat de overeenstemming tussen personen die deze
radiologische criteria toepassen laag is (Cohen’s κ 0.29), en dat het operationaliseren van de
criteria de overeenstemming alleen vergroot tussen personen die al ervaring hebben met het
beoordelen van vasculaire lesies op MRI (κ = 0.62). Overeenstemming tussen beoordelaars
zonder deze ervaring neemt hierdoor niet toe (van κ = 0.17 tot κ = 0.18) (hoofdstuk 2.2).
Vasculaire lesies in de thalamus kunnen, volgens het radiologische deel van de NINDS-
AIREN criteria, leiden tot VaD. In hoofdstuk 2.3 bleek TSE sensitiever dan FLAIR voor de
detectie van deze lesies bij de beoordeling van MRI scans van 73 personen, die voldeden aan de
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NINDS-AIREN radiologische criteria. Met FLAIR werd 55% van de lesies gevonden, met TSE
97%. Dit suggereert dat voor de beoordeling van vasculaire lesies FLAIR niet de enige T2-
gewogen serie zou moeten zijn.
WMH kunnen kwalitatief beoordeeld worden, door middel van een visuele schaal, of
kwantitatief, met volumetrische methoden. In hoofdstuk 3.1 worden de kenmerken van diverse
visuele schalen en een semi-automatische volumetrische methode beschreven. De visuele
schalen blijken plafond-effecten te vertonen en hoogte van WMH score met behulp van deze
schalen houdt niet lineair verband met het WMH volume. Hierdoor zou het onderscheidend
vermogen van de schalen om verschillen tussen groepen te detecteren verminderd kunnen zijn.
Diverse visuele schalen voor de beoordeling van WMH waren ook beoordeeld met betrekking
tot hun vermogen om verschillen in tijd te detecteren. In hoofdstuk 3.2 wordt beschreven dat de
visuele WMH schalen, die ontwikkeld zijn voor de cross-sectionele beoordeling van WMH,
grotendeelds niet in staat zijn om veranderingen in volume van WMH, beoordeeld op MRI’s met
een drie-jaar interval van personen met verschillende ernst van WMH, in score tot uitdrukking te
brengen. Volumetrische methoden en een visuele schaal die specifiek voor de beoordeling van
verandering van WMH is ontworpen zijn sensitiever voor progressie van lesies en daarom te
verkiezen in longitudinale studies.
Cerebrale vasculaire lesies kunnen leiden tot VaD, maar er zijn steeds meer aanwijzingen
dat ze ook een rol spelen bij de ontwikkeling van AD. Ze komen vaker voor bij personen met
AD in vergelijking met leeftijdgenoten zonder de ziekte. De aanwezigheid en uitgebreidheid van
WMH werd beoordeeld in een groep personen met amnestische MCI, hetgeen mogelijk een
transitiefase is tussen normale veroudering en AD. Met een visuele schaal werden WMH
gescoord in verschillende hersengebieden (subcorticaal, periventriculair, in de basale kernen en
infratentorieel) op baseline MRI (hoofdstuk 4.1). Personen die na 3 jaar follow-up AD hadden
ontwikkeld, bleken significant vaker WMH in de periventriculaire gebieden te hebben dan de
personen die na 3 jaar geen AD hadden.
Ischemische cerebrale vasculaire lesies kunnen worden onderverdeeld in corticale infarcten,
lacunaire (subcorticale) infarcten en WMH (subtotale ischemie). In hoofdstuk 4.2 worden de
vasculaire risicoprofielen beschreven van deze drie typen CVD. Mannelijk geslacht was een
risicofactor voor alle typen CVD.
List of abbreviations AD Alzheimer’s disease ADDTC Alzheimer’s Disease Diagnostic and Treatment Centers ARWMC Age-related White Matter Hyperintensities CAA Cerebral Amyloid Angiopathy CADASIL Cerebral Autosomal Dominant Angiopathy with Subcortical Infarcts
and Leukencephalopathy CSF Cerebrovascular disease CT Computed Tomography CVD Cerebrovascular disease DSM-IV Diagnostic and Statistical Manual of Mental Disorders FLAIR Fluid Attenuated Inversion Recovery HIS Hachinski Ischemic Scale ICD-10 International Statistical Classification of Disease - 10th edition LADIS Leukoaraiosis and Disability in the Elderly MCI Mild cognitive impairment MRA Magnetic Resonance Angiography MRI Magnetic Resonance Imaging MTL Medial temporal lobe NINDS-AIREN National Institute of Neurological Disorders and Stroke and Association
Internationale pour la Recherche et l’Enseignement en Neurosciences RSS Rotterdam Scan Study T2-WI T2-weighted image TE Echo Time TR Repetition Time TSE Turbo Spin Echo VaD Vascular dementia VCI Vascualr cognitive impairment WMH White Matter Hyperintensities WML White Matter Lesions
Dankwoord Ik wil de vele mensen bedanken die betrokken zijn geweest bij het tot stand komen van dit proefschrift en een antal mensen in het bijzonder. Allereerst ben ik alle vrijwillige deelnemers aan het dementie-onderzoek en hun partners dank verschuldigd, en met name de deelnemers aan het LADIS onderzoek. Naast het feit dat het hard werken was om alle benodigde gegevens te verzamelen was het ook vaak gezellig en kijk ik terug op een prettige samenwerking. Mijn promotoren prof.dr. Ph. Scheltens en prof.dr. F. Barkhof. Beste Philip, het was een voorrecht om onderzoek te doen onder jouw hoede. Je bent zeer betrokken bij al je onderzoekers en laagdrempelig te bereiken: je vindt zelfs tijd om te informeren hoe de onderzoeks-zaken ervoor staan tijdens je vakantie, gezeten in de ski-lift, en je beantwoordt ook e-mails als je met griep in bed ligt (dit laatste geldt overigens ook voor professor Barkhof hetgeen komische berichten opleverde toen jullie toevallig tegelijkertijd ziek waren). Aan de andere kant wordt zelfstandig werken ook zeer door jou gestimuleerd en is er zeker ruimte om eigen ideëen uit te werken. Je bent steeds bezig met vernieuwing en verbetering van het Alzheimer Centrum op een manier die respect afdwingt en dit maakt de sfeer zeer stimulerend en prettig. Daarnaast vind je tijd voor het leggen en onderhouden van vele (inter)nationale contacten tot profijt van de hele onderzoeksgroep. Ik ben je er erg dankbaar voor dat ik hierin ook heb mogen delen met o.a. een stage in Californië. In het kader van het onderzoek heb ik, net als met Frederik, met jou vele MRI scans bekeken, waarbij er altijd wel wat te leren viel. Beste Frederik, jouw snelle en scherpe geest zorgde vaak voor de nodige bijsturing in de goede richting. Je bezit de gave om mensen tijdens het proces van het schrijven van een artikel gemotiveerd te houden. Hier heb ik menigmaal plezier van gehad, als ik dankzij je aanmoedigingen weer vol goede moed begon aan de volgende versie. Je goede samenwerking met Philip was voor mij altijd een genoegen en de vergelijking met de ‘balkon-mannetjes’ van de Muppetshow drong zich zo nu en dan op (alleen in goede zin: jullie hebben volgens mij veel plezier in het observeren en elkaar op humoristische wijze van commentaar voorzien van gebeurtenissen). Daarnaast heb ik het genoegen gehad om bijna 2000 MRI scans met je te hebben beoordeeld. Dit was ongelooflijk leerzaam, ik heb er in mijn huidige werkzaamheden nog dagelijks plezier van. Jouw inspanningen voor het Image Analysis Center leidden onder anderen tot overvloedig en interessant scanmateriaal. Prof.dr. J. Heimans, beste Jan. Ik dank je voor de mogelijkheid die je me hebt geboden om de opleiding tot neuroloog te volgen. De prettige werksfeer die ik op de afdeling ervaar is mijns inziens voor een groot deel aan jou te danken. Giorgos Karas, beste Giorgos, met veel plezier kijk ik terug op de samenwerking en de tijd dat we in de Meander en later in de kelder van de polikliniek een kamer deelden. Jouw hulp bij het bewerken van de scan-beelden en op ICT-gebied anderszins was onmisbaar. Ik ben ervan overtuigd dat de personen die meer met computers kunnen dan jij in het VUmc op 1 hand te tellen zijn. Daarnaast ben je ook een erg prettige persoon om mee samen te werken en viel er vaak en veel te lachen. Dankzij jou (en je moeder, die ik af en toe aan de telefoon kreeg) ken ik nu enkele woorden Grieks. Ik ben erg blij dat je paranimf wil zijn. De leden van de promotiecommissie, prof. dr. M.M.A. Breteler, dr. F-E. de Leeuw, prof.dr. C. Jonker, Prof. dr. J.A. Castelijns en dr. M. Visser wil ik bedanken voor hun bereidheid in de
promotiecommissie plaats te nemen en voor de samenwerking op verschillend gebied in de loop van de jaren. Prof. C. DeCarli, dear Charlie, thank you very much for the collaboration and the opportunity to do research in your center, the IdEA lab. It has been a fruitful, and wonderful time. You manage the group with great energy which projects to all employers. I will love California for ever. Dear collaborators of the LADIS study: I really enjoyed working together on this project. The meetings were always very stimulating, and the results of everybodies enthousiasm and hard work are now becoming visible. De mensen van het IAC, met name Tineke van IJken en Maikel Jonker. Jullie waren mijn steun en toeverlaat in de Meander-tijd. Jullie hulp was zeer gewaardeerd! Daarnaast was het erg prettig samenwerken en hebben we naast hard gewerkt ook erg veel lol gehad. Ronald van Schijndel, beste Ronald, jou hulp was onmisbaar voor dit proefschrift. Met name de heterogeniteit van de LADIS-scans en het reduceren hiervan heeft je de nodige hoofdbrekens maar vooral kostbare tijd gekost. Altijd dook er wel weer een nieuw probleem op waarvoor ik bij je aanklopte. Ik waardeer het zeer dat dit nooit voor niets was en je altijd weer aan de oplossing werkte. Antonio, Esther, Jasper, Alida, Alie, Niki, Yolande, Rutger, Laura, Wouter, Els, Anita, Marjan, Freek, Rolinka en alle andere onderzoekers en medewerkers van het Alzheimercentrum en de afdeling neurologie, dank voor jullie hulp, samenwerking en gezelligheid. Niels Prins en Ewout van Dijk, dank voor de samenwerking aan het artikel van hoofdstuk 3.2. Lieve vrienden en vriendinnen, dank voor jullie lieve support de afgelopen jaren. Alleen werken is ook niets, ik ben blij dat ik mijn vrijetijd met jullie kon delen, ook al was dat bij tijd en wijle niet zo vaak. Pap, mam: jullie zijn ongelooflijk belangrijk voor mij en ik ben zeer dankbaar voor jullie onvoorwaardelijke steun en blindelings vertrouwen. Ik heb me atijd mogen verheugen op een zeer warm (zeg maar heet) nest. Ook in de afronding van het preofschrift is jullie steun onmisbaar gebleken! Pap, bedankt dat je de taak van paranimf met veel energie op je hebt genomen. Marlein: een betere en lievere zus kan ik me niet wensen, dank voor al je hulp, energie en vrolijkheid. Lieve Christian, jij hebt nog het meeste geduld moeten hebben met mij tijdens het schrijven van dit proefschrift. Ik ben je dankbaar voor je niet-aflatende steun en hulp, jij bent alles wat ik me kan wensen…. En Meer! Ten slotte: de kleine Hugo met zijn leuke snoet zorgde in de afrondingsfase van het proefschrift voor de nodige afleiding en relativering.