MRI correlates of vascular cerebral lesions and cognitive ... dissertation.pdf · MRI correlates of vascular cerebral lesions and cognitive impairment ACADEMISCH PROEFSCHRIFT ter

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


Chapter 1

8


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


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


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.


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


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


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.


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.


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


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.


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


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


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.


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.


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.


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.


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.


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


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.


45

References

1. Papez JW. A proposed mechanism of emotion. Arch Neurol Psychiatry 1937;38:725-34.

2. Bogousslavsky J, Regli F, Uske A. Thalamic infarcts: clinical syndromes, etiology, and prognosis. Neurology 1988;38:837-48.

3. Clarke S, Assal G, Bogousslavsky J et al. Pure amnesia after unilateral left polar thalamic infarct: topographic and sequential neuropsychological and metabolic (PET) correlations. J Neurol Neurosurg Psychiatry 1994;57:27-34.

4. Gaffan EA, Gaffan D, Hodges JR. Amnesia following damage to the left fornix and to other sites. A comparative study. Brain 1991;114 ( Pt 3):1297-313.

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6. Schott JM, Crutch SJ, Fox NC et al. Development of selective verbal memory impairment secondary to a left thalamic infarct: a longitudinal case study. J Neurol Neurosurg Psychiatry 2003;74:255-7.

7. Bogousslavsky J, Regli F, Delaloye B et al. Loss of psychic self-activation with bithalamic infarction. Neurobehavioural, CT, MRI and SPECT correlates. Acta Neurol Scand 1991;83:309-16.

8. Guard O, Bellis F, Mabille JP et al. [Thalamic dementia after a unilateral hemorrhagic lesion of the right pulvinar]. Rev Neurol (Paris) 1986;142:759-65.

9. Sandson TA, Daffner KR, Carvalho PA et al. Frontal lobe dysfunction following infarction of the left-sided medial thalamus. Arch Neurol 1991;48:1300-3.

10. Tatemichi TK, Desmond DW, Prohovnik I et al. Confusion and memory loss from capsular genu infarction: a thalamocortical disconnection syndrome? Neurology 1992;42:1966-79.

11. Van Der Werf YD, Weerts JG, Jolles J et al. Neuropsychological correlates of a right unilateral lacunar thalamic infarction. J Neurol Neurosurg Psychiatry 1999;66:36-42.

12. Ladurner G, Lliff LD, Sager WD et al. A clinical approach to vascular (multiinfarct) dementia. Exp Brain Res 1982;Suppl 5:243-50.

13. Ladurner G, Sager WD, Flooh E. Computertomography and vascular (multiinfarct) dementia: a qualitative and quantitative investigation. Exp Brain Res 1982;Suppl 5:264-71.


15. Leys D, Scheltens P, Vermersch P et al. [Morphological imaging in the diagnosis of dementia. II. Vascular dementia]. Rev Med Interne 1995;16:195-200.

16. O'Brien JT, Wiseman R, Burton EJ et al. Cognitive associations of subcortical white matter lesions in older people. Ann N Y Acad Sci 2002;977:436-44.

17. Udaka F, Sawada H, Kameyama M. White matter lesions and dementia: MRI-pathological correlation. Ann N Y Acad Sci 2002;977:411-5.


19. Jungreis CA, Kanal E, Hirsch WL et al. Normal perivascular spaces mimicking lacunar infarction: MR imaging. Radiology 1988;169:101-4.

20. De Coene B, Hajnal JV, Gatehouse P et al. MR of the brain using fluid-attenuated inversion recovery (FLAIR) pulse sequences. AJNR Am J Neuroradiol 1992;13:1555-64.

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.

28. Hashemi RH, Bradley WG, Jr., Chen DY et al. Suspected multiple sclerosis: MR imaging with a thin-section fast FLAIR pulse sequence. Radiology 1995;196:505-10.

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.


47

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


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.


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.


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)


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.


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


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.


63

References

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.


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.


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.


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.


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),


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


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,


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.


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.


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.


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.


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

83

Chapter 4


Chapter 4

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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.


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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 –


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


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


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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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


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

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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.


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.

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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.

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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.


101

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,


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).


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


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


107

Table 3. Associations between vascular risk factors and large vessel infarcts

risk factors % infarcts


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.


109

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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.

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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


Axial T2* gradient-echo TR 650 ms

TE 15 ms


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

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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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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.


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.


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.

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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

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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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Summary

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Chapter 7

Nederlandse samenvatting

Chapter 7

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Summary

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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.

MRI correlates of vascular cerebral lesions and cognitive ... dissertation.pdf · MRI correlates of vascular cerebral lesions and cognitive impairment ACADEMISCH PROEFSCHRIFT ter

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