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MRI and histopathology in multiple sclerosis Seewann-Gaitatzis, A.

2019

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Download date: 04. Oct. 2021 ALEXANDRA SEEWANN-GAITATZIS

MRI AND HISTOPATHOLOGY IN MULTIPLE SCLEROSIS

ALEXANDRA SEEWANN-GAITATZIS The studies described in this thesis were carried out at the Department of Neurology, Medical University Graz Austria, and at the Department of Neurology, Radiology and the Department of Pathology, Amsterdam University Medical Center, located at the VU University Medical Center. MS research at the VUmc is organized within the MS Center Amsterdam and supported by grants from the Dutch MS Research Foundation.

Cover design and lay-out: Elisa Calamita, persoonlijkproefschrift.nl. Printing: Ridderprint BV | www.ridderprint.nl ISBN: 978-94-6375-657-0

© 2019, Alexandra Seewann-Gaitatzis All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without the prior written permission of the author. Vrije Universiteit

MRI and histopathology in multiple sclerosis

Academisch proefschrift

ter verkrijging van de graad Doctor of Philosophy aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op donderdag 19 december 2019 om 11.45 uur in de aula van de universiteit, De Boelelaan 1105

door

Alexandra Seewann-Gaitatzis geboren te Graz, Oostenrijk Promotoren prof.dr. J.J.G. Geurts prof.dr. F. Barkhof prof.dr. P. van der Valk For Thomas

TABLE OF CONTENTS

Chapter 1 Introduction p. 9

Chapter 2 The post-mortem method p. 29 2.1 Translating pathology in multiple sclerosis: The p. 31 combination of post mortem imaging, histopathology and clinical findings

Chapter 3 Grey matter in multiple sclerosis p. 47 3.1 Postmortem verification of MS cortical lesion detection p. 49 with 3D-DIR 3.2 Imaging the tip of the iceberg: visualisation of cortical p. 63 lesions in multiple sclerosis

Chapter 4 White matter in multiple sclerosis p. 81 4.1 Diffusely abnormal white matter in chronic multiple p. 83 sclerosis: imaging and histopathologic analysis 4.2 Diffusely abnormal white matter in progressive multiple p. 103 sclerosis: In vivo quantitative MR imaging characterization and comparison between disease types

Chapter 5 Atypical lesions in multiple sclerosis p. 123 5.1 MRI characteristics of atypical idiopathic inflammatory p. 125 demyelinating lesions of the brain: A review of reported findings

Chapter 6 Beyond multiple sclerosis: neurodegenerative and p. 145 vascular diseases 6.1 Heterogeneity of small vessel disease: A systematic review p. 147 of MRI and histopathology correlations 6.2 Heterogeneity of white matter hyperintensities in p. 177 Alzheimer’s disease: Post-mortem quantitative MRI and neuropathology

Chapter 7 Summarizing discussion p. 203

Chapter 8 Summary in Dutch / Nederlandse samenvatting p. 246 Bibliography p. 250 Curriculum Vitae p. 252 Acknowledgements/ Dankwoord p. 254

1 INTRODUCTION Chapter 1

GENERAL INTRODUCTION

Multiple sclerosis (MS) is a demyelinating inflammatory and neurodegenerative disease of the central nervous system (CNS), which affects nearly 2.5 million people worldwide (1). The disease typically starts in early adulthood, effecting females approximately twice as often than males (2). The cause of MS is unknown, however the strong geographical variation in the prevalence of the disease, which increases with distance from the equator, and the higher occurrence in families, suggest involvement of genetic as well as environmental and lifestyle factors (3). Recently, several alleles, most of them located within the major histocompatibility complex (MHC) region, have been associated with susceptibility to the disease (4,5). Reported environmental factors include infections, low sunlight exposure, vitamin D deficiency, cigarette smoking and toxins (6). In the last two decades, published research on MS has doubled, and currently the search term “multiple sclerosis” produces more than 80,000 hits on ‘pub med’ (https:// www.ncbi.nlm.nih.gov/pubmed/?term=multiple+sclerosis). In multiple sclerosis, the disciplines of Neuropathology, Clinical Neurology and Neuroradiology are seen as the classical cornerstones of research. In order to further increase our insight into the disease, it is important to understand the various backgrounds from which the different specialities view MS. While all disciplines address the same disease, their methods, priorities and viewpoints differ. In order to understand research findings it is important to be aware of the strengths and limitations of each viewpoint and to be fluent in the “languages” spoken in the specific fields. In the following paragraphs I have therefore given an overview of MS from the vantage point of each specialty as relevant to the studies in this thesis.

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Under the microscope, MS is characterised by multifocal demyelination in the CNS, with lesions of different ages. Focal demyelination occurs both in the white- and grey matter; some of the bigger lesions are already macroscopically visible and palpable (Figure 1).

10 Introduction

Figure 1: Brain slice with a macroscopically visible lesion in the subcortical white matter Chapter 1

Hallmark of MS are multiple, sharply demarcated “sclerotic” plaques (arrow) in the white matter. Some MS plaques can be seen with the naked eye, as in this example. The lesions feel hard when touched, which is a consequence of the scar formed by astrocytes. This gave the disease its name; “Sclerosis” originates from the Greek word “ਯਧਨਤਭ੏ਮ”, which means “hard”.

White matter (WM) lesions typically develop around small veins and venules on the background of an inflammatory reaction, which is mainly composed of lymphocytes and macrophages. The process of demyelination is followed by reactive astrocytic scar formation and a variable degree of axonal injury. Also, attempts of repair occur in the form of remyelination. Based on the absence and presence of active demyelinating immune cells, WM lesions are classified in different stages, which are thought to reflect their development in time (7) (Figure 2). Active WM lesions can have different structural and immunological features, which are consistent for all plaques within one patient, suggesting pathogenetic heterogeneity (9). Although MS is classically considered a white matter disease, circumscribed lesions in the grey matter are frequent, especially in progressive disease (10,11). Grey matter (GM) lesions are demonstrated immunohistochemically (Box 2) and occur not only in the cortical GM, but also in deep grey matter structures such as the basal ganglia, brainstem and spinal cord (15-17). Within the cortex, lesions can be classified according their extent and location in different types Figure( 3). Unlike WM lesions, GM lesions do not show macrophages, blood-brain-barrier leakage or gliosis and are solely characterized by loss of myelin. This suggests a different mechanism of lesion formation as compared to WM lesions (19,20).

11 Chapter 1

Figure 2: Stages of white matter lesions, modified after van der Valk and de Groot (7)

• Pre-active lesions: consist of clusters of activated microglia cells, without demyelination. • Active lesions: contain demyelinating macrophages with myelin degradation products. • Chronic active lesions: show an inactive centre with a peripheral rim of activated microglia/ macrophages. From there, demyelination is ongoing. • Inactive lesions: are completely demyelinated, sharply demarcated ares devoid of inflammatory cells. Demyelinated axons are embedded in a glial scar, which is formed by densely packed fibrillary astrocytic cell processes. • Remyelinated lesions: Lesions can remyelinate partly or fully. The areas of remyelination are sharply demarcated and show reduced myelin density as compared to the surrounding white matter. They are also referred to as “shadow plaque (8).

12 Introduction

Box 2: Tinctorial and immunohistochemical staining techniques used in this thesis Chapter (12,13).

Conventional staining: • Hematoxilin and eosin (H&E): assessment of general cytoarchitecture 1 • Luxol fast blue (LFB): traditional stain for myelin, which is often combined with cresyl violet, or periodic acid Schiff (PAS). This lipid stain does not allow for the sensitive detection of grey matter lesions. Due to the lower myelin density in grey- as compared to white matter, cortical myelin stays largely unstained and lesions go undetected. • Bodian silver: used for visualization of axons. • Nissl (e.g. cresyl fast violet): traditional stain for neurons, and assessment of their general cytoarchitecture.

Immunohistochemistry: enables detection of specific proteins using antibodies. • Fibrinogen immunohistochemistry: antibodies against fibrinogen are used for detection of serum protein leakage from vessels. A blood- brain barrier leakage is most pronounced in acute lesions. • GFAP (glial fibrillary acidic protein) immunohistochemistry: antibodies against GFAP are used to visualise astrocytes. • ȕAPP (ȕ-Amyloid precursor protein) immunohistochemisty: marker for acute axonal damage. Positivity indicates interruption to fast intra-axonal transport (14). • HLA-DR immunohistochemistry: marker for antigen presenting cells (microglial cells/ macrophages).

Besides the well circumscribed areas of demyelination, the WM and GM outside lesions are affected by a various degree of diffuse changes, which become most prominent in the progressive phase of the disease. Microscopic changes include microglial activation, gliosis, and, most importantly, axonal loss (11). As a consequence of longstanding disease, the brain shrinks. Atrophy affects both the white and grey matter, and is evident by the presence of wide sulci and ventricles and loss of brain weight (21,22). These degenerative changes are a major cause of permanent neurological disability in MS patients (23).

13 Chapter 1

Figure 3: Types of cortical lesions

The classification of Bö et al (18) distinguishes mixed GM-WM lesions (Type I) from purely intracortical lesions (Type II-IV). Type I: lesions constitute approximately 15% of cortical lesions and affect both grey-, and subcortical white matter. Type II: lesions are small lesions, entirely within the cerebral cortex. Type III: or subpial lesions extend from the pia downwards without reaching subcortical WM. These are the most frequent lesion types with 60% and account for up to 67% of the total cortical demyelinated area, especially in progressive MS. They can spread over all cortical areas, leading to a picture of “general cortical subpial demyelination”. (19) Type IV: lesions affect the whole span of the cortex, and reach from the pia to the subcortical WM.

The problem pathologists face: Despite showing good resolution and specificity, histopathological specimen are handicapped by the limitations of 2D sampling (slice thickness of micrometers). Pathological assessment allows only one snapshot in time, and gives no information about the evolution of pathological changes. Therefore, the complex series of events leading to MS lesions are not yet fully understood, and the evolution of the earliest demyelinating lesions is still discussed. Equally, factors influencing the considerable interindividual variation in lesion burden and distribution are unknown. Damage to the brain is caused both by inflammation (leading to focal demyelination) and neurodegeneration (leading to axonal loss and atrophy), but the temporal and causal relationship of these two forces is also still a matter of debate.

14 Introduction

06)520$1(852/2*,67·69,(: Chapter To a Neurologist, MS patients can present in many different ways, as MS lesions can 1 develop anywhere in the central nervous system. Examples include disturbed vision, numbness, weakness, ataxia, incontinence, fatigue, neuropsychological impairment, or depression. As opposed to these “negative” symptoms, which are characterised by loss of function due to loss of axonal conduction, “positive” symptoms frequently occur. These are caused by hyperexcitability of axons and can cause tingling paraesthesiae, trigeminal neuralgia and pain (3). The clinical course of MS varies considerably between patients, however, the majority of MS patients (~85%) have a biphasic disease course, beginning with the primary phase termed relapsing-remitting MS (RR-MS) (Figure 4). This period is characterised by alternating episodes of neurological disability (so called exacerbations or relapses) separated by periods of recovery that can last for many years. A relapse is defined as a period of neurological worsening, which lasts for at least 24 hours and occurs in the absence of fever and infection. However, lesions are also frequently clinically silent and do not cause clinical symptoms. Approximately 90% of RR-MS patients turn into a secondary-progressive phase (SP-MS) within 25 years. This phase is characterised by steady neurological worsening without recovery. A small group of MS patients (10%) follow a primary progressive disease course (PP-MS), which is characterised by steady decline in neurological functioning from the beginning, usually presenting as a myelopathic gait disorder without relapses. About 5% of MS patients suffer from a progressive disease course accompanied by relapses with or without recovery, which is referred to as progressive-relapsing MS (PR-MS) (25-29). The description of the clinical course has been recently refined and includes now also a consideration of disease activity and progression (30). The diagnosis of MS is based on the principle that demyelination occurs on more than one occasion (so-called dissociation in time) and in more than one part of the CNS (so-called dissociation in space)(Figure 5). In principle, the diagnosis can be made on clinical grounds alone, however, additional paraclinical tests can be useful to rule out other diagnoses (31-33). Presently, magnetic resonance imaging (MRI) has become a prominent role in the diagnosis of the disease (34). The differential diagnosis of MS includes diseases which mimic clinical features (relapsing course), and/or imaging appearances of MS (multiple lesions affecting the CNS). Examples include infectious diseases (e.g.: lyme borreliosis, HIV, PML, Whipple’s disease), other demyelinating diseases (e.g.: ADEM, NMO), immunologically mediated disorders (e.g.: Sarcoidosis, Sjögren’s syndrome, systemic lupus erythematodes, cerebral vasculitis), adult onset leucodystrophies, and vascular disease (35,36). Rarely, lesions produce clinical features and imaging appearances that mimic those of a brain neoplasm and lead to biopsy (37).

15 Chapter 1

Figure 4: The majority of MS patients show a biphasic disease course

The very first clinical episode in which a previously healthy person has symptoms and signs suggestive of MS, is referred to as clinically isolated syndrome (CIS) (rhomb). 30-70 percent of these patients will experience another episode, which is the defining event for the diagnosis of RR-MS (red arrow) (24). Disability caused by relapses is likely to resolve in the relapsing remitting phase of the disease due to numerous adaptive and compensatory mechanisms (left side of figure). Permanent disability predominantly occurs in the progressive phase and is caused by axonal loss and exhaustion of compensatory capacity (right side of figure). Arrows indicate relapses.

Figure 5: The clinical diagnosis of MS

For the diagnosis of MS, lesions/symptoms have to show dissemination in space (DIS) and time (DIT). If a patient has at least 2 clearly distinguishable episodes in 2 diff erent anatomical locations of the CNS, the diagnosis of RR-MS can be made. (N=no need for further confirmation) If a patient has only one episode eff ecting one site, either presence of DIS and DIT on MRI is necessary to confirm the diagnosis, or a second attack has to be awaited.

16 Introduction

Therapeutic options for MS have rapidly increased in the recent years. All approved Chapter disease modifying treatments target inflammation (that is, the relapsing phase of the disease), and reduce the frequency and severity of clinical attacks. First generation 1 treatments include interferon-beta products and glatiramer acetate, which reduce the annualised relapse rate by one third, but cannot anticipate disease progression. Newer, “second generation” treatments (for example natalizumab, fingolimod, cladribine, teriflunomide and alemtuzumab) are 50% to more than 60% effective against relapses, but more serious side effects and an unknown long term safety profile restricts their use to more severe cases (38-40).

The problems Neurologists face Unsolved questions accompany the Neurologist in almost all clinical aspects of the disease, from diagnosis, to prognosis and treatment. Finding a treatment for the progressive forms of MS remains an unsolved challenge, as presently no treatment can reliably repair injured axons or protect neurons. Furthermore, there is limited evidence to guide treatment decisions in those patients who can be treated: Selecting the most suitable medication would be easier with a notion of the future disease course to estimate its risks and benefits. The disease course, however, is unpredictable and highly variable in the individual. The spectrum ranges from mild to very aggressive courses, which may even lead to death due to extensive demyelination. In addition, the time to conversion from RR to SPMS and therefore to permanent disability is difficult to predict in the individual and can presently only be made in retrospect. The classification of the MS phenotypes, although recently revised and more diverse (30), is still suboptimal, as is our clinical grading system, the EDSS. These systems do not account for differences within the groups/patients and have unsatisfactory imaging correlates.

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For a radiologist, MR imaging is the modality of choice for the assessment of patients with (suspected) MS. WM lesions appear hyperintense (i.e. brighter than the surrounding brain tissue) on T2-weighted, Proton Density (PD) and Fluid Attenuated Inversion Recovery (FLAIR) images (Figure 6). They show a predominance for areas with a high venular density, such as the periventricular and subcortical white matter of the forebrain, the optic nerves, cerebellar peduncles, subcortical U fibers and spinal cord (41,42).

17 Chapter 1

On T1-weighted images, 10-30% of MS lesions appear hypointense (i.e. darker than the surrounding brain tissue), and are referred to as ‘black holes’. T1 hypo-intensity can be transient in acute lesions and reflects oedema and demyelination. If back holes persist after the inflammatory phase is over, they indicate areas of axonal loss and therefore severe tissue destruction, and correlate with disability (43). Both T1- and T2- weighted images cannot distinguish between pre-active, active, chronic active or re-myelinated lesions (44,45). A greater pathological specificity can be obtained by using gadolinium based contrast agents on T1 weighted images: Active lesions typically enhance and appear “bright” on T1-weighted images (see Figure 6E)(46,47). The mentioned conventional MRI sequences are recommended in the work-up of MS patients, in which lesion quantification and localisation is used as a tool for diagnosis and follow-up. As opposed to histopathology, MRI allows for sampling of the whole brain and is able to sensitively reflect dissemination in time and space. Therefore, MRI has developed to the most important paraclinical tool in the diagnosis and follow up. More than half of adults with CIS and over 95% of patients with clinically definite MS have positive findings on MR scans (48). It is however not possible to diagnose MS on radiological grounds alone: In the radiologically isolated syndrome, MRI findings are characteristic of MS, but clinically, typical squelae are absent and therewith a diagnosis of MS cannot be made. The white matter outside lesions as well as the grey matter can appear remarkably normal on conventional MRI despite considerable histopathological damage. Advanced MRI techniques, including quantitative MRI techniques, (ultra) high field applications, and new sequences such as DIR, are superior in the examination of tissue damage in both lesions and so called ‘normal appearing’ grey and white matter. Box 3 gives an overview of the advanced imaging techniques used in this thesis.

18 Introduction

Figure 6: Typical MS lesions as seen on magnetic resonance imaging Chapter 1

MS lesions are round to ovoid in shape and located in specific areas of the brain. Their size can range from a few millimeters to more than a centimeter in diameter. A: Multiple periventricular lesions, which are typically oriented perpendicular to the lateral ventricles (axial PD-weighted image). This centripetal perivenular extension is common, causing the appearance of so-called “ dawson’s fingers”. B: Infratentorial lesions (axial T2-weighted image), C: Lesion in the dorsal aspect of the cervical spine. Typical spinal cord lesions are cigar shaped and eccentrically located, do not involve the entire diameter of the medulla, and do not exceed more than two vertebral segments in length (sagittal T2-weighted image). D: (juxta)cortical lesions (sagittal FLAIR image). E: Contrast enhancing, active, juxtacortical lesion. In active lesions, the blood-brain barrier brakes down and gadolinium extravasates to the CNS. Enhancement is detectable on average 3 weeks after formation of the lesion. (sagittal T1-weighted image with Gadolinium), F: Hypointense (=black) lesions on T1 weighted images are called “black holes”. They are characteristic for MS and represent severe tissue damage and axonal loss (axial T1-weighted image), or inflammation related oedema.

19 Chapter 1

Box 3: MRI techniques used in this thesis

DIR (double inversion recovery sequence) This technique simultaneously suppresses white matter and CSF signals, which leads to a superior delineation of the grey matter (49). The use of DIR sequences has improved the sensitivity of MRI to detect cortical lesions in vivo (50) (Figure 7).

Quantitative MRI The images or ‘maps’ which are produced by quantitative MRI are conceptually different from conventional MRI images. Individual pixel values have a numerical meaning (for example, milliseconds), rather than representing a signal intensity (grey value) on an arbitrary scale. It can therefore reflect the severity and nature of underlying tissue changes. Quantitative MR images were analysed by a region of interest (ROI) approach and/or a histogram analysis in this thesis. ROIs: Are drawn manually on predefined structures of interest (for example on lesions and normal appearing white matter), which makes the ROIs spatially specific. The ‘output’ is the mean value of all pixels within the ROI (for example, milliseconds). Histogram analysis: With histograms, the whole brain, or whole brain regions (for example the grey matter) can be analysed. The histogram is a frequency distribution showing the number of voxels with a particular range of MR parameter values. The peak location of the histogram corresponds to the parameter value that is most common in the brain tissue. The peak height corresponds to the number of voxels with this parameter value. In histograms, any information on the location of abnormalities is lost (51).

DTI (diffusion tensor imaging) DTI reflects the diffusion of water molecules by estimating the average distance water molecules displace during a period of time. The movement of water molecules depends on the surrounding structures: Fibre tracts allow diffusion along their direction but hinder diffusion perpendicular to them. Diffusion is therefore anisotropic (i.e.: directionally dependent). On the other hand, water molecules in the cerebrospinal fluid can move unrestrictedly in all directions, which is referred to as isotropy ( i.e.: uniformity of diffusion in all directions). Diffusion can be mathematically characterised by a 3x3 tensor, which represents an ellipsoid. To measure the diffusion tensor at least 6 measurements from different directions are necessary per voxel (52,53).

FA (fractional anisotropy) Is a scalar value between zero and one that describes the degree of anisotropy (= restrictedness) in an diffusion process. A value of “zero” means that diffusion is unrestricted or isotropic (and equal in all directions). A value of “one” means that diffusion is anisotropic, or restricted, and occurs only along one axis. Therefore, FA quantifies the pointedness of the ellipsoid or the degree of diffusion directionality. A decrease in FA reflects a reduction in white matter tract integrity.

20 Introduction

Figure 7: Example of Double Inversion Recovery (DIR) images Chapter 1

DIR images in A: axial and B: coronal planes. Arrows indicate cortical lesions.

The problem with imaging In comparison to histopathology, MRI allows for sampling of the whole brain in a 3-dimensional way. This advantage however, comes with the downside of poorer resolution (millimeters vs micrometers in histopathology) and poorer contrast (specificity). This is reflected in an only moderate correlation between lesion load as seen on conventional MRI, and disability (60,61). Insufficient contrast and resolution are one explanation for the so called “clinico-radiological paradox” , namely the inability of conventional MRI to quantify the full extent and nature of MS related damage (for example, cortical lesions are nearly completely invisible with conventional MRI) (62). In addition, not all visible changes (for example, diffuse abnormalities) are considered in the radiological evaluation of MS. The radiologist is confronted with the bewildering situation that one can use MRI as tool to diagnose MS if a typical clinical sign is present, however, the lesions seen on MRI cannot be used to inform about the clinical state (i.e. relapsing or progressive) of a patient. Many of the advanced MRI techniques are still confined to research settings, or can only applied in specialised centres. Due to a lack of standardisation, they are not (yet) adopted in the MR consenus guidelines and do not yet play a role in clinical routine.

21 Chapter 1

AIMS AND OUTLINE OF THIS THESIS:

The last paragraphs demonstrate how each field “sees” MS from different angles, therewith offering its own specific view on the disease. The limitations that are attached to the various research methods and disciplines can be partly overcome by combining different methods and fields. Therefore, in this thesis, the three cornerstones of MS research, pathology, imaging and clinical neurology, were integrated with the main focus on cortical pathology, diffuse white matter damage and atypical MS lesions. Most of the studies in this thesis combine quantitative and qualitative MRI techniques and histopathology and are conducted as post-mortem studies. An overview over the postmortem technique as we used it is given in Chapter 2. In Chapter 3, grey matter damage in MS was addressed by two studies. The sensitivity of DIR for GM lesions is evaluated in Chapter 3.1, and the characteristics of cortical lesions are studied in Chapter 3.2. Chapter 4 presents our research on diffusely abnormal white matter (DAWM), which is introduced as a new entity in Chapter 4.1. These post-mortem results were applied in progressive MS in vivo in Chapter 4.2. Chapter 5 addresses atypical white matter lesions in multiple sclerosis and a new classification based on conventional MRI is suggested. In Chapter 6, the same technique is applied to a different field, and used to characterize white matter hyperintensities in patients with and without dementia (Chapter 6.2.). Before, an overview over post-mortem studies in cerebral small vessel disease is given in Chapter 6.1. The results of these chapters will be summarized and discussed in Chapter 7.

22 Introduction

REFERENCES Chapter

1. PETERSON JW, TRAPP BD. 12. DAVID ELLISON SL, LEILA MARIA CARDAO Neuropathobiology of multiple sclerosis. CHIMELLI, BRIAN HARDING, JAMES LOWE, 1 Neurol Clin. 2005;23(1):107-29, vi-vii. HARRY V VINTERS, SEBASTIAN BRANDNER, 2. COYLE PK. Gender issues. Neurol Clin. WILLIAM YOUNG. Neuropathology. David 2005;23(1):39-60, v-vi. Ellison SL, editor: Elsevir; 2012. 3. COMPSTON A, COLES A. Multiple sclerosis. 13. JEANS A, ESIRI M. Brain histology. Pract Lancet. 2008;372(9648):1502-17. Neurol. 2008;8(5):303-10. 4. SAWCER S. The major cause of multiple 14. GEDDES JF, VOWLES GH, BEER TW, sclerosis is environmental: genetics ELLISON DW. The diagnosis of diffuse has a minor role--no. Mult Scler. axonal injury: implications for forensic 2011;17(10):1174-5. practice. Neuropathol Appl Neurobiol. 5. SAWCER S. The complex genetics of 1997;23(4):339-47. multiple sclerosis: pitfalls and prospects. 15. BROWNELL B, HUGHES JT. The distribution Brain : a journal of neurology. 2008;131(Pt of plaques in the cerebrum in multiple 12):3118-31. sclerosis. J Neurol Neurosurg Psychiatry. 6. MARRIE RA. Environmental risk factors 1962;25:315-20. in multiple sclerosis aetiology. Lancet 16. GEURTS JJ, BO L, POUWELS PJ, Neurol. 2004;3(12):709-18. CASTELIJNS JA, POLMAN CH, BARKHOF 7. VAN DER VALK P, DE GROOT CJ. Staging of F. Cortical lesions in multiple sclerosis: multiple sclerosis (MS) lesions: pathology combined postmortem MR imaging and of the time frame of MS. Neuropathol Appl histopathology. AJNR Am J Neuroradiol. Neurobiol. 2000;26(1):2-10. 2005;26(3):572-7. 8. PATRIKIOS P, STADELMANN C, KUTZELNIGG 17. GILMORE CP, BO L, OWENS T, LOWE J, A, RAUSCHKA H, SCHMIDBAUER M, ESIRI MM, EVANGELOU N. Spinal cord LAURSEN H, et al. Remyelination is gray matter demyelination in multiple extensive in a subset of multiple sclerosis sclerosis-a novel pattern of residual patients. Brain : a journal of neurology. plaque morphology. Brain Pathol. 2006;129(Pt 12):3165-72. 2006;16(3):202-8. 9. LUCCHINETTI C, BRUCK W, PARISI 18. BO L, GEURTS JJ, MORK SJ, VAN DER J, SCHEITHAUER B, RODRIGUEZ M, VALK P. Grey matter pathology in multiple LASSMANN H. Heterogeneity of multiple sclerosis. Acta Neurol Scand Suppl. sclerosis lesions: implications for the 2006;183:48-50. pathogenesis of demyelination. Ann 19. BO L, VEDELER CA, NYLAND H, TRAPP Neurol. 2000;47(6):707-17. BD, MORK SJ. Intracortical multiple 10. GEURTS JJ. Is progressive multiple sclerosis lesions are not associated with sclerosis a gray matter disease? Ann increased lymphocyte infiltration. Mult Neurol. 2008;64(3):230-2. Scler. 2003;9(4):323-31. 11. KUTZELNIGG A, LUCCHINETTI CF, 20. VAN HORSSEN J, BRINK BP, DE VRIES STADELMANN C, BRUCK W, RAUSCHKA H, HE, VAN DER VALK P, BO L. The blood- BERGMANN M, et al. Cortical demyelination brain barrier in cortical multiple sclerosis and diffuse white matter injury in multiple lesions. J Neuropathol Exp Neurol. sclerosis. Brain : a journal of neurology. 2007;66(4):321-8. 2005;128(Pt 11):2705-12.

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21. GR A SSIOT B, DESGR ANGES B, 30. LUBLIN FD, REINGOLD SC, COHEN JA, EUSTACHE F, DEFER G. Quantification CUTTER GR, SORENSEN PS, THOMPSON and clinical relevance of brain atrophy AJ, et al. Defining the clinical course of in multiple sclerosis: a review. J Neurol. multiple sclerosis: the 2013 revisions. 2009;256(9):1397-412. Neurology. 2014;83(3):278-86. 22. KUTZELNIGG A, LASSMANN H. Cortical 31. MCDONALD WI, COMPSTON A, EDAN G, lesions and brain atrophy in MS. J Neurol GOODKIN D, HARTUNG HP, LUBLIN FD, Sci. 2005;233(1-2):55-9. et al. Recommended diagnostic criteria 23. DUTTA R, TRAPP BD. Mechanisms of for multiple sclerosis: guidelines from neuronal dysfunction and degeneration the International Panel on the diagnosis in multiple sclerosis. Prog Neurobiol. of multiple sclerosis. Ann Neurol. 2011;93(1):1-12. 2001;50(1):121-7. 24. FISNIKU LK, BREX PA, ALTMANN DR, 32. POLMAN CH, REINGOLD SC, EDAN G, MISZKIEL KA, BENTON CE, LANYON R, et FILIPPI M, HARTUNG HP, KAPPOS L, et al. al. Disability and T2 MRI lesions: a 20-year Diagnostic criteria for multiple sclerosis: follow-up of patients with relapse onset 2005 revisions to the “McDonald Criteria”. of multiple sclerosis. Brain : a journal of Ann Neurol. 2005;58(6):840-6. neurology. 2008;131(Pt 3):808-17. 33. POSER CM, PATY DW, SCHEINBERG L, 25. CONFAVREUX C, VUKUSIC S, MOREAU T, MCDONALD WI, DAVIS FA, EBERS GC, et Adeleine P. Relapses and progression of al. New diagnostic criteria for multiple disability in multiple sclerosis. N Engl J sclerosis: guidelines for research Med. 2000;343(20):1430-8. protocols. Ann Neurol. 1983;13(3):227-31. 26. COTTRELL DA, KREMENCHUTZKY M, RICE 34. POLMAN CH, REINGOLD SC, BANWELL GP, KOOPMAN WJ, HADER W, BASKERVILLE B, CLANET M, COHEN JA, FILIPPI M, et al. J, et al. The natural history of multiple Diagnostic criteria for multiple sclerosis: sclerosis: a geographically based study. 2010 revisions to the McDonald criteria. 5. The clinical features and natural history Ann Neurol. 2011;69(2):292-302. of primary progressive multiple sclerosis. 35. BRINAR VV, HABEK M. Rare infections Brain : a journal of neurology. 1999;122 ( mimicking MS. Clin Neurol Neurosurg. Pt 4):625-39. 2010;112(7):625-8. 27. LUBLIN FD, REINGOLD SC. Defining the 36. ECKSTEIN C, SAIDHA S, LEVY M. A clinical course of multiple sclerosis: differential diagnosis of central nervous results of an international survey. National system demyelination: beyond multiple Multiple Sclerosis Society (USA) Advisory sclerosis. J Neurol. 2012;259(5):801-16. Committee on Clinical Trials of New 37. LUCCHINETTI CF, GAVRILOVA RH, METZ I, Agents in Multiple Sclerosis. Neurology. PARISI JE, SCHEITHAUER BW, WEIGAND S, 1996;46(4):907-11. et al. Clinical and radiographic spectrum 28. THOMPSON AJ, POLMAN CH, MILLER DH, of pathologically confirmed tumefactive MCDONALD WI, BROCHET B, FILIPPI M, et multiple sclerosis. Brain : a journal of al. Primary progressive multiple sclerosis. neurology. 2008;131(Pt 7):1759-75. Brain : a journal of neurology. 1997;120 ( 38. CASTRO-BORRERO W, GRAVES D, Pt 6):1085-96. FROHMAN TC, FLORES AB, HARDEMAN 29. WEINSHENKER BG. Natural history of P, LOGAN D, et al. Current and emerging multiple sclerosis. Ann Neurol. 1994;36 therapies in multiple sclerosis: a Suppl:S6-11. systematic review. Ther Adv Neurol Disord. 2012;5(4):205-20.

24 Introduction

39. KILLESTEIN J, RUDICK RA, POLMAN CH. 49. BEDELL BJ, NARAYANA PA. Implementation Oral treatment for multiple sclerosis. and evaluation of a new pulse sequence Chapter Lancet Neurol. 2011;10(11):1026-34. for rapid acquisition of double inversion 40. RIO J, COMABELLA M, MONTALBAN X. recovery images for simultaneous Multiple sclerosis: current treatment suppression of white matter and CSF. J 1 algorithms. Curr Opin Neurol. Magn Reson Imaging. 1998;8(3):544-7. 2011;24(3):230-7. 50. GEURTS JJ, POUWELS PJ, UITDEHAAG BM, 41. FAZEKAS F, BARKHOF F, FILIPPI M, POLMAN CH, BARKHOF F, CASTELIJNS GROSSMAN RI, LI DK, MCDONALD WI, et al. JA. Intracortical lesions in multiple The contribution of magnetic resonance sclerosis: improved detection with 3D imaging to the diagnosis of multiple double inversion-recovery MR imaging. sclerosis. Neurology. 1999;53(3):448-56. Radiology. 2005;236(1):254-60. 42. LASSMANN H. The pathologic substrate 51. TOFTS PS, DAVIES GR, DEHMESHKI J. of magnetic resonance alterations in Histograms: Measuring Subtle Diffuse multiple sclerosis. Neuroimaging Clin N Disease. Quantitative MRI of the Brain: Am. 2008;18(4):563-76, ix. John Wiley & Sons, Ltd; 2004. p. 581-610. 43. VAN WAESBERGHE JH, KAMPHORST W, 52. LE BIHAN D, BRETON E, LALLEMAND D, DE GROOT CJ, VAN WALDERVEEN MA, GRENIER P, CABANIS E, LAVAL-JEANTET CASTELIJNS JA, RAVID R, et al. Axonal M. MR imaging of intravoxel incoherent loss in multiple sclerosis lesions: motions: application to diffusion and magnetic resonance imaging insights perfusion in neurologic disorders. into substrates of disability. Ann Neurol. Radiology. 1986;161(2):401-7. 1999;46(5):747-54. 53. PIERPAOLI C, JEZZARD P, BASSER PJ, 44. Barkhof F, Bruck W, De Groot CJ, Bergers BARNETT A, DI CHIRO G. Diffusion E, Hulshof S, Geurts J, et al. Remyelinated tensor MR imaging of the human brain. lesions in multiple sclerosis: magnetic Radiology. 1996;201(3):637-48. resonance image appearance. Arch 54. BEAULIEU C. The basis of anisotropic Neurol. 2003;60(8):1073-81. water diffusion in the nervous system - a 45. DE GROOT CJ, BERGERS E, KAMPHORST technical review. NMR Biomed. 2002;15(7- W, RAVID R, POLMAN CH, BARKHOF F, et 8):435-55. al. Post-mortem MRI-guided sampling 55. LAULE C, KOZLOWSKI P, LEUNG E, LI of multiple sclerosis brain lesions: DK, MACKAY AL, MOORE GR. Myelin increased yield of active demyelinating water imaging of multiple sclerosis at and (p)reactive lesions. Brain : a journal 7 T: correlations with histopathology. of neurology. 2001;124(Pt 8):1635-45. Neuroimage. 2008;40(4):1575-80. 46. COTTON F, WEINER HL, JOLESZ FA, 56. LAULE C, LEUNG E, LIS DK, TRABOULSEE GUTTMANN CR. MRI contrast uptake in AL, PATY DW, MACKAY AL, et al. Myelin water new lesions in relapsing-remitting MS imaging in multiple sclerosis: quantitative followed at weekly intervals. Neurology. correlations with histopathology. Mult 2003;60(4):640-6. Scler. 2006;12(6):747-53. 47. FILIPPI M. Enhanced magnetic resonance 57. THOMAS JD. Magnetization transfer in imaging in multiple sclerosis. Mult Scler. magnetic resonance imaging. Radiol 2000;6(5):320-6. Technol. 1996;67(4):297-306. 48. PATY DW. Magnetic resonance imaging 58. FILIPPI M, AGOSTA F. Magnetization in the assessment of disease activity transfer MRI in multiple sclerosis. J in multiple sclerosis. Can J Neurol Sci. Neuroimaging. 2007;17 Suppl 1:22S-6S. 1988;15(3):266-72.

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59. SCHMIERER K, SCARAVILLI F, ALTMANN DR, BARKER GJ, MILLER DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol. 2004;56(3):407-15. 60. BARKHOF F. MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS). Mult Scler. 1999;5(4):283-6. 61. BREX PA, CICCARELLI O, O’RIORDAN JI, SAILER M, THOMPSON AJ, MILLER DH. A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N Engl J Med. 2002;346(3):158-64. 62. BARKHOF F. The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol. 2002;15(3):239-45.

26 Introduction

Chapter 1

27

2

THE POSTMORTEM METHOD

2.1

TRANSLATING PATHOLOGY IN MULTIPLE SCLEROSIS: THE COMBINATION OF POST MORTEM IMAGING, HISTOPATHOLOGY AND CLINICAL FINDINGS

Alexandra Seewann* Evert-Jan Kooi* Stefan D. Roosendaal Frederik Barkhof Paul van der Valk Jeroen J.G. Geurts

* both authors contributed equally

Acta Neurol Scand. 2009 Jun; 119(6):349-55. Chapter 2

ABSTRACT

Background: Studies combining postmortem magnetic resonance imaging (MRI) and histopathology have provided important insights into the abnormalities reflected by MRI. Materials and methods: A short overview of these studies applied to multiple sclerosis (MS) is provided in this review, and the Amsterdam postmortem imaging protocol is specifically highlighted. Conclusion: Postmortem MRI and histopathology correlation studies have enabled a direct translation of basic pathology in MS to the clinical setting, and have simultaneously served as a biological validation of new MRI techniques.

32 The post-mortem method

INTRODUCTION

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, most commonly affecting young adults and leading to substantial neurological disability (1, 2). The majority (݂80%) of MS patients present with a relapsing-remitting disease course, which is characterized by a waxing and waning of Chapter neurological symptoms. Over time, the initial (partial) recovery from relapses gives way to a steady progression and irreversible neurological deficits, the so-called secondary 2 progressive phase. About 15% of the cases follow a primary progressive course, which is characterized by disease progression from onset without intermittent remissions (3-7). Clinically, MS is characterized by a wide range of symptoms, dependent on the localization of the damage. The most common symptoms, include visual deterioration, sensory problems, central paresis, brainstem symptoms and neuropsychological impairment including memory deficits (8). Histopathologically, MS is characterized by multifocal lesions in the white matter (WM), with various degrees of de- and remyelination, axonal damage and ⁄ or loss, gliosis, blood-brain barrier leakage, infiltrated T lymphocytes and macrophages and complement deposition (9, 10). Besides the occurrence of MS lesions in the WM, lesions are also extensively present in the cerebral and cere-bellar cortex, deep grey matter (GM) and spinal cord (11-18). Due to the possibility of following lesions over time in vivo, magnetic resonance imaging (MRI) has considerably contributed to the understanding and early diagnosis of MS (19-21). MRI is also routinely used to monitor disease course and progression in MS patients and as a surrogate outcome marker in clinical trials. However, for correlations with clinical measures, it is not only important to be able to identify abnormalities on MRI, but also to reliably interpret these abnormalities in terms of underlying pathology. Post mortem MRI and histopathology correlation studies have provided important insights into the pathology reflected by MRI. In this review we provide a brief overview of these post mortem studies, with a special emphasis on the Amsterdam post mortem imaging protocol that was set up as a tool to integrate histopathology, MRI and, ultimately, clinical findings in MS. Future challenges and limitations of combined post mortem MRI and histopathology research will also be discussed.

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Figure 1: Combined MRI and histopathology results can be translated to the clinical setting

Comparison of magnetic resonance images (MRI, top) with histopathology (left) is essential to resolve the discrepancies between MRI and clinical measures (right). The post mortem method is not only valuable in translating MRI features in histopathological terms but is also used vice versa in translating pathology to MRI, thereby integrating findings from bench to bedside.

TRANSLATING MS PATHOLOGY TO THE CLINIC: A MULTIDISCIPLINARY APPROACH

In MS, only modest correlations have been found between abnormalities visible on conventional MRI and clinical disability (22, 23). This discrepancy has been referred to as the clinicoradiological paradox (24). Several explanations were proposed in an attempt to explain this discrepancy. First, the lack of correlation between conventional MRI and disability may be explained by a lack of pathological specificity of conventional MRI techniques. For example, using standard T2-weighted MRI sequences, it is generally possible neither to distinguish between lesions with varying degrees of demyelination, gliosis or axonal damage nor to distinguish between remyelinated and non-remyelinated lesions (20, 25). Second, it was suggested that factors other

34 The post-mortem method than the MRI visible focal WM lesions might contribute to the development of clinical disability, i.e. invisible pathology in the so-called normal-appearing WM and in the GM. Over the last years, research activities in different fields have considerably contributed to our understanding of disease mechanisms in MS. Pathological studies have identified diffuse and subtle damage in the WM outside focal WM lesions as well as extensive cortical demyelination in progressive MS patients (26-29). Subsequently, new Chapter MRI tech-niques were introduced that proved more sensitive to these abnormalities than conventional MRI (30-33). Clinical studies identified factors that contribute to or 2 even predict future disability (34, 35). Direct comparison of MRI findings with histopathology has proved essential to bridge the marked discrepancies between MRI and clinical measures. The Amsterdam post mortem protocol (see Box) is not only valuable in terms of translating MRI features into histopathological terms (i.e. what do we see on MRI?), but is also used vice versa in translating pathology to MRI (i.e. can we image cortical GM lesions, diffuse WM damage and remyelination?). Successfully combined MRI and histopathology results can then be further translated to the clinical setting (e.g. are T1-hypointense lesions, which were shown to reflect more extensive axonal loss, predictive of a higher disability?) Thus, correlative post mortem studies have an important bridging function: they have the ultimate potential to translate pathological processes to the clinical situation, thereby integrating findings from bench to bedside (Figure 1).

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Figure 2: Post mortem brain tissue sampling procedures

1a: Brain slice holder (BSH) containing a coronally cut brain slice; 1b: The BSH fits exactly in the standard head coil of the magnetic resonance imaging (MRI) system and allows scanning of up to seven brain slices per scan session. 2 & 3: MRI to (histo)pathology matching: areas of interest are chosen on the basis of MRI. 2a: 3D-fluid attenuated inversion recovery image, showing a selected area (red box) adjacent to the lateral ventricle (star), containing a subcortical lesion (arrowheads), and also including part of the gyrus cinguli (arrow). 2b: After scanning, the brain slices are cut in half(5mm) to reveal the imaged plane, and areas of interest can be excised. Adjacent structures (e.g. ventricle, cortex) are important for subsequent matching of tissue to MRI. 3: matching is facilitated greatly when using fully hemispheric brain slices, where cortical anatomy and WM lesions (arrowheads) may serve as landmarks. 3a: Luxol fast blue-PAS stain and 3b: T2 weighted image of the corresponding brain slice.

36 The post-mortem method

Box: Post mortem brain tissue sampling: ‘The Amsterdam protocol’ (36)

To use magnetic resonance imaging (MRI) for selection of tissue at autopsy, an adequate logistical setup is required. Our university medical centre closely cooperates with the Netherlands Brain Bank that runs a multiple sclerosis (MS) tissue donor program. Correlative neuropathology MRI studies are approved by the Institutional Ethics Review board, and prior to death, donors provide written informed consent for brain autopsy and use of material and clinical information for research purposes. To minimize the time to imaging and fixation, Chapter patients are quickly transported for autopsy, and a neuropathologist, autopsy technicians and MRI investigators are on call according to a rotation scheme. This procedure guarantees ultra-short postmortem delays (≤7 h). 2 Postmortem MRI may comprise whole-body (in situ) imaging or imaging of selected brain and spinal cord slices (ex vivo). When imaging in situ, the cerebrospinal fluid is in place and end-stage lesion loads and atrophy can be assessed. Also, focal or diffuse MR signal changes in the spinal cord can be investigated (23). Ex vivo brain slice imaging is performed on 10-mm-thick coronal brain slices and allows for a good matching with histology. Four or five brain slices are selected at autopsy based on the presence of macroscopically visible abnormalities. These slices are put into a specially devised perspex brain slice holder, which fits into a standard circularly polarized MRI head coil (Fig. 2). Standard proton density ⁄ T2- and T1-weighted images as well as more advanced 3D techniques like 3D-fluid attenuated inversion recovery (3D-FLAIR) are acquired. Additionally, quantitative MR sequences, such as magnetization transfer imaging, T1- and T2-relaxation time measurements and diffusion tensor imaging may be included in the protocol. T2- weighted and 3D-FLAIR images of the slice centres are printed and used at autopsy to select areas of abnormal signal intensity. The 10-mm slices are cut into two 5-mm-thick halves to correspond optimally with the imaged plane. From the two adjacent brain slice halves, and guided by MR imaging, tissue blocks are cut that can be either fixed in 4% formalin or snap- frozen in liquid nitrogen. Cut samples are photographed with a digital camera, and the cutting borders are outlined on the prints of the MR images to facilitate tissue-to-MRI matching at a later stage (Fig. 2). The fixed tissue is characterized by a histological staining panel consisting of conventional histochemistry like haematoxylin-eosin, Luxol Fast Blue-periodic acid Schiff and Bodian silver stains (which show general changes in cellularity and cell morphology and myelin and axonal density, respectively), and of immunohistochemistry (for staining of cell-specific markers like glial fibrillary acidic protein). During the matching procedure, the outline of a histological sample is digitally copied onto the postmortem T2-weighted MR image, making use of the photographs obtained at autopsy (see above. With the matching successfully completed, MRI and histopathological measures can be directly correlated (Fig. 2).

A BRIEF HISTORY OF FINDINGS

Magnetic resonance imaging-guided sampling of MS tissue at autopsy has significantly improved the yield of tissue block selection from postmortem brain slices, particularly concerning (p)reactive lesions (37) and postmortem MS studies have attempted to close

37 Chapter 2 the aforementioned clinicoradiological gap by studying cerebral WM (38-43), cortical GM (44, 45) and spinal cord tissue (23, 46-48), therewith improving MRI specificity. Post mortem MRI to histopathology studies in chronic patients showed that persisting T1- hypo intense lesions, so-called “black-holes”, contain a marked loss of myelinated axons (49,50). However, not only axonal loss, but also oedema, demyelination, cellular infiltration, and astrogliosis can lead to T1 hypointensity by increasing free extracellular water in so-called “acute black holes” (51,52). These findings explain the dynamic behaviour of black holes as seen in longitudinal MRI studies (53) with either persisting hypointensity, so-called “persisting black holes”, reflecting a more destructive pathology with both demyelination and axonal loss, or transient black holes, reflecting remyelination or resolving extracellular edema. This underlying pathological heterogeneity might also clarify inconclusive results of clinical studies correlating disability with T1 hypointensities (54-56). With this in mind, persistent black holes may be most useful as an MRI marker for treatment efficacy (57). Similarly, the combination of post mortem MRI and histopathology was used to study the radiological appearance of remyelination (25). Furthermore, although classically considered a WM disease, the introduction of myelin protein immuno-histochemistry spotlighted the high prevalence of GM lesions in MS (18,58,59). However, post mortem MRI and histopathology quantification of cortical MS lesions revealed that up to 95% percent of intracortical lesions may go undetected when using conventional MRI techniques (44). Subsequently, similar results were found using high-field MRI (45). GM abnormalities were also found outside the neocortex in MS patients, e.g. in the hippocampus (13). Hippocampal lesions as defined histopathologically, were later visualized by a newly developed in vivo MRI sequence (60). As 45-65% of MS patients are known to suffer from cognitive deficits (8,61), the next step would be to study the impact of these hippocampal lesions, as visualized by MRI, on the neuropsychological profiles of these patients. In a recent study (15), inflammatory lesions in the MS hypothalamus (62) could be related to abnormal cortisol levels and a worse disease course. Schmierer and colleagues (43,63-65) reported on the histopathological correlates of quantitative MRI measurements such as magnetisation transfer ratio (MTR) and diffusion tensor imaging (DTI), as well as on the effects of formalin-fixation on these quantitative MRI measures. They found that MTR is affected by myelin content in MS white matter (43), and that the fraction of macromolecular protons is dependent upon myelin density (63). FA and MD were found to be affected by myelin content and to a lesser degree by axonal density in post mortem MS brain (64), and finally, when compared to published in vivo data, all diffusivity measures were lower in unfixed MS brain material, but dropped even further following fixation (65).

38 The post-mortem method

CHALLENGES IN POST MORTEM MRI RESEARCH

Decrease of tissue quality is inevitable and one of the biggest challenges for postmortem research as it influences the interpretability of both MRI and histopathology measures. Quick work-up of tissue to keep the decay within limits is essential but requires more intricate logistics when MRI is to precede tissue sampling, Chapter including trained pathology and radiology staff on-call around the clock (seeBox ). Besides postmortem delay, excessive handling (risk of tissue damage as unfixed brain 2 is delicate), risk of dehydration and high temperature are potential threats to tissue quality. Although the imaging of fresh tissue may be preferable, the use of formalin-fixed material is a common alternative in postmortem MRI research. Formalin fixation does affect MRI measures, as relaxation times shorten and free-water diffusion decreases. This may complicate direct comparisons of postmortem and in vivo MRI values (65- 67). However, MRI of fixed material can still generate clinically relevant conclusions (65). Also, postmortem degeneration of tissue and decrease of pH may lead to a poorer quality of MRI data, which especially holds true for MR spectroscopy (68). As an alternative to MR spectroscopy on postmortem tissue, it was suggested that high- performance liquid chromatography can be used to correlate N-acetylaspartate levels to axonal density and volume in MS tissue (69)

FUTURE PLANS AND POSSIBILITIES

Recently, new MRI sequences and post-processing tools have been introduced in vivo, showing promising results and relevant associations with clinical measures in MS patients. However, future combined post mortem MRI and histopathological studies are needed for biological confirmation of some of these results. Among the new post-processing tools that have been developed are those that allow for visualization and subsequent quantification of WM tracts in vivo (see for instance the FMRIB Software Library, http://www.fmrib.ox.ac.uk). Although several DTI studies were conducted in MS (70), tools that allow for quantification of WM fibre tract “connectivity”, using DTI tractography, have only recently become available (71). Evaluation of the structural connectivity of a priori selected tracts, combined with relevant functional measures, promise to shed more light on interesting phenomena like functional reorganization (72). However, pathobiological correlates of connectivity decreases and changes in DTI eigenvalues have not yet been investigated. These correlates could be studied using in situ MRI.

39 Chapter 2

Similarly, tissue correlates of cortical thinning and atrophy could be investigated using post mortem MRI and histopathology correlations. Regional quantification of cortical thickness is possible using new post-processing tools and T1-weighted imaging. Focal thinning of the frontal and temporal cortex was reported in MS (73), and was related to WM lesion load (74) and clinical progression (75). Possible pathological correlates of cortical thickness reduction in MS may be loss of axons, damage to the neuropil or cortical lesions. In histopathological studies, cortical lesions were shown to be less inflammatory than WM lesions (51), which may explain their poor conspicuity on MRI (44). A relatively new MRI sequence, 3D double-inversion recovery showed improved cortical lesion detection compared with conventional MRI techniques in vivo (76). However, whether all cortical lesions in the MS brain are picked up by this new sequence should also be investigated in the postmortem setting. Finally, diffuse changes were described in the WM of patients with MS. As yet, there is still controversy whether this so-called “diffusely abnormal” or “dirty appearing” WM (77, 78) represents areas of newly developing lesions or a more chronic pathology. Unravelling the underlying pathology of those diffuse changes may be expected to contribute to a better understanding of disease outside focal abnormalities.

CONCLUSION

Correlative post mortem MRI-histopathology studies have the potential to provide crucial insights into the pathological specificity of (new) MRI methods and the interpretation of MRI results, which contributes to our understanding of the clinicoradiological paradox in MS. The Amsterdam post mortem imaging protocol has been developed as a tool to integrate histopathology and MRI data, which enables a direct translation of basic MS pathology into clinically relevant terms.

40 The post-mortem method

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20. MILLER DH, GROSSMAN RI, REINGOLD 30. FILIPPI M, CAMPI A, DOUSSET V et al: A SC, MCFARLAND HF: The role of magnetic magnetization transfer imaging study of resonance techniques in understanding normal-appearing white matter in multiple and managing multiple sclerosis. Brain sclerosis. Neurology 1995;45:478-482. 1998;121:3-24. 31. MACKAY A, LAULE C, VAVASOUR I, 21. RASHID W, MILLER DH: Recent advances in BJARNASON T, KOLIND S, MADLER B: neuroimaging of multiple sclerosis. Semin Insights into brain microstructure from Neurol 2008;28:46-55. the T2 distribution. Magn Reson Imaging 22. The IFNB Multiple Sclerosis Study Group 2006;24:515-525. and The University of British Columbia 32. VRENKEN H, GEURTS JJ, KNOL DL et al: MS/MRI Analysis Group: Interferon beta-1b Whole-brain T1 mapping in multiple in the treatment of multiple sclerosis: final sclerosis: global changes of normal- outcome of the randomized controlled appearing gray and white matter. trial. Neurology 1995;45:1277-1285. Radiology 2006;240:811-820. 23. BERGERS E, BOT JC, VAN DER VALK P 33. VRENKEN H, POUWELS PJ, GEURTS JJ et et al: Diffuse signal abnormalities in al: Altered diffusion tensor in multiple the spinal cord in multiple sclerosis: sclerosis normal-appearing brain tissue: direct postmortem in situ magnetic cortical diffusion changes seem related resonance imaging correlated with in to clinical deterioration. J Magn Reson vitro high-resolution magnetic resonance Imaging 2006;23:628-636. imaging and histopathology. Ann Neurol 34. TROJANO M, AVOLIO C, MANZARI C et 2002;51:652656. al: Multivariate analysis of predictive 24. BARKHOF F: The clinico-radiological factors of multiple sclerosis course with paradox in multiple sclerosis revisited. a validated method to assess clinical Curr Opin Neurol 2002;15:239-245. events. J Neurol Neurosurg Psychiatry 25. BARKHOF F, BRUCK W, DE GROOT CJ et al: 1995;58:300-306. Remyelinated lesions in multiple sclerosis: 35. VUKUSIC S AND CONFAVREUX C: Prognostic magnetic resonance image appearance. factors for progression of disability in the Arch Neurol 2003;60:1073-1081. secondary progressive phase of multiple 26. ALLEN IV, MCQUAID S, MIRAKHUR M, NEVIN sclerosis. J Neurol Sci 2003;206:135-137. G: Pathological abnormalities in the 36. BO L, GEURTS JJ, RAVID R, BARKHOF F: normal-appearing white matter in multiple Magnetic resonance imaging as a tool to sclerosis. Neurol Sci 2001;22:141-144. examine the neuropathology of multiple 27. BO L, GEURTS JJ, MORK SJ, VAN DER VALK sclerosis. Neuropathol Appl Neurobiol P: Grey matter pathology in multiple 2004;30:106-117. sclerosis. Acta Neurol Scand Suppl 37. DE GROOT CJ, BERGERS E, KAMPHORST W 2006;183:48-50. et al: Post mortem MRI-guided sampling of 28. KUTZELNIGG A, LUCCHINETTI multiple sclerosis brain lesions: increased CF, STADELMANN C et al: Cortical yield of active demyelinating and (p) demyelination and diffuse white matter reactive lesions. Brain 2001;124:1635- injury in multiple sclerosis. Brain 1645. 2005;128:2705-2712. 38. LAULE C, LEUNG E, LIS DK et al: Myelin water 29. VERCELLINO M, PLANO F, VOTTA B, imaging in multiple sclerosis: quantitative MUTANI R, GIORDANA MT, CAVALLA P: Grey correlations with histopathology. Mult matter pathology in multiple sclerosis. J Scler 2006;12:747-753. Neuropathol Exp Neurol 2005;64:1101-1107.

42 The post-mortem method

39. LAULE C, KOZLOWSKI P, LEUNG E, LI DK, 47. MOTTERSHEAD JP, SCHMIERER K, MACKAY AL, MOORE GR: Myelin water imaging CLEMENCE M et al: High field MRI correlates of multiple sclerosis at 7 T: correlations with of myelin content and axonal density in histopathology. Neuroimage 2008;40:1575- multiple sclerosis--a post mortem study 1580. of the spinal cord. J Neurol 2003;250:1293- 40. MOORE GR, LEUNG E, MACKAY AL et al: 1301. A pathology-MRI study of the short-T2 48. NIJEHOLT GJ, BERGERS E, KAMPHORST component in formalin-fixed multiple W et al: Post mortem high-resolution MRI Chapter sclerosis brain. Neurology 2000;55:1506- of the spinal cord in multiple sclerosis: a 1510. correlative study with conventional MRI, 41. NEWCOMBE J, HAWKINS CP, HENDERSON histopathology and clinical phenotype. 2 CL et al: Histopathology of multiple Brain 2001;124:154-166. sclerosis lesions detected by magnetic 49. VAN WAESBERGHE JH, KAMPHORST W, DE resonance imaging in unfixed postmortem GROOT CJ et al: Axonal loss in multiple central nervous system tissue. Brain sclerosis lesions: magnetic resonance 1991;114:1013-1023. imaging insights into substrates of 42. NIJEHOLT GJ, VAN WALDERVEEN MA, disability. Ann Neurol 1999;46:747-754. CASTELIJNS JA et al: Brain and spinal 50. VAN WALDERVEEN MA, KAMPHORST cord abnormalities in multiple sclerosis. W, SCHELTENS P et al: Histopathologic Correlation between MRI parameters, correlate of hypointense lesions on clinical subtypes and symptoms. Brain T1-weighted spin-echo MRI in multiple 1998;121:687-697. sclerosis. Neurology 1998;50:1282-1288. 43. SCHMIERER K, SCARAVILLI F, ALTMANN DR, 51. BRUECK W, BITSCH A, KOLENDA H, BRUECK BARKER GJ, MILLER DH: Magnetization Y, STIEFEL M, LASSMANN H: Inflammatory transfer ratio and myelin in postmortem central nervous system demyelination: multiple sclerosis brain. Ann Neurol correlation of magnetic resonance 2004;56:407-415. imaging findings with lesion pathology. 44. GEURTS JJ, BO L, POUWELS PJ, Ann Neurol. 1997;42:783-793. CASTELIJNS JA, POLMAN CH, BARKHOF 52. BITSCH A, KUHLMANN T, STADELMANN F: Cortical lesions in multiple sclerosis: C, LASSMANN H, LUCCHINETTI C, combined postmortem MR imaging and BRUECK W: A longitudinal MRI study of histopathology. AJNR Am J Neuroradiol histopathologically defined hypointense 2005;26:572-577. multiple sclerosis lesions. Ann Neurol. 45. GEURTS JJ, BLEZER EL, VRENKEN H et 2001;49:793-796. al: Does high-field MR imaging improve 53. VAN WAESBERGHE JH, VAN WALDERVEEN cortical lesion detection in multiple MA, CASTELIJNS JA et al: Patterns of sclerosis? J Neurol 2008;255:183-191. lesion development in multiple sclerosis: 46. BOT JC, BLEZER EL, KAMPHORST W et longitudinal observations with T1- al: The spinal cord in multiple sclerosis: weighted spin-echo and magnetization relationship of high-spatial-resolution transfer MR. AJNR Am J Neuroradiol quantitative MR imaging findings to 1998;19:675-683. histopathologic results. Radiology 54. VAN WALDERVEEN MA, BARKHOF F, 2004;233:531-540. 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.

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55. TRUYEN L, VAN WAESBERGHE JH, VAN 65. SCHMIERER K, WHEELER-KINGSHOTT CA, WALDERVEEN MA et al: Accumulation TOZER DJ et al: Quantitative magnetic of hypointense lesions (“black holes”) resonance of postmortem multiple on T1 spin-echo MRI correlates with sclerosis brain before and after fixation. disease progression in multiple sclerosis. Magn Reson Med 2008;59:268-277. Neurology 1996; 47:1469-1476 66. FOX CH, JOHNSON FB, WHITING J, ROLLER 56. O’RIORDAN JI, GAWNE CAIN M, COLES PP: Formaldehyde fixation. J Histochem A et al: T1 hypointense lesion load Cytochem 1985;33:845-853. in secondary progressive multiple 67. PFEFFERBAUM A, SULLIVAN EV, sclerosis: a comparison of pre versus ADALSTEINSSON E, GARRICK T, HARPER C: post contrast loads and of manual versus Postmortem MR imaging of formalin-fixed semi automated threshold techniques human brain. Neuroimage 2004;21:1585- for lesion segmentation. Mult Scler 1595. 1998;4:408-412. 68. ITH M, BIGLER P, SCHEURER E, KREIS R, 57. VAN DEN ELSKAMP IJ, LEMBCKE J, DATTOLA HOFMANN L, DIRNHOFER R, BOESCH V et al: Persistent T1 hypointensity as an MRI C: Observation and identification of marker for treatment efficacy in multiple metabolites emerging during post- sclerosis. Mult Scler 2008;14:764-769. mortem decomposition of brain tissue by 58. BO L, VEDELER CA, NYLAND H, TRAPP means of in situ 1H-magnetic resonance BD, MORK SJ: Intracortical multiple spectroscopy. Magn Reson Med. 2002;48: sclerosis lesions are not associated with 915-920. increased lymphocyte infiltration. Mult 69. BJARTMAR C, KIDD G, MOERK S, RUDICK Scler 2003;9:323-331. R, TRAPP BD: Neurological disability 59. KIDD D, BARKHOF F, MCCONNELL R, ALGRA correlates with spinal cord axonal loss PR, ALLEN IV, REVESZ T: Cortical lesions and reduced N-acetyl aspartate in in multiple sclerosis. Brain 1999;122 ( Pt chronic multiple sclerosis. Ann Neurol. 1):17-26. 2000;48:893-901. 60. ROOSENDAAL SD, MORAAL B, VRENKEN H 70. ROVARIS M, GASS A, BAMMER R et al: et al: In vivo MR imaging of hippocampal Diffusion MRI in multiple sclerosis. lesions in multiple sclerosis. J Magn Reson Neurology 2005;65:1526-1532. Imaging 2008;27:726-731. 71. CICCARELLI O, TOOSY AT, HICKMAN SJ 61. RAO SM: Neuropsychology of multiple et al: Optic radiation changes after optic sclerosis. Curr Opin Neurol 1995;8:216-220. neuritis detected by tractography- 62. HUITINGA I, DE GROOT CJ, VAN DER VALK based group mapping. Hum Brain Mapp P, KAMPHORST W, TILDERS FJ, SWAAB 2005;25:308-316. DF: Hypothalamic lesions in multiple 72. ROCCA MA, PAGANI E, ABSINTA M et sclerosis. J Neuropathol Exp Neurol al: Altered functional and structural 2001;60:1208-1218. connectivities in patients with MS: a 3-T 63. SCHMIERER K, TOZER DJ, SCARAVILLI F et study. Neurology 2007;69:2136-2145. al: Quantitative magnetization transfer 73. SAILER M, FISCHL B, SALAT D: Focal imaging in postmortem multiple sclerosis thinning of the cerebral cortex in multiple brain. J Magn Reson Imaging 2007;26:41-51. sclerosis. Brain 2003;126:1734-1744. 64. SCHMIERER K, WHEELER-KINGSHOTT CA, 74. CHARIL A, DAGHER A, LERCH JP, ZIJDENBOS BOULBY PA et al: Diffusion tensor imaging AP, WORSLEY KJ, EVANS AC: Focal cortical of post mortem multiple sclerosis brain. atrophy in multiple sclerosis: relation to Neuroimage 2007;35:467-477. lesion load and disability. Neuroimage 2007;34:509-517.

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75. CHEN JT, NARAYANAN S, COLLINS DL, SMITH SM, MATTHEWS PM, ARNOLD DL: Relating neocortical pathology to disability progression in multiple sclerosis using MRI. Neuroimage 2004;23:1168-1175. 76. GEURTS JJ, POUWELS PJ, UITDEHAAG BM, POLMAN CH, BARKHOF F, CASTELIJNS JA: Intracortical lesions in multiple Chapter sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 2005;236:254-260. 2 77. GE Y, GROSSMAN RI, BABB JS, HE J, MANNON LJ: Dirty-appearing white matter in multiple sclerosis: volumetric MR imaging and magnetization transfer ratio histogram analysis. AJNR Am J Neuroradiol 2003;24:1935-1940. 78. VOS CM, GEURTS JJ, MONTAGNE L et al: Blood-brain barrier alterations in both focal and diffuse abnormalities on postmortem MRI in multiple sclerosis. Neurobiol Dis 2005;20:953-960.3.1

45

3

GRAY MATTER IN MULTIPLE SCLEROSIS

3.1

32670257(09(5,),&$7,212)06 CORTICAL LESION DETECTION WITH 3D DIR.

Alexandra Seewann Evert-Jan Kooi Stefan D. Roosendaal Petra J Pouwels Mike P. Wattjes Paul van der Valk Frederik Barkhof Chris H. Polman Jeroen J.G. Geurts

Neurology 2012:31; 78(5):302-308. Comment in: Neurology 2012:31; 78(5):296-297

Oral presentation at the 26th congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), Gothenburg, Sweden, 2010.

Presented in ECTRIMS scientific highlights Chapter 3

ABSTRACT

Objective: To assess the sensitivity and specificity of 3D double inversion recovery (DIR) MRI for detecting multiple sclerosis (MS) cortical lesions (CLs) using a direct postmortem MRI to histopathology comparison. Methods: Single-slab 3D DIR and 3D fluid-attenuated inversion recovery (FLAIR) images of 56 matched fresh brain samples from 14 patients with chronic MS were acquired at 1.5 T. The images of both sequences were prospectively scored for CLs in consensus by 3 experienced raters who were blinded to histopathology and clinical data. Next, CLs were identified histopathologically and were scored again on 3D DIR and 3D FLAIR (retrospective scoring). CLs were classified as intracortical or mixed gray matter (GM)-white matter lesions. Deep GM lesions were also scored. False-positive scores were noted and, from this, specificity was calculated. Results: We found a sensitivity for 3D DIR to detect MS CLs of 18%, which is 1.6-fold higher than 3D FLAIR (improves to 37% with retrospective scoring; 2.0-fold higher than 3D FLAIR). We detected mixed GM-white matter lesions with a sensitivity of 83% using 3D DIR (65% sensitivity for 3D FLAIR), which improved to 96% upon retrospective scoring (91% for 3D FLAIR). For purely intracortical lesions, 3D DIR detected more than 2-fold more than 3D FLAIR (improved to 3-fold upon retrospective scoring). The specificity of 3D DIR to MS CLs was found to be 90%. Conclusions: In this postmortem verification study, we have shown that 3D DIR is highly pathologically specific, and more sensitive to CLs than 3D FLAIR in MS.

50 Grey matter in multiple sclerosis

INTRODUCTION

Cortical lesions (CLs) are thought to contribute significantly to disease severity in multiple sclerosis (MS) (1–5), and dominate disease pathology in the progressive phase (6). Therefore, reliable in vivo detection of CLs is crucial. Conventional MRI pulse sequences were found to largely miss cortical MS lesions (7,8) and even with the use of newer MRI techniques such as fluid-attenuated inversion recovery (FLAIR) (7,9–11), CL detection remained suboptimal. With the introduction of double inversion recovery (DIR) MRI, which simultaneously suppresses the signals from white matter (WM) and CSF (12–14), a substantial increase of MRI-detected CLs in patients with MS was found when compared to more conventional sequences (15,16). Subsequently, Chapter several cross-sectional and longitudinal DIR studies showed that CLs are associated with increased clinical, especially cognitive, impairment in MS(4,17–21). A drawback of 3 DIR as an imaging technique is its poor signal-to-noise ratio and the presence of flow and pulsation artifacts in 2D sequences (12–14,22). An improvement can be obtained with 3D single-slab methods, although the signal-to-noise ratio generally remains low (23). Together with regional variations in gray matter (GM) signal intensity (23) this may introduce difficulties when scoring cortical MS lesions. Recently, international consensus recommendations for CL scoring with 3D DIR were introduced (24), but sensitivity and pathologic specificity of 3D DIR have never been formally assessed by comparison to the gold standard of histopathology. In the current study we aimed to verify CL scoring on postmortem 3D DIR images by directly comparing them to histopathology. This way, sensitivity and specificity of 3D DIR as a technique could be determined.

METHODS

Patients and autopsy For this study, 40 brain slices of 14 patients with chronic MS were studied after rapid autopsy. Patients’ characteristics are shown in Table 1. As part of the MS Center Amsterdam autopsy protocol, areas of interest are generally sampled from a maximum of 5 coronally cut brain slices, under guidance of postmortem T2-weighted MRI (25). As T2-weighted scans are usually not helpful in detecting GM lesions (7), GM areas of interest were selected randomly from the slices for the current study. A total of 60 cortical areas and 8 deep GM areas were selected and used for further histopathologic examination.

51 Chapter 3

Standard protocol approvals, registrations, and patient consents Ethics approval was obtained from the institutional ethics review board. Prior to death, all donors were registered at the Netherlands Brain Bank, Amsterdam, the Netherlands, and all donors gave written informed consent for the use of their tissue and medical records for research purposes.

Postmortem MRI Single-slab 3D FLAIR images (repetition time (TR)/echo time (TE)/inversion time (TI)/ number of excitations (NEX) 6,500/355/2,200/1; echo train length 191; measured voxel size 1.1 x 1.1 x 1.3 mm3) and 3D DIR images (TR/TE/TI1/TI2/NEX 6,500/355/2,350/350/1; echo train length 191; measured voxel size 1.1 x 1.1 x 1.3 mm3) of selected brain slices were acquired using a whole body 1.5 T magnetic resonance system (Sonata and Avanto, Siemens Medical Systems, Erlangen, Germany) by using a standard circularly polarized head coil (Sonata) or a 12-channel phased-array head coil (Avanto).

Histopathology and immunohistochemistry After MRI, we selected a total of 68 tissue samples containing GM. The samples were fixed in 10% formalin and subsequently embedded in paraffin. Of these samples, 5-çm- thick sections were cut, mounted onto glass slides (Superfrost, VWR international, Leuven,Belgium), and dried overnight at 37°C. Sections were deparaffinized in a series of xylene, 100% ethanol, 96% ethanol, 70% ethanol, and water. Endogenous peroxidase activity was blocked by incubating the sections in methanol with 0.3% H2O2 for 30 minutes. After this, the sections were 3x 10 minutes rinsed with 0.01 mol/L phosphate-buffered saline (PBS, pH 7.4). Then, sections were incubated with antiproteolipid protein (PLP; mouse IgG2a; 1:3,000; Serotec, Oxford, UK) diluted in PBS containing 1% bovine serum albumin (BSA; Roche Diagnostics, Mannheim, Germany) for 1 hour. Bound primary antibodies were detected using EnVision horseradish peroxidase complex (DAKO Cytomation, Glostrup, Denmark) and 3,3 diaminobenzidinetetrahydrochloridedihydrate (DAB) was used as a chromogen. Sections were counterstained with hematoxylin.

52 Grey matter in multiple sclerosis

Table 1: Patient demographics

Patient/Sex/ Post mortem Disease Disease type Cause of death Age (y) delay (h:min) duration (y)

1/F/47 4:25 16 SPMS Rectum carcinoma

2/M/50 5:25 17 PPMS Pulmonary carcinoma

3/F/66 6:00 23 SPMS Unknown

4/M/55 6:20 32 SPMS Respiratory insufficiency

5/M/61 9:15 19 SPMS Euthanasia

6/F/40 9:00 9 RRMS Hypovolaemic shock Chapter

7/M/45 7:45 19 SPMS Cardiac arrest

8/M/72 7:55 13 SPMS Pneumonia 3

9/M/50 9:30 24 SPMS Unknown

10/M/76 7:35 44 SPMS Cardiovascular accident

11/M/44 12:00 14 PPMS Pneumonia

12/M/66 7:30 26 PPMS Unknown

13/M/57 7:55 25 SPMS Euthanasia

14/F/88 7:55 25 SPMS Cardiorespiratory insufficiency

Matching and analysis Cortical and deep GM lesions were scored (in consensus by A.S., S.D.R., and J.J.G.G.) on all 3D DIR and 3D FLAIR images which were viewed in a randomized fashion, and readers were blinded to histopathology and clinical information (i.e., prospective scoring). To avoid bias toward scoring in the sampled areas, CLs were assessed throughout the entire MRI of the brain slices, and thus before matching of selected tissue samples to the postmortem MRI. CLs were defined based on prior experience with scorings of cortical GM lesions in in vivo studies using different magnetic field strengths (4,16,26,27), and was consistent with the recently published consensus recommendations (24). Among other points, these guidelines offer a scoring strategy to avoid mistaking CLs for cortical vasculature or artifacts caused by magnetic field inhomogeneities. For the histopathologic scoring, we classified CLs as mixed WM-GM lesions (type I lesions) or purely intracortical lesions. Intracortical lesions included type II lesions

53 Chapter 3

(small, round intracortical lesions), type III or subpial lesions, and type IV lesions, which affect the entire width of the cortex (28). The pathology reader (E.-J.K.) scored cortical lesion types and numbers in PLP-stained tissue sections and was blinded to MRI and clinical data. Deep GM lesions were also scored. After separate prospective MRI and histopathology scorings, PLP-stained tissue sections were carefully matched to the corresponding plane of the 3D DIR and 3D FLAIR images. Matching was performed as described previously (29). For an example of successfully matched tissue samples, see Figure 1. After the blinded, prospective scoring of the postmortem MRI and the tissue-to-MRI matching, only those lesions that were present in brain areas sampled at autopsy were taken into account, and were used for further (retrospective) analysis. After histopathology scores had been made available to the MRI readers, a second, retrospective, unblinded scoring was performed, within the same areas from which tissue was sampled at autopsy. The sensitivity of the MRI sequences for detecting GM lesions was determined by dividing the number of lesions scored in the prospective or retrospective ratings by the number of lesions assessed on histopathology, times 100%. The specificity of the different MRI sequences was determined as follows: 100% - ((the number of false-positive scorings, i.e., hyperintensities on MRI without a corresponding lesion in histopathology/false- positives + total number of true CLs detected with 3D DIR or 3D FLAIR) x 100%). To assess relative gain or loss of lesions detected on 3D DIR vs 3D FLAIR, relative comparisons of lesion counts on these sequences were expressed as percentages, i.e. ((lesions detected by 3D DIR - lesions detected by 3D FLAIR)/lesions detected by 3D FLAIR) x 100%. Statistical analyses were performed using SPSS 15.0 for Windows (SPSS, Inc., Chicago, IL).

RESULTS

In total, we sampled 68 tissue samples containing GM under guidance of postmortem MRI for further histopathologic analysis. Of these samples, we discarded 12 due to suboptimal matching with MRI (resulting from tissue processing, i.e., distortion and dehydration of the tissue or from a priori obvious partial volume artifacts), resulting in a final set of 56 samples of 14 patients with chronic MS. Of this final set, 8 samples were deep GM and 48 samples contained cortical GM. Results of histopathology and MRI ratings are shown in Table 2 and Figure 1. In total, we identified 211 GM lesions on the PLP-stained tissue sections, consisting of 175 purely intracortical lesions (types II–IV), 23 mixed WM-GM lesions (type I), and 13 deep GM lesions. Prospectively, we were able to detect 35 of the in total 198 CLs with 3D DIR

54 Grey matter in multiple sclerosis

MRI. As such, the sensitivity of 3D DIR for CL detection was 18%, which is 1.6-fold higher than the sensitivity of 3D FLAIR. Retrospective scoring improved the sensitivity of 3D DIR for CL detection to 37%, which is 2.0-fold higher than 3D FLAIR. The pathologic specificity for 3D DIR was 90% and for 3D FLAIR 81%. Those scored hyperintensities that were discarded as a GM lesion after comparison with histopathology (i.e., false- positives) later appeared to be explained by either sulci with blood and fluid that had been misinterpreted for superficial lesions in the prospective scoring or by juxtacortical lesions that had been mistaken for type I (mixed WM-GM) lesions. The advantage of 3D DIR compared to 3D FLAIR was most evident for the purely intracortical lesions (type II–III–IV lesions; see panels H, I, K, and L of Figure 1), showing a gain of 129% in the prospective scoring (i.e., 9 lesions more), which increased to 240% Chapter (i.e., 36 lesions more) in the retrospective scoring. Note that although the sensitivity for detecting intracortical lesions is enlarged with 3D DIR compared to 3D FLAIR, the 3 majority of the CLs are still missed (Figure 2). Mixed (type- I) lesions were detected with a slightly higher sensitivity using 3D DIR (83% sensitivity) when compared to 3D FLAIR (65% sensitivity) in the prospective rating,and reached almost equal numbers for 3D DIR and 3D FLAIR in the retrospective scoring (96% sensitivity with 3D DIR vs 91% with 3D FLAIR). In terms of both prospective and retrospective detection of deep GM lesions, 3D DIR showed similar sensitivity compared to 3D FLAIR, confirming previous in vivo results (27).

DISCUSSION

Postmortem verification of 3D DIR hyperintensities in the GM of patients with MS has long been unavailable. In the current study, we demonstrated that although 3D DIR does not detect most GM lesions (especially not purely intracortical lesions), it has excellent pathologic specificity and higher sensitivity compared to 3D FLAIR (i.e., a relative gain of 3D DIR over 3D FLAIR in the detection of purely intracortical lesions of up to 240%). Naturally, the scoring criteria and the images used to define lesions in the GM influence the eventual number of scored lesions. Hence, the results of the present study are especially true for the 3D DIR protocol and the scoring criteria used here. However, for CL identification in the current study, we followed recently proposed CL scoring guidelines of an international panel of experts (24). Hyperintensities were not excessively scored as CLs, as is proven, e.g., by the low number of false-positive scorings (reflected in high specificity) in our study. The apparent distinction in CL numbers scored prospectively and retrospectively also shows that our scoring was conservative. Prospectively, especially type II intracortical and type III subpial lesions were missed (92% and 93%, respectively), and a considerable portion (more than 2 thirds) of all intracortical lesions were still missed when scored retrospectively.

55 Chapter 3

Table 2: Comparison of gray matter lesion scores between 3D DIR and 3D FLAIR, and PLP stained tissue sections*

Histopathology Prospective rating MRI Retrospective rating MRI

Lesion Type Lesion count 3D-FLAIR 3D-DIR 3D-FLAIR 3D-DIR

I 23 15 (65.2) 19 (82.6) 21 (91.3) 22 (95.7)

II 61 5 (8.2) 5 (8.2) 6 (9.8) 10 (16.4)

III 103 0 (0) 7 (6.8) 6 (5.8) 34 (33.0)

IV 11 2 (18.2) 4 (36.4) 3 (27.3) 7 (63.6)

II-IV 175 7 (4) 16 (9.1) 15 (8.6) 51 (29.1)

DGM 13 1 (7.7) 1 (7.7) 5 (38.5) 4 (30.8)

Total 211 23 (10.9) 36 (17.1) 41 (19.4) 77 (36.5)

Abbreviations: DGM= deep gray matter; DIR= double inversion recovery; FLAIR= fluid attenuated inversion recovery; PLP=proteolipid protein *Numbers in parenthesis indicate the percentage of MRI-detected lesions as compared to histopathology (i.e., the pathologic sensitivity)

To explain why some CLs are better visible than others, it is important to understand which specific properties of CLs determine their contrast and therefore their relative (in)visibility on MRI. Several factors are known to be responsible for the low contrast between CLs and surrounding GM, including the generally noninflammatory characteristics of cortical lesions (i.e., no complement deposition, gliosis, or blood– brain barrier disruption), the intrinsically low myelin density in the upper cortical layers, and the small size of CLs (30–33). The 3D DIR and 3D FLAIR sequences that were used for the current postmortem study are also used in vivo, and image contrasts are comparable. However, a difficulty in the postmortem setting is that additional artifacts caused by blood and water in the sulci may hamper CL detection. As such, it might well be that the sensitivity of 3D DIR and 3D FLAIR for detecting CLs is slightly higher in the in vivo setting, where these artifactually high signal intensities are adequately suppressed by the inversion pulses. However, the current results cannot lend sufficient force to this expectation. Unfortunately, as a result of the consensus approach for scoring of CLs that was adopted in this study, an interrater consistency score could not be provided. Furthermore, as sequential imaging on the 2 scanner systems used in this study was not possible due to time constraints and for reasons of decaying tissue quality, putative effects of the different scanner types on the numbers of detected CLs were not explored.

56 Grey matter in multiple sclerosis

These issues should be regarded as limitations of the current work, and will need to be investigated in future studies. Ongoing and future imaging of patients with MS at higher magnetic field strengths may further increase the sensitivity for the detection of GM lesions (34). This has already been shown by in vivo studies using 3D DIR MRI at 3 T (27) and T2*- weighted imaging at 7 T (35,36). However, whether imaging techniques at higher magnetic field strengths also show high(er) pathologic specificity remains to be determined. With 3D DIR being increasingly used for CL detection in MS, and consensus recommendations (24) and postmortem verification of this technique now being available, the need for a standardized acquisition protocol also becomes more pressing, as comparison of CL scores between centers will otherwise remain difficult. Chapter The current postmortem study is the first to verify CLs as scored on 3D DIR in the postmortem setting, by direct comparison to histopathology. It was shown that 3 single-slab 3D DIR has excellent pathologic specificity and a higher sensitivity than 3D FLAIR in detecting CLs in patients with MS. The latter confirms previous in vivo data (16). These findings now further establish the usefulness of 3D DIR for the MS clinical and research setting, and may be used to further fine-tune the discussion revolving around the imaging of CLs. Specifically, other MRI techniques (e.g., T1-based, phase- sensitive inversion recovery techniques) may now be further investigated to determine their sensitivity for CL detection in MS relative to 3D DIR, and future protocols might also explore the added value of combining sequences to optimally visualize lesions in the GM of patients with MS.

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Figure 1: Examples of postmortem MRI at 1.5 T, with corresponding histopathology

(A, D, G, J): Proteolipid protein (PLP) stained tissue sections; dotted lines indicate borders between white and gray matter; cortical lesions are encircled by thin black lines. (B, E, H, K): Postmortem 3D double inversion recovery (DIR) images corresponding with the tissue sections. (C, F, I, L): Corresponding 3D fluid-attenuated inversion recovery (FLAIR) images. (A–C): Multiple sclerosis (MS) cortex with rather inhomogeneous signal intensity on MRI, but without any demyelinated lesions. The bright signal indicated by the arrowheads (B, C) is caused by blood and other fluid within the sulci, which should not be mistaken for subpial (type III) cortical pathology. (D–F): Mixed gray-white matter (type I) lesion (asterisk), which is seen on both 3D DIR and 3D FLAIR images. However, the gray matter border (arrowheads in E) is often easier identified on 3D DIR (E) as compared to 3D FLAIR (F). (G–I):Subpial (type III) cortical lesions (indicated by thin line in G and arrowheads in H and I) are slightly more conspicuous on 3D DIR (H) than on 3D FLAIR (I). (J–L): Mixed gray-white matter (type I) lesion (asterisks). Arrowhead in J–L: an intracortical lesion, which was prospectively scored on 3D DIR (K) and only retrospectively (i.e., with knowledge of histopathology) on 3D FLAIR (L).

58 Grey matter in multiple sclerosis

Figure 2: Two examples of cortical lesions (CLs) that were not scored on 3D double inversion recovery (DIR) and 3D fluid-attenuated inversion recovery (FLAIR) in the prospective scorings and were also not discriminated in the retrospective phase for different reasons

Chapter 3

(A, D): Proteolipid protein (PLP) stained tissue sections. (B, E): Postmortem 3D DIR images corresponding with the tissue sections. (C, F): Corresponding 3D FLAIR images. (A): PLP staining indicates an extensive subpial (type III) CL involving the superficial layers of the cerebral cortex (arrowheads); (B) corresponding 3D DIR image indicates a hyperintense area in the cortex (arrowhead) that was not scored as a lesion due to nuisance of the high (artifactual) signal produced by fluid in the sulcus (see also figure 1, A through C, and text). (D): PLP-stained tissue section showing an extensive subpial CL, in some areas affecting all layers of the cerebral cortex; despite this extensive demyelination, the lesion was not scored on the MRI (E, F) during prospective and retrospective scorings, because the subtly increased signal intensity on both 3D DIR and 3D FLAIR throughout the entire cortex (indicated by arrowheads) made the distinction between the signal of the lesion and that of normal cortex nearly impossible. White matter lesions were always readily visible on both MRI sequences (asterisks).

59 Chapter 3

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1. FEINSTEIN A, ROY P, LOBAUGH N, 10. GAWNE-CAIN ML, O’RIORDAN JI, FEINSTEIN K, O’CONNOR P, BLACK S. THOMPSON AJ, MOSELEY IF, MILLER Structural brain abnormalities in multiple DH. Multiple sclerosis lesion detection sclerosis patients with major depression. in the brain: a comparison of fast fluid- Neurology 2004;62:586-590. attenuated inversion recovery and 2. LAZERON RH, LANGDON DW, FILIPPI M, conventional T2-weighted dual spin echo. et al. Neuropsychological impairment Neurology 1997;49:364-370. in multiple sclerosis patients: the role of 11. SHARMA R, NARAYANA PA, WOLINSKY JS. (juxta)cortical lesion on FLAIR. Mult Scler Grey matter abnormalities in multiple 2000;6:280-285. sclerosis: proton magnetic resonance 3. MORIARTY DM, BLACKSHAW AJ, TALBOT spectroscopic imaging. Mult Scler PR, et al. Memory dysfunction in multiple 2001;7:221-226. sclerosis corresponds to juxtacortical 12. BEDELL BJ, NARAYANA PA. Implementation lesion load on fast fluid-attenuated and evaluation of a new pulse sequence inversion-recovery MR images. AJNR Am for rapid acquisition of double inversion J Neuroradiol 1999;20:1956-1962. recovery images for simultaneous 4. ROOSENDAAL SD, MORAAL B, POUWELS suppression of white matter and CSF. J PJ, et al. Accumulation of cortical lesions Magn Reson Imaging 1998;8:544-547. in MS: relation with cognitive impairment. 13. REDPATH TW, SMITH FW. Technical Mult Scler 2009;15:708-714. note: use of a double inversion recovery 5. ROVARIS M, FILIPPI M, MINICUCCI L, et pulse sequence to image selectively al. Cortical/subcortical disease burden grey or white brain matter. Br J Radiol and cognitive impairment in patients with 1994;67:1258-1263. multiple sclerosis. AJNR Am J Neuroradiol 14. TURETSCHEK K, WUNDERBALDINGER 2000;21:402-408. P, BANKIER AA, et al. Double inversion 6. KUTZELNIGG A, LUCCHINETTI CF, recovery imaging of the brain: initial STADELMANN C, et al. Cortical experience and comparison with fluid demyelination and diffuse white matter attenuated inversion recovery imaging. injury in multiple sclerosis. Brain Magn Reson Imaging 1998;16:127-135. 2005;128:2705-2712. 15. CALABRESE M, DE STEFANO N, ATZORI M, 7. GEURTS JJG, BØ L, POUWELS PJ, et al. Detection of cortical inflammatory CASTELIJNS JA, POLMAN CH, BARKHOF lesions by double inversion recovery F. Cortical lesions in multiple sclerosis: magnetic resonance imaging in patients combined postmortem MR imaging and with multiple sclerosis. Arch Neurol histopathology. AJNR Am J Neuroradiol 2007;64:1416-1422. 2005;26:572-577. 16. GEURTS JJG, POUWELS PJ, UITDEHAAG 8. KIDD D, BARKHOF F, MCCONNELL R, ALGRA BM, POLMAN CH, BARKHOF F, CASTELIJNS PR, ALLEN IV, REVESZ T. Cortical lesions in JA. Intracortical lesions in multiple multiple sclerosis. Brain 1999;122:17-26. sclerosis: improved detection with 3D 9. BAKSHI R, ARIYARATANA S, BENEDICT RH, double inversion-recovery MR imaging. JACOBS L. Fluid-attenuated inversion Radiology 2005;236:254-260. recovery magnetic resonance imaging 17. CALABRESE M, ATZORI M, BERNARDI V, et detects cortical and juxtacortical multiple al. Cortical atrophy is relevant in multiple sclerosis lesions. Arch Neurol 2001;58:742-748. sclerosis at clinical onset. J Neurol 2007;254:1212-1220.

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18. CALABRESE M, FILIPPI M, ROVARIS M, et 28. BØ L, VEDELER CA, NYLAND HI, TRAPP al. Morphology and evolution of cortical BD, MORK SJ. Subpial demyelination in lesions in multiple sclerosis. A longitudinal the cerebral cortex of multiple sclerosis MRI study. Neuroimage 2008;42:1324- patients. J Neuropathol Exp Neurol 1328. 2003;62:723-732. 19. CALABRESE M, DE STEFANO N, ATZORI 29. BØ L, GEURTS JJ, RAVID R, BARKHOF F. M, et al. Extensive cortical inflammation Magnetic resonance imaging as a tool to is associated with epilepsy in multiple examine the neuropathology of multiple sclerosis. J Neurol 2008;255:581-586. sclerosis. Neuropathol Appl Neurobiol 20. CALABRESE M, AGOSTA F, RINALDI F, et al. 2004;30:106-117. Cortical lesions and atrophy associated 30. BØ L, VEDELER CA, NYLAND H, TRAPP with cognitive impairment in relapsing- BD, MORK SJ. Intracortical multiple remitting multiple sclerosis. Arch Neurol sclerosis lesions are not associated with Chapter 2009;66:1144-1150. increased lymphocyte infiltration. Mult 21. CALABRESE M, ROCCA MA, ATZORI M, et al. Scler 2003;9:323-331. A 3-year magnetic resonance imaging study 31. BRINK BP, VEERHUIS R, BREIJ EC, VAN of cortical lesions in relapse-onset multiple DER VALK P, DIJKSTRA CD, BØ L. The 3 sclerosis. Ann Neurol 2010;67:376-383. pathology of multiple sclerosis is location- 22. WATTJES MP, LUTTERBEY GG, GIESEKE dependent: no significant complement J, et al. Double inversion recovery brain activation is detected in purely cortical imaging at 3T: diagnostic value in the lesions. J Neuropathol Exp Neurol detection of multiple sclerosis lesions. 2005;64:147-155. AJNR Am J Neuroradiol 2007;28:54-59. 32. SEEWANN A, VRENKEN H, KOOI EJ, et al. 23. POUWELS PJ, KUIJER JP, MUGLER JP, Imaging the tip of the iceberg: visualization GUTTMANN CR, BARKHOF F. Human of cortical lesions in multiple sclerosis. gray matter: feasibility of single-slab 3D Mult Scler 2011; 17(10): 1202-1210 double inversion-recovery high-spatial- 33. VAN HORSSEN J, BRINK BP, DE VRIES resolution MR imaging. Radiology HE, VAN DER VALK P, BØ L. The blood- 2006;241:873-879. brain barrier in cortical multiple sclerosis 24. GEURTS JJG, ROOSENDAAL SD, lesions. J Neuropathol Exp Neurol CALABRESE M, et al. Consensus 2007;66:321-328. recommendations for MS cortical lesion 34. WATTJES MP, BARKHOF F. High field MRI scoring using double inversion recovery in the diagnosis of multiple sclerosis: MRI. Neurology 2011;76:418-424. high field-high yield? Neuroradiology 25. SEEWANN A, KOOI EJ, ROOSENDAAL SD, 2009;51:279-292. BARKHOF F, VAN DER VALK P, GEURTS JJG. 35. HAMMOND KE, METCALF M, CARVAJAL Translating pathology in multiple sclerosis: L, et al. Quantitative in vivo magnetic the combination of postmortem imaging, resonance imaging of multiple sclerosis at histopathology and clinical findings. Acta 7 Tesla with sensitivity to iron. Ann Neurol Neurol Scand 2009;119:349-355. 2008;64:707-713. 26. MORAAL B, ROOSENDAAL SD, POUWELS 36. MAINERO C, BENNER T, RADDING A, et al. PJ, et al. Multi-contrast, isotropic, single- In vivo imaging of cortical pathology in slab 3D MR imaging in multiple sclerosis. multiple sclerosis using ultra-high field Eur Radiol 2008;18:2311-2320. MRI. Neurology 2009;73:941-948. 27. SIMON B, SCHMIDT S, LUKAS C, et al. Improved in vivo detection of cortical lesions in multiple sclerosis using double inversion recovery MR imaging at 3 Tesla. Eur Radiol 2010;20:1675-1683. 61

3.2

IMAGING THE TIP OF THE ICEBERG: 9,68$/,6$7,212)&257,&$//(6,216,1 MULTIPLE SCLEROSIS.

Alexandra Seewann Hugo Vrenken Evert-Jan Kooi Paul van der Valk Dirk L. Knol Chris Polman Petra J. Pouwels Frederik Barkhof Jeroen J.G. Geurts

Mult Scler. 2011; 17(10):1202-1210.

Awarded the 2nd poster prize at the 25th congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), Düsseldorf, Germany, 2009. Chapter 3

ABSTRACT

Background: Cortical lesions (CLs) occur frequently in multiple sclerosis (MS), but only few CLs are observed on conventional magnetic resonance imaging (MRI). Why some CLs are visible and others are not is currently unknown. Here, we investigated whether CLs that are visible on conventional MRI differ from MRI-invisible CLs in terms of underlying histopathology and quantitative MRI (qMRI) measures. Methods: A total of 16 brain slices from 10 patients with chronic MS were analysed histopathologically and with conventional and qMRI. A region-of-interest approach was used to compare MRI-visible CLs with MRI-invisible CLs. Results: Although under-powering cannot be completely excluded in this study, MRI- visible CLs did not seem to differ from MRI-invisible CLs in terms of histopathology or qMRI measures. They were, however, significantly larger than their invisible counterparts (mean 13.3±1.7mm2 versus 6.9 ±1.3mm2; p=0.001). Furthermore, the number of MRI-visible lesions correlated with the overall number of CLs in the brain slice (r=0.96, p<0.01) and with the overall percentage of demyelination (r=0.78, p<0.01) per hemispheric brain slice. Conclusion: MRI visibility of CLs is determined by lesion size, and not by any distinctive underlying pathology. Visible CLs are associated with a higher total cortical lesion load, which suggests that when CLs in patients with MS become detectable on MRI, they merely represent ‘the tip of the pathological iceberg’.

64 Grey matter in multiple sclerosis

INTRODUCTION

Although multiple sclerosis (MS) is classically considered a typical white matter (WM) disease, recent histopathology studies have shown that a substantial part of the cortical grey matter (GM) is involved in the pathological process as well (1–3). Cortical demyelination may occur in four different patterns (4), based on the location of the lesion within the cortex and/or subcortical WM: mixed GM–WM lesions (type I lesions) which affect both the GM and subcortical WM; smaller perivascular intracortical lesions (type II); widespread subpial demyelination(type III); and type IV lesions, which affect the entire width of the cortex. Besides demyelination, subtle neuroaxonal and glial loss, remyelination and inflammation have been found in the MS GM (5–9). Chapter It has been suggested that GM lesions are important contributors to clinical disease severity and disability in MS (10–12). Clinically, cortical lesions (CLs) have 3 been correlated with cognitive impairment, epilepsy, depression, fatigue and physical disability (13–19). However, studies of the direct effects of focal CLs on clinical measures have been handicapped by the fact that only a small percentage of CLs can be detected by conventional magnetic resonance imaging (MRI) (20,21). So far, it is unclear why it is possible to detect some CLs with conventional T2-weighted MRI, but not others. Understanding the selective conspicuity of cortical MS lesions on conventional MRI will enable a more accurate and reliable quantification of cortical tissue damage in vivo, and hence a more accurate correlation with clinical, and especially cognitive, deficits. The goal of the current study was therefore to investigate whether MRI-visible CLs differ from MRI invisible cortical lesions. In addition to histopathological measures, we employed three quantitative MRI (qMRI) techniques, T1- and T2-relaxation time measurements (22,23), and magnetization transfer ratio (MTR) (24–26), to characterize the cortical tissue. These qMRI techniques have all detected GM changes early on in the disease and could be related to, or even predict, clinical disability (27–30).

MATERIALS AND METHODS

Subjects Sixteen coronally cut, 10-mm-thick full-hemispheric brain slices of 10 patients with chronic MS (mean age 68.9 years; six women) were selected at autopsy and were formalin-fixed for several weeks. Table 1 provides demographic details of the MS donors. Clinical courses were determined by means of retrospective medical record reviews according to established criteria (31). Ethics approval was obtained by the institutional ethics review board, and all the donors, or their next of kin, gave informed consent for the use of their tissue and medical records prior to death.

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Table 1: Demographic data of the studied cases

Case Sex Age (years) No PMD DD Type COD

1 F 48 1 04:50 20 SP Euthanasia

2 M 59 2 22:15 22 SP Myocardial infarct

3 F 65 1 06:00 25 PR Cardiac failure

4 F68207:3023SPPneumonia

5 F72212:0014PPPneumonia

6 M73206:4516SPShock

7 F 74 2 06:00 15 SP Cardiac arrest

8 M77104:1527PPCVA

9 F84208:4512SPEuthanasia

10 F 48 1 05:50 18 SP Heart failure

No, number of hemispheric slices included per case; PMD, post-mortem delay (h:min); DD, disease duration in years; Type, type of disease; SP, secondary progressive; PR, progressive relapsing; RR, relapsing-remitting; COD, cause of death.

Post-mortem MRI Examinations were performed with a 1.5-T MR system (Magnetom Vision scanner, Siemens, Erlangen, Germany) by using a standard circularly polarised head coil. For reduction of tissue boundary artefacts (21), each brain slice was immersed in a non-magnetic oil (Fomblin; perfluorinated polyether, Solvay Solexis, Weesp, The Netherlands) (29). Dual-echo T2-weighted spin-echo (T2SE) images (TR/TE1/TE2/NEX 2755 ms/45 ms/90 ms/2; field-of-view (FoV) 80mm 128 mm; matrix size 160 256; slice thickness: 3mm), as well as 3D fluid-attenuated inversion recovery (3D-FLAIR) images (TR/TE/TI/NEX: 6500ms/120ms/2200ms/1; FoV: 125x200mm; matrix size:160x256; slice thickness 1.25mm) were obtained from each brain slice. For the T1 measurements, six sets of 3D fast low-angle shot (FLASH) images were acquired (TR/TE/NEX:15ms/4ms/4; slice thickness: 3mm; FoV: 80x128mm; matrix size:

80x128) with nominal flip-angles between 2°-25°. B1-maps were generated from five additional sets of 3D-FLASH images (TR/TE/NEX:15ms/5ms/4; partition thickness: 3mm; FoV: 80x128mm; matrix size: 80x128) with nominal flip-angles varying between 140° and 220°(30). Post processing yielded T1 maps and corresponding PD maps. For T2 measurement, a multi-echo Carr Purcell Meiboom Gill sequence (TR/ NEX:2500ms/1; FoV:80x128mm, matrix size: 80x128 mm, slice thickness: 3mm) with

66 Grey matter in multiple sclerosis alternating 180° pulses and 16 equidistant echoes, starting from 20.5ms, was applied, yielding a single T2 relaxation time per pixel, after a mono-exponential T2-fit. MTR-maps were acquired with a 3D-FLASH sequence (TR/TE/NEX:27ms/4ms/1 partition thickness:5 mm; FoV:80x128mm; matrix size:80x128; flip angle: 20°), one with a Gaussian MT pre-pulse (MS), and one without (M0). (See Figure 1 D-F for quantitative MRI maps)

Neuropathology and immunohistochemistry After MR imaging, the brain slices were cut in half to reveal the imaged plane, and embedded in paraffin. Stainings and immunohistochemistry were performed on 10-çm-thick sections. To assess tissue morphology and general tissue quality, as well as density of the neuropil (neuronal cell bodies, axons, dendrites), haematoxylin-eosin Chapter (HE), Nissl and Bodian silver stains were performed. Immunohistochemistry was performed on adjacent sections with antibodies against 3 the following targets: glial fibrillary acidic protein (GFAP; DakoCytomation, Glostrup, Denmark), antigen presenting cells / microglia, (HLA-DR; courtesy of Dr. Hilgers, Amsterdam), proteolipid protein (PLP; Serotec, Oxford, UK) and fibrinogen (indicative of blood brain barrier leakage; DakoCytomation, Glostrup, Denmark). Bound primary antibodies were detected using the EnVision® method (DakoCytomation, Glostrup, Denmark).

Regional analysis of cortical GM: ROI placement After visual matching of the hemispheric tissue sections to the corresponding MRI planes using as many anatomical landmarks as possible (32), CLs were counted and classified (type I-IV) on PLP-stained sections. CLs were defined as areas of complete demyelination on those sections (3,6,20,21). Size of CLs was calculated on scanned PLP-stained sections using ImageJ_1.37 (http://rsbweb.nih.gov/ij). Non-lesional GM (NLGM) was defined histopathologically on PLP stained sections as areas devoid of any demyelination. CLs on MRI were scored in consensus by two experienced raters according to the following criteria (and as published previously)(21): (1) lesions should appear hyperintense on MRI, intermediate to the signal intensities of WM lesions and adjacent normal cortex; (2) lesional borders should be irregularly shaped (as in histology); and (3) lesions should not be clearly traceable over several subsequent slices (to avoid aberrant scoring of vascular structures). qMRI measures and neuropathological measurements were calculated within regions of interest (ROIs), which were placed onto CLs and NLGM in all tissue sections and in corresponding MRI areas. To avoid bias, ROI sizes were kept as constant as possible. However, to ensure that ROIs were accurately placed within T2 hyper intensities only (so as to avoid partial voluming with NLGM), ROI shapes were sometimes slightly modified (elongated but running less deep) to fit the more superficial hyperintensities (likely type

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III CLs). ROIs were placed on the PD maps, which were calculated from the flip angle arrays, and were subsequently copied onto the qMRI maps; matching accuracy was checked once more using PD-weighted images as reference. To assess the reproducibility of ROI placement, 50% of the ROIs were placed twice by the same observer (with 60 days in between the ROI placements) and intraclass correlation coefficients were calculated (See Figure 1, A-D for placement of ROIs and matching procedure). Neuropathological abnormalities within the ROIs were quantified by measuring staining intensity on digital images of histological sections, using ImageJ_1.37 as analysis software. The program was set in 8-bit mode and the mean staining intensity within the ROIs was measured (arbitrary units, ranging from 0- 255). High values (increased light transmittance) correspond with low staining intensity. For each analysed ROI, mean T1 and T2 relaxation times, MTR, and the presence of histopathological abnormalities was assessed. Variation in staining intensity in cortical layers was accounted for by selecting NLGM areas of the same size and in the same cortical layers as the corresponding CLs. Light transmittance was used to quantify neuroaxonal densities on Bodian (Tm(Bodian)) and Nissl (Tm(Nissl)) stainings. Gliosis was assessed by light transmittance measurements on GFAP stainings (Tm(GFAP)), and blood- brain barrier leakage on fibrinogen stainings (Tm(fibrinogen)). The presence of myelin (PLP stainings) within the ROIs was assessed as either present or absent; in addition, staining intensity was measured on PLP stained sections. Similarly, microglial cells were scored on the HLA-DR stained sections (400x magnification) as present or absent.

Global analysis of cortical GM: histograms Besides regional (ROI) analyses, the entire cortex (including MRI-visible lesions) was manually outlined on the calculated PD maps, and these outlines were copied onto the T1- T2-, and MTR-maps. This was done to calculate correlations between qMRI measures and histopathology stainings on a more global basis, as ROIs were conservatively placed and the total amount of lesional pathology may thus have been underrepresented. Pixels with partial volume at the inner and outer borders of the cortex were carefully excluded. Histograms were calculated for the manually outlined areas of the GM, and were normalized and smoothed by using a running average. Mean T1, T2, and MTR values, peak height, peak location and peak width (full width at half maximum) were then extracted from each histogram. To guarantee an accurate matching of the MR images to the corresponding tissue sections, each of the calculated PD maps were separately matched with the corresponding tissue section (Figure 1, A-D).

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Figure 1: Demonstration of MRI-to-histopathology matching and placement of regions of interest (ROIs) on a post-mortem multiple sclerosis brain slice.

Chapter 3

A: Calculated proton density (PD) map; B: PD-weighted image; C: Tissue section stained for glial fibrillary acidic protein (GFAP) (original magnification 0.7), D: T1 map, E: T2 map, F: Magnetization transfer ratio (MTR) map. Each of the tissue sections (C) were matched separately with the calculated PD map (A), using the PD-weighted image as reference (B). Arrows on image A and C indicate inconsistent areas which were carefully considered in the matching procedure. For global analysis of the gray matter, the whole cortex was outlined on calculated PD maps and histological sections, and these ROIs were copied onto the calculated T1-, T2- and MTR-maps (blue areas, demonstrated on the upper half of images A, C and D). Similarly, small ROIs were placed for focal analysis of the grey matter. (yellow: ROIs in the non-lesional grey matter, orange: ROIs in grey matter lesions; demonstrated on the lower half of images A, C, and D; displayed ROIs here were redrawn for illustrative purposes, according to original size and positioning).

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Next, the cortical GM was manually outlined in all tissue sections, and neuroaxonal densities, myelin densities, gliosis and blood-brain barrier leakage were assessed by light transmittance as described above. The overall percentage of demyelination was measured on PLP stained sections and was calculated as (lesion volume/overall cortical volume)*100. Lesion volumes were measured with ImageJ_1.37.

Statistical analysis Data analysis was performed by using SPSS version 14.0 for Windows (SPSS, Inc., Chicago, IL, USA). qMRI measurements and quantitative neuropathological data were compared by means of a general linear mixed model analysis, accounting for a nested design, where required (i.e. when there were significant interactions on slice and ROI levels). Pairwise comparisons were performed between MR-visible and MR-invisible CLs and between cortical lesions and NLGM. Bonferroni-corrected p<0.05 was considered statistically significant. Pearson’s correlation coefficient r( ) was used to investigate correlations of qMRI ROI and histogram parameters with transmittance measurements. For nonparametric correlations, Spearman’s rank correlation coefficient (В) was used. Significance was accepted at the level of p<0.05. The intrarater variability for ROI positioning was expressed as intraclass correlation coefficient between subjects variance and total variance, calculated on basis of a restricted maximum likelihood method.

RESULTS

On the PLP-stained sections, 187 cortical GM lesions were counted, the majority of which consisted of type III lesions (151 lesions, 80.7%). In total, 24 lesions (12.8%) were classified as type II, seven lesions (3.7%) as type IV, and five lesions (2.7%) as type I, consistent with previous histopathology results (4,20). The average amount of overall cortical demyelination per hemispheric brain slice was 7.7%, varying between 0% and 39.5% per section.

Regional analysis of the cortical GM (ROI analysis) qMRI and histopathological comparison between MRI- visible and MRI- invisible CLs. A total of 91 ROIs were placed and analysed in representative cortical areas; 42 ROIs were placed in a cortical GM lesion each, and 49 ROIs in non-lesional GM. Of the 42 ROIs, 26 were placed in MRI-visible CLs, which consisted of five type I, one type II, 18 type III and two type IV lesions. The remaining 16 invisible lesions consisted of one type I, one type II, 11 type III and three type IV lesions. Intraclass correlation coefficients showed good reliability for the placement of cortical ROIs on the qMRI maps

70 Grey matter in multiple sclerosis

(ICCT1 = 0.95, ICCT2 = 0.83, ICCMTR = 0.82). Neither qMRI measurements, nor transmittance measurements showed a significant difference between visible and invisible CLs (Table

2). Both Tm(Bodian) and Tm(Nissl) measurements differed significantly between NLGM and visible CLs, as well as between NLGM and invisible CLs, respectively. T2 relaxation times differed significantly between visible lesions and NLGM (see Table 2). qMRI measures within the ROIs correlated with several transmittance values: longer T2 relaxation times and lower MTR values correlated with demyelination (В=0.30, p<0.01, and В=-0.27, p<0.05). Associations of T1, T2 and MTR with gliosis, microglial activation or fibrinogen leakage did not reach statistical significance. A correlation was detected between higher T1 relaxation times and lower staining intensities for Nissl, but not for Bodian stainings (В=0.27, p<0.05). Chapter

Table 2: Regional analysis of quantitative magnetic resonance imaging measurements 3 and transmittance measurements in the cortical grey matter; model-based estimates

NLGM CL MRI-invisible MRI-visible CL Measurement (n= 49) (n= 42) CL (n=16) (n=26) T1, mean (SE), ms 323.7 (22.1) 349.9 (22.4)a 348.3 (24.3) 350.9 (23.2) T2, mean (SE), ms 72.1 (2.6) 77.1 (2.7)a 74.8 (3.0) 79.3 (2.8)f MTR, mean (SE), % 15.6 (0.6) 14.8 (0.6) 15.3 (0.7) 14.5 (0.6)

b c e Tm(Bodian), mean (SE) 190.4 (2.3) 198.8 (2.4) 199.1 (2.9) 196.3 (2.8) b d f Tm(Nissl), mean (SE) 155.8 (4.6) 177.4 (4.7) 177.9 (5.4) 176.4 (5.1)

Tm(GFAP), mean (SE) 210.0 (4.9) 213.9 (5.3) 208.3 (5.9) 215.9 (5.7)

Tm(fibrinogen), mean (SE) 235.0 (1.9) 234.3 (1.9) 236.8 (2.5) 232.9 (2.2)

MRI, magnetic resonance imaging; MTR, magnetization transfer ratio; Tm, transmittance of histopathological sections stained for neuroaxonal density (Nissl and Bodian), astrocytes (GFAP), and blood-brain barrier leakage (fibrinogen); NLGM, non-lesional grey matter; CL, cortical lesion; SE, standard error of the mean. ap < 0.05 for NLGM vs CL bp < 0.001 for NLGM vs CL cp < 0.05 for NLGM vs MRI-Invisible CL dp < 0.001 for NLGM vs MRI-Invisible CL ep < 0.05 for NLGM vs MRI-Visible CL fp < 0.001 for NLGM vs MRI-Visible CL

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Figure 2: Comparison between magnetic resonance imaging (MRI)-visible and invisible cortical lesions.

1: Cortical lesions were assessed on proteolipid protein-stained tissue sections (original magnification 0.7), and after comparison with the corresponding MRI images, marked as visible (red) and invisible (blue). 2: Corresponding proton density-weighted MR image of the same brain slice. 2a: MRI-visible lesion. Note the subtle signal intensity increase that could be detected after direct comparison with the proteolipid protein-stained tissue section. 2b: MRI invisible lesion. 3a-7b: MRI visible lesions did not differ from MRI-invisible lesions in terms of histopathology. Left column, 3a-7a: histological sections of the MRI-visible lesion shown in 2a. Right column, 3b-7b: corresponding MRI-invisible lesion of 2b. Sections (magnification 200) stained for neurons (3a, b: Nissl stain; 7a, b: Bodian silver), antigen-presenting cells (4a, b: HLA-DR), astroglia (5a, b: glial fibrillary acidic protein stain), blood-brain barrier leakage (6a, b: fibrinogen).

72 Grey matter in multiple sclerosis

Upon comparison with the PLP-stained sections, 70 (37.4%) of the CLs were visible on conventional MRI (PD and/or FLAIR) (Figure 2; 1,2). Among those lesions, 50 (71.4%) were scored as type III lesions, 10 (14.3%) as type II lesions, 5 (7.1%) as type I, and 5 (7.1%) type IV lesions, respectively. Some 33.1% of all type III lesions and 41.7% of all type II lesions were detected on MRI. Type I and IV lesions were best detected, with a visibility of 100% and 71.4% respectively. MRI-visible cortical lesions were significantly larger than invisible lesions (mean 13.3mm2 ± 1.7mm2 , versus 6.9mm2 ± 1.3mm2, respectively; p=0.001), and visible type III lesions extended deeper into the lower cortical layers than invisible type III lesions (lesion width as calculated from the pia downwards: 1.9mm for visible versus 1.3mm for invisible lesions, p<0.05). Semiquantitative assessment of microglial activation showed no difference between Chapter MRI visible and MRI invisible cortical lesions (Figure 2; 3a-7b). Visible CLs correlated with the overall number of lesions in the brain slice (r=0.96, p<0.01) and with the overall 3 percentage of demyelination (r=0.78, p<0.01) per hemispheric brain slice. qMRI and histopathology comparison between CLs and NLGM T1 and T2 relaxation times differed significantly between CLs and NLGM, and there was a tendency for lower MTR in cortical lesions, but this difference did not reach significance. CLs showed significantly higher transmittance values in sections stained for neurons and axons as compared with NLGM (4.2% loss of staining intensity in Bodian stainings and 12.3% loss of Nissl staining intensity), whereas no difference was detected for Tm(GFAP) and Tm(Fibrinogen) (Table 2).

Histogram analysis of the cortical GM Histogram peak positions, peak heights and means were correlated with the percentage of demyelination and transmittance measurements for neuroaxonal and myelin density, blood-brain barrier leakage and astrogliosis. The peak position of the

T2 histograms correlated with Tm(PLP) (r=0.56, p<0.05), and MTR peak position and mean MTR correlated with the percentage of cortical demyelination (r=0.76, p<0.01, and r=0.77, p<0.01). No associations were found between gliosis, fibrinogen and axons (neurons) and T1, T2 and MTR histogram parameters.

DISCUSSION

The present study demonstrates that CLs that are visible on conventional MR images are not different from MRI-invisible CLs in terms of underlying histopathology. Instead, visibility of CLs was exclusively determined by size. Furthermore, visibility of CLs, and

73 Chapter 3 therewith greater lesion size, was associated with a higher overall CL load, as assessed by histopathology. This indicates that when CLs become visible on MRI, we are only seeing the “tip of the pathological iceberg” and many more demyelinated areas may be present, though invisible on MRI. qMRI measures were shown to sensitively reflect demyelination in the cortical GM of patients with MS, and detected both MRI-visible and MRI-invisible lesions. Both in vivo MRI and histopathology studies have shown that GM pathology is already present in the earliest disease stages, and becomes increasingly prominent as disease progresses (25, 33-35). However, an accurate estimation of GM pathology in vivo is challenging, as up to 91% of CLs are missed on conventional MRI (20). No explanation exits why the remaining 9% are in fact visible. As the greatest part of CLs consist of type III subpial lesions (6), which cover only the upper (more thinly myelinated) cortical layers, it may be expected that MRI-visible lesions involve the lower, more densely myelinated cortical layers. Alternatively, they may consist of a more destructive or inflammatory pathology; both options would lead to notable differences in MRI contrast. Here we show, for the first time, that the latter hypothesis is unlikely, as neither axonal or glial loss, nor microglial activation or blood-brain barrier leakage were significantly different between MRI-visible and MRI-invisible lesions. Lesion size was the only discriminatory variable between MRI- visible and MRI-invisible CLs. Moreover, MRI-visible type III (subpial) lesions showed a more extensive infiltration into deeper, more heavily myelinated layers of the cortex, therewith generating better contrast on MRI. The fact that pathology does not differ between MRI-visible and MRI-invisible CLs was mirrored by the qMRI measures, which were very sensitive to overall cortical demyelination, confirming previous studies (22- 26,36), but could not specifically distinguish between MRI-visible and MRI-invisible CLs. Despite the low overall percentage of demyelination (7.7%, varying between 0% and 39.5%) in our brain slices, histogram parameters showed significant correlations with cortical demyelination. Interestingly, increasing total cortical demyelination, as measured in PLP stainings and as reflected by the qMRI measures, was associated with an increased number of MRI-visible CLs (r=0.96, p < 0.01). This ‘tip-of-the-iceberg phenomenon’ may be a specifically interesting observation for the clinical setting, as patients who show more CLs on their in vivo MRIs are likely to already have more severe cortical damage. However, a few limitations apply to this study. First, our analysis may not be exhaustive. Other histopathological parameters than those selected here could contribute to the visibility of CLs on MRI. Furthermore, the ROI analysis may have been under-powered to detect differences in T1 and T2 relaxation times between MRI-visible and MRI-invisible lesions. The exclusion of cortical hyperintensities smaller than 5 pixels

74 Grey matter in multiple sclerosis in order to compensate for volume averaging may have underpowered the study with respect to the detection of MR-visible CLs. For this study, formalin-fixed material was used, which slightly hampers comparisons of qMRI measures with the in vivo situation. The latter issue may not be detrimental, however, as qMRI results obtained from fixed material are still useful in terms of investigating histopathological underpinnings of qMRI changes in the post-mortem MS brain, and hence are still sufficiently clinically relevant (37). Finally, it would have been highly interesting to study CLs with a Double Inversion Recovery (DIR) sequence in the current study, as this technique has shown to be sensitive to cortical MS lesions in vivo (38,39). However, this technique is not yet applicable to the post-mortem setting, and DIR imaging of post-mortem MS brain material therefore remains an interesting target for future research. Chapter In conclusion, this study shows that visibility of CLs on conventional MRI seems to be predominantly determined by lesion size, and not by any distinctive underlying 3 histopathology. Furthermore, greater CL size was associated with a higher overall CL load, indicating that once CLs become visible on MRI, these lesions represent only the ‘tip of the pathological iceberg’. qMRI measurements sensitively reflected the percentage of cortical demyelination, but did not distinguish between MRI-visible and MRI-invisible CLs. These findings substantially increase our understanding of the radiologically reported cortical qMRI abnormalities reported in MS.

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REFERENCES

1. BROWNELL B, HUGHES JT. The distribution 10. CHEN JT, NARAYANAN S, COLLINS DL, of plaques in the cerebrum in multiple SMITH SM, MATTHEWS PM, ARNOLD sclerosis. J Neurol Neurosurg Psychiatry DL. Relating neocortical pathology to 1962 November;25:315-20. disability progression in multiple sclerosis 2. KIDD D, BARKHOF F, MCCONNELL R, ALGRA using MRI. Neuroimage 2004;23:1168-75. PR, ALLEN IV, REVESZ T. Cortical lesions in 11. DE STEFANO N, MATTHEWS PM, FILIPPI M multiple sclerosis. Brain 1999;122:17-26. ET AL. Evidence of early cortical atrophy 3. PETERSON JW, BO L, MORK S, CHANG A, in MS: relevance to white matter changes TRAPP BD. Transected neurites, apoptotic and disability. Neurology 2003;60:1157-62. neurons, and reduced inflammation in 12. SAILER M, FISCHL B, SALAT D ET AL. Focal cortical multiple sclerosis lesions. Ann thinning of the cerebral cortex in multiple Neurol 2001 September;50:389-400. sclerosis. Brain 2003;126:1734-44. 4. BO L, GEURTS JJ, MORK SJ, VAN DER 13. CHRISTODOULOU C, KRUPP LB, LIANG VALK, P. Grey matter pathology in multiple Z ET AL. Cognitive performance and MR sclerosis. Acta Neurol Scand Suppl markers of cerebral injury in cognitively 2006;183:48-50. impaired MS patients. Neurology 5. ALBERT M, ANTEL J, BRUCK W, 2003;60:1793-8. STADELMANN C. Extensive cortical 14. FEINSTEIN A, ROY P, LOBAUGH N, remyelination in patients with chronic FEINSTEIN K, O’CONNOR P, BLACK S. multiple sclerosis. Brain Pathol 2007 Structural brain abnormalities in multiple April;17:129-38. sclerosis patients with major depression. 6. BO L, VEDELER CA, NYLAND H, TRAPP Neurology 2004;62:586-90. BD, MORK SJ. Intracortical multiple 15. LAZERON RH, LANGDON DW, FILIPPI M sclerosis lesions are not associated with ET AL. Neuropsychological impairment increased lymphocyte infiltration. Mult in multiple sclerosis patients: the role of Scler 2003;9:323-31. (juxta)cortical lesion on FLAIR. Mult Scler 7. BRINK BP, VEERHUIS R, BREIJ EC, VAN 2000;6:280-5. DER VALK P, DIJKSTRA CD, BO L. The 16. MORIARTY DM, BLACKSHAW AJ, TALBOT pathology of multiple sclerosis is location- PR ET AL. Memory dysfunction in multiple dependent: no significant complement sclerosis corresponds to juxtacortical activation is detected in purely cortical lesion load on fast fluid-attenuated lesions. J Neuropathol Exp Neurol inversion-recovery MR images. AJNR Am 2005;64:147-55. J Neuroradiol 1999;20:1956-62. 8. VAN HORSSEN J, BRINK BP, DE VRIES 17. ROVARIS M, FILIPPI M. MRI correlates of HE, VAN DER VALK P, BO L. The blood- cognitive dysfunction in multiple sclerosis brain barrier in cortical multiple sclerosis patients. J Neurovirol 2000;6:S172-S175. lesions. J Neuropathol Exp Neurol 18. SOKIC DV, STOJSAVLJEVIC N, DRULOVIC 2007;66:321-8. J ET AL. Seizures in multiple sclerosis. 9. WEGNER C, ESIRI MM, CHANCE SA, PALACE Epilepsia 2001;42:72-9. J, MATTHEWS PM. Neocortical neuronal, 19. SPATT J, CHAIX R, MAMOLI B. Epileptic synaptic, and glial loss in multiple and non-epileptic seizures in multiple sclerosis. Neurology 2006;67:960-7. sclerosis. J Neurol 2001;248:2-9.

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20. GEURTS JJ, BO L, POUWELS PJ, 28. VRENKEN H, POUWELS PJ, ROPELE S, CASTELIJNS JA, POLMAN CH, BARKHOF ET AL. Magnetization transfer ratio F. Cortical lesions in multiple sclerosis: measurement in multiple sclerosis combined postmortem MR imaging and normal-appearing brain tissue: limited histopathology. AJNR Am J Neuroradiol differences with controls but relationships 2005;26:572-7. with clinical and MR measures of dis-ease. 21. GEURTS JJ, BLEZER EL, VRENKEN H ET Mult Scler 2007; 13: 708-716. AL. Does high-field MR imaging improve 29. FATTERPEKAR GM, NAIDICH TP, DELMAN cortical lesion detection in multiple BN ET AL. Cytoarchitecture of the human sclerosis? J Neurol 2008;255:183-91. cerebral cortex: MR microscopy of excised 22. MANFREDONIA F, CICCARELLI O, KHALEELI specimens at 9.4 Tesla. AJNR Am J Z ET AL. Normal-appearing brain t1 Neuroradiol 2002;23:1313-21. relaxation time predicts disability in early 30. VENKATESAN R, LIN W, HAACKE EM. Chapter primary progressive multiple sclerosis. Accurate determination of spin- Arch Neurol 2007;64:411-5. density and T1 in the presence of RF- 23. VRENKEN H, GEURTS JJ, KNOL DL ET field inhomogeneities and flip-angle AL. Whole-brain T1 mapping in multiple miscalibration. Magn Reson Med 3 sclerosis: global changes of normal- 1998;40:592-602. appearing gray and white matter. 31. LUBLIN FD, REINGOLD SC. Defining the Radiology 2006;240:811-20. clinical course of multiple sclerosis: 24. CERCIGNANI M, BOZZALI M, IANNUCCI G, results of an international survey. National COMI G, FILIPPI M. Magnetisation transfer Multiple Sclerosis Society (USA) Advisory ratio and mean diffusivity of normal Committee on Clinical Trials of New appearing white and grey matter from Agents in Multiple Sclerosis. Neurology patients with multiple sclerosis. J Neurol 1996;46:907-11. Neurosurg Psychiatry 2001;70:311-7. 32. BO L, GEURTS JJ, RAVID R, BARKHOF F. 25. KHALEELI Z, CERCIGNANI M, AUDOIN B, Magnetic resonance imaging as a tool to CICCARELLI O, MILLER DH, THOMPSON examine the neuropathology of multiple AJ. Localized grey matter damage in early sclerosis. Neuropathol Appl Neurobiol primary progressive multiple sclerosis 2004;30:106-17. contributes to disability. Neuroimage 33. FISHER E, LEE JC, NAKAMURA K, RUDICK 2007;37:253-61. RA. Gray matter atrophy in multiple 26. RAMIO-TORRENTA L, SASTRE-GARRIGA sclerosis: a longitudinal study. Ann Neurol J, INGLE GT ET AL. Abnormalities in 2008;64:255-65. normal appearing tissues in early primary 34. FISNIKU LK, CHARD DT, JACKSON JS ET progressive multiple sclerosis and their AL. Gray matter atrophy is related to long- relation to disability: a tissue specific term disability in multiple sclerosis. Ann magnetisation transfer study. J Neurol Neurol 2008;64:247-54. Neurosurg Psychiatry 2006;77:40-5. 35. KUTZELNIGG A, LUCCHINETTI CF, 27. KHALEELI Z, ALTMANN DR, CERCIGNANI M, STADELMANN C ET AL. Cortical CICCARELLI O, MILLER DH AND THOMPSON demyelination and diffuse white matter AJ. Magnetization transfer ratio in gray injury in multiple sclerosis. Brain matter: a potential surrogate marker for 2005;128:2705-12. progression in early primary progressive 36. SCHMIERER K, PARKES HG, SO PW, ET AL. multiple sclero-sis. Arch Neurol 2008; 65: High field (9.4 Tesla) magnetic resonance 1454-1459. imaging of cortical grey matter lesions in multiple sclerosis. Brain 2010; 133: 858-867.

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37. SCHMIERER K, WHEELER-KINGSHOTT CA, TOZER DJ ET AL. Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn Reson Med 2008;59:268-77. 38. GEURTS JJ, POUWELS PJ, UITDEHAAG BM, POLMAN CH, BARKHOF F AND CASTELIJNS JA. Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 2005; 236: 254-260. 39. CALABRESE M, DE STEFANO N, ATZORI M, ET AL. Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis. Arch Neurol 2007; 64: 1416-1422.

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79

4 WHITE MATTER IN MULTIPLE SCLEROSIS

4.1

DIFFUSELY ABNORMAL WHITE MATTER IN CHRONIC MULTIPLE SCLEROSIS: IMAGING AND HISTOPATHOLOGIC ANALYSIS.

Alexandra Seewann Hugo Vrenken Paul van der Valk Erwin Blezer Dirk L. Knol Jonas Castelijns Chris Polman Petra J. Pouwels Frederik Barkhof Jeroen J.G. Geurts

Arch Neurol. 2009;66(5):601-609

Awarded the 3rd poster prize at the 23rd congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), Prague, Czech Republic, 2007.

Oral presentation at the 17th meeting of the European Neurological Society (ENS), Rhodos, Greece, 2007.

Oral presentation at the 16th meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Toronto 2008. Chapter 4

ABSTRACT

Background: Diffuse abnormalities in the white matter (WM), ie, the so-called diffusely abnormal WM (DAWM), as observed on magnetic resonance imaging (MRI), may contribute to the development of clinical disability in multiple sclerosis (MS). Underlying pathologic and MRI characteristics of DAWM are largely unknown. Objectives: To explore and describe the histopathologic and radiologic characteristics of DAWM in chronic MS. Methods: We analyzed 17 formalin-fixed hemispheric brain slices from 10 patients with chronic MS using histopathologic analysis and qualitative and quantitative MRI. A region-of-interest approach was applied to compare radiologically defined DAWM, normal-appearing WM, and focal WM lesions and to correlate quantitative MRI mea- sures with histopathologic findings. Main Outcome Measures: The DAWM consisted of extensive axonal loss, decreased myelin density, and chronic fibrillary gliosis, all of which were substantially abnormal compared with normal-appearing WM and significantly different from focal WM lesion pathology. Increased T1- and T2-relaxation times and decreased fractional anisotropy values were found in DAWM regions of interest, in association with extensive axonal loss and reduced myelin density. Increased T1- and T2-relaxation times were associated with chronic gliosis. Conclusions: This study classifies DAWM in chronic MS as an abnormality that is different from normal-appearing WM and focal WM lesions, most likely resulting from the cumulative effects of ongoing inflammation and axonal pathology. As such, DAWM is likely to substantially contribute to disease progression and may prove to be an important new disease marker in clinical trials focusing on the neurodegenerative aspects of MS.

84 White matter in multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating disease of the central nervous system typically characterized by focal lesions in the white matter (WM). Mag- netic resonance imaging (MRI) is the most sensitive imaging technique for detecting MS lesions in vivo, and lesion load measurements based on conventional T2-weighted MRI are widely used to monitor treatment effects in therapeutic trials (1) However, there is only a modest correlation between the lesion load on conventional MRI and the clinical disability of patients with MS, a phenomenon referred to as clinicoradiologic dissociation (2). To explain this dissociation, it was suggested that factors other than focal WM lesions on MRI might contribute to the development of disability, that is, invisible pathologic abnormalities in the so-called normal-appearing white matter (NAWM) and normal- appearing gray matter (3-6). Quantitative MRI techniques (eg, magnetization transfer imaging, diffusion tensor imaging (DTI), and T1 and T2 relaxation time measurements), and high-field MRI have proved to be more sensitive in detecting subtle abnormalities (7-10), overcoming some of the limitations of conventional MRI (11-13). Chapter Apart from this invisible pathology on conventional T2-weighted imaging, there is also pathology that is visible but unaccounted for in standard MS disease burden 4 ratings. These diffuse and subtle signal hyperintensities in the WM are commonly referred to as diffusely abnormal WM (DAWM) or dirty WM, and the signal intensity is higher in these areas than in the surrounding NAWM but lower than in focal WM lesions (14). Although not rated in a standard clinical and re-search setting, DAWM may be expected to contribute to the clinicoradiologic dissociation if considerable pathologic abnormalities are present in these regions. So far, no systematic studies exist, to our knowledge, that have characterized the pathologic features of DAWM in MS; hence, it is unclear whether DAWM represents an early stage of inflammation and lesion development, a more chronic ongoing pathologic abnormality, or even a reparative process (remyelination). Assessment of underlying pathologic abnormalities and a clear definition of DAWM should provide a more accurate estimation of disease burden and disease progression in MS. We investigated fixed postmortem brain tissue of patients with chronic MS using qualitative MRI at 1.5 and 4.7 T and quantitative MRI (magnetization transfer imaging, diffusion tensor imaging and T1 and T2 relaxation time mapping) at 1.5 T to answer the following questions: (1) What are the underlying qualitative MRI and histopathologic characteristics of DAWM compared with NAWM and focal WM lesions? (2) Do quantitative MRI measures at standard field strength (1.5 T) specifically reflect the extent and nature of the underlying pathologic changes in DAWM?

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METHODS

Patients and autopsy procedure Seventeen coronally cut, 10-mm-thick hemispheric brain slices of 10 patients with chronic MS (7 women; mean age, 66.8 years) were selected after rapid autopsy (mean post-mortem delay, 8.5 hours) and were formalin fixed for several weeks. Table 1 provides demographic and neuropathologic details of the patients. Clinical courses were determined by means of retrospective medical record reviews according to established criteria (15). Ethics approval was obtained from the VU University Medical Centre, and all the donors gave informed consent before their death for the use of their tissue and medical records.

Table 1: Demographic and Neuropathologic Data for the 10 Study Patients

Hemispheric Patient No./ Postmortem Disease Type of Brain Slices Cause of Death Sex/Age, y Delay, h:min Duration, y Disease Included, No.

1/F/74 2 06:00 15 SP cardiac arrest

2/F/48 1 04:50 20 SP euthanasia

3/F/72 2 12:00 14 PP pneumonia

4/M/77 1 04:15 27 PP stroke

5/F/48 1 05:50 18 SP cong. heart failure

6/F/65 2 06:00 25 PR cardiac failure

7/F/84 2 08:45 12 SP euthanasia

8/M/73 2 06:45 16 SP septicaemic shock

9/M/59 2 22:15 22 SP myocardial infarct

10/F/68 2 07:30 23 SP pneumonia

MRI protocol and image postprocessing Imaging at 1.5 T was performed using a Magnetom Vision scanner (Siemens AG, Erlangen, Germany) (16). Conventional measurements consisted of the following: (1) dual-echo T2-weighted spin-echo images (repetition time [TR]/echo time [TE] 1/TE2/ number of signals acquired, 2755 milli-seconds/45 milliseconds/90 milliseconds/2; field of view (FOV), 80x128 mm; matrix size, 160x256; and section thickness, 3 mm); and (2) multislab 3-dimensional fluid-attenuated inversion recovery (17) images (TR/

86 White matter in multiple sclerosis

TE/inversion time/number of signals acquired, 6500 milliseconds/120 milliseconds /2200 milliseconds/1; echo train length, 27; 8 partitions per slab; partition thickness, 1.25 mm; FOV, 125x200 mm; and matrix size, 160x256). Quantitative imaging at 1.5 T consisted of the following: (1) T1 relaxation time mapping: 6 sets of 3-dimensional fast low-angle shot (3D-FLASH) images were acquired (TR/TE/ number of signals acquired, 15 milliseconds/4 milliseconds/4; partition thickness, 3 mm; FOV, 80x128 mm; and matrix size, 80x128), with nominal flip angles of 2° to 25°. B1 maps were generated from 5 additional sets of 3D-FLASH images (TR/ TE/ number of signals acquired, 15 milliseconds/5 milliseconds/4; partition thickness, 3 mm; FOV, 80x128 mm; and matrix size, 80x128), with nominal flip angles varying between 140° and 220°(9). (2) T2 relaxation time mapping: a multi-echo Carr-Purcell- Meiboom-Gill sequence (TR/number of signals acquired, 2500 milliseconds/1; FOV, 80x128 mm; matrix size, 80x128 mm, and section thickness, 3 mm), with alternating 180° pulses and 16 equidistant echoes, starting from 20.5 milliseconds, was applied. (3) Magnetization transfer imaging maps: a 3D-FLASH sequence (TR/TE/number of signals acquired, 27 milliseconds/4 milliseconds/1; partition thickness, 5 mm; FOV, Chapter 128 mm rectangular; matrix size, 128 mm; and flip angle, 20°) was acquired, 1 with a gaussian magnetization transfer prepulse and 1 without. (4) Diffusion tensor imaging: 4 a diffusion-weighted single-shot stimulated echo acquisition mode sequence (TR/TE/ number of signals acquired, 6000 milliseconds/65 milliseconds/84; FOV, 80x128 mm; matrix size, 40x64; flip angle, 11°; and section thickness, 8 mm) was used (10). Seven volumes were acquired: 6 with different noncollinear directions and 1 without diffusion weighting. These volumes were used to calculate the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps. Examples of different quantitative MRI maps and image quality are seen in Figure 1.

Figure 1: Quantitative magnetic resonance images of postmortem multiple sclerosis brain slices.

A: Apparent diffusion coefficient map. B: Fractional anisotropy map. C: T2 map. D: T1 map. E: magnetization transfer map.

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Measurements at high field (4.7 T) were performed using an experimental animal scanner (Varian Inc, Palo Alto, California) (horizontal bore) and consisted of a mainly proton density (PD)-weighted sequence (3-dimensional fast spin echo) (TR/TE/number of signals acquired, 4000 milliseconds/9 milliseconds/2; echo train length, 8; partition thick-ness, 0.39 mm; 64 partitions per slab; FOV, 100x100 mm; matrix size, 256x256; and acquisition time, 4 hours 55 minutes).

Neuropathologic and immunohistochemical analysis After MRI, the brain slices were cut in half to reveal the centre of the imaged plane and were embedded in paraffin. Stainings and immunohistochemical analyses were performed on 10-µm-thick sections. To assess tissue morphologic features and quality and myelin and axonal density, hematoxylin-eosin, Luxol fast blue (LFB)-periodic acid- Schiff (PAS), and Bodian silver stains were performed. Immunohistochemical analysis was performed on adjacent sections with antibodies against the following targets: glial fibrillary acidic protein (GFAP) (Dako; Glostrup, Denmark), antigen-presenting cells and microglia (HLA-DR, courtesy of J Hilgers, PhD, Amsterdam), proteolipid protein (PLP) (AbD Serotec; Oxford, England), -amyloid precursor protein (APP) (Zymed Laboratories Inc; South San Francisco, California), and fibrinogen (Dako). Bound primary antibody was detected using the EnVision method (Dako).

Selection of regions of interest and MRI-to-histopathology matching The DAWM and focal WM lesions were defined using 1.5-T PD images; NAWM was defined using 4.7-T PD images to avoid inclusion of focal WM lesions undetected on the 1.5-T images (Figure 2). Regions of interest were placed in selected representative DAWM areas and in focal WM lesions and NAWM. Quantitative MRI values were measured in the regions of interest for comparisons among DAWM, NAWM, and focal WM lesions.

88 White matter in multiple sclerosis

Figure 2: Magnetic resonance imaging to histopathology matching and comparison of 1.5-T and high-field (4.7-T) images.

A Luxol fast blue-periodic acid-Schiff-stained tissue section (A) was matched to a 4.7-T proton Chapter density (PD) image (partly reproduced from Journal of Neurology. 2008;255(2):183-191, Does High-Field MR Imaging Increase Cortical Lesion Detection in Multiple Sclerosis? Geurts et al, 4 Figure 1B, with permission from Springer Science and Business Media) (B) and to a 1.5-T PD image of the same brain slice (C). Orange arrowheads indicate areas of diffusely abnormal white matter (DAWM); yellow stars, normal-appearing white matter (NAWM); and green dots, focal WM lesions. In the inset, note the unsharply defined borders of the DAWM compared with the borders of focal WM lesions and the signal intensity, which is more subtle than that of focal WM lesions, although clearly abnormal compared with that of NAWM (see the “Methods” section for the DAWM inclusion criteria).

The DAWM was defined as a uniform, nonfocal area of signal increase on the PD-weighted sequence at 1.5 T, with a subtly increased signal intensity compared with the signal intensity of focal WM lesions. The DAWM signal hyperintensity usually tapered off toward the NAWM, leading to a relatively unsharply defined border of DAWM areas, again compared with focal WM lesions. Classification of NAWM and DAWM was performed blinded to histopathologic features. After MRI-to-histopathology matching (16,17) Figure( 2), regions of interest were placed onto the corresponding areas in the tissue sections. Histologic scorings were performed blinded to MRI data.

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4XDQWLWDWLYHHYDOXDWLRQRIKLVWRSDWKRORJLFÀQGLQJV Staining intensity was measured by means of light transmittance on digital images of histologic sections using ImageJ_1.37 (http://rebweb.nih.gov/ij). High values (increased light transmittance) correspond with low staining intensity. Transmittance of Bodian

(TmBodian, PLP-(Tmplp), LFB-PAS-(TMLFB-PAS), GFAP-(TmGFAP), and fibrinogen-(Tmfibrinogen) stained sections, was used to quantify axonal density, myelin density, gliosis, and blood-brain barrier leakage, respectively. In addition, axons were counted in 3 random areas per region of interest using a morphometric grid (10 mm2, original magnification x1250) and were averaged per region of interest (CountAxon). To assess the extent of astrogliosis, glial filament staining and the morphologic aspect of cell bodies were scored separately, because gliosis may show different features depending on the stage of astrocytic activation (Figure 3)(18). The GFAP- positive cell bodies were scored as 0 (not enlarged) or 1 (enlarged). Increase of GFAP staining in glial filaments (FSQ GFAP) was assessed in 3 steps: 0, normal appearance; 1, increased glial processes; and 2, severely increased glial processes. Acute axonal injury was scored as 0 (no APP positivity) or 1 (APP-positive beads or end bulbs) (19). The antigen-presenting cells were scored as follows: 0, no increase; 1, mild increase (10-15 APCs); or 2, severe increase (>15 APCs). The (p)reactive lesions were noted separately as present or absent. Remyelination was assessed by evaluating the absence or presence of shadow plaques (20). Lesion activation stage was evaluated in the regions of interest as described previously (21), and care was taken not to mistake DAWM areas for confluent (chronic) WM lesions. Cortical lesions were counted and classified into 4 lesion subtypes as described elsewhere (22).

Statistical analysis Data analysis was performed using a statistical software program (SPSS 14.0 for Windows; SPSS Inc, Chicago, Illinois). Quantitative MRI measurements and quantitative neuropatho-ogic data were compared using a general linear mixed-model analysis, accounting for a nested design. Pairwise comparisons were performed between tissue types (DAWM and NAWM, DAWM and focal WM lesions, and focal WM lesions and NAWM). Bonferroni-corrected P< 0.05 was considered to be statistically significant. Results of semiquantitative histopathologic scorings were compared using the Mann-Whitney test. Spearman rank correlation coefficient was used to assess correlations between histopathologic findings and quantitative MRI variables. Significance was accepted at the level of P< 0.05.

90 White matter in multiple sclerosis

RESULTS

A total of 42 regions of interest in 17 tissue blocks from 10 patients with chronic MS were analyzed. Of these 42 regions of interest, 16 were placed in DAWM, 15 in NAWM, and 11 in focal WM lesions. The MRI-to-histopathology matching was successful in almost all cases; only 8% of 630 measurements had to be excluded from further analysis.

DAWM at 1.5T is also DAWM at 4.7T Mostly, DAWM was seen in periventricular WM or in the centrum semiovale of frontal and parietal slices and was found in direct proximity of focal WM lesions and in areas with no visible focal abnormalities. The DAWM usually extended over large parts of the WM (Figure 2). At both field strengths, DAWM displayed uniform, nonpatchy signal hyperintensity. The DAWM is characterized by significant axonal loss, myelin pallor, and chronic fibrillary gliosis.

The TmBodian and TmLFB-PAS differed significantly among DAWM, NAWM, and focal WM lesions (Figure 3). The TmPLP, however, showed no difference between DAWM Chapter and NAWM (Table 2). The TmFibrinogen did not differ among the 3 tissue types, whereas

Tm GFAP showed equal values for focal WM lesions and NAWM and significantly lower 4 values (corresponding to a higher staining intensity of GFAP) for DAWM.

The CountAxon also differed significantly among the investigated areas. The number of axons was reduced by 40% in DAWM and by 67% in focal WM lesions compared with the average number of axons in NAWM.

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Figure 3: Diffusely abnormal white matter (DAWM) is a common feature in progressive multiple sclerosis and seems to reflect a separate pathologic process.

A: Schematic illustration of a representative multiple sclerosis brain section from this study. Green indicates focal demyelinated lesions in the WM; yellow, DAWM; red, cortical demyelination; light blue and dark blue dots, areas of fibrinogen leakage; and orange dots, areas of antigen-presenting cell activation. 1a, Glial fibrillary acidic protein stain (original magnification x400) of a chronic gliotic area in DAWM. Note the small cell bodies compared with 2a. 2a, Glial fibrillary acidic protein stain (original magnification x400) of active gliosis with large cell bodies and gemistocytes (inset, original magnification x400) in normal-appearing WM (NAWM). B, Luxol fast blue-periodic acid-Schiff stain for myelin (original magnification x0.7) of the same brain section; original magnification x400 in NAWM (1b), DAWM (2b) (note the large cell bodies), and focal WM lesions (3b). Myelin pallor is most severe in focal WM lesions but also differs significantly between DAWM and NAWM. C, Bodian silver stain (original magnification x0.7) of the same brain section; original magnification x400 in NAWM (1c), DAWM (2c), and focal WM lesions (3c), showing significant axonal loss in focal WM lesions and in DAWM compared with in NAWM. D, HLA-DR immunostain of the same hemispheric brain section (original magnification x0.7). Antigen- presenting cell (APC) activation was more pronounced in DAWM. 1d, No activated APCs were found in NAWM (original magnification x400). 2d, Some scattered activated APCs were found in DAWM (original magnification x400). 3d, Chronic active lesion with a rim of activated APCs (original magnifications x25 and x400 [inset]). 92 White matter in multiple sclerosis

Abnormally enlarged astrocytic cell bodies were seen in 37 of 39 analyzed regions of interest (95%) (Table 2). Semiquantitative scores for glial processes (FSQ GFAP) were highest in focal WM lesions, followed by DAWM and NAWM. Acute axonal pathology and remyelination were absent in DAWM. Activated antigen-presenting cells were found in all areas but were significantly higher in DAWM than in NAWM. Five of 11 focal WM lesions were classified as chronic active and 6 as chronic inactive. No lesions ([p]reactive or otherwise) were found in DAWM. A total of 133 cortical lesions were counted in the brain sections, 5 of which were classified as type I, 22 as type II, 101 as type III, and 5 as type IV. No spatial relation was detected between the extent of DAWM and the occurrence of gray matter lesions.

Table 2: Quantitative MRI Measurements, Transmittance Measurements, Axonal Counts, and Semiquantitative Scores of Astrogliosis and APC Activation.

Measurement NAWM (n=15 ) DAWM (n=16) Focal WM lesion (n=11) T1, mean (SD), ms 194.6 (30.9) 327.3 (30.9)a,b 533.5 (23.7)c Chapter T2, mean (SD), ms 50.0 (4.3) 75.7 (4.3)b,d 113.2 (5.1)c FA, mean (SD) 0.713 (0.05) 0.498 (0.05)a 0.433 (0.06)c 4 ADC, mean (SD), çm2/s 274.6 (41.9) 320.9 (41.8) 408.4 (47.2)e MTR, mean (SD), (%) 21.4 (0.9) 18.7 (0.9)a 16.9 (1.0)e Axonal count, mean (SD) 32.9 (1.6) 19.6 (1.6)d,f 11.0 (1.9)c

d,f c Tm(Bodian), mean (SD) 182.8 (2.8) 204.0 (2.7) 222.0 (4.0) d,f c Tm(LFB-PAS), mean (SD) 174.4 (5.0) 214.8 (4.9) 244.5 (7.1) b c Tm(PLP), mean (SD) 138.6 (7.1) 154.6 (7.0) 220.4 (10.9) a,f Tm(GFAP), mean (SD) 176.7 (6.3) 148.3 (6.1) 176.0 (9.2)

Tm(Fibrinogen), mean (SD) 220.7 (7.6) 224.7 (7.6) 205.4 (10.3) a SQ(LN3), median (range) 0 (0-1) 1 (0-2) 0 (0-2) d,f c F(SQ) GFAP, median (range) 1 (0.5-2) 1.75 (1.5-2) 2 (2-2)

Abbreviations: ADC, apparent diffusion coefficient; APC, antigen-presenting cell; DAWM, diffusely abnormal white matter; FA, fractional anisotropy; (SQ)F GFAP, semiquantitative rating of fibrillary gliosis; GFAP, glial fibrillary acidic protein; LFB-PAS, Luxol fast blue-periodic acid- Schiff; MRI, magnetic resonance imaging; MTR, magnetic transfer imaging; NAWM, normal- appearing white matter; PLP, proteolipid protein; SQ(LN3), semiquantitative rating of APC activity; Tm, transmittance of slides stained for axon density (Bodian), myelin density (LFB-PAS and PLP), astrocytes (GFAP), and blood-brain barrier (fibrinogen); WM, white matter. a P < 0.05 for DAWM vs NAWM. b P < 0.001 for DAWM vs focal WM lesion. c P < 0.001 for focal WM lesion vs NAWM. d P < 0.001 for DAWM vs NAWM. e P < 0.05 for focal WM lesion vs NAWM. f P < 0.05 for DAWM vs focal WM lesion. 93 Chapter 4

The DAWM can be distinguished from NAWM and focal WM lesions using quantitative MRI. Quantitative MRI values in DAWM were intermediate to those of focal WM lesions and NAWM (Table 2). T1 and T2 relaxation times were significantly different among NAWM, DAWM, and focal WM lesions. For FA and magnetic transfer image (MTR) measurements, no significant difference was detected between DAWM and focal WM lesions. The ADC measurements differed significantly only between focal WM lesions and NAWM.

Table 3: Pearson correlations between quantitative MRI and histopathology.

Measurements Tm(Bodian) Count(axons) Tm(LFB-PAS) Tm(PLP) Tm(Fibrinogen) Tm(GFAP) SQF(GFAP) T1, (ms) 0.69a -0.80a 0.68a 0.41b 0.18 -0.08 0.70a T2, (ms) 0.77a -0.79a 0.79a 0.48a -0.07 -0.02 0.57a FA -0.61a 0.58a -0.63a -0.35b -0.02 0.01 -0.20 ADC, çm2/s 0.12 -0.42a 0.28 0.16 0.01 -0.06 0.23 MTR, % -0.57a 0.54a -0.63a -0.27 0.08 -0.04 -0.15

Abbreviations: ADC, apparent diffusion coefficient; Count(Axon), mean number of axons, as counted in the regions of interest; FA, fractional anisotropy; GFAP, glial fibrillary acidic protein; LFB-PAS, Luxol fast blue-periodic acid-Schiff; MRI, magnetic resonance imaging; MTR, magnetic transfer imaging; PLP, proteolipid protein; SQF(GFAP), semiquantitative count of glial fibers; Tm, transmittance of slides stained for axons (Bodian), myelin lipid (LFB-PAS), myelin protein (PLP), blood-brain barrier (fibrinogen), and astrocytes (GFAP). a P < 0.01. b P < 0.05.

DAWM tissue changes correlate with quantitative MRI measures Higher T1 and T2 relaxation times and lower FA values correlated with axonal loss

(lower CountAxon and higher TmBodian) and with reduced myelin density (higher TmLFB-PAS and TmPLP)(Table 3 and Figure 4). In addition, T1 and T2 relaxation times correlated with increased fibrillary gliosis (higher FSQ GFAP) but not with TmGFAP. Further-more, a correlation was found between decreased MTR and axonal (TmBodian and CountAxon) and myelin lipid (TmLFB-PAS) loss. The ADC correlated with CountAxon.

Semiquantitative rating of gliosis may be more informative than light transmittance

Both CountAxon and TmBodian showed significant differences among the 3 tissue types and correlated with each other (r = −0.81, P < 0.001). In contrast, TmGFAP and semiquantitative counts of fibre density were not correlated (r = −0.29, P = 0.08). As glial cell bodies were rated as abnormal in almost all cases, no correlations between semiquantitative scores of glial cell bodies and TmGFAP were calculated (Figure 4). 94 White matter in multiple sclerosis

DISCUSSION

This study shows that DAWM in chronic MS has distinct radiologic and histopathologic characteristics that are significantly different from those of focal WM lesions and NAWM. Higher magnetic field strengths may be expected to improve the detection of inflammatory brain lesions in the MS WM (12,13). Specifically, one might expect to see multiple smaller or confluent lesions in DAWM when using high-field MRI, as DAWM or dirty WM (11,14,23) was discussed to represent areas with newly developing, so-called (p)reactive, lesions (24). In the present study, high-field MRI of DAWM did not support this, and DAWM on 1.5 T was also diffusely abnormal on 4.7 T. This seems to indicate that DAWM represents a pathologic process distinct from (developing) focal WM lesions. This finding was confirmed in these histopathologic results. So far, histopathologic studies of DAWM are scarce, partly because the definition of DAWM has so far been equally “diffuse”: until now, it was unclear whether DAWM consists of (early) inflammatory lesions or of more chronic pathologic abnormalities. The DAWM areas histopathologically showed reduced myelin density and axonal Chapter loss and chronic fibrillary gliosis. The chronic character of this process was supported by the absence of acute axonal pathology, (p)reactive lesions, and blood-brain barrier 4 leakage. The chronic gliosis was characterized by small glial cell bodies and a dense glial fibre meshwork (Figure 2). Shadow plaques, indicative of remyelination, were absent in DAWM. The observed mild activation of antigen-presenting cells in the WM is consistent with the recently described findings of widespread microglial activation (5) and was more pronounced in DAWM than in NAWM. Injury to axons and myelin in the central nervous system can occur through at least 2 mechanisms: as a consequence of direct injury (eg, by acute inflammatory pathology (25)) or as a result of degeneration distant to focal lesions (eg, wallerian degeneration (26)). In the present study, histopathologic analysis confirmed the chronic character of DAWM, which leads to the suspicion that its pathologic condition results from a secondary degenerative process remote from acute, focal damage. It is well established that secondary axonal degeneration proceeds through various stages, each with characteristic histologic and MRI features (27). In the chronic phase, it appears hyperintense on T2-weighted images (28), and histopathologic studies on wallerian degeneration (29) are consistent with the present histopathologic findings in DAWM.

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Figure 4: Scatterplots showing relations between quantitative magnetic resonance imaging measurements and histologic findings and between quantitative and quali- tative histologic measurements.

Circles represent normal-appearing white matter; triangles, diffusely abnormal white matter; and squares, focal white matter lesions.

Relations could be detected between axonal counts (Count[Axon]) and T1, T2, magnetic transfer image (MTR), and apparent diffusion coefficient (ADC) measurements. T1 and T2 relaxation times were related to myelin density, measured as light transmittance on sections stained for

Luxol fast blue-periodic acid-Schiff (Tm(LFB-PAS)). No relation was found between MTR and transmittance (TM) of sections stained for proteolipid protein (Tm[PLP]). T2 relaxation time was related to semiquantitative ratings of gliosis (F[SQ] GFAP [glial fibrillary acidic protein]). Quantitative histopathologic methods (TM) showed strong relations with axon counts, whereas quantitative and semiquantitative assessments of gliosis (Tm[GFAP] and F[SQ] GFAP) showed no relation. See Table 3 for correlation coefficients. 96 White matter in multiple sclerosis

The DAWM appeared slightly more frequent in the centrum semiovale in this study, which may be due to the fact that this WM region harbours many crossing fibers and inflammatory lesions, affecting many descending and ascending WM tracts. This “projecting pathology” was suggested previously in an MRI study showing secondary axonal degeneration in the corticospinal tracts of patients with MS with isolated neurologic syndromes (28). Therefore, DAWM may be found in the direct surroundings of focal WM lesions but may also be ob-served in areas not directly related to focal WM lesions. Whether distinct effects of axonal transection in focal WM lesions alone or whether gray matter damage additionally accounts for the full extent of pathologic features in DAWM could not be shown in this study.

Axonal counts and TmBodian showed good correlation. However, semiquantitative ratings of gliosis and TmGFAP did not correlate. Mean TmGFAP was equal in NAWM and focal WM lesions, whereas semiquantitative scores of glial fibre density showed a significant increase in DAWM compared with NAWM and also in focal WM lesions compared with DAWM. This heterogeneity of staining patterns may be responsible for the fact that the semiquantitative ratings of gliosis (taking the different stages of gliosis into account) Chapter correlated well with T1 and T2 relaxation time measurements in the present study, whereas TmGFAP showed no correlation with any of the quantitative MRI measurements. 4 These findings now indicate that using transmittance for measuring gliosis may yield insufficiently precise information. Of all quantitative MRI measurements, T1 and T2 relaxation time measurements showed highly significant differences among DAWM, NAWM, and focal WM lesions and produced the strongest correlations with axonal and myelin density and gliosis. Therefore, they should be considered most specific in detecting any tissue ab- normalities. However, the clinical implementation of these sequences is technically more challenging, and, consequently, they are less commonly distributed. On the other hand, diffusion tensor imaging and MTR measures are more widely used in MS research and clinical trials, and in this study, FA (and to a lesser extent MTR) enabled distinction between DAWM and NAWM and between DAWM and focal WM lesions. Moreover, FA and MTR correlated strongly with axonal loss and reduced myelin density. The ADC showed few differences between regions of interest and weaker correlations with histopathologic findings. This finding can be explained by the use of formalin-fixed material, which is known to affect MRI measures (eg, shortening of relaxation times), thus affecting direct comparisons with in vivo measurements (30,31). Nevertheless, fixed material can still be suitable for providing clinically relevant conclusions (31). How-ever, interpretation of MRI techniques, such as ADC (and MTR), which depend on free mobility of water molecules throughout the brain parenchyma, may be more challenging (32).

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This study characterized DAWM in chronic MS and showed that it differs from focal WM lesions and NAWM in pathologic and imaging terms. Areas of DAWM consist of reduced myelin density, extensive axonal loss, and chronic fibrillary gliosis, differing from focal WM lesions and NAWM pathologic findings in that they seem to reflect the secondary, cumulative effects of ongoing (focal) pathologic abnormalities in the MS brain instead of focal inflammation. From these data, we conclude that tissue damage in DAWM, although mostly more subtle than that of focal WM lesions, is substantial. Clinical effects of DAWM could not be investigated in the present postmortem study and should be investigated in future studies focusing on DAWM in the in vivo situation using a validated scoring system for DAWM. Given its extent in these brain samples and its discriminate pathologic findings, inclusion of DAWM in lesion load assessments should be expected to result in a more accurate estimation of total disease burden than evaluation of the focal WM lesion load alone. Furthermore, it can potentially be of use as a new disease marker in future clinical trials that focus on the neurodegenerative aspects of MS.

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REFERENCES

1. PATY DW, OGER JJ, KASTRUKOFF LF 10. VRENKEN H, POUWELS PJ, GEURTS JJ et et al. MRI in the diagnosis of MS: a al. Altered diffusion tensor in multiple prospective study with comparison of sclerosis normal-appearing brain tissue: clinical evaluation, evoked potentials, cortical diffusion changes seem related oligoclonal banding, and CT. Neurology. to clinical deterioration. J Magn Reson 1988; 38:180-185 Imaging. 2006; 23:628-636 2. BARKHOF F. The clinico-radiological 11. KEIPER MD, GROSSMAN RI, HIRSCH JA paradox in multiple sclerosis revisited. et al. MR identification of white matter Curr Opin Neurol. 2002; 15:239-245 abnormalities in multiple sclerosis: a 3. ALLEN IV, MCQUAID S, MIRAKHUR M et al. comparison between 1.5 T and 4 T. AJNR Pathological abnormalities in the normal- Am J Neuroradiol. 1998; 19:1489-1493 appearing white matter in multiple 12. SICOTTE NL, VOSKUHL RR, BOUVIER S et al. sclerosis. Neurol Sci. 2001; 22:141-144 Comparison of multiple sclerosis lesions 4. BO L, VEDELER CA, NYLAND HI et al. at 1.5 and 3.0 Tesla. Invest Radiol. 2003; Subpial demyelination in the cerebral 38:423-427 cortex of multiple sclerosis patients. J 13. WATTJES MP, LUTTERBEY GG, HARZHEIM M Neuropathol Exp Neurol. 2003; 62:723-732 et al. Higher sensitivity in the detection of Chapter 5. KUTZELNIGG A, LUCCHINETTI inflammatory brain lesions in patients with CF, STADELMANN C et al. Cortical clinically isolated syndromes suggestive of demyelination and diffuse white matter multiple sclerosis using high field MRI: an 4 injury in multiple sclerosis. Brain. 2005; intraindividual comparison of 1.5 T with 128:2705-2712 3.0 T. Eur Radiol. 2006; 16:2067-2073 6. PETERSON JW, BO L, MORK S et al. 14. GE Y, GROSSMAN RI, BABB JS et al. Dirty- Transected neurites, apoptotic neurons, appearing white matter in multiple and reduced inflammation in cortical sclerosis: volumetric MR imaging and multiple sclerosis lesions. Ann Neurol. magnetization transfer ratio histogram 2001; 50:389-400 analysis. AJNR Am J Neuroradiol. 2003; 7. FILIPPI M, CAMPI A, DOUSSET V et al. A 24:1935-1940 magnetization transfer imaging study 15. LUBLIN FD, REINGOLD SC. Defining the of normal-appearing white matter in clinical course of multiple sclerosis: multiple sclerosis. Neurology. 1995; results of an international survey. National 45:478-482 Multiple Sclerosis Society (USA) Advisory 8. MACKAY A, LAULE C, VAVASOUR I et al. Committee on Clinical Trials of New Insights into brain microstructure from Agents in Multiple Sclerosis. Neurology. the T2 distribution. Magn Reson Imaging. 1996; 46:907-911 2006; 24:515-525 16. BO L, GEURTS JJ, RAVID R et al. Magnetic 9. VRENKEN H, GEURTS JJ, KNOL DL et al. resonance imaging as a tool to examine Whole-brain T1 mapping in multiple the neuropathology of multiple sclerosis. sclerosis: global changes of normal- Neuropathol Appl Neurobiol. 2004; 30:106- appearing gray and white matter. 117 Radiology. 2006; 240:811-820

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17. GEURTS JJ, BO L, POUWELS PJ et al. 27. INOUE Y, MATSUMURA Y, FUKUDA T et al. Cortical lesions in multiple sclerosis: MR imaging of Wallerian degeneration in combined postmortem MR imaging and the brainstem: temporal relationships. histopathology. AJNR Am J Neuroradiol. AJNR Am J Neuroradiol. 1990; 11:897-902 2005; 26:572-577 28. SIMON JH, KINKEL RP, JACOBS L et al. 18. LUDWIN SK. The pathogenesis of multiple A Wallerian degeneration pattern in sclerosis: relating human pathology to patients at risk for MS. Neurology. 2000; experimental studies. J Neuropathol Exp 54:1155-1160 Neurol. 2006; 65:305-318 29. MATSUSUE E, SUGIHARA S, FUJII S 19. GEDDES JF, VOWLES GH, BEER TW et al. et al. Wallerian degeneration of the The diagnosis of diffuse axonal injury: corticospinal tracts: postmortem MR- implications for forensic practice. pathologic correlations. Acta Radiol. 2007; Neuropathol Appl Neurobiol. 1997; 48:690-694 23:339-347 30. PFEFFERBAUM A, SULLIVAN EV, 20. PATRIKIOS P, STADELMANN C, KUTZELNIGG ADALSTEINSSON E et al. Postmortem MR A et al. Remyelination is extensive in a imaging of formalin-fixed human brain. subset of multiple sclerosis patients. Neuroimage. 2004; 21:1585-1595 Brain. 2006; 129:3165-3172 31. SCHMIERER K, WHEELER-KINGSHOTT CA, 21. VAN DER VALK P, DE GROOT CJ. Staging of TOZER DJ et al. Quantitative magnetic multiple sclerosis (MS) lesions: pathology resonance of postmortem multiple of the time frame of MS. Neuropathol Appl sclerosis brain before and after fixation. Neurobiol. 2000; 26:2-10 Magn Reson Med. 2008; 59:268-277 22. BO L, VEDELER CA, NYLAND H et al. 32. FOX CH, JOHNSON FB, WHITING J et al. Intracortical multiple sclerosis lesions are Formaldehyde fixation. J Histochem not associated with increased lymphocyte Cytochem. 1985; 33:845-853 infiltration. Mult Scler. 2003; 9:323-331 23. CHEN SC, CHUNG HW, LIOU M. Measurement of volumetric lesion load in multiple sclerosis: moving from normal- to dirty-appearing white matter. AJNR Am J Neuroradiol. 2003; 24:1929-1930 24. VOS CM, GEURTS JJ, MONTAGNE L et al. Blood-brain barrier alterations in both focal and diffuse abnormalities on postmortem MRI in multiple sclerosis. Neurobiol Dis. 2005; 20:953-960 25. TRAPP BD, PETERSON J, RANSOHOFF RM et al. Axonal transection in the lesions of multiple sclerosis. N Engl J Med. 1998; 338:278-285 26. WALLER AV. Experiments on the section of the glossopharyngeal and hypoglossal nerves of the frog, and observations of the alterations produced thereby in the structure of the primitive fiber. Philos Trans R Soc Lond 1850; 140:123-429

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DIFFUSELY ABNORMAL WHITE MATTER ,1352*5(66,9(08/7,3/(6&/(526,6 IN VIVO QUANTITATIVE MR IMAGING CHARACTERIZATION AND COMPARISON BETWEEN DISEASE TYPES.

Hugo Vrenken Alexandra Seewann Dirk Knol Chris Polman Frederik Barkhof Jeroen Geurts

AJNR Am J Neuroradiol 2010;31(3): 541-548 Comment in: AJNR Am J Neuroradiol. 2010: 31(3):390-391 Chapter 4

INTRODUCTION

Background: Recent postmortem studies in MS brain suggest that the severity of changes in DAWM can be measured by using quantitative MR imaging. This study aimed to characterize DAWM in vivo by using 4 quantitative MR imaging measures and to explore differences between MS disease types. Materials and methods: In 17 patients with chronic MS (7 PP, 10 SP), quantitative MR imaging was performed at 1.5T, yielding whole-brain voxelwise maps of T1, MTR, ADC, and FA. ROIs were placed to obtain values for DAWM, NAWM, and WM lesions. A general linear mixed-model analysis was used to compare T1, MTR, ADC, and FA between tissue types and disease types. Results: Values of T1, MTR, ADC, and FA for DAWM were intermediate to those observed in NAWM and WM lesions. In patients with SPMS, DAWM was significantly different from both WM lesions and NAWM regarding all 4 measures, while in patients with PPMS, DAWM differed significantly from NAWM regarding T1, MTR, and FA and from lesions only regarding FA. Most interesting, DAWM differed between disease types: DAWM in patients with SPMS exhibited significantly higher T1 and lower MTR than did DAWM in patients with PPMS. Conclusions: In vivo T1, MTR, ADC, and FA reflect the variable severity of pathologic changes in DAWM in MS. Moreover, these quantitative MR imaging measures suggest that DAWM may differ between PPMS and SPMS.

Abbreviations: ADC = apparent diffusion coefficient; DAWM = diffusely abnormal white matter; EDSS = Expanded Disability Status Scale; FA = fractional anisotropy; FLASH = fast low-angle shot; IQR = interquartile range; MS = multiple sclerosis; kfor = forward magnetization exchange rate; MTR = magnetization transfer ratio; NAWM = normal-appearing white matter; Pd = proton density; PP = primary progressive; PPMS = primary progressive multiple sclerosis; ROI = region of interest; SP = secondary progressive; SPMS = secondary progressive multiple sclerosis; STEAM = stimulated echo acquisition mode; T1 = T1 relaxation time; T1free = native T1 relaxation time; WM = white matter

104 White matter in multiple sclerosis

INTRODUCTION

In the brain of patients with MS, in vivo MR imaging often shows fuzzy-bordered areas of subtly increased signal intensity on Pd or T2-weighted images. These abnormalities have been referred to as dirty WM, dirty-appearing WM, or DAWM (1-4). The use of varying criteria renders comparisons among these studies difficult. A previous article has proposed a set of MR imaging criteria to define DAWM, applied them in a postmortem setting, and concluded that the use of these specific criteria yields only areas with truly diffuse pathology, without including multifocal lesional pathology (4). Recently, these areas of DAWM have been characterized histopathologically and were found to contain tissue changes indicating a chronic pathology, including axonal loss, myelin loss, and chronic isomorphic gliosis (4,5). However, the severity of these changes may vary among patients. On the basis of previous histopathology and MR imaging results, DAWM is believed to represent a separate pathologic entity in MS (4,5), in addition to MR imaging-visible focal WM lesions, focal lesions in the gray matter, and subtle changes in the so-called normal-appearing brain tissue. Because DAWM Chapter pathology appears to be chronic, most likely reflecting secondary axonal degeneration, one could hypothesize that monitoring DAWM in vivo may give important information 4 on the progression of the disease. In any case, the histopathologic changes observed in DAWM suggest that to obtain a full in vivo assessment of the total disease burden, the tissue damage in DAWM should be taken into account. Beyond the mere detection of DAWM areas on T2-weighted images, measures are required that can reflect the severity of the pathology in DAWM regions because this severity appears to be variable. Using quantitative MR imaging, Seewann et al (4) demonstrated that in the postmortem setting, quantitative MR imaging measures reflect the degree of tissue change as measured by histopathologic methods. These quan-titative MR imaging measures are, therefore, important candidates for quantifying the DAWM disease burden in patients in vivo. To date, few studies have investigated diffuse WM changes by using quantitative MR imaging techniques in an in vivo setting. In 2 studies, both with small numbers of patients and ROIs, values for MTR, kfor, and T1free of diffuse WM changes were intermediate to those of NAWM and focal WM le-sions (3,6), and 1 of these observed lower kfor and higher T1free in diffuse abnormalities compared with those in NAWM (6). A larger histogram-based study found that MTR values distinguished diffuse changes from NAWM and focal WM lesions (2). Finally, in a study of 13 patients, T2 relaxation times of diffuse changes were higher than those of NAWM and lower than those of focal WM lesions (7). The varying criteria used to define diffuse abnormalities in these studies, specifically regarding whether multifocal lesions were included, make it difficult to

105 Chapter 4 draw reliable conclusions about the nature of the areas of truly diffuse pathology. Furthermore, no comparisons between MS disease types have been reported. The present study aimed to characterize and quantify changes in purely diffuse DAWM in vivo in progressive MS, defined following previously proposed MR imaging criteria (4), by using quantitative MR imaging techniques. This study investigated the progressive disease types because DAWM seems to be more prevalent in progressive MS than in relapsing-remitting MS (Seewann et al, unpublished general observations). Second, we aimed to investigate whether differences in the severity of DAWM pathology exist between PP and SPMS.

MATERIALS AND METHODS

Patients Data previously acquired as part of an academic quantitative MR imaging study were retrospectively selected, including only patients with progressive MS who had results for all 3 techniques: diffusion tensor imaging, MTR, and T1, yielding a subgroup of the total group of progressive patients originally studied (8-10).

Quantitative MR Imaging Techniques This section briefly describes the 3 quantitative MR imaging techniques used in this study. T1 mapping produces voxelwise maps of the longitudinal relaxation time, T1, assuming monoexponential decay. In MS, T1 has been shown to be increased in different pathologic processes, most prominently axonal damage but also edema, demyelination, and gliosis (4,11-16). MTR mapping produces voxelwise maps of the relative amount of signal-intensity decrease that results from adding a magnetization- preparation prepulse to a pulse sequence. The MTR is a semiquantitative measure that is ultimately determined by more fundamental parameters such as the fraction of bound pool protons, the magnetization exchange rate between bound and free protons, and the relaxation rates. As such, MTR can theoretically be expected to be altered especially by demyelination and edema, though it has been suggested that inflammation per se may also decrease MTR (17). Correlative imaging-histopathologic studies in MS have found reduced MTR to be associated with a lower grade of myelination (4,18,19); similar relations of MTR with axonal loss are thought to result from the close relation between demyelination and axonal loss (17). Finally, diffusion tensor imaging uses magnetic field gradients to sensitize the MR imaging signal intensity to the effects of water diffusion in several directions; and from the results of these multiple measurements, the water self-diffusion tensor is

106 White matter in multiple sclerosis calculated. Two important parameters that can be deduced from this tensor are the ADC, which can be interpreted as reflecting the overall magnitude of diffusion, and the FA, which reflects the degree to which the diffusion has a preferential direction. Diffusion tensor imaging was used in this study to create voxelwise maps of both ADC and FA. Increased ADC and decreased FA in MS have both been associated with a lower grade of myelination and with axonal loss, and it has been suggested that the relation with axonal loss is largely determined by its relation to demyelination (4,20).

MR Image Acquisition MR imaging investigations were performed on a 1.5T Magnetom Vision MR imaging system (Siemens, Erlangen, Germany) with the standard circularly polarized head coil. A fast spin-echo technique was used to generate Pd and T2-weighted images (TR, 2625 ms; TE, 16/98 ms) for 2 interleaved sets of 16 sections covering the whole brain (section thickness, 4 mm; in-plane resolution, 1x1 mm2). For T1 mapping, 21 six 3D FLASH image sets were acquired (TR/ TE, 20/4 ms; NEX, 1; bandwidth, 244 Hz/pixel), with nominal flip angles of 2°, 5°, 10°, 15°, 20°, and 25° (small Chapter flip angle array), by using a 128-mm 3D slab consisting of thirty-two 4-mm sections, with the same position, orientation, and resolution as the Pd/T2-weighted images. 4 Acquisition took approximately 8 minutes. For MTR mapping, 2 sets of 3D FLASH images covering the same volume were acquired (TR/TE, 27/4 ms; flip angle, 20°; NEX, 2; 1x1x4 mm3 voxels), 1 with a magnetization transfer prepulse (gaussian pulse shape, 7.68-ms duration; frequency offset, 1500 Hz; equivalent flip angle, 500°), and 1 without. Acquisition took approximately 5 minutes. For B1 correction of the T1 and MTR measurements, sagittal 3D FLASH images (TR/ TE, 25/5 ms; NEX, 1; bandwidth, 244 Hz/pixel; 200-mm 3D slab; 2x2x4 mm3 voxels) were acquired with nominal flip angles of 140°, 160°, 180°, 200°, and 220° (large flip angle array). Acquisition took approximately 12 minutes. Diffusion tensor mapping was performed for 20 contiguous 6-mm sections with 2x2 mm2 pixels, again with the same orientation and in-plane FOV as the T2-weighted images, by using a diffusion-weighted single-shot short TE STEAM sequence (22), with gradients in 6 noncollinear directions; b-value, 750 s/mm2; flip angle, 11°; effective TE, 65 ms; and 4 acquisitions. Acquisition took approximately 6 minutes.

MR Image Analysis Calculation of Supratentorial Lesion Volume. To calculate supratentorial lesion loads, we manually marked MR imaging-visible abnormalities and semiautomatically outlined them on the Pd-weighted images by using in-house-developed software

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(Show_Im-ages, VU University Medical Centre, Amsterdam, the Netherlands), using a seed-growing technique with a local threshold.

Quantitative MR Imaging Analysis T1 Mapping. Whole-brain T1 maps (Figure 1) were obtained as previously described (9). Briefly, the small flip angle array images were coregistered with FLIRT (FMRIB’s linear image registration tool) (23,24); T1 was then calculated (21) for each pixel by nonlinear least-squares fitting by using a hill-climbing algorithm, correcting the flip angle values for the local effective B1 field strength, which was calculated from the signal-intensity zero crossing (21) in the large flip angle array images.

Figure 1: Sample of maps of quantitative MR imaging measures for 1 axial section.

Images shown are from a female patient with PPMS (59.5 years of age; disease duration, 8.3 years; EDSS score, 4.0; total focal WM lesion volume on T2, 14.9 mL). Images show maps of the T1 (A), MTR (B ), ADC (C ), and FA (D ). The empty (black) voxels located centrally in the maps of T1 and MTR are voxels excluded from analyses because of insufficient accuracy in determining the local effective B1 field strength in and close to the CSF. Details of methods are provided in the text.

MTR Mapping. Whole-brain MTR maps were obtained as previously described (10). Briefly, 3D FLASH images acquired with and without magnetization transfer prepulse were coregistered, MTR maps were generated, and a correction for B1-induced variation was applied for each subject by using the method described by Ropele et al (25). ADC and FA Mapping. ADC and FA maps were generated as previously described (8). Briefly, maps of the 6 unique diffusion tensor elements were calculated from the raw data and then used to calculate ADC and FA maps (26).

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ROI for Quantitative MR Imaging Analysis Figures 2 and 3 illustrate how the ROIs were placed in the different tissue types. In this study, DAWM was defined as a uniform nonfocal area of signal increase on the Pd-weighted sequence, in which the signal increase is more subtle than the signal increase of focal WM lesions. ROIs were placed by manual outlining on the Pd-weighted images. To avoid regional bias, we aimed to sample all 3 tissue types (NAWM, DAWM, and focal WM lesions) in 4 locations: left and right frontal WM and left and right parieto- occipital WM, leading to a total of 12 ROIs per patient. Another interesting region, the periventricular region, unfortunately had to be discarded because images of too many patients did not exhibit NAWM in that region, which would have led to systematic bias in the sampling. If a tissue type was not present in a particular location, the corresponding ROI was not placed. To provide good sampling of NAWM, we used ROIs in NAWM that were fairly large compared with those in DAWM and lesions, in which the sizes of the pathologic areas present in the 4 anatomic regions studied here created limitations on the size of ROIs that could be drawn, especially sometimes leading to small ROIs in lesions. Within these limitations, we aimed to draw ROIs of roughly similar Chapter size in DAWM and lesions. For each tissue type, we attempted to draw similar-sized ROIs in the different anatomic regions as much as possible. 4 Within each patient, ROIs were then combined for each tissue type, giving 1 combined ROI for DAWM, 1 for NAWM, and 1 for focal WM lesions. Using FLIRT(23,24), we registered the patient’s T2-weighted images to their MTR, T1, and ADC/FA maps, by using the corresponding native images as a reference. The transformation matrices thus obtained were applied to the binary ROIs, by using trilinear interpolation and a 25% intensity threshold, yielding binary ROIs in the MTR, T1, ADC, and FA map spaces, which were used to calculate the average MTR, T1, ADC, and FA for each tissue type for that patient.

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Figure 2: Early-echo (Pd) images from a T2-weighted 2D dual-echo fast spin-echo se- quence used to identify and outline focal WM lesions, DAWM, and NAWM.

Images shown are from a male patient with SPMS (45.7 years of age; disease duration, 11.1 years; EDSS score, 4.0; total focal WM lesion volume on T2, 21.9 mL). The top row shows the images of 5 consecutive sections used for placing ROIs in this patient. The bottom row shows the same images but with the ROIs overlaid. The ROIs placed in each section are left frontal focal WM lesion (blue), left frontal DAWM (green) (A); right parieto- occipital DAWM (blue) (B ); right frontal DAWM (red) (C ); right frontal focal WM lesion (green), right parieto-occipital focal WM lesion (yellow), left parieto-occipital focal WM lesion (pink), and left parieto-occipital DAWM (yellow) (D ); and right frontal NAWM (blue), left frontal NAWM (green), right parieto-occipital NAWM (pink), and left parieto-occipital NAWM (yellow) (E ).

Statistical Analysis Clinical variables and supratentorial lesion volumes were compared between patients with PPMS and SPMS by using the Mann-Whitney U test, except for sex for which the Pearson ई2 test was used. For each quantitative MR imaging parameter (MTR, T1, ADC, FA), a model was constructed incorporating all 3 tissue types and all patients. A general linear mixed model was used with the tissue types nested within patients and an unstructured covariance matrix. The model further contained disease type (PPMS or SPMS) and the interaction between disease type and tissue type. If the interaction between disease type and tissue type was not significant at the P = 0.05 level, differences between PPMS and SPMS and pair-wise differences among the 3 tissue types were assessed by using post hoc Bonferroni-adjusted contrasts. If there was a significant interaction between

110 White matter in multiple sclerosis dis-ease type and tissue type, post hoc Bonferroni-adjusted contrasts were set up to assess differences between PPMS and SPMS within each of the 3 tissue types separately and, similarly, to assess differences among the 3 tissue types within patients with PPMS and SPMS separately. P values below 0.05 were considered statistically significant.

RESULTS

Patients Demographic and clinical characteristics are given in Table 1. Sex distributions in both groups were as expected, with relatively more women in the SPMS group, but this difference was not significant P=( 0.6). Disease durations were shorter in the PPMS group (P = 0.3), while patients with PPMS were significantly older than those with SPMS (P = 0.002), as expected from the higher average age at onset of PPMS. EDSS scores were higher in the SPMS group (P = 0.2). The T2 focal WM lesion volumes were higher in the SPMS group compared with the PPMS group (P = 0.07). Chapter

Figure 3: Further illustration of the radiologic definition of DAWM and placement of ROIs. 4

Images shown are from a male patient with PPMS (52.3 years of age; disease duration, 2.3 years; EDSS score, 3.0; total focal WM lesion volume on T2, 6.9 mL). The left part of the figure shows the early-echo (Pd) image of 1 section from the T2-weighted 2D dual-echo fast spin-echo sequence. Images are in radiologic convention: The right side of the body is on the left side of the image. The right part of the figure shows the same image but with the parieto-occipital DAWM ROIs used in this study as a color overlay. The right parieto-occipital DAWM ROI is shown in light blue; the left parieto-occipital DAWM ROI is shown in yellow.

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ROIs A total of 190 ROIs were placed in the 17 patients included in this study, with 12 ROIs per patient except in the follow-ing cases: In 2 patients, no parieto-occipital lesions were found. In another patient, no parieto-occipital DAWM was found. In 1 patient with PPMS, no lesion was found in the frontal or parieto-occipital WM, so no lesion ROI could be placed. Similarly, in 1 patient with SPMS, no NAWM ROI could be placed. The numbers of patients analyzed in each tissue category were therefore the following: DAWM: 7 PP, 10 SP; focal WM lesions: 6 PP, 10 SP; NAWM: 7 PP, 9 SP. Table 2 lists the median numbers and volumes of analyzed voxels for each quantitative MR imaging measure in each tissue type, as well as the same number for the original ROIs drawn on the Pd-weighted images. Table 3 provides more detail on the ROIs as drawn on the Pd-weighted images, listing the median numbers and IQRs for each patient group by anatomic region and tissue type. As indicated in “Materials and Methods,” NAWM ROIs were larger than those for DAWM or lesions. Also, DAWM ROIs were larger than lesion ROIs. The largest discrepancies between the PPMS and SPMS groups were seen for parieto-occipital DAWM and parieto-occipital lesions.

Table 1: Demographic and clinical characteristics

P-value for PPMS SPMS comparison PPMS vs SPMS

No. of patients 710

Gender: M/F 3 / 4 3 / 7 P=0.06

Age (y): mean ± SD 59.2 ± 5.9 43.8 ± 10.3 P=0.002 Disease duration (y): 8.3 [3.8 – 16.4] 15.9 [8.7 – 23.7] P=0.3 Median [IQR]

EDSS score: median [IQR] 4.5 [3.0 – 4.5] 5.5 [4.0 – 6.75] P=0.2 Supratentorial focal WM lesion volume (mL): median 3.6 [1.4 – 13.8] 15.5 [3.9 – 23.6] P=0.07 [IQR]

P values for the comparison between patients with PPMS and SPMS are derived from the Mann- Whitney U test, except for sex, for which Pearson ई2 was used.

112 White matter in multiple sclerosis

Quantitative MR Imaging characterization of DAWM Table 4 and Fig 4 show the observed values of the quantitative MR imaging measures. Values observed in DAWM were intermediate to those observed in focal WM lesions and those observed in NAWM. For ADC, MTR, and T1, the interaction between disease type and tissue type was significant, whereas for FA, the interaction between disease type and tissue type was not significant; in both cases, the appropriate method was used to assess pair-wise contrasts.

Table 2: Median number (volume) of analyzed voxels per patient in each tissue type for each quantitative MR imaging measurea

Technique

Tissue type ADC / FA MTR T1 Pd

NAWM 117.5 (2.8 mL) 624 (2.5 mL) 660 (2.6 mL) 660 (2.6 mL)

DAWM 53 (1.3 mL) 327 (1.3 mL) 283 (1.1 mL) 200 (0.8 mL) Chapter Focal WM lesions 19.5 (0.5 mL) 117 (0.5 mL) 123.5 (0.5 mL) 80.5 (0.3 mL) a Median values are given for the entire group of patients in this study. A more detailed 4 subdivision by anatomic region and disease type is provided in Table 3. The size of each region of interest was defined as the total number of voxels included in the ROI, after warping it to the corresponding quantitative MR imaging maps as described in the text. The corresponding ROI volume was calculated by multiplying the number of voxels by the appropriate voxel volume, which was 4 mm3 for the T1 and MTR maps, and 24 mm3 for the ADC and FA maps. The column headed “Pd” gives the values for the original ROIs as drawn on the Pd-weighted images.

In patients with SPMS, the discrimination among tissue types was clearest: DAWM differed significantly from both focal WM lesions and NAWM regarding all 4 quantitative MR imaging measures. ADC and T1 were significantly higher (by between 10% and 20%) in DAWM than in NAWM and significantly lower than in focal WM lesions (by approximately 25%). FA and MTR were significantly lower in DAWM compared with NAWM (by approximately 10%) and significantly higher compared with lesions (by approximately 30%). In patients with PPMS, DAWM differed significantly from NAWM, in that FA and MTR were significantly lower in DAWM (by between 5% and 10%) and T1 was significantly higher in DAWM (by approximately 10%), whereas DAWM differed significantly from focal WM lesions only regarding FA, which was approximately 10% higher in DAWM compared with focal WM lesions.

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Table 3: Number of voxels (medians and IQRs) in the ROI drawn on the Pd-weighted imagesa

Disease type, NAWM DAWM Lesions Region Median IQR Median IQR Median IQR Median

PPMS

Frontal 314.0 247.5-338.5 89.0 79.5-120.0 25.0 17.0-56.0

Parieto- 318.0 305.0-379.5 128.0 111.0-156.0 32.0 26.0-52.0 occipital

Combined 593.0 528.0-713.0 212.0 190.5-265.5 57.0 17.0-80.0 SPMS

Frontal 394.0 238.0-452.0 99.0 69.0-121.0 40.5 32.0-87.0

Parieto- 390.0 319.0-501.0 94.5 76.0-114.0 56.0 36.0-61.0 occipital

Combined 703.0 629.0-921.0 193.0 141.0-238.0 95.0 71.0-144.0 a The “Combined” ROI for each patient is the combination of the frontal and parieto-occipital ROIs for that tissue type (NAWM, DAWM, or lesions).

Comparison between PPMS and SPMS DAWM differed between PPMS and SPMS regarding MTR and T1, with MTR lower and T1 higher in SPMS DAWM compared with PPMS DAWM, both associated with more severe MS-related tissue changes. No differences were observed between PPMS and SPMS regarding ADC or FA of DAWM. Similarly, focal WM lesions had lower MTR and higher T1 in SPMS compared with PPMS, again suggesting more severe damage in the patients with SPMS, while lesional ADC and FA did not differ between SPMS and PPMS.

114 White matter in multiple sclerosis

Table 4: Quantitative MR imaging parameters by tissue type and clinical group*

Significant Significant pairwise comparison NAWM DAWM Lesions comparisons between between tissue disease types types

ADC PP 805 ± 53 842 ± 48 1042 ± 216 Lesions vs NAWM a ( m2 s-1) ѥ b DAWM vs NAWM - SP 798 ± 40 903 ± 77 1201 ± 131 DAWM vs Lesions b Lesions vs NAWM b

FA DAWM vs NAWM c PP 0.383 ± 0.058 0.350 ± 0.047 0.318 ± 0.110 DAWM vs Lesions a Lesions vs NAWM b - 0.242 ± 0.054 DAWM vs NAWM b SP 0.365 ± 0.033 0.322 ± 0.030 DAWM vs Lesions b Lesions vs NAWM b Chapter

MTR (%) DAWM vs NAWM c PP 32.9 ± 0.6 31.1 ± 1.0 28.2 ± 3.6 Lesions vs NAWM a DAWM SP vs 4 PP b b DAWM vs NAWM Lesions SP b SP 32.8 ± 1.2 28.8 ± 0.8 21.3 ± 3.3 DAWM vs Lesions vs PP c Lesions vs NAWM b

a T1 (ms) PP 759 ± 27 823 ± 38 1040 ± 185 DAWM vs NAWM DAWM SP vs b DAWM vs NAWM b PP SP 815 ± 47 958 ± 67 1419 ± 298 DAWM vs Lesions b Lesions SP a Lesions vs NAWM b vs PP

Means and SDs of ADC, FA, MTR, and T1 in NAWM, DAWM, and lesions by clinical group (PP or SPMS). Bonferroni-corrected P values derived from general linear mixed model analysis are indicated for statistically significant pair-wise differences between tissue types and disease types. Details of statistical analyses are provided in the text. a P < 0.05 b P < 0.001 c P < 0.01

115 Chapter 4

Figure 4: Boxplots showing median, range, and 25th and 75th percentiles for T1 (A), MTR (B ), ADC (C ), and FA (D ).

Each boxplot shows data split according to patient group (either PP or SPMS) as well as according to tissue type (NAWM, DAWM, or focal WM lesions). The plots clearly demonstrate that the values for the quantitative MR imaging measures observed in DAWM are intermediate to those observed in NAWM and focal WM lesions.

Focal WM lesions and NAWM were significantly different regarding all 4 quantitative MR imaging measures and in both disease types (Table 3, Fig 4), with the exception of T1 in PPMS, which did not significantly differ between NAWM and focal WM lesions.

116 White matter in multiple sclerosis

DISCUSSION

This study demonstrates that 4 in vivo quantitative MR imaging measures enable a clear discrimination between DAWM and other tissue pathologies in MS brain and that T1 and MTR may differentiate between DAWM in PPMS and SPMS. The ability of quantitative MR imaging to reflect histopathologic changes in DAWM has recently been demonstrated in postmortem tissue (4,5). In the in vivo imaging setting used in the present study, no distinction can be made between truly diffuse pathology and areas containing multiple microscopic lesions. Therefore, DAWM was selected on the basis of well-defined MR imaging criteria that have been shown in postmortem imaging to lead to inclusion of truly diffuse abnormalities, without including multifocal lesional pathology. Hence, this study is the first to give real in vivo insight in the pathology of “pure” diffusely abnormal WM, by using a pathologically confirmed method to select it. The results are in agreement with the limited data available from previous in vivo quantitative MR imaging studies, in which MTR (2,3), kfor (3,6), T1free (3,6), and T2 (7) were measured in “diffuse” WM changes, sometimes Chapter including multifocal lesions. The present study demonstrates that “pure” DAWM without multifocal lesions exhibits in vivo quantitative MR imaging measures intermediate to 4 and significantly different from those of focal WM lesions and those of NAWM. These results are in agreement with findings of a recent postmortem MR imaging- histopathology correlation study that used the same quantitative MR imaging measures in fixed brain sections (4) and that also observed that quantitative MR imaging measures of DAWM showed intermediate values between those of focal WM lesions and those of NAWM. We, therefore, conclude that the applied quantitative MR imaging techniques are useful as paraclinical tools for measuring the severity and evolution of DAWM in patients with MS. Previous postmortem histopathologic findings evidenced that though quantitative MR imaging measures of DAWM are intermediate to those of NAWM and focal WM lesions, the disease process in DAWM is by no means a stage preceding focal lesions, but a different pathologic phenomenon altogether, involving old chronic gliosis with extensive axonal and myelin loss (4). Remyelination or other evidence of resolution of pathology was not observed in that study, but it would certainly be important to scrutinize those findings in independent studies on different samples. Combined with a moderate activation of microglia, these observations are certainly compatible with secondary (wallerian) degeneration, similar to what has been reported in white matter hyperintensities in Alzheimer disease by Gouw et al (27). If DAWM indeed represents secondary axonal degeneration, studying DAWM changes in vivo will be of paramount importance for under-standing and monitoring the disease process.

117 Chapter 4

Although groups in this study were relatively small, the results for T1 and MTR suggest that DAWM in PPMS may be different from DAWM in SPMS. This finding is important because it establishes that pathologic changes in DAWM are not the same in all patients with MS and, in fact, may differ significantly between 2 clinical groups. Whether the differences observed between the PPMS and SPMS patients in the present study are strictly the result of a fundamental difference between PPMS and SPMS cannot be concluded from our results. The retrospective nature of the present study implies that we have been unable to influence the size and composition of the patient groups. From the groups of patients with PPMS and SPMS included in the original study, we had to exclude several patients because data were not complete for all 4 measures (MTR, T1, ADC, and FA). Although the 2 resulting patient groups were representative of their respective disease types, they did differ in terms of disease durations and EDSS scores (though not significantly). Therefore, it cannot be excluded that the observed difference between our groups of patients with PPMS and SPMS is partly or wholly the result of a longer and/or more severe disease process in the patients with SPMS. Lower numbers of inflammatory infiltrates were reported in the WM in PPMS compared with SPMS in a post-mortem study (28). It has been hypothesized that PPMS may be more primarily neurodegenerative in nature, compared with the relapse-onset type of disease (29). Further in vivo studies should address the putative DAWM differences between PPMS and SPMS in well-matched groups. The nature and severity of tissue changes in DAWM should also be compared between PPMS and SPMS through postmortem histopatho-logic studies. Although in this study, ROI sizes differed somewhat between patient groups and tissue types, this difference is unlikely to have affected the results. Most important, the ROIs were placed in the same anatomic regions, thus controlling for regional variations in the quantitative MR imaging measures, and were of sufficient size to provide good sampling of each tissue type. Because of the small sizes of the lesions present in these regions in many patients, the lesion ROIs were overall smaller than the DAWM ROIs, which can be expected to result in somewhat less accurate determination of the average values of the quantitative MR imaging measures in lesional tissue but would not have any systematic effect on the values themselves or the statistical comparisons performed in this study. The comparison between clinical groups demonstrates that the severity of tissue damage in DAWM may vary between patients; and as argued by Chen et al (1), quantitative MR imaging will be a valuable tool for studying DAWM changes com- prehensively. The most important next step in this process would be to investigate the clinical correlate of the observed quantitative MR imaging changes in DAWM, by assessing relations with clinical measures, by comparing DAWM across the course

118 White matter in multiple sclerosis of relapse-onset disease, and by measuring changes with time through prospective follow-up studies applying quantitative MR imaging of DAWM regions. Furthermore, whether DAWM severity is related to the abundance of or severity of the damage in the focal WM lesions should be investigated as well as the relations of DAWM with cortical at-rophy, to scrutinize the hypothesis that DAWM may consist of secondary axonal degeneration. In the current retrospective study, groups were too small and available image data inadequate to address these issues. The current study was designed to investigate DAWM, not to compare MR imaging techniques, and the different MR imaging techniques were, therefore, not designed to be as comparable as possible. However, our results suggest that though T1, MTR, ADC, and FA all show the same trends, which are in agreement with postmortem findings, of these 4, T1 and MTR may be especially sensitive to the changes occurring in DAWM. These, therefore, seem the most promising candidates for use in future studies investigating the clinical and imaging correlates of DAWM. It is likely that previous histogram studies of quantitative MR imaging measures obtained from NAWM have been influenced by changes in DAWM. The present study Chapter demonstrates that DAWM exhibits the quantitative MR imaging measures that are more abnormal than those of “real” NAWM, differing by as much as 30%. While taking care to 4 exclude focal or confluent WM lesions from the analyses, previous histogram studies have most likely included DAWM as defined according to the criteria of Seewann et al (4), because DAWM as defined by these criteria exhibits fairly subtle signal-intensity increases. To understand the respective roles of NAWM and DAWM in MS, future studies should also investigate the extent of DAWM in addition to studying its severity with quantitative MR imaging techniques. In conclusion, the present retrospective study demonstrated that 4 widely used in vivo quantitative MR imaging measures can discriminate “pure” DAWM—without multifocal lesional abnormalities—from both focal WM lesions and NAWM, confirming earlier postmortem results in vivo and laying the foundations for future prospective clinical studies of DAWM. Moreover, the results suggest that DAWM may be more abnormal in patients with SPMS than in patients with PPMS.

119 Chapter 4

REFERENCES

1. 143. CHEN SC, CHUNG HW, LIOU M. 8. VRENKEN H, POUWELS PJ, GEURTS JJ, Measurement of volumetric lesion load et al. Altered diffusion tensor in multiple in multiple sclerosis: moving from normal- sclerosis normal-appearing brain tissue: to dirty-appearing white matter. AJNR Am Cortical diffusion changes seem related J Neuroradiol 2003;24:1929-30 to clinical deterioration. J Magn Reson 2. GE Y, GROSSMAN RI, BABB JS, et al. Imaging 2006;23:628-36 Dirty-appearing white matter in multiple 9. VRENKEN H, GEURTS JJ, KNOL DL, et sclerosis: volumetric MR imaging and al. Whole-brain T1 mapping in multiple magnetization transfer ratio histogram sclerosis: global changes of normal- analysis. AJNR Am J Neuroradiol appearing gray and white matter. 2003;24:1935-40 Radiology 2006;240:811-20 3. ROPELE S, STRASSER-FUCHS S, AUGUSTIN 10. VRENKEN H, POUWELS PJ, ROPELE S, et al. M, ET AL. A comparison of magnetization Magnetization transfer ratio measurement transfer ratio, magnetization transfer rate, in multiple sclerosis normal-appearing and the native relaxation time of water brain tissue: limited differences with protons related to relapsing-remitting controls but relationships with clinical multiple sclerosis. AJNR Am J Neuroradiol and MR measures of disease. Mult Scler 2000;21:1885-91 2007;13:708-16 4. SEEWANN A, VRENKEN H, VAN DER VALK 11. BITSCH A, KUHLMANN T, STADELMANN P, et al. Diffusely abnormal white matter C, et al. A longitudinal MRI study of in chronic multiple sclerosis: imaging and histopathologically defined hypointense histopathologic analysis. Arch Neurol multiple sclerosis lesions. Ann Neurol 2009;66:601-09 2001;49:793-96 5. MOORE GR, LAULE C, MACKAY A, et al. 12. BREX PA, PARKER GJ, LEARY SM, et Dirty-appearing white matter in multiple al. Lesion heterogeneity in multiple sclerosis : Preliminary observations of sclerosis: a study of the relations between myelin phospholipid and axonal loss. J appearances on T1 weighted images, Neurol 2008;255:1802-11 T1 relaxation times, and metabolite 6. KARAMPEKIOS S, PAPANIKOLAOU N, concentrations. J Neurol Neurosurg PAPADAKI E, et al. Quantification of Psychiatry 2000;68:627-32 magnetization transfer rate and native 13. MACKAY AL, VAVASOUR IM, RAUSCHER A, T1 relaxation time of the brain: correlation ET AL. MR relaxation in multiple sclerosis. with magnetization transfer ratio Neuroimaging Clin N Am 2009;19:1-26 measurements in patients with multiple 14. VAN WAESBERGHE JH, KAMPHORST W, DE sclerosis. Neuroradiology 2005;47:189-96 GROOT CJ, et al. Axonal loss in multiple 7. PAPANIKOLAOU N, PAPADAKI E, sclerosis lesions: magnetic resonance KARAMPEKIOS S, et al. T2 relaxation imaging insights into substrates of time analysis in patients with multiple disability. Ann Neurol 1999;46:747-54 sclerosis: correlation with magnetization 15. VAN WALDERVEEN MA, KAMPHORST transfer ratio. Eur Radiol 2004;14:115-22 W, SCHELTENS P, et al. Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 1998;50:1282-88

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16. VAVASOUR IM, LI DK, LAULE C, et al. Multi- 26. BASSER PJ, PIERPAOLI C. Microstructural parametric MR assessment of T(1) black and physiological features of tissues holes in multiple sclerosis : evidence that elucidated by quantitative-diffusion-tensor myelin loss is not greater in hypointense MRI. J Magn Reson B 1996;111:209-19 versus isointense T(1) lesions. J Neurol 27. GOUW AA, SEEWANN A, VRENKEN H, 2007;254:1653-59 et al. Heterogeneity of white matter 17. ROPELE S, FAZEKAS F. Magnetization hyperintensities in Alzheimer’s disease: transfer MR imaging in multiple sclerosis. post-mortem quantitative MRI and Neuroimaging Clin N Am 2009;19:27-36 neuropathology. Brain 2008;131:3286-98 18. SCHMIERER K, SCARAVILLI F, ALTMANN 28. KUTZELNIGG A, LUCCHINETTI CF, DR, et al. Magnetization transfer ratio and STADELMANN C, et al. Cortical myelin in postmortem multiple sclerosis demyelination and diffuse white matter brain. Ann Neurol 2004;56:407-15 injury in multiple sclerosis. Brain 19. SCHMIERER K, WHEELER-KINGSHOTT CA, 2005;128:2705-12 TOZER DJ, et al. Quantitative magnetic 29. MILLER DH, LEARY SM. Primary- resonance of postmortem multiple progressive multiple sclerosis. Lancet sclerosis brain before and after fixation. Neurol 2007;6:903-912 Magn Reson Med 2008;59:268-77 20. SCHMIERER K, WHEELER-KINGSHOTT CA, Chapter BOULBY PA, et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 2007;35:467-77 4 21. VENKATESAN R, LIN W, HAACKE EM. Accurate determination of spin- density and T1 in the presence of RF- field inhomogeneities and flip-angle miscalibration. Magn Reson Med 1998;40:592-602 22. NOLTE UG, FINSTERBUSCH J, FRAHM J. Rapid isotropic diffusion mapping without susceptibility artifacts: whole brain studies using diffusion-weighted single-shot STEAM MR imaging. Magn Reson Med 2000;44:731-36 23. JENKINSON M, SMITH S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143-56 24. JENKINSON M, BANNISTER P, BRADY M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825-41 25. ROPELE S, FILIPPI M, VALSASINA P, et al. Assessment and correction of B1- induced errors in magnetization transfer ratio measurements. Magn Reson Med 2005;53:134-40

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5 ATYPICAL LESIONS IN MULTIPLE SCLEROSIS

5.1

MRI CHARACTERISTICS OF ATYPICAL IDIOPATHIC INFLAMMATORY DEMYELINATING LESIONS OF THE BRAIN: $5(9,(:2)5(3257('),1',1*6

Seewann A. Enzinger C. Filippi M. Barkhof F. Rovira A. Gass A. Miller D. Montalban X. Thompson A. Yousry T. Tintore M. de Stefano N. Palace J. Rovaris M. Polman C. Fazekas F. for the MAGNIMS network

J Neurol. 2008 Jan; 255(1):1-10

Oral presentation at the16th meeting of the European Neurological Society (ENS), Lausanne, Switzerland, 2006. Selected as “best of session” presentation. Chapter 5

ABSTRACT

Background: Idiopathic inflammatory demyelinating lesions (IIDL) of the brain usually present with a morphologic pattern characteristic of multiple sclerosis (MS). Atypical appearances of IIDLs also exist, however, and can pose significant diagnostic problems and uncertainty regarding prognosis and adequate therapy. We attempted to improve upon this situation by reviewing the literature. Methods: We performed a PubMed search from January 1984 through December 2004 for articles in English reporting on IIDLs which had been considered as morphologically atypical (66 articles; 270 cases reported). From these publications 69 individual patient reports allowed the extraction of adequate information on magnetic resonance imaging (MRI) and associated disease characteristics. Results: Reported atypical IIDLs most frequently manifested as large ring-like lesions (n=27) which are now considered quite suggestive of an antibody-mediated form of MS. Truly atypical IIDLs were less common and exhibited appearances which we termed megacystic (n=8), Balo-like (n=11) and diffusely infiltrating (n=11). Despite limitations imposed by the absence of original data the inter-rater agreement in defining these subtypes of atypical IIDLs was moderate to substantial (kappa 0.48 – 0.68) and we noted trends for their association with certain demographic, clinical and paraclinical variables. Interpretation: We suggest that IIDLs reported as atypical in the literature can be segregated into several distinct subtypes based on their MRI appearance. The recognition of these patterns may be useful for the differential diagnosis and for a future classification. Because of the limitations inherent in our review this will have to be confirmed by a prospective registry.

126 Atypical lesions in multiple sclerosis

INTRODUCTION

There is evidence for a large spectrum of idiopathic inflammatory demyelinating disorders of the central nervous system which can cause a wide range of morphologic abnormalities as shown by magnetic resonance imaging (MRI) of the brain and spinal cord (1, 2). Within this spectrum, multiple sclerosis (MS) clearly constitutes the main entity and shows – in most cases – typical findings on MRI (3, 4). Apart from MS, however, only few other subtypes of idiopathic inflammatory demyelinating disorders have been recognized so far. These include acute disseminated encephalomyelitis (ADEM) and neuromyelitis optica (NMO) which have also been associated with rather characteristic patterns of morphologic abnormalities (5, 6). Otherwise there exists no systematic assessment of those idiopathic inflammatory demyelinating lesions (IIDLs) which cannot be linked with or appear atypical for any of the disorders noted above. In the absence of such assessment even the term “atypical” remains difficult to define and the definition will vary between investigators as evidenced by respective case reports. Atypical MRI appearances may include an unusual size (e.g. very large and with mass effect), unusual morphology (e.g. irregular lesion appearance, indistinct lesion borders, marked heterogeneity within a lesion), an intriguing pattern of contrast uptake of lesions (e.g. formation of rings) or other unusual characteristics. Lack of uniform definitions for and descriptions of atypical IIDLs thus limit their recognition in the differential diagnosis of brain lesions, although some attempts have been made to Chapter evoke parallels with earlier pathologic descriptions such as Balo’s concentric sclerosis (7). In addition, these deficits and the relatively rare occurrence of atypical IIDLs have 5 also prohibited the collection of sufficient data regarding their clinical implications and treatment. We, therefore, decided to review the literature for specific patterns of atypical IIDLs that were more frequently encountered and attempted to establish associations with demographic, clinical and paraclinical variables which might help to characterize them.

METHODS

A literature search using PubMed© database was performed from January 1984 through December 2004. The search included all publication types, sexes and medical subsets, but was restricted to studies written in English on humans aged 18 years or older. Since a number of terms has been used to describe patients with atypical IIDLs, we searched for the following diagnostic terms: “atypical”, “Marburg”, “variant”, “Balo”, “Schilder”, “tumefactive”, “tumor like” and “tumour like”, “tumor” and “tumour”,

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“transitional”, “fulminant”, “mimicking”, “mass lesion”, “mass effect” in association with and without “demyelinating”. Sixty-six articles reporting a total number of 270 cases were found. Two of the authors (A.S. and C.E.) reviewed these for consistency with an “atypical” IIDL. In this effort, we selected those articles in which 1) available clinical, histopathologic, and CSF data were compatible with the presumed diagnosis of an IIDL, 2) other etiologies, e.g. infectious, neoplastic, vascular, etc., had been ruled out, and 3) the authors had felt that the lesion appearance was atypical for MS, ADEM or NMO. Further prerequisites for inclusion were an MRI illustration of the respective lesions and an individual case description, i.e. we disregarded reports where it was impossible to correlate MRI findings and clinical course on an individual basis. As we wanted to concentrate only on atypical IIDLs of the brain, we excluded those articles reporting on patients with atypical lesions of the spinal cord. A total of 69 cases fulfilled these inclusion and exclusion criteria with MRI illustration of at least one atypical IIDL and appropriate complementary clinical and paraclinical data (8-53). In the next step, we reviewed the MRI findings of each case independently by three of the investigators (A.S., C.E., F.F.). The article figures provided information on baseline T2-weighted MRI examinations (including FLAIR and PD-weighted images) in 56 cases. T1-weighted images were shown in 19 patients and MRI scans following the administration of contrast material in 28 patients. We used all the available material to assess the specific morphologic features of individual lesions which we tabulated including lesion location and size, signal homogeneity and intensity, mass effect, edema and the pattern of contrast enhancement. This was done without any clinical information as we had extracted the illustrations from the articles for this purpose. We also recorded the number of lesions with both atypical and MS like or non-specific appearance. Based on this detailed analysis, we attempted to group lesions according to common patterns and found four subtypes which appeared to cluster within distinct morphological patterns. Beyond providing diagnostic insights, this review was also intended to define patterns of atypical IIDLs which could be used in a prospective registry. We, therefore, felt it important to test how well the defined patterns would be separable by other investigators. For this purpose, we also assessed the interobserver variability of the proposed classification of atypical IIDLs among four experienced neurologists/ neuroradiologists (A.G, M.F., F.B., A.R.), who had not been involved in the lesion type classification before. These readers received an illustrative example and the corresponding morphologic description of the suggested atypical IIDL subtypes and were then asked to independently assign all article illustrations accordingly. Again,

128 Atypical lesions in multiple sclerosis all raters were blinded for clinical data and we calculated kappa scores to assess the extent of interobserver agreement for all MRI interpretations and IIDL subgroups (54). To obtain preliminary insights into potential pathogenetic, clinical and prognostic differences between atypical IIDL subtypes, we also performed between-group comparisons regarding the available demographic, clinical, MRI and CSF variables. We tested the categoric variables by Pearson’s chi-square test or by 2x2 Fisher’s exact test in case of contingency tables containing less than 5 cases. Normally distributed continuous variables were compared with Student’s t-test.

RESULTS

The total number of patients with atypical IIDLs as suggested by MRI and appropriate clinical and paraclinical findings was 69 and comprised 26 men and 43 women with an age range from 18 to 69 years (mean age 34.5 years) at the onset of symptoms. Follow-up information was available in 52 cases. The mean duration of follow-up was 96 weeks, ranging from 61 to 108 weeks. A repeat MRI was performed in 41 patients. The majority of reportedly atypical IIDLs exhibited morphologic characteristics on MRI which could be classified into one of four different subtypes. We described these as “megacystic”, “Balo-like”, “infiltrative” and “ring-like”, according to the most prominent radiological features (Figures 1-4). Eleven cases did not fall into any of these Chapter four categories and were classified as non-specific. The megacystic type was characterized by extremely large (≥3 cm in diameter) 5 cyst-like lesions within the hemispheric white matter which expanded towards and along the cortical ribbon (Figure 1). Some of these lesions showed a moderate mass effect and an incomplete rim of contrast enhancement on T1-weighted images. The borders of the lesion were uniformly well defined. This lesion type had been reported in eight patients and was the only type of abnormality seen in seven of them.

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Figure 1: Megacystic type of atypical IIDL

Note the large (≥3 cm in diameter) cyst-like lesion in the left parietal lobe which expands from the hemispheric white matter into and along the cortical ribbon. The axial FLAIR (a) and T2- weighted (b) images show a clear demarcation of the lesion against the white matter (arrows) and some perifocal edema. Axial (c) and coronal (d) T1-weighted images after application of contrast material (0.1 mmol/kg bodyweight Gd-DTPA) show faint enhancement of the centripetal lesion borders (arrows).

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Balo-like IIDLs consisted of lesions with multiple concentric rings or a pattern of alternating bands of signal intensity (≥2 alternations) on any sequence (Figure 2). Mass effect was minimal to absent. They were reported in eleven patients and eight of them showed at least two Balo-like IIDLs.

Figure 2: Balo-like IIDL

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Note the alternating bands of signal intensity on proton-density weighted (a), T2-weighted (b,c) and FLAIR (d) images. The pattern of multiple concentric rings is best seen on contrast enhanced T1-weighted scans (e,f). Images c and f are dissected magnifications of the original scan. A few other non-enhancing areas of signal hyperintensity partly adjacent to the right lateral ventricle (white arrows) are also seen which may suggest foci of earlier demyelination.

The infiltrative type, present in eleven patients, was characterized by large, ill- defined areas of T2 abnormality with inhomogeneous contrast uptake, which appeared to suggest a diffusely infiltrating process (Figure 3). In four cases with infiltrative lesions, serial MRI showed slowly increasing expansion of the lesion over a period of two to six weeks with concomitant changes in the uptake of contrast material. Interestingly, high-dose prednisolone treatment appeared to have little effect on lesion growth in all of these cases.

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Figure 3: Infiltrative type of atypical IIDL

Different columns show the evolution of this lesion over time. The first row shows T2-weighted scans, the second row FLAIR images, and the third row contrast-enhanced T1-weighted scans. Note the rapid increase of the lesion with ill-defined areas of T2 abnormality and some inhomogeneous contrast enhancement only after 5 weeks. The patient received two high- dose corticosteroid pulses following the first examination which did not prevent further lesion growth. After 6 months, lesion size strikingly decreased and the extent of tissue destruction as indicated by T1-hypointensity appears to have been relatively minor (arrows).

Ring-like lesions were reported most frequently as atypical IIDLs (27 patients) in the reviewed literature. They consisted of single or multiple round lesions predominantly in the white matter which showed a strong ring-like enhancement after the administration of contrast material (Figure 4). Where available, heavily T2-weighted or gradient-echo T2*-weighted images showed a small outer rim of hypointensity. The lesions tended to exhibit no or only a mild mass effect and were frequently surrounded by an ill-defined zone of T2 hyperintensity suggestive of edema.

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Figure 4: Ring-like type

Chapter Ring-like lesions on FLAIR (a), T2-weighted (b) and T1-contrast-enhanced (c) scans. Two lesions show circular contrast enhancement (c) and one of them is surrounded by an ill-defined zone of T2-hyperintensity suggestive of edema (black arrows in a and b). Several other foci of signal 5 hyperintensity and contrast enhancement are also noted throughout both hemispheres.

MRI findings in the remaining 12 extracted cases were either non-specific or “intermediate” in terms of prior subgroups. We, therefore, decided to treat them separately in order not to dilute the subgroup-specific analyses. The kappa coefficient for interobserver agreement was moderate (0.48 ± 0.02) for the total cohort. Substantial agreement was achieved for the megacystic and “Balo- like” IIDL subtypes and it was moderate regarding the infiltrative and ring-like subtypes. Not unexpectedly, for unclassified lesions there was almost no agreement (Table 1). Overall, megacystic and infiltrative atypical IIDL subtypes most often consisted of a solitary lesion, whereas Balo and ring-like lesions tended to occur as multiple lesions (Table 1). Except for one case in the “unclassified” category, all atypical IIDLs were reported in supratentorial portions of the brain. Additional lesions typical of MS were reported in at least one patient of every group but were rare with the megacystic

133 Chapter 5 atypical IIDL type (Table 1). A more rapid accumulation of typical MS lesions on MRI follow-up was reported for patients with infiltrative atypical IIDLs compared to patients with the Balo and ring-like lesion subtypes as listed in Table 1. Table 1 also shows the age and gender distribution among atypical IIDL subgroups. The mean age of patients with Balo-like IIDLs was non-significantly higher than that of patients showing a ring-like or infiltrative atypical IIDL subtype. There were also some between-group differences in the distribution of gender, but these did not reach a level of significance and should be viewed with caution given the relatively small number of cases that we were able to analyze. Clinical presentations at onset were quite similar in all groups and consisted of hemiparesis, hemianopia, hemisensory loss, gait ataxia, aphasia, dysphagia, memory dysfunction or seizures (i.e. mostly atypical for MS). A prior diagnosis of MS was reported in two cases and a previous history of neurological dysfunction in four cases only.

Table 1: Demographic and morphologic variables according to type of atypical IIDLs

Total Megacystic Balo Infiltrative Ring-like Unclassified N=69 N=8 N=11 N=11 N=27 N=12

Mean age, 34.5 (18-69) 37.6 (21-56) 43 (33-56) 28 (19-54) 30.8 (18-69) 38.9 (22-64) years (range)

Male/Female 26/43 3/5 5/6 3/8 8/19 7/5 Number of atypical IIDLs

1 39 7 3 10 13 6

2 71 2130

>3 23 0 6 0 11 6 Additional MS typical lesions

Baseline 21/69 1/8 6/11 3/11 10/27 1/12

Follow-up 28/41 2/3 6/11 6/7 10/13 1/7

Inter- 0.48 ± 0.02 0.62 ± 0.04 0.68 ± 0.48 ± 0.04 0.42 ± 0.04 0.17 ± 0.04 observer 0.04 agreement Kappa- coefficient

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No consistent rating or scale for describing the patients’ clinical outcome was available from the reviewed articles. We, therefore, just adhered to the descriptive terms that were used in the reports, i.e. fully recovered, improved, worse, death and relapse at follow-up, to assess the clinical consequences of the reported atypical IIDLs. We list these outcome variables in Table 2 both for the entire group of patients and for the atypical IIDL subtypes. Of all 52 patients with a mean clinical follow-up of 96 weeks, 16 (31%) were considered free of symptoms and reported as fully recovered. Improvement of neurological symptoms was reported in 26 patients (50%) and relapses occurred in 14 patients (30%). Five patients (9.5%) had a fatal outcome. When looking at the outcome characteristics within the different groups, a full recovery was found in 50% of all megacystic patients, whereas only 10% of the infiltrative group were reported free of neurological symptoms at follow-up (Table 2). The highest number of relapses also occurred in the infiltrative group (4 of 10 patients with follow-up information). Approximately half of the patients of every group showed improvement of clinical symptoms. Notably, the unclassified group showed the highest number of fatal outcomes (n=4; 44%). Causes were neurologic deterioration in two, and pulmonary embolism and not indicated in one patient each.

Table 2: Clinical outcome at follow-up according to type of atypical IIDLs

Mega- Balo- Clinical Total Infiltrative Ring-like Unclassified Chapter cystic like outcome N=52 N=10 N=20 N=9 N=6 N=7 5 Fully recovered 16 (30%) 3 (50%) 3 (43%) 1 (10%) 9 (45%) 0

Improved 26 (50%) 3 (50%) 3 (43%) 5 (50%) 9 (45%) 5 (56%)

Worse 5 (10%) 0 1 (14%) 3 (30%) 2 (10%) 0

Dead 5 (10%) 0 0 1 (10%) 0 4 (44%)

Relapse at 14 (20%) 1 (17%) 1 (14%) 4 (40%) 5 (25%) 3 (33%) follow up

Histopathological data were provided in 43 cases. Tissue was obtained by stereotactic biopsy in 38 cases and by surgical resection in 5 cases. In one of these patients, a second histopathologic exam was performed at autopsy. Histopathological examination was performed from 1 to 15 weeks (mean 5.5 + 5 weeks) after disease onset. Biopsy was performed most commonly because of the atypical clinical or radiological features (mass effect, infiltration, discordant symptoms) in order to rule

135 Chapter 5 out a neoplasm. The used stains were listed in 25 cases. The most common stains used were H&E in 76% and Luxol fast blue in 48%. In addition, several other stains were used (Heidenhain myelin stain, Bodian Silver, Sudan Black, Kluever-Barrera, Gomori and Elastica van Gieson, Bielschowsky’s silver impregnation, Myelin basic protein stain, PAS). In 17 cases, more than one staining technique was used to verify the diagnosis. In Balo-like lesions, the characteristic pattern of alternating bands of demyelination intermingled with preserved areas of myelin was noted (30, 31, 34). Histopathological findings were otherwise non-specific for all other subtypes of our MRI classification. There was invariably loss of myelin with relative preservation of axons; foamy macrophages, perivascular inflammation and reactive astrocytes were all seen. Although the cerebrospinal fluid was reported in 43 cases, the data provided were limited. A pathological IgG-Index was described in 8 cases, oligoclonal bands occurred in 16 and an elevated cell count in 15 patients. Pathological abnormalities of all 3 parameters occurred in two patients only. Oligoclonal bands were noted more commonly in the infiltrative (54%) and ring-like type (30%) of atypical IIDLs and less often in patients with Balo-like (9%) lesions. No oligoclonal bands were found with megacystic IIDLs.

DISCUSSION

Recognition of definable types of atypical IIDLs of the brain may have two important clinical implications. First, such knowledge could help in the diagnostic workup of patients presenting with unclear focal lesions of the brain. A recent review of the MAGNIMS group has successfully attempted to summarize so-called “red flags” which should raise the suspicion of a lesion etiology other than MS in patients with multiple focal CNS abnormalities (55). Complementing these efforts, it also appears necessary to expand our diagnostic awareness of the spectrum of atypical IIDLs which may otherwise be mistaken for neoplastic, infectious or even vascular pathologies. Inclusion of rather characteristic though atypical IIDLs might thus speed up the initiation of appropriate anti-inflammatory measures and could even help to avoid invasive diagnostic procedures like a biopsy. Second, the definition of specific types of atypical IIDLs, if easily and reproducibly recognizable on MRI, could serve to start a registry to prospectively collect data both on prognostic factors and on the response of such lesions to specific acute and longer-term treatments. Such an approach should facilitate a more evidence-based management of patients which atypical IIDLs. By reviewing the literature for reports of atypical IIDLs, we have obtained a preliminary impression on the range of imaging abnormalities that may occur and their association with demographic,

136 Atypical lesions in multiple sclerosis clinical and other paraclinical variables. Based on MRI features, the lesions clustered into four subtypes, i.e. “megacystic”, “Balo-like”, “infiltrative” and “ring-like”. The reported ring-like lesions probably need not be considered as atypical IIDL any longer. In recent years, ring enhancement has been reported to occur in as much as one quarter of active MS patients (56) and has also been tentatively linked with an antibody-mediated pattern of MS (57, 58). In this context, it is noteworthy that most descriptions of ring-like IIDLs as atypical brain lesions were published before the histopathologic and immunologic evidence that such lesions are part of the spectrum of MS. Accordingly, in our series, this type of IIDL was frequently associated with other abnormalities typical for MS, and the demographic and clinical findings in the “ring- like” subgroup were also quite consistent with those typically seen in MS. “Ring-like” IIDLs may appear quite similar to cerebral abscesses, parasitic disease and neoplasms at least on conventional MRI. In a careful comparison of enhancement patterns of rounded lesions of different etiologies, however, it was noted that demyelinating lesions quite often present with an open-ring of enhancement which may help in their differential diagnosis (59). Abscesses and neoplasms tend to have a closed ring of enhancement which is also thicker and may be more irregular. They also tend to be associated with more extensive perilesional edema (60). Diffusion-weighted imaging (DWI) can provide additional insights for separating infectious and neoplastic brain masses (61). The contributory role of DWI regarding the differential diagnosis of atypical IIDLs is not yet fully clear, however. Less prominent than with brain abscesses, Chapter ring-like IIDLs frequently also show a small ring of hypointensity on T2-weighted and especially T2*-weighted images which has been attributed mostly to the accumulation 5 of macrophages (62). Balo-like lesions have already been recognized earlier within the spectrum of idiopathic demyelinating disorders and our review supports their recognition by characteristic morphologic features. The pathophysiologic mechanisms responsible may consist of an interplay between histotoxic hypoxia from extensive local production of nitric oxide intermediates and oxygen radicals which impair mitochondrial function and subsequent tissue protection by the expression of molecules involved in the tissue preconditioning proposed (63). During radial lesion growth, layers of active inflammation and tissue destruction thus appear to interchange with layers of more preserved tissue leading to the concentric patterning of demyelination and preserved myelin which is characteristic of Balo’s disease histopathologically (63). In our analysis, we saw this type of atypical IIDL especially in an older age group and most often it was not accompanied by other signal abnormalities typical for MS. This may support a rather specific and individual predisposition for the development of such lesions.

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Importantly, patients with MRI findings of Balo-like lesions in our series did not exhibit the poor clinical prognosis that was suggested in earlier pathological descriptions. Both the megacystic and infiltrating subtypes are IIDL variants which have not been labeled as such before and appear especially critical in terms of their separation from brain tumors. Even with careful attention to the features summarized, it may not always be possible to immediately rule out a neoplastic or infectious process. Arguments against a neoplastic etiology of megacystic IIDLs come primarily from the absence of apparent cortical involvement at least in terms of diffuse infiltration and swelling. Rather, a thinning of the cortical ribbon appears to be the case in most instances which would not be expected with neoplasia. Also, contrast enhancement, where present, appeared in a smooth, rim-like fashion while irregular or nodular enhancement would be expected with cystic brain tumors, such as pilocytic astrocytoma, hemangioblastoma or metastasis. To what extent the suggested open-ring sign of demyelinating lesions can also contribute to the differential diagnosis of these lesions cannot be readily answered from our material (56). Contrast material was not given in all instances and we also were able to review only selected imaging slices. Clearly, the absence of supportive CSF findings or of other lesions suspicious for MS does not argue against this subtype of atypical IIDL. Given their giant size, it also appears of interest that these lesions were associated mostly with a very good prognosis both in terms of regression of clinical symptoms and regarding a low tendency for recurrence. The infiltrating subtype of atypical IIDLs appears especially difficult to separate from a diffusely infiltrating tumor or another specific infectious demyelinating process like progressive multifocal leukoencephalopathy when first seen and enhancement is not yet present (53). Therefore, the recognition of this atypical IIDL subtype gains further importance from the reported association of PML with natalizumab treatment (64). The usually rapid evolution of atypical IIDLs clearly argues against a low-grade glioma while CSF findings may serve to rule out other infectious etiologies. However, continuing growth despite high-dose steroid treatment does not exclude an IIDL as evident from our review. Additional lesions typical for MS appear to frequently coexist and may assist diagnosis. Obvious and significant limitations of this review and analysis need to be acknowledged. Investigators are likely to have reported primarily those cases which appeared intriguing and unexpected. This certainly will have caused bias both in terms of the range of abnormalities and in regard to the associated clinical and demographic findings. Equally important, the workup of these patients has not been performed in a uniform manner. This includes the clinical and imaging examinations, the collection of paraclinical data and their follow-up. Our analysis on the interobserver agreement regarding the definition of lesion subtypes was also hampered by large variations

138 Atypical lesions in multiple sclerosis in the quality and quantity of imaging material available and reflects a worst-case scenario rather than an ideal and uniform patient work-up. To mitigate this problem, we also have not attempted to forcefully assign each case to a specific subtype which left a rather large number of cases as “unclassifiable”. Our attempt of a classification of atypical IIDLs thus needs to be viewed as a working hypothesis and the tabulated results can serve primarily to identify future aspects of interest. As an example, it is quite difficult from our review to speculate on the diagnostic contribution of other laboratory examinations like CSF analysis for oligoclonal bands. On the one hand, it appears that in general oligoclonal bands tend to be less frequently observed in atypical IIDLs than in MS; on the other hand, the absence of such findings may have triggered the report of such cases. Our review also cannot serve to determine the possible contribution of non-conventional MRI techniques, such as proton magnetic resonance spectroscopy, to the classification of atypical IIDLs. More recent reports show that the longitudinal observation of such lesions with these techniques may serve to define abnormalities which would not be expected to occur in neoplasms. Whether such a decision can already be made on the basis of a single first examination using non-conventional MRI techniques is not yet completely clear. Quite interestingly, the reported histopathologic and immunopathologic results did not help in a further separation of the described lesion types apart from Balo-like lesions. It may, therefore, be questioned whether terms like Marburg variant or Schilder’s disease still provide useful classifications. Where available, the histopathologic data simply confirmed the Chapter absence of other pathology and supported the diagnosis of an idiopathic inflammatory demyelinating process. 5 With this review, we also wanted to develop a proposal for the morphologic classification of atypical IIDLs that could be used for a prospective registry of these abnormalities. We, therefore, tested the interobserver reliability of lesion classification by experienced readers and found moderate to substantial interobserver agreement. This is a likely consequence of the limitations of the reviewed material with only selected images as well as the absence of contrast material in many cases and attests to the necessity of very close definitions and a comprehensive and standardized MRI evaluation. Our review calls attention to several lesion patterns which may be associated with atypical idiopathic inflammatory disorders of the brain. As a consequence, idiopathic inflammation should be included in the differential diagnosis when a patient presents with a lesion resembling these patterns. Prospectively collected data will be necessary, however, to define the appropriate strategies for a rapid and non-invasive diagnosis of such abnormalities, to confirm their suggested associations with specific demographic and clinical characteristics, and to gain insights regarding their response to treatment.

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40. ERDEM H, STALBERG E, CAGLAR I (2001) 51. WURM G, PARSAEI B, SILYE R, FELLNER Aphasia in multiple sclerosis. Upsala J F (2004) Distinct supratentorial lesions Med Sci 106: 205-210. mimicking cerebral gliomas. Acta 41. KARAARSLAN E, ALTINTAS A, SENOL U, YENI Neurochir (Wien) 146: 19–26. N, DINCER A, BAYINDIR C, KARAAGAC N, SIVA 52. NAGI S, MEGDICHE H, MRABET H, SEBAI R, A (2001) Balo’s concentric sclerosis: clinical CHAABANE S, BELGHITH L, TOUIBI S (2005) and radiologic features of five cases. AJNR Sclérose concentrique de Balò chez un Am J Neuroradiol 22: 1362-1367. patient nord-africaine. Balo’s concentric 42. MOORE G, BERRY K, OGER J, PROUT sclerosis in a North-African patient. Rev A, GRAEB D, NUGENT R (2001) Balo’s Neurol (Paris) 161: 78-80. concentric sclerosis: surviving normal 53. ENZINGER C, STRASSER-FUCHS S, ROPELE myelin in a patient with a relapsing- S, KAPELLER P, KLEINERT R, FAZEKAS F remitting clinical course. Mult Scler 7: (2005) Tumefactive demyelinating lesions: 375-382. conventional and advanced magnetic 43. SUGITA Y, TERASAKI M, SHIGEMORI M, resonance imaging. Mult Scler 11: 135-139. SAKATA K, MORIMATSU M (2001) Acute 54. FLEISS J (2003) Statistical Method for focal demyelinating disease simulating Rates and Proportions. John Wiley & Sons, brain tumors: histopathologic guidelines New York for an accurate diagnosis. Neuropathology 55. CHARIL A, YOUSRY T, ROVARIS M, BARKHOF 21: 25-31. F, DE STEFANO N, FAZEKAS F, MILLER D, 44. KASTRUP O, STUDE P, LIMMROTH V (2002) MONTALBAN X, SIMON J, POLMAN C, Balo’s concentric sclerosis: Evolution of FILIPPI M (2006) MRI and the diagnosis of active demyelination demonstrated by multiple sclerosis: expanding the concept serial contrast-enhanced MRI. J Neurol of “no better explanation”. Lancet Neurol 249: 811–814. 5: 841–852. 45. KHOSHYOMN S, BRAFF S, PENAR P (2002) 56. HE J, GROSSMAN R, GE Y, MANON L (2001) Tumefactive multiple sclerosis plaque. J Enhancing patterns in multiple sclerosis: Neurol Neurosurg Psychiatry 73: 85. evolution and persistence. AJNR Am J 46. KOTIL K, KALAYCI M, KOSEOGLU T, TUGRUL Neuroradiol 22: 64-669. A (2002) Myelinoclastic diffuse sclerosis 57. LUCCHINETTI C, BRÜCK W, PARISI (Schilder’s disease): report of a case and J, SCHEITHAUER B, RODRIGUEZ M, review of the literature. Br J Neurosurg LASSMANN H (2000) Heterogeneity of 16: 516-519. Multiple Sclerosis Lesions: Implications 47. DI PATRE P, CASTILLO V, DELAVELLE J, for the Pathogenesis of Demyelination. VUILLEMOZ S, PICARD F, LANDIS T (2003) Ann Neurol 47: 707-717. “Tumor-mimicking” multiple sclerosis. 58. BRUCK W, NEUBERT K, BERGER T, Clin Neuropathol 22: 235-239. WEBER JR (2001) Clinical, radiological, 48. DOUSSET V (2003) Case no 2. Balo immunological and pathological findings concentric sclerosis. J Radiol 84: 80-81. in inflammatory CNS demyelination- 49. HAYASHI T, KUMABE T, JOKURA H, -possible markers for an antibody- FUJIHARA K, SHIGA Y, WATANABE M, mediated process. Mult Scler 7: 173-177. HIGANO S, SHIRANE R (2003) Inflammatory 59. MASDEU J, QUINTO C, OLIVERA C, TENNER demyelinating disease mimicking M, LESLIE D, VISINTAINER P (2000) Open- malignant glioma. J Nucl Med 44: 565-569. ring imaging sign: highly specific for 50. WAMOTO K, OKA H, UTSUKI S, OZAWA atypical brain demyelination. Neurology T, FUJII K (2004) Late-onset multiple 54: 1427-1433. sclerosis mimicking brain tumor: a case report. Brain Tumor Pathol 21: 83-86.

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60. OMURO A, LEITE C, MOKHTARI K, DELATTRE J (2006) Pitfalls in the diagnosis of brain tumours. Lancet Neurol 5: 937-948. 61. GUZMAN RB, A, LOVBLAD K, EL-KOUSSY M, WEIS J, SCHROTH G, SEILER R (2002) Use of diffusion-weighted magnetic resonance imaging in differentiating purulent brain processes from cystic brain tumors. J Neurosurg 97: 1101-1107. 62. SCHWARTZ K, ERICKSON B, LUCCHINETTI C (2006) Pattern of T2 hypointensity associated with ring-enhancing brain lesions can help to differentiate pathology. Neuroradiology 48: 143-149. 63. STADELMANN C, LUDWIN S, TABIRA T, GUSEO A, LUCCHINETTI C, LEEL-ÖSSY L, ORDINARIO A, BRÜCK W, LASSMANN H (2005) Tissue preconditioning may explain concentric lesions in Balo’s type of multiple sclerosis. Brain 128: 979-987. 64. YOUSRY T, MAJOR E, RYSCHKEWITSCH C, FAHLE G, FISCHER S, HOU J, CURFMAN B, MISZKIEL K, MUELLER-LENKE N, SANCHEZ E, BARKHOF F, RADUE E, JAGER H, CLIFFORD D (2006) Evaluation of patients Chapter treated with natalizumab for progressive multifocal leukoencephalopathy. N Engl J Med 354: 924-933. 5

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6 BEYOND MULTIPLE SCLEROSIS: NEURODEGENERATIVE AND VASCULAR DISEASES

6.1

+(7(52*(1(,7<2)60$//9(66(/ ',6($6($6<67(0$7,&5(9,(:2)05, AND HISTOPATHOLOGY CORRELATIONS.

Alida Gouw Alexandra Seewann Wiesje van der Vlier Frederik Barkhof Annemieke Rozemuller Philip Scheltens Jeroen Geurts

J Neurol Neurosurg Psychiatry 2011; 82:126-135 Chapter 6

ABSTRACT

Background: White matter hyperintensities (WMH), lacunes and microbleeds are regarded as typical MRI expressions of cerebral small vessel disease (SVD) and they are highly prevalent in the elderly. However, clinical expression of MRI defined SVD is generally moderate and heterogeneous. By reviewing studies that directly correlated postmortem MRI and histopathology, this paper aimed to characterise the pathological substrates of SVD in order to create more understanding as to its heterogeneous clinical manifestation. Summary: Postmortem studies showed that WMH are also heterogeneous in terms of histopathology. Damage to the tissue ranges from slight disentanglement of the matrix to varying degrees of myelin and axonal loss. Glial cell responses include astrocytic reactions - for example, astrogliosis and clasmatodendrosis - as well as loss of oligodendrocytes and distinct microglial responses. Lipohyalinosis, arteriosclerosis, vessel wall leakage and collagen deposition in venular walls are recognised microvascular changes. Suggested pathogenetic mechanisms are ischaemia/hypoxia, hypoperfusion due to altered cerebrovascular autoregulation, blood-brain barrier leakage, inflammation, degeneration and amyloid angiopathy. Only a few postmortem MRI studies have addressed lacunes and microbleeds to date. Cortical microinfarcts and changes in the normal appearing white matter are ‘invisible’ on conventional MRI but are nevertheless expected to contribute substantially to clinical symptoms. Conclusion: Pathological substrates of WMH are heterogeneous in nature and severity, which may partly explain the weak clinicoradiological associations found in SVD. Lacunes and microbleeds have been relatively understudied and need to be further investigated. Future studies should also take into account ‘MRI invisible’ SVD features and consider the use of, for example, quantitative MRI techniques, to increase the sensitivity of MRI for these abnormalities and study their effects on clinical functioning.

148 Beyond multiple sclerosis: neurodegenerative and vascular diseases

BACKGROUND

MRI and clinical expression of small vessel disease White matter hyperintensities (WMH), lacunes and microbleeds are regarded as MRI expressions of small vessel disease (SVD) and are commonly found on brain MRI of elderly subjects. WMH are visible as hyperintense areas on T2 weighted MRI scans (including FLAIR), while lacunes are identified on MRI as small cavities with a diameter of 3 mm to 10 - 15 mm, and signal intensities comparable with CSF. Lacunes are located in the white matter (WM) or subcortical grey matter and often have a surrounding hyperintense halo. Microbleeds are small, round, hypointense foci on gradient echo T2* weighted MRI and are mostly located in the basal ganglia or cortical - subcortical areas (1,2). Examples of these abnormalities are given in Figure 1. Unfortunately, definition and quantification of these MRI expressions of SVD vary between studies. This warrants a standardisation of SVD rating on MRI, as extrapolation of results from different studies to more general conclusions may be severely hampered otherwise. In non-demented elderly subjects, WMH, lacunes and microbleeds have been associated with cognitive decline, including reduced mental speed and impaired executive functions (3-7). WMH have also been related to other potentially disabling symptoms, such as gait disturbances, depression and urinary incontinence (8-11). SVD is even more common in subjects with Alzheimer ’s disease (AD), and it might interact with the neurodegenerative changes in AD and with their effect on cognitive decline (12,13). Thus SVD probably contributes significantly to clinical disability in the elderly. As it can potentially be treated or prevented, increased insight into the underlying pathological mechanisms of SVD is of paramount importance. Chapter 6

149 Chapter 6

Figure 1: MRI expressions of small vessel disease.

Axial fluid attenuated inversion recovery images with periventricular and deep white matter hyperintensities (A); and an illustration of two lacunes in the right hemisphere (arrows) and deep white matter hyperintensities (B). T2* gradient echo image with multiple cortical-subcortical microbleeds (C).

The value of postmortem MRI studies The association between SVD features on MRI and clinical symptoms is modest. An explanation for this may be heterogeneity of the neuropathological substrates underlying SVD. T2 weighted MRI dichotomises the white matter as ‘hyperintense’ (WMH) or ‘normal’ whereas the hyperintense areas may reflect pathological tissue changes that vary in type and severity. It further reveals the presence of lacunes and microbleeds but it has been suggested that expressions of SVD that are not readily detectable on conventional MRI - that is, cortical microinfarcts and tissue changes in the normal appearing white matter (NAWM) - may play an even more important clinical role in terms of clinical symptomatology (Figure 2). These abnormalities can now only be revealed post mortem (14). To better understand the pathological changes involved in SVD, postmortem MRI scanning and direct correlation with pathology is a valuable tool, as it bridges the gap between MRI findings and clinical studies (15,16). As it has been shown for other neurological diseases, such as multiple sclerosis (15-17), postmortem-MRI- histopathology correlation studies may help to solve the weak clinical-radiological associations in SVD.

150 Beyond multiple sclerosis: neurodegenerative and vascular diseases

AIM

This paper aimed to investigate the published pathological substrates of WMH and other features of SVD, by comprehensively reviewing studies that have directly compared post-mortem MRI and histopathology. Another aim was to pinpoint the gaps in our knowledge and provide the readership with suggestions for further studies, which will hopefully contribute to the development of future treatment options for demented and non-demented elderly patients suffering from SVD.

Figure 2: A schematic representation of small vessel disease (SVD) expression is shown, including illustrative postmortem MRI and histological sections.

Chapter 6

SVD expression visible on MRI is illustrated as: a prefrontal coronal fluid attenuated inversion recovery (FLAIR) image and a matching Bodian Silver stained section of white matter hyperintensities (WMH); a parietal coronal FLAIR image and a Klüver - Barrera stained section of two lacunes (two arrows in the magnification); and a cerebellar axial T2* image and haematoxylineeosin stained section of a microbleed (reproduced with permission from Fazekas, AJNR 1999 [84]). SVD expression that is not readily detected by conventional MRI includes: cortical microinfarcts, illustrated by microglial/ macrophage activation on a HLA-DR stained section; and changes in the normal appearing white matter (NAWM) - for example, astrogliosis (glial fibrilary acidic protein stained section). Future studies should be directed towards assessing the whole spectrum of SVD because all expressions may contribute to clinical symptoms in the elderly subject.

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A small proportion of patients with SVD features on their MRI suffer from genetic disorders such as cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) or hereditary cerebral amyloid angiopathy (CAA). These diseases have a distinct aetiology, and we will only focus on SVD that is observed in ‘normal’ ageing and AD here.

METHOD (SEARCH STRATEGY)

We have systematically searched PubMed for scientific reports correlating postmortem MRI and histopathological assessment of WMH, lacunes and microbleeds until December 2009. The following search terms were used: postmortem, MRI, magnetic resonance, white matter (hyperintensities/lesion(s)), lacune(s), lacunar infarct(ion), microbleed(s).

WHITE MATTER HYPERINTENSITIES

Studies having correlated postmortem MRI to histopathology of WMH are summarised in Table 1. These studies confirmed in vivo studies, stating that WMH are highly prevalent (94%) in elderly populations (33). The first studies are small and descriptive. However, subsequent studies have specified WMH by distinguishing periventricular (PVL) versus deep (DWMH) WMH and the extent of DWMH (Box 1).

Box 1: Sensitivity and specificity of postmortem MRI

All studies identified by the above-mentioned search criteria have used formalin fixed brain specimens. Fixation duration and time to autopsy influence the reliability of postmortem MRI measurements. It has been shown that tissue fixation decreases both T1 and T2 relaxation times in both gray and white matter (42,43). Early descriptive studies have claimed that postmortem MRI of 0.25 T to 1.5 T can already visualise WMH with sufficient image quality (16,18,22). However, the sensitivity of postmortem MRI does appear to be dependent on the size of WMH. Smaller punctate WMH, thought to have little clinical impact, can be less clearly visible on postmortem MRI (24,44,45). A sensitivity of 95% (range 87-99%) and specificity of 71% (range 44-90%) was found for PVL on T2 weighted postmortem MRI, which could be directly compared with myelin loss in Luxol Fast Blue stainings. For DWMH, the sensitivity was 86% (range 79-93%) and specificity was 80% (range 72-88%) (31,44). Overall, post- mortem MRI was considered a valuable technique for translating pathological findings to the clinical setting.

152 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Descriptive MRI and histopathology studies Using direct post mortem MRI and histopathology correlations, a plethora of histopathological alterations in WMH was described in several studies, including studies with AD patients, patients with cortical infarctions and patients with Binswanger ’s disease. WMH was shown to reflect partial loss of myelin, axons and oligodendroglial cells, astrogliosis, dilatation of perivascular spaces, activated macrophages and fibrohyalinotic vessel changes (16,18,22,23). This range of tissue changes was suggested to be collectively suggestive of incomplete infarcts. Also, complete deep white matter infarcts were found, mostly in WMH with arteriolosclerotic vessel changes (16,19,23).

Distinct types of white matter hyperintensities In clinical studies using in vivo MRI, an attempt to improve specificity for WMH was made by distinguishing between periventricular WMH (thin hyperintense line, smooth halo or irregular bands/caps) and WMH in the deep WM (punctate, early confluent and confluent WMH) (46). Postmortem MRI and histopathology correlation studies have described that each type of WMH reflects distinct pathological changes (24,26-28, 47). Mild periventricular WMH presents with discontinuity of the ependyma, mild- moderate gliosis in the subependymal layer, loosening of the fibre network and myelin loss around so-called ‘tortuous venules’ and dilated perivascular spaces. No arteriolosclerotic vessel changes were found in these regions (24,30). Irregular PVL was shown to correspond to more severe, partly confluent, areas with varying fibre and myelin loss and reactive gliosis. Some complete infarcts were seen in irregular PVL regions, in combination with fibrohyalinotic and arteriosclerotic vessels. Punctate, early confluent and confluent WMH in the deep WM were found to be Chapter associated with increasing severity of tissue changes. In punctate DWMH, tissue changes were generally mild and confined to the area around dilated perivascular spaces with myelin loss and atrophic neuropil around fibrohyalinotic arterioles. In 6 early confluent DWMH, perivascular rarefaction of myelin was accompanied by varying degrees of axonal loss and astrogliosis. In confluent DWMH, diffuse areas of incomplete parenchymal destruction were observed, together with loss of myelin, axons and oligodendrocytes, astrogliosis, spongiosis and focal transitions to complete infarcts (24, 26-28,29,31) Examples of pathological samples with periventricular and deep WMH, defined on postmortem MRI, are shown in Figure 3. The above described studies imply that smooth periventricular WMH and punctate WMH are mild forms of WMH and may therefore not be clinically relevant or even detectable (29). Irregular periventricular WMH and confluent DWMH, however, corre- spond to more severe tissue changes, probably of ischaemic origin, and are more

153 Chapter 6 likely to produce clinical symptoms (19,26,27,29). Of note is the dependence on subject selection: in the relatively healthy NUN study cohort, evidence of ischaemia was not found in extensive DWMH (30).

Figure 3: Pathological samples with periventricular and deep WMH, which were defined on postmortem MRI

Prefrontal coronal fluid attenuated inversion recovery image (A) of an 88-year-old female with Alzheimer’s disease. Regions of interest represent white matter hyperintensities (WMH) in the periventricular area (green; B1 to E1); WMH in the deep white matter (yellow; B2 to E2); and an area of normal appearing white matter (NAWM) (white, B3 to E3). Bodian Silver stained sections (B, original magnification 200X) showed lower axonal density in WMH (B1 and B2) than in NAWM (B3); more microglial activation (C) was observed in WMH (C1 and C2) than in NAWM (C3) on HLA-DR immunohistochemical sections (original magnification 200X); WMH also showed more myelin loss (D1 and D2) compared with NAWM (D3) in Luxol Fast Blue/ Cresyl Violet stained sections (original magnification 100X); and the severity of astrogliosis (E) (glial fibrilary acidic protein immunostained sections, original magnification 400X) was not clearly diff erent between WMH and NAWM in this patient. Adapted with permission from Gouw, Brain 2008 (41).

154 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Pathogenetic mechanisms underlying white matter hyperintensities Recently, several studies further assessed possible pathogenetic mechanisms of WMH by quantitative assessment of immunohistochemical stainings. These studies include important work on the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) cohort that prospectively collects unselected brain specimens from a large community based cohort (48). Firstly, the role of hypoxia in the pathogenesis of WMH was investigated using specific markers for vascular morphology and tissue hypoxia (34). Thicker vessel walls and larger perivascular spaces were found in WMH. In DWMH specifically, capillary endothelial cells were found to be activated and an increased expression of several hypoxia markers was observed. Other studies on characterisation of afferent vessels showed arteriolar tortuosity and decreased vessel densities in WMH (32,49). These findings support an ischaemic pathogenesis of WMH, especially in DWMH. Secondly, blood-brain barrier dysfunction was demonstrated in a proportion of WMH (19,35). This was shown by the presence of swollen, eosinophilic, glial fibrilary acidic protein positive astrocytes in both DWMH and PVL (50). These clasmatodendritic astrocytes stained positively for serum fibrinogen implying leakage of the blood-brain barrier (51). Furthermore, vascular integrity (as determined by CD31 staining) and P-glycoprotein, an important constituent of the blood-brain barrier, were decreased in WMH (37). Concentric collagen deposition in venular walls may cause intramural thickening, stenosis and eventually venous insufficiency. Venous collagenosis then induces ischaemic stress and may cause dysfunction of the bloodebrain barrier (49,50 ). Thirdly, the role of microglial cells in the pathogenesis of WMH was investigated (36). Microglial cells in PVL showed a greater tendency to be immunologically activated than DWMH, as shown by the expression of MHC class II. DWMH contained microglial cells Chapter with an amoeboid morphology, which were less immune activated but were likely involved in the phagocytosis of myelin breakdown products. Alternative pathogenetic mechanisms of WMH included altered cerebral blood flow autoregulation, axonal 6 depletion from Wallerian degeneration or toxic effects of amyloid on vascular permeability in AD patients (52). These findings have illustrated that MRI visible WMH are associated with various underlying pathological features and (patho)biological responses in the MRC CFAS cohort (53). This cohort, however, consists of a heterogeneous community based group of subjects, including healthy elderly, AD patients and subjects with other neurological disorders. The large heterogeneity encountered in this group may therefore be partly artificial and WMH may be pathologically distinct for patient (and control) groups (35).

155 Chapter 6

:KLWHPDWWHUK\SHULQWHQVLWLHVLQSDWLHQWVZLWK$O]KHLPHU·VGLVHDVH MRI expressions of SVD are more prevalent in patients with AD than in non-demented elderly (12). In addition to the prototypical neuropathological characteristics of AD - that is, amyloid plaques and neurofibrillary tangles - cerebrovascular pathology is also more frequently observed in AD compared with the general elderly population (48,54). The impact of cerebrovascular pathology on cognitive decline in AD patients remains to be established. For WMH, some studies have suggested a synergistic effect with common AD pathology on cognitive decline (13,55) whereas others have not found a distinct role for WMH in AD (56). Although WMH was found to be more extensive in AD patients than in controls, the nature of pathological correlates, including vascular morphological changes and specific markers for hypoxia were comparable between these groups (34,57,58). An exception is microglial activation, which was specific for WMH in AD patients (41). The severity of tissue changes, however, differed with more severe loss of myelinated axons, ependyma denudation, gliosis and thicker adventitia of the deep white matter arteries in AD patients (57,58). In vascular dementia (VaD) patients, the histopathological profile of WMH was comparable with that of AD patients (59). This generally comparable pathology suggests that WMH associated with ageing, AD and VaD does not have a distinct pathogenesis but instead may be part of a pathological continuum (34). A specific pathology possibly linking SVD and AD is CAA (60,61). CAA is characterised by amyloid deposition in the smooth muscle cells of cortical, subcortical and leptomeningeal small arteries and arterioles (28,62). Patients with CAA can present with intracerebral haemorrhage, transient neurological events and cognitive decline (62). In CAA patients, the severity of CAA was found to be associated with WMH severity (60), possibly due to global vascular dysfunction, which includes the vasculature in the white matter (60). In AD, some studies have found weak correlations between WMH and CAA in AD (34,63,64) whereas other studies failed to find any correlations (19,23,54,57).

27+(5(;35(66,2162)69'

Lacunes In pathological terms a ‘lacune’ corresponds to small (lacunar) infarcts, dilated perivascular spaces or old small haemorrhages (65 66). However, the term ‘lacune’ in MRI studies is generally used for a lacunar infarct. These are focal CSF filled cavities, often surrounded by a hyperintense rim on FLAIR images. Lacunes are typically located in the areas supplied by the deep thalamo-perforant, lenticulostriate or pontine

156 Beyond multiple sclerosis: neurodegenerative and vascular diseases paramedian arterioles - that is, basal ganglia, thalamus, internal capsule, pons and centrum semiovale (67 68). The few postmortem MRI studies that have focused on lacunes are summarised in Table 2. On histological examination, MRI defined lacunes were found to correspond to irregular cavitations with scattered fat laden macrophages, which can be accompanied by surrounding reactive gliosis and myelin and axonal loss (22,66,71,72). With increasing age of the lacune, the density of macrophages diminishes and gliosis becomes more fibrillar (71). A subtype of lacunes may be seen that is not yet cavitated but shows selective neuronal loss with relative preservation of glial elements (73). Several postmortem MRI studies have compared lacunes to dilated perivascular (VirchoweRobin) spaces as these structures appear similar on MRI which generally hinders a clear distinction (18,26,28). The clinical relevance of enlarged perivascular spaces, if any, is not yet fully elucidated. In general, enlarged perivascular spaces are considered to be asymptomatic but a relation with SVD may exist. A discriminating feature between lacunes and enlarged perivascular spaces may be that lacunes are commonly larger (>3 mm) and can be accompanied by perifocal signal changes (20,70). Focal cavities in the anterior perforated substance and the lower part of the basal ganglia/putamen have been reported to generally refer to perivascular spaces rather than to lacunes (69,74,75). The most frequently reported cause of lacunes is acute arteriolar occlusion by arteriosclerosis/thrombosis but the existence of non-cavitated lacunes and the relationship with WMH suggest that there may be other pathogenetic mechanisms with a more gradual development (65,73,76-80). Possible alternatives include thromboembolism, general ongoing hypoxia or tissue damage by extravasated toxic serum proteins due to blood-brain barrier leakage (76, 81). Future postmortem MRI studies with histopathological Chapter confirmation is warranted to further investigate these mechanisms.

Microbleeds 6 Clinical MRI studies have generally regarded small foci of signal loss on gradient echo T2* MRI sequences as micro bleeds (1,82). They are not only a predictor of future lobar intracerebral haemorrhage but are also independently associated with cognitive decline (6,7,83). Only a few studies have used direct postmortem MRI pathological correlations to establish the pathological changes responsible for these MRI hypointensities (see Table 3) (84-86). A recent study that systematically correlated susceptibility weighted imaging, an advanced T2* MRI sequence, to tissue pathology of hypointensities in AD patients (87) found that most lesions indeed seem to be microscopic bleedings. A minority of these lesions, however, corresponded to small

157 Chapter 6 lacunes, dissections of a vessel wall or to microaneurysms. Microbleeds may also correspond to focal accumulations of hemosiderin containing macrophages in the perivascular space and there is evidence of haeme degradation activity with a surrounding inflammatory reaction with activated microglial cells, late complement activation and apoptosis (87). Microbleeds were found to be occasionally surrounded by gliosis and incomplete ischaemic changes. The walls of ruptured arterioles may show CAA related vascular damage, with thickened, acellular morphology, lack of the muscularis layer and Є-amyloid deposition. CAA related microbleeds tended to be localised at the grey-white matter junction and in superficial cortical layers of the parietal and occipital lobes. Microbleeds in hypertensive subjects, however, were more often seen in the basal ganglia, brainstem and cerebellum (88). Arteriosclerosis of the vessel walls was often present in these subjects (84,89).

05,´,19,6,%/(µ(;35(66,2162)69'

As noted above, there is accumulating evidence that there are also pathological changes associated with SVD which are ‘invisible’ to conventional MRI, such as tissue changes in white matter areas appearing normal on postmortem MRI (NAWM) and cortical microinfarcts. Pathologically, NAWM may correspond to mild tissue changes with a slightly lower myelin density, activated endothelium, a looser but still largely intact axonal network and a normal glial density (31,33). Furthermore, it has been shown that the density of small afferent vessels is not only decreased in WMH but extends into NAWM and the cortex (32). Cortical microinfarcts are microscopically small lesions. They are attributed to ischaemia, consisting of complete or incomplete cavitation with myelin pallor and neuronal loss, surrounded by glial cells and/or macrophages (90). Cystic micro- infarcts tend to be larger (up to 5 mm) than non-cystic microinfarcts (0.05-0.4 mm) (90 91). Several population based prospective autopsy studies suggested that cortical microinfarcts are major determinants of dementia (90-92). Microinfarcts also have an independent influence on cognitive decline in the non-demented elderly, with only little or moderate AD changes on histology (14,93). Moreover, microinfarcts were associated with CAA in patients with VaD (90). All of these findings suggest that SVD is a widespread disease and has various expressions throughout the brain of which only some aspects can be visualised with conventional MRI. As illustrated by Figure 2, MRI ‘invisible’ pathologies, including cortical microinfacts and tissue changes in the NAWM, hence contribute to the clinical- radiological association that is found in SVD.

158 Beyond multiple sclerosis: neurodegenerative and vascular diseases

3RVWPRUWHPTXDQWLWDWLYH05,PRUHVSHFLÀFIRU69'UHODWHGSDWKRORJ\ than T2 weighted MRI? To be able to draw conclusions on the clinical relevance of SVD, additional pathology specific tools are needed in vivo. Recently, quantitative MRI techniques (QMRI) have been suggested to be more specific for underlying pathology (31,44,59). It has been shown that magnetisation transfer imaging and diffusion tensor imaging (DTI) distinguish between WMH and NAWM in elderly subjects (52,94). When correlating postmortem QMRI and pathology, it needs to be taken into account that both the time to autopsy and fixation duration have an effect on T1 and T2 relaxation times and diffusivity measures (95,96). Although several postmortem studies using QMRI have been performed in patients with multiple sclerosis and other neurological diseases (97-99), postmortem QMRI studies in the elderly with SVD are still scarce. Two small studies showed that DTI measures in WMH seem to correspond to the degree of myelin loss. Also, the area of diffusivity and pathological changes was found to be more spatially extensive than indicated by the hyperintense areas on conventional T2 weighted MRI (39,40). Pathological correlates of fractional anisotropy in DTI and T1 relaxation time were established in a recent study on WMH in AD patients and controls. Fractional anisotropy reflected axonal loss whereas T1 relaxation time corresponded with axonal loss, myelin loss and microglial activation (41). These few studies reveal that QMRI techniques may be promising in assessing tissue damage in vivo because they sensitively and specifically reflect the severity of pathological substrates and reveal tissue changes in areas that appear normal on conventional MRI (NAWM).

Chapter CONCLUSIONS AND CONSIDERATIONS FOR FUTURE RESEARCH 6

This review has considered the pathological correlates of SVD, as reflected on MRI. The available literature suggests that several explanations may exist for the weak clinical- pathological associations. Firstly, pathological substrates of SVD expressions on MRI, such as WMH, lacunes and microbleeds, are heterogeneous in nature and differ in severity. Relative to WMH, postmortem MRI pathology correlation studies of lacunes and microbleeds are still scarce. For lacunes, pathological correlation studies are certainly warranted to be able to further investigate their hypothesised multiple aetiologies, including acute thromboembolism, continuing moderate ischaemia with eventual focal tissue loss and

159 Chapter 6 inflammation. For microbleeds, the pathogenetic mechanisms and their relationship with WMH and lacunes also needs to be further unravelled. It should be noted that in this review, we have only focused on normal ageing and dementia. In specific brain diseases, such as CADASIL and hereditary CAA, SVD features are also present but may differ with regard to their MRI and histopathology profiles. Secondly, until recently, clinical MRI studies have often focused on separate aspects of SVD, such as WMH, and found only weak associations with clinical symptoms. However, not only WMH but also lacunes and microbleeds are bound to contribute to clinical symptoms such as cognitive decline (3,6). Furthermore, the previously discussed MRI ‘invisible’ lesions - that is, microinfarcts and tissue changes in the NAWM - may be clinically relevant, independently of MRI ‘visible’ characteristics of SVD (93). The combination of SVD features is therefore a better predictor of cognitive decline than separate SVD expressions (100). Moreover, SVD should be assessed together with frequently coexisting large vessel infarcts, which may improve insight into the mechanisms of vascular cognitive impairment. In addition to vascular disease, other (degenerative) brain changes - for example, AD pathology, CAA or cortical Lewy bodies - may interact and modulate their specific contributions to cognitive decline (92,101-104). Future studies should therefore consider the full spectrum of SVD expression, together with vascular and degenerative pathologies coexisting in the ageing brain. Unfortunately, MRI sequences that are commonly used in radiological practice are insufficiently sensitive and specific to detect all the tissue changes related to SVD. Novel QMRI techniques, such as DTI, magnetisation transfer imaging, and T1 and T2 relaxation time measurements have two advantages over conventional MRI. Firstly, QMRI better reflects the severity of underlying pathological substrates. Secondly, it may better reveal clinically relevant tissue changes in the WM that appears normal on conventional MRI (39,40). QMRI techniques are therefore more adequate at reflecting the full range of SVD expressions. Several QMRI abnormalities were confirmed histopathologically, and these techniques are promising, pathology specific tools for future in vivo studies. It should be noted that, even though study logistics will become more complex, the use of fresh brain tissue in exploratory postmortem MRI studies is preferred above the use of fixed specimens, as formalin fixation has been shown to influence tissue proton relaxation characteristics. Unlike tissue changes in the NAWM, QMRI techniques have not yet been investigated to visualise cortical microinfarcts. The future use of high resolution MRI, using 7 T MRI scanners, may achieve in vivo assessment of these lesions, which are probably beyond the resolution of 1.5 or 3 T MRI.

160 Beyond multiple sclerosis: neurodegenerative and vascular diseases WMH correspond to a spectrum of histological alterations. tissueMild changes=enlarged perivascular spaces, vascular ectasia. Severe tissue changes=degeneration axons, gliosis. WMH correspond to intermediate and old infarctions: intermediate infarctions=necrosis, minimal cavitation, gliosis. old infarction=cavitation, surrounded by demyelination, fibrohyalinosis and isomorphic gliosis (swollen reactive astrocytes, positive for IgG and Findingsalbumin). imply blood-brain barrier leakage. No correlation with CAA. WMH. astrogliosis, oligodendroglial swelling, a few macrophages and slight oedema. Arterial/arteriolar wall changes: severe thickening with stenosis, hyperplasia and hyalinosis. Scarce vascular amyloid deposition. Vascular wall changes=arteriosclerotic wall Vascular with thickening, changes fibrosis and splitting of the internal elastic lamina, dilated perivascular spaces. wall thickening in moderate/severe WMH. MRI findings Main 1.5T WMH vary from subtle gliosis and demyelination to frank infarction. 0.35T 0.5T Good correlation of extent of WMH on MRI and pathological changes. 1.5T WMH=demyelination, gliosis and arteriolosclerotic changes. Arteriolar

Chapter (Immuno)- Histochemistry LFBHE, 0.25T and T2 T1 relaxation times increased with severity of tissue changes in HE, Weil, LFB, Bodian, HE, Woelke, Congo Red GFAP, HE, LFB,GFAP, 1.5T HE, Congo Red, Alcian Blue, IgG, albumin LFB, GFAP, axons, cresyl violet, EvG, myelin, Heidenhain’s PTAH Bielschowsky, HE, LFB, Weil, EvG, PTAH, PAS, Congo Red, Bodian 6 controls controls controls controls 11 neur (-) neur11 (-) controls Englund 1987 (16) 21 AD 19 neur (+) Braffmann 1988(20) (+/-) 23 neur Refer-ence Subjects Marshall 1988 (19) neur (-) 16 (21)1989 Mascalchi VaD 1 RedCongo Woelke, HE, 0.5T Case report: WMH reflect demyelination, axonal loss, moderate Awad 1986 (18) 7 neur (-) Revesz 1989 (22) 4 VaD HE, KB, Nissl, Holzer Van Swieten Van 1991 (23) Table 1: PostmortemTable studies on characterisation of white matter hyperintensities using postmortem MRI-pathology correlation

161 Chapter 6 anges; demyelination, atrophic Punctate ischaemic DWMH =no ch : Smooth PVL= loose myelin fibres, pallor, tortuous venules, no PVL gliosis. mild-moderate ependym, arteriolosclerosis, discontinuity wall Irregular PVL= varying fibre loss, gliosis and cavitation, fibrohyalinosis. vascular DWMH: or Early arterioles perivenous and damage. fibrohyalinotic around neuropil infarction Confluent No DWMH=perivascular rarefaction of myelin, mild-moderate fibre loss, varying gliosis. spongiosis. Confluent DWMH= irregular areas of incomplete parenchymal destruction and infarcts. true to transitions focal with Extensive DWMH=broad areas of loss of myelin, axons and cells glial (oligodendrocytes) changes. Punctate WMH=dilated perivascular spaces. PVL=atherosclerotic changes, vacuolisation of the myelin, neuropil and fibrous gliosis with proliferation of ependymal cells. DWMH=vacuolated arteries/arterioles, perivascular widened atherosclerotic around myelin spaces with degenerated myelin and recent infarction. PVL/focal DWMH no clinical consequences, whereas confluent DWMH are potentially pathological. atrophic neuropil and rarefaction myelinated fibres. PV=myelin gliosis and widened pallor, perivascular spaces. DWMH did not correlate with any neuropathological measure. rims=ependymal loss and subependymal gliosis. Periventricular caps/ Periventricular gliosis. subependymal and loss rims=ependymal spaces. perivascular DWMH=widened Punctate pallor. patches=myelin MRI findings Main 1.5T 1.5T Each type of WMH correlated with distinct PA: Periventricular Periventricular PA: distinct with correlated WMH of type Each 1.5T (Immuno)- Histochemistry HE, chromoxane cyanin, Bielschowsky, Congo red HE, LFB, GFAP 1.0T HE, Masson’s trichrome, KBtrichrome, Masson’s HE, 1.5T Punctate WM=spectrum of perivascular damage with fibrohyalinosis, LFB-PAS, Holzer astrocytes, Holzer LFB-PAS, Holmes, gallocyanin, GFAP KB, Congo red, desmin, GFAP HE, Masson’s trichrome, KB 1.5T controls controls, 5 neur (+) controls neur, 4 neur (+) 4 neurneur, (+) controls controls controls controls Refer-ence Subjects Munoz 1993 (28) 13 2 AD, neur (-) Scarpelli 1994 (29) neur (-) 16 Fazekas 1991 (24) 1991 Fazekas 2 controls - Grafton(25) 1991 (+/-) AD, 4 3 neur Chimowitz (26) 1991 7 neur (-) Fazekas 1993 (27) neur 11 (+) Table 1: Continued 1: Table

162 Beyond multiple sclerosis: neurodegenerative and vascular diseases More extensive than on MRI. lesions= WMH +PA/-MRI on PA mild changes=lower myelin density, loose but intact fibre network, normal density. glial lesions=variable+PA/+MRI axonal myelin/ loss, irregular and fragmented perivascular spaces, decreased dilated axons, density, vacuolation, cell smooth muscle degeneration. no gliosis/ infarction. capillaries). Subjectscapillaries). with WMH have a decreased vascular density in WMH, NAWM and cortex, but especially apparent in youngest subjects. WMH=loss of myelinated axons, gliosis, no atherosclerosis. the In AD, nature of pathological changes is comparable with but more severe than in controls. sensitivity=86% specificity=80% (79-93%), (72-88%). Weighted kappa DWMH=0.3;MRI-PA: PVL=0.4, underestimation lesions. small MRI+ versus MRI- lesions: difference in myelin loss and endothelial upregulation. No difference in microglial activation. No ischaemic changes (microinfarcts or lacunes) in a sample with few cardiovascular risk factors. MRI findings Main 1.5T WMH correlates with decreased vascular density (arteries/ arterioles/ 0.6T AD versus controls: some AD pts show more extensive WMH than controls. 1.0T sensitivity=95% PVL-MRI: (44-90%), specificity=71% DWMH-MRI: (87-99%),

Chapter (Immuno)- Histochemistry phosphatase, Alkaline Congo red, Masson trichrome, Kultchitsky haematoxylin/LFB, acetate violet Cresyl plus light green and Gill haematoxylin. HE, PTAH, KB,HE, Bodian, PTAH, EvG, Congo Red HE, LFB/Loyez method, CD68, collagen-IV, ICAM-1 LFB pathology. WM of extent with well - correlates MRI on WMH of Extent 6 controls controls controls, 17 ADcontrols, 17 + VaD specified Moody 2004 (32) 21 neur (-) Refer-ence Subjects Fernando 2004 (33) neur (-) 16 Scheltens 1995 (12) 6 AD, 9 neur (-) Smith 2000 (30) 12 pts not Bronge 2002 (31) 6 AD HE, KB 1.5T Table 1: Continued 1: Table

163 Chapter 6 . Both હ reactive ݂ R/OPC). હ vascular integrity. WMH not associated with ݂ DWMH and PVL: C/hypoxia upregulation plays a role MMP7. in a part of WMH. Differences DWMH versus PVL. No differences demented and non-demented subjects. In PVL: ependym denudation. In DWMH specifically: higher capillary hypoxia upregulation activationnetwork (CD68), microglial density, factors (HIF1/2alpha, VEGFR2, and correlation Ngb) CAA with HIF1 biological responses: More microglial activation (CD68) in DWMH > > DWMH in (CD68) activation microglial More responses: biological (42% proteins serum for astrocytosis, positive PVL. Clasmatodendritic DWMH, 67% PVL), suggesting blood-brain barrier dysfunction. Attempts at regeneration/ remyelination PDGF in PVL (MAP-2+13, stimulatory and CD40, suggesting B7-2 a more proliferative/ immune phagocytosis for microglia amoeboid DWMH: In environment. reactive of myelin breakdown products. microglia, DWMH extentmicroglia, Modelmyelin for pallor. prediction of WMH extent (correctly classifies NAWMWMH/ in 80%): only independent predictor=decreased vascular P-gp:integrity WMH > NAWM, but no difference (CD31). with IgG → Blood- brain barrier dysfunction. MRI findings Main 1.0T Vascular changes in WMH: wall thickening, dilated perivascular spaces. 1.0T DWMH and PVL differ with regard to pathological profiles and and profiles pathological to regard with differ PVL and 1.0T DWMH 1.0T Microglial responses: PVL: more MHCII positive microglia and co- 3.0T WMH extent scored using Scheltens Scale PVL (38). extent A4 Є , MMP7, Ngb, , MMP7, હ R, MAP-2(+13), R, MAP-2(+13), /2 હ હ (Immuno)- Histochemistry HE, LFB, MBP, GFAP, CD68, GFAP, HE, LFB, MBP, PDGF CD68, ICAM1, Col IV, HIF1 NMBR, VEFGR2, fibrinogen HLA-DR, CD40, Mcm2, B7-2, Ki67 PCNA, dMBP, CD31, GFAP, hGLUT-5, hGLUT-5, GFAP, CD31, dMBP, HLA-DR,APP, P-gp, IgG tissue blocks: 12 12 blocks: tissue PVL, 12 DWMH, NAWM 15 108 neur (+/-) neur108 (+/-) controls tissue blocks: 12 12 blocks: tissue PVL, 12 DWMH, NAWM 15 (various), 3 neur (various), controls. (+/-) Refer-ence Subjects Simpson 2007 (35) Unselected Fernando 2006 (34)Fernando demented 99 Simpson 2007 (36) Unselected Young 2008 (37)Young demented 17 Table 1: Continued 1: Table

164 Beyond multiple sclerosis: neurodegenerative and vascular diseases , , HLA- , matrix severity severity LFB-PAS ݂ , pathology; MMP7 PA ; , frontotemporal , phosphotungstic neur (+) controls FTD , vascular endothelial endothelial vascular , Mcm2 PTAH and CD40 ligand, immune , Luxol Blue; Fast VEGFR2 LFB CD40 , hypoxia inducible factor; predictor of fractionalpredictor of anisotropy HIF , P-glycoprotein; , Elastic van Gieson; , myelin basic protein, EvG P-gp , oligodendrocyte precursor cells; MBP , Klüver-Barrera; vascular dementia. dementia. vascular KB , vascular integrity; OPC VaD, CD31 , human glucose transporter-5; , immunoglobulin; activation in WMH than NAWM in AD patients specifically. QMRI QMRI specifically. patients AD in NAWM than WMH in activation of pathological changes. Independent in DTI=axonal loss. Independent predictors relaxation of T1 time=axonal loss, myelin loss, microglial activation. , non-demented subjects without neurological disease; non-demented, disease; subjects neurological without , (quantitative) MRI; IgG , neuromedin B receptor; hGLUT-5 MRI findings Main 1.5T Quantitative MRI distinguishes WMH from AD and controls. More microglial (Q)MRI NMBR , (deep) white, (deep) matter hyperintensities; , cerebral amyloid angiopathy; , platelet derived growth factor a receptor; neur (-) controls CAA (D)WMH , neuroglobin; PDGFaR Chapter , haematoxylin-eosin; Ngb , microtubule associated protein 2 expressing exon 13; HE , intercellular adhesion molecule; (Immuno)- Histochemistry HE, LFB-CV, Bodian, GFAP, HE, LFB-CV, Bodian, GFAP, HLA-DR 6 MAP-2 +13 MAP-2 ICAM1 , diffusion tensor imaging; tensorimaging; diffusion , , amyloid precursor protein; , normal appearing white matter; DTI APP , periventricular white matter hyperintensities; hyperintensities; matter white periventricular , controls NAWM PVL , glial fibrilary, glial acidic protein; , cell proliferation related molecules; GFAP Ki67 and , human leucocyte antigen-DR; , Alzheimer’s disease; Refer-ence Subjects Gouw 2008 (41) AD, 7 neur 11 (-) Table 1: Continued 1: Table non-demented patients disease; with neurological PCNA DR Luxol Blue/periodic Fast acid-Schiff; metalloproteinase 7; 7; metalloproteinase acid haematoxylin; haematoxylin; acid Abbreviations: AD dementia; dementia; co-stimulatory molecules; growth factor receptor 2.

165 Chapter 6 lia, , Luxol Blue/ Fast LFB-PAS white matter. white , non-demented patients with neurological neurological with non-demented patients , , Luxol Blue; Fast WM, LFB neur (+) controls (+) neur 14 asymptomatic14 lesions detected on PA. Basal (76%): ganglia n= 87 , Klüver-Barrera; 29 lacunes, 58 VRS. lacunes, 11 WM: 8 VRS. 2 lacunes, Thalamus: 3 VRS. Brainstem: 3 lacunes, no VRS. In basal ganglia more VRS, in WM more lacunes. Morphology: lacunes VRS more round/ linear, more wedge shaped, but both could be ovoid. VRS are smaller than lacunes (72% of VRS < 2x1mm, 60% of lacunes > 2x2mm). irregular margin, with surrounding mild loss of myelin and axons with mild gliosis. 2 subjects with 3 lacunes in posterior fossa and 2 subjects with 5 supratentorial white matter lacunes. morphology:Usual mm, varying 3-14 or ovoid, size slit-like degrees of cavitation. criblé in 4 subjects. ´Etat Usual morphology 1 mm-5was round ormm. linear, subject: a lacune in internal capsule. In 2 cases old cystic macrophages containing hemosiderin by densely cavities lined were (possibly reflect microbleeds). demarcated walls, some arteries within cavity were tortuous tortuous were cavity within arteries some enlarged perivascular spaces. Histology: regular and clearly walls, demarcated occluded),(not thickened adventitia. Surrounding white matter showed corpora amylacea, vacuolation, myelin loss and minimal gliosis, but no infarction. KB , VirchoweRobin spaces; MRI findings Main 1.5T Description of old lacune: cystic cavity with ill-defined and 0.5T All subjects: lacunes in the white matter, basal ganglia and pons. 1 - Infraputaminal cavities or ‘lacunes’ are single or grouped VRS , haematoxylin-eosin; HE , vascular dementia; VaD (Immuno)- Histochemistry GFAP axons, cresyl violet, EvG, myelin, Heidenhain’s PTAH Bielschowsky, HE, LFB-PAS, Gomori’s Verhoeff’s trichrome, Actin elastic, HE, KB 1.5T 1 non-demented subjects without neurological disease; non-demented subjects disease; neurological without , glial fibrilary, glial acidic protein; GFAP MRI, 1 in vivo MRI. Not specified (cerebro-vascular disease), disease), (cerebro-vascular controls3 neur (-) neur (-) controls, , phosphotungstic acid haematoxylin; PTAH , Elastic van Gieson; Refer-ence Subjects Matsusue 2006 (71) not specified HE, LFB, axon staining, Braffmann 1988(20) controls 36 neur(+/-) HE, Weil, LFB, Bodian 1.5T Lacunes in 6subjects: 4subjects with 6 lacunes in basal gang Revesz 1989 (22) 4 VaD HE, KB, Nissl, Holzer Pullicino 1995 (69) 2 subjects post-mortem Bokura 1998 (70) controls 9 neur (+) Table 2 :PostmortemTable studies on characterisation of lacunes using postmortem MRI-pathology correlation periodic acid-Schiff; disease; EvG

166 Beyond multiple sclerosis: neurodegenerative and vascular diseases m, surrounded by gliosis and incomplete ischaemic ç necrosis. Identified as hemosiderin pigments within the perivascular perivascular the within pigments hemosiderin as Identified necrosis. space and as an organised pseudoaneurysm case). (1 9 MRI hypointensities: could all be identified as brown spots on the cut surface. 8 hemosiderin laden macrophages, 1 vascular pseudocalcification had5/8 vascular abnormalities: (left pallidum). degenerated endothelial lining and hyalinosis. of hemosiderin Size withdeposit MRI hypointensity. similar Associated with tissue rarefaction, arteriolar or gliosis changes. Some deposits hemosiderin not observed on MRI. Ante- and postmortem MRI comparable. basal ganglia/infratentorial, 4 pts both locations. WMH patients, in all lacunes in 5/7 pts. In 62% of MRI signal loss: focal accumulation of hemosiderin-containing macrophages adjacent blood to small vessels, sometimes minute areas of tissue necrosis. The remainder of MRI signal loss: no pathological substrate. Also MR negative hemosiderin only a few perivascular,smaller, hemosiderin laden macrophages. deposits: No calcification or vascular malformations. 2 subjects had cerebral amyloid angiopathy of variable extent in multiple vessels, associatedremote blood leakage. Brains with with fibrohyalinosis showed microbleeds foci of preferentially in the basal ganglia/thalami, but also cortical-subcortical. microvessels <200 mortem and in mortem in and MRI vivo 1.5T subjects. MRI signal loss in 7/11 2 pts: only cortical-subcortical, 1 pt only MRI vivo In Foci of old haemorrhages caused1.0T by rupture of arteriosclerotic

Chapter 6 HE, MassonHE, trichrome, KB, Berlin Blue HE, MassonHE, trichrome, KB, Congo Red, iron 1 neur (+) control1 neur (+) HE, Berlin Blue 1.5T, both post- controls, with MRI signal loss intra-cerebral haemor-rhage Tatsumi 2008Tatsumi (86) Tanaka 1999 (85)Tanaka 3 neur (+) Ref-erence 1999Fazekas (84) 11 subjects Subjects with Histo-chemistry MRI findings Main Table 3: PostmortemTable studies on characterisation of microbleeds using postmortem MRI-pathology correlation

167 Chapter 6 , MAP-2 , microbleed; MB controls, non-demented patients neur (+) , Klüver-Barrera; KB amyloid deposition and lacking Activated muscularis layer. Є haeme oxygenase 1; 1; oxygenase haeme haematomas, 7 small cavities, 3 microscopic hemosiderin cavities, microscopic hemosiderin small 3 7 haematomas, granules+haematoidin deposition, 1 dissection vessel wall, 1 microaneurysm. Location: 79% grey-white junction, superficial 21% cortex. CAA related vascular thickened, damage: arteriolar acellular, walls with evidencemicroglia (CD68), of haeme late degradation (HO-1), complement activation, apoptosis. Inflammatory reaction along local microvasculature. HO-1, , cerebral autosomal dominant arteriopathy with subcortical infarcts and CADASIL 3.0T 38 MBs: correspond to variety of pathological changes=16 old old changes=16 pathological of variety to 3.0T correspond MBs: 38 , haematoxylin-eosin; HE controls, non-demented subjects without neurological disease; non-dementedcontrols, disease; subjects neurological without 1-42, CD68,1-42, HO-1, Є A complement C6, CD3, CD20, Prussian Blue, fluorescent study TUNEL + MAP-2), (HO-1 neur (-) , white matter, white hyperintensities. , cerebral amyloid angiopathy; , Elastic van Gieson; WMH CAA EvG advanced CAA), advanced controls2 neur (-) , Alzheimer’s disease; Ref-erence(87)2009 Schrag which 8 AD 6 (of Subjects Histo-chemistry MRI findings Main Table 3: Continued 3: Table leukoencephalopathy; AD microtubule associated protein 2; with neurological disease;

168 Beyond multiple sclerosis: neurodegenerative and vascular diseases

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99. SIMONYAN K, TOVAR-MOLL F, OSTUNI J, et al. Focal white matter changes in spasmodic dysphonia: a combined diffusion tensor imaging and neuropathological study. Brain 2008;131:447-59. 100. GOLD G, KOVARI E, HOF PR, et al. Sorting out the clinical consequences of ischemic lesions in brain aging: a clinicopathological approach. J Neurol Sci 2007;257:17-22. 101. VAN DER FLIER WM, VAN STRAATEN EC, BARKHOF F, et al. Medial temporal lobe atrophy and white matter hyperintensities are associated with mild cognitive deficits in non-disabled elderly people: the LADIS study. J Neurol Neurosurg Psychiatry 2005;76:1497-500. 102. GIANNAKOPOULOS P, GOLD G, KOVARI E, et al. Assessing the cognitive impact of Alzheimer disease pathology and vascular burden in the aging brain: the Geneva experience. Acta Neuropathol 2007;113:1-12. 103. JAGUST WJ, ZHENG L, HARVEY DJ, et al. Neuropathological basis of magnetic resonance images in aging and dementia. Ann Neurol 2008;63:72-80. 104. BARKHOF F, POLVIKOSKI TM, VAN STRAATEN EC, et al. The significance of medial temporal lobe atrophy: a postmortem MRI study in the very old. Neurology 2007;69:1521-7.

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175

6.2

HETEROGENEITY OF WHITE MATTER +<3(5,17(16,7,(6,1$/=+(,0(5·6 ',6($6(32670257(048$17,7$7,9( MRI AND NEUROPATHOLOGY

Alida Gouw Alexandra Seewann Hugo Vrenken Wiesje van der Vlier Annemieke Rozemuller Frederik Barkhof Philip Scheltens Jeroen Geurts

Brain 2008; 131:3286-3298 Chapter 6

ABSTRACT

Background: White matter hyperintensities (WMH) are frequently seen onT2- weighted MRI scans of elderly subjects with and without Alzheimer’s disease. WMH are only weakly and inconsistently associated with cognitive decline, which may be explained by heterogeneity of the underlying neuropathological substrates. The use of quantitative MRI could increase specificity for these neuropathological changes. We assessed whether post-mortem quantitative MRI is able to reflect differences in neuropathological correlates of WMH in tissue samples obtained post-mortem from Alzheimer’s disease patients and from non-demented elderly. Methods: Thirty-three formalin-fixed, coronal brain slices from 11 Alzheimer’s disease patients (mean age: 83±10 years, eight females) and 15 slices from seven non- demented controls (mean age: 78±10 years, four females) with WMH were scanned at 1.5 T using qualitative (fluid-attenuated inversion recovery, FLAIR) and quantitative MRI (diffusion tensor imaging (DTI) including estimation of apparent diffusion coefficient (ADC) and fractional anisotropy (FA), and T1-relaxation time mapping based on flip-angle array). A total of 104 regions of interest were defined on FLAIR images in WMH and normal appearing white matter (NAWM). Neuropathological examination included (semi-)quantitative assessment of axonal density (Bodian), myelin density (LFB), astro -gliosis (GFAP) and microglial activation (HLA-DR). Patient groups (Alzheimer’s disease versus controls) and tissue types (WMH versus NAWM) were compared with respect to QMRI and neuropathological measures. Results: Overall, Alzheimer’s disease patients had significantly lower FA (P< 0.01) and higher T1-values than controls (P = 0.04). WMH showed lower FA (P < 0.01) and higher T1- values (P < 0.001) than NAWM in both patient groups. A significant interaction between patient group and tissue type was found for the T1 measurements, indicating that the difference in T1-relaxation time between NAWM and WMH was larger in Alzheimer’s disease patients than in non-demented controls. All neuropathological measures showed differences between WMH and NAWM, although the difference in microglial activation was specific for Alzheimer’s disease. Multivariate regression models revealed that in Alzheimer’s disease, axonal density was an independent determinant of FA, whereas T1 was independently determined by axonal and myelin density and microglial activation. Conclusion: Quantitative MRI techniques reveal differences in WMH between Alzheimer’s disease and non-demented elderly, and are able to reflect the severity of the neuropathological changes involved.

Abbreviations: 3D-FLAIR = 3D-fluid-attenuated inversion recovery; ADC = apparent diffusion coefficient; CERAD = Consortium to establish a Registry for Alzheimer’s disease; DTI = diffusion tensor imaging;FA = fractional anisotropy; FLASH = fast low-angle shot; GFAP = glial fibrillary acidic protein; HE = haematoxylin - eosin; LFB = Luxol Fast Blue; NAWM = normal appearing white matter; NBB = Netherlands Brain Bank; PBS = phosphate-buffered saline; QMRI = quantitative MRI techniques; ROIs = regions of interest; T2SE = T2-weighted spin-echo; WM = white matter; WMH = white matter hyperintensities

178 Beyond multiple sclerosis: neurodegenerative and vascular diseases

INTRODUCTION

White matter hyperintensities (WMH) form an important expression of small vessel disease on MRI and are observed in a significant proportion of demented and non- demented elderly subjects (De Leeuw et al., 2001). WMH have been distinguished by location into periventricular WMH, which are directly adjacent to the lateral ventricles, and deep WMH, which are located in the subcortical white matter (WM). Furthermore, the severity of deep WMH is generally scored into punctate, early confluent and confluent WMH (Fazekas et al., 1987). In healthy elderly, WMH are mostly regarded as a normal ageing phenomenon, but especially confluent WMH have been associated with loss of specific cognitive functions, such as psychomotor speed (De Groot et al., 2000). However, the associations are only weak and inconsistently found across MRI studies (O’Brien et al., 2002; Schmidt et al., 2005; van der Flier et al., 2005). MRI studies have shown that WMH are prevalent in patients with Alzheimer’s disease (Scheltens et al., 1992; Barber et al., 1999). Although Alzheimer’s disease is considered to be a mainly cortical dementia with senile plaques and neurofibrillary tangles in the grey matter of the brain (Braak and Braak, 1991), cerebrovascular pathology often coexists in brains of Alzheimer’s disease patients (Smith et al., 2000, MRC CFAS, 2001). The clinical significance of WMH in Alzheimer’s disease patients is insufficiently known. Some authors reported that WMH have an additive effect on cognitive decline in dementia, whereas others could not confirm this association (Stout et al., 1996; Hirono et al., 2000; Mungas et al., 2001). A possible explanation for the inconsistent and weak associations between WMH and clinical symptoms in demented and non-demented elderly would be heterogeneity of the neuropathological substrates underlying WMH (Scheltens et Chapter al., 1992). Although with the above mentioned classification system an attempt was made to specify WMH on T2-weighted images, these MRI sequences are generally not sufficiently specific for the demonstration of underlying pathological changes in 6 the composition of the brain. It is well conceivable that the high signal intensity on T2-based images (e.g. fluid-attenuated inversion recovery, FLAIR) actually reflects a spectrum of neuropathological substrates or tissue damage with varying severity. Whether this is the case for Alzheimer’s disease patients and non-demented subjects with WMH, can be investigated by post-mortem MRI scanning and direct matching of the MRI hyper-intensities to pathological stainings (Geurts et al., 2005). Previous post-mortem MRI—pathology correlation studies in Alzheimer’s disease or in non- demented elderly showed that the pathological correlates of WMH include myelin and axonal loss, astrogliosis, reduction of oligodendrocytes, mild microglial activation and dilated perivascular spaces to variable degrees providing support for the notion

179 Chapter 6 of heterogeneity in WMH pathology (Fazekas et al., 1991, 1993; Scheltens et al., 1995). Recent studies provided detailed immunohistochemical characterization of different expressions of WMH including associations with specific hypoxia markers, altered fluid dynamics or discontinuity of the ependymal lining (Bronge et al., 2002; Fernando et al., 2006; Simpson et al., 2007a). From a clinical standpoint, investigations with sufficient pathological specificity are needed in vivo to be able to assess the clinical impact of WMH. In vivo studies using more advanced quantitative MRI techniques (QMRI), such as diffusion tensor imaging (DTI) and T1-relaxation time measurements, claim that these techniques are more specific to the presence of structural brain damage than the current gold standard for the detection of WMH (conventional T2-weighted MRI) (Jones et al., 1999; Pierpaoli et al., 2001; Shenkin et al., 2005). DTI quantifies the extent of diffusivity of water molecules as well as tissue anisotropy, which is the spatial restriction of water movements in certain directions (Basser et al., 1994). Normal WM, for example, is highly anisotropic because diffusion is more readily directed along the long axis of fibre bundles whereas the perpendicular movement of water molecules is relatively restricted (Basser and Pierpaoli, 1996; Pierpaoli et al., 2001). T1-mapping that determines tissue-specific T1-relaxation times, may reflect pathological processes related to intraparenchymal changes in water content such as oedema, widening of the extracellular space, subtle blood-brain barrier leakage or glial proliferation (Vrenken et al., 2006a). These in vivo QMRI techniques are promising to further improve under-standing of WMH in aging. However, the neuropathological substrates that define changes in QMRI parameters in WMH are still unknown (Bronge et al., 2002; Fernando et al., 2004). In this study, we therefore investigated whether post-mortem QMRI reflects heterogeneity in basic neuropathological hallmarks of WMH, i.e. whether QMRI shows differences between WMH of Alzheimer’s disease patients and of non-demented controls. Also, we aimed to identify which of these neuropathological substrates most strongly determines the possible changes in QMRI parameters of white matter.

METHODS

Patients Eleven consecutive brain specimens of patients older than 70 years with a clinical diagnosis of Alzheimer’s disease were prospectively selected from the Netherlands Brain Bank (NBB). Controls were selected from the VU University Medical Center (i.e. hospitalized patients at the VU University Medical Center who died during the admission). Controls were defined as non-demented subjects according to clinical records. Patients and controls were only included when extensive periventricular WMH

180 Beyond multiple sclerosis: neurodegenerative and vascular diseases and/or (beginning) confluent areas of deep WMH were present on post-mortem MRI (Fazekas et al., 1987). Subjects were excluded if they had a history of resuscitation and if they had suffered from a neurological disease other than Alzheimer’s disease or small cerebral infarcts outside the selected tissue samples, as revealed by clinical, neuroradiological or neuropathological evaluation. General pathological assessment was performed according to the dementia protocol of the NBB. The clinical diagnosis of Alzheimer’s disease was pathologically confirmed using the Braak criteria and Consortium to Establish a Registry for Alzheimer’s disease (CERAD) criteria (Braak et al., 1991; Mirra et al., 1991), whereas control subjects had to have a Braak stage scoring for neurofibrillary tangles <2. All subjects were excluded when pathological evidence of other types of dementia was present. After rapid autopsy (all within 24 h, except for one subject) and at least 4 weeks of fixation in 10% formalin (mean fixation time ± SD: 10 ± 4 weeks) one hemisphere was cut into 10 mm thick coronal brain slices using a 10 mm deep cutting panel to improve precision. Two to four slices per subject were selected for MRI scanning including one pre-frontal slice (first slice cranial of frontal horns of the lateral ventricle), one frontal slice (at the level of the hippocampus, caudate and lenticular nuclei) and one parietal slice (at the level of the cella media). The study was approved by the institutional ethics review board, and all patients approved of the use of their brain tissue for research and review of their medical history by signing an informed consent prior to their death. Patients’ characteristics are listed in Table 1.

Post-mortem MRI protocol Post-mortem MRI scanning was performed using a 1.5-T MRI scanner (Vision, Siemens AG, Erlangen, Germany). The brain slices were separately placed in a purpose-built perspex multi-slice holder that exactly fits into the head-coil of the MRI system. The Chapter MRI protocol that was optimized for fixed brain slices included: 6

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Table 1: Patient characteristics

Post- Number Age Gender Case mortem Cause of Death of slices (years) (M/F) delay (left/right)

Alzheimer’s disease

1 93 F 6h 45min Cerebral infarction 3 (0/3)

2 88 F 4h 35min Cachexia and dehydration 3 (1/2)

372M5h 25minDehydration 2 (0/2)

4 73 M 6h 15min Sudden death 3 (0/3)

5 77 F 3h 05min Dehydration, mamma carcinoma 2 (0/2)

699F5h 10minDehydration 3 (0/3)

7 75 F 15h Cardiac arrest 4 (0/4)

8 86 F 5h 55min Cachexia and dehydration 4 (0/4)

970M4h 50minPneumonia 2 (0/2)

10 90 F 5h 35min Colon carcinoma, dehydration 3 (0/3)

11 86 F 5h 05min Pneumonia 4 (0/4) Control

1 98 M 10h 46min Unknown 2 (0/2)

2 76 F <24h Myocardial infarction 2 (2/0)

3 71 F <24h Ruptured abdominal aorta 3 (1/2)

4 83 F 3h 20min Euthanasia 1 (1/0)

5 70 M <24h Myocardial infarction 3 (0/3)

6 79 F <24h Bronchopneumonia 1 (0/1)

7 71 M <24h Myocardial infarction 3 (2/1)

Qualitative MRI sequences: (1) Dual-echo T2-weighted spin-echo (T2SE) images—TR/TE/NEX: 2755ms/45ms and 90ms/2; field of view: 80x128 mm; matrix size: 160x256; slice thickness: 3mm; acquisition time: 7min 25s. (2) (2) 3D-FLAIR images—TR/TE/TI/NEX: 6500ms/120ms/2200ms/1; 8 partitions per slab; partition thickness: 1.25 mm; field of view: 125x200mm; matrix size: 160x256; acquisition time: 7min 54s.

182 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Quantitative MRI sequences: (2) T1-relaxation time measurements: 3D fast low-angle shot (FLASH), TR/TE/NEX: 15ms/4ms/4; partition thickness: 3mm; field of view: 80x128 mm; matrix size: 80x128, with flip angles of 2o ,5o ,10o ,15o ,20o , and 25o , respectively. The T1- relaxation times (T1) was then calculated for each pixel through a non-linear least- squares fit (Vrenken et al., 2006b). (3) (2) DTI: diffusion-weighted single shot STEAM sequence, TR/TE/ NEX: 6000 ms/65 ms/8; slice thickness: 8mm; field of view: 80x128 mm; matrix size: 40x64; flip angle: 11o. Diffusion encoding gradients were applied in six non-collinear directions: (0, 1, 1), (0, 1, -1), (1, 0, 1), (1, 0, -1), (1, 1, 0) and (1, -1, 0), given in the gradient coordinate system (x, y, z), using a b-value of 750 s/mm2 (Vrenken et al., 2006c). For each slice, one image without use of a diffusion gradient b( = 0) was also acquired. Pixelwise maps of the apparent diffusion coefficient (ADC) and fractional anisotopy (FA) maps were calculated. ADC reflects the magnitude of water diffusivity, whereas the FA indicates spatial directionality of water diffusivity. DTI measurements had to be excluded in seven brain slices due to image artifacts and were therefore available for 41 brain slices.

'HÀQLWLRQRI:0+DQGVHOHFWLRQRIUHJLRQVRILQWHUHVW 52,V The scans and quantitative maps were displayed on a Sun workstation (Sun Microsystems, Mountainview, CA, USA) using an in-house developed image viewer (Show_Images) for analyses. Rectangular regions of interest (ROIs) were defined on 3D-FLAIR images in a consensus meeting of two experienced raters. The ROIs (mean size: 9.7x3.0 mm2 and minimum size: 4.9 mm2) were drawn within areas of WMH and normal appearing white matter (NAWM). Care was taken not to include the borders Chapter of the area of WMH or NAWM in the ROIs to avoid miscategorization. WMH included extensive periventricular WMH and (beginning) confluent areas of deep WMH, according to the widely used Fazekas scale (Fazekas et al., 1987). Rim-like periventricular WMH 6 or small punctate deep WMH were not included, because it is more difficult to match these small areas exactly with (immuno)-histochemical stainings (Fazekas et al., 1991). NAWM was defined as white matter that showed no visually appreciable signal intensity increase on T2-weighted or 3D-FLAIR images. Only scans of brain slices with at least one WMH ROI were included for analyses. Forty-five ROIs in WMH and 30 ROIs in NAWM were defined in 33 brain slices of Alzheimer’s disease patients; 16 WMH and 13 NAWM ROIs were defined in 15 slices of controls (totalling 104 ROIs).

183 Chapter 6

Neuropathology and matching procedure As part of routine pathological description of each brain specimen, Braak staging was performed for confirmation of the clinical diagnosis of Alzheimer’s disease and controls (Braak et al., 1991). Furthermore, evaluation of the agonal state was performed on haematoxylin-eosin (HE) stainings by assessment of red (hypoxic) neurons and cortical pericellular oedema in the frontal, temporal and occipital lobes (graded as absent/mild/moderate/severe). Hemispheric brain slices were paraffin-embedded and subsequently cut until halfway to reveal the centre of the imaged plane on which the ROIs were defined. This way, the brain slices could be reliably matched to the MR images. Serial 8-mm thick sections were then cut, mounted onto glass slides and stained with HE, Luxol Fast Blue/Cresyl Violet (LFB) and Bodian silver to assess general tissue morphology, myelin density, and axonal density, respectively. Standard immunohistochemical stainings were performed on adjacent sections, which were mounted on poly-L-lysine-coated glass slides for microglial activation with HLA-DR antibodies and for astrogliosis with antibodies directed against glial fibrillary acidic protein (GFAP).

184 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Figure 1: Prefrontal brain slice of an 88-year-old female Alzheimer’s disease patient (case 2 in Table 1).

ROIs were defined on the FLAIR image (A) and copied onto the quantitative MRI maps: T1-map (B), fractional anisotropy-map (C); and apparent diff usion coeff icient-mapD ( ) and Chapter histochemically stained whole hemispheric sections including Bodian Silver (E) and Luxol- Fast-Blue/Cresyl-Violet (F ) stained sections aft er careful matching, using cortical anatomy as landmarks. Microscopic sections of the ROIs are illustrated in G to J. 6 The green ROIs represent white matter hyperintensities (WMH) in the periventricular area (G1 to J1). The yellow ROIs are located in WMH in the deep white matter (G2 to J2). The white ROIs are drawn in an area of normal appearing white matter (NAWM, G3 to J3). Bodian Silver stained sections (G, original magnification 200x) showed lower axonal density in WMH (G1 and G2) than in NAWM (G3); more microglial activation (H) was observed in WMH (H1 and H2) than in NAWM (H3) on HLA-DR immunohisto-chemical sections (original magnification 200x); WMH also showed more myelin loss (I1 and I2) compared to NAWM (I3) in Luxol-Fast- Blue/Cresyl-Violet stained sections (original magnification 100x); the diff erence in severity of astrogliosis (J) between WMH and NAWM (GFAP-immunostained sections, original magnification 400x), varied between patients: in this patient, the degree of astogliosis was roughly comparable between tissue types, but at a group level, astrogliosis was more severe in WMH than in NAWM.

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Endogenous peroxidase activity was blocked by incubating the sections in methanol with 0.3% H2O2 for 30 min. The sections were rinsed for 30 min with 0.01M phosphate- buffered saline (PBS; pH 7.4). For HLA-DR staining, sections were pretreated by heating in a microwave oven (750W) at 100C for 10min in a citrate buffer (0.01 M, pH 6.0), cooling down to room temperature and rinsing with PBS. Primary antibodies (HLA-DR, clone LN3: mouse antibody, dilution 1:50, gift from Dr. J.H. Hilgers, Department of Obstetrics and Gynaecology, VUmc); GFAP: rabbit polyclonal antibody, dilution 1:2000, gift from Dr Hilgers) were diluted in PBS containing 1% bovine serum albumin and were incubated overnight at 4C. Primary antibodies were omitted for negative controls. After rinsing, immunolabeling with primary antibodies was detected with the EnVision-HRP complex (DakoCytomation, Glostrup, Denmark). Sections were lightly counterstained with haematoxylin and mounted with Depex (BDH, Poole, UK). The FLAIR images, on which the ROIs were defined, were carefully matched to the corresponding QMRI maps and to the hemispheric tissue sections. The WMH and NAWM ROIs were then copied onto the corresponding areas in the QMRI maps and tissue sections by visual inspection using cortical anatomy and WM abnormalities as landmarks (Geurts et al., 2005). A representative matching of post-mortem MRI to histopathology is illustrated in Figure 1. All measurements were performed within the ROIs and were assessed blinded to clinical, pathological and MRI information. Myelin and axonal densities were quantified by assessing light transmittance of digital images of the LFB and Bodian Silver stainings

(TmLFB and TmBodian, respectively), using the software programme ImageJ version 1.37 (freely downloadable from rsb.info.nih.gov). Images were converted into 8-bit grey scale images and the mean light transmittance within the ROIs was measured (arbitrary units, ranging from 0 to 255). High values (increased light transmittance) correspond to low staining intensity. Severity of microglial activation was scored on HLA-DR stained sections using templates (Figure 2A): (0) no increase of microglia, only peri- vascular macrophages are stained; (1) mild microglial activation: slight increase of activated microglial cells; (2) moderate increase of activated microglial cells; (3) severe microglial activation with severe increase of activated microglial cells. Furthermore, the most predominant microglial phenotype was assessed: predominantly microglial cells or predominantly macrophages (Simpson et al., 2007b). Severity of astrogliosis was assessed using templates on the GFAP-immunostained sections according to the following semi-quantitative score (range 0-3, see Figure 2B): (0) normal: normal cell bodies with visible ramifications and low staining of glial processes; (1) mild reactive astrogliosis: slight enlargement of cell bodies and slightly increased staining of glial processes; (2) moderate reactive astrogliosis: significant enlargement of cell bodies, ramifications of glial processes not visible due to increased staining; (3) severe reactive astrogliosis: gemistocytic appearance of cell bodies and dense staining of glial processes.

186 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Figure 2: Templates illustrate the semi-quantitative assessment of microglial activa- tion (A) and astrogliosis (B).

A score of 0 - 3 could be given for the severity of microglial activation assessed on HLA-DR immunostained sections (A): (0) no increase of microglia; (1) mild microglial activation with a slight increase of microglial cells; (2) moderate increase of microglial cells; and (3) severe microglial activation with significant increase of microglial cells. Severity of astrogliosis (B) was assessed on GFAP-immunostained sections according to the following semi-quantitative score (range 0 - 3): (0) normal tissue with normal cell bodies and visible ramifications and low staining of glial processes; (1) mild reactive astrogliosis with slight enlargement of cell bodies and slightly increased Chapter staining of glial processes; (2) moderate reactive astrogliosis: significant enlargement of cell bodies, ramifications of glial processes are not visible due to increased staining; 6 (3) severe reactive astrogliosis with a gemistocytic appearance of cell bodies and dense staining of glial processes.

Statistical analysis Data analysis was performed by using SPSS version 12.0.1 for Windows (SPSS, Inc., Chicago, IL, USA). Patient demographics were compared between groups using Student’s t-tests and Chi-squared tests, where appropriate. Neuropathological and quantitative MRI measures were compared between patient groups (Alzheimer’s disease and non-demented controls) and tissue types (WMH and NAWM). Quantitative variables of the QMRI (T1, FA and ADC) and neuropathological (TmBodian and TmLFB)

187 Chapter 6 measures were compared using linear mixed model analyses, accounting for the nested design where several brain slices were selected from each subject and one or more ROIs were drawn on each brain slice. For these analyses, the quantitative neuropathological and MRI measures were entered as the dependent variables, while the patient groups (Alzheimer’s disease versus controls) and tissue types (WMH versus NAWM) were the independent variables, and variables for both patient and slice were entered as repeated measures. Semi-quantitative neuropathological scorings (HLA-DR scores and GFAP scores) were evaluated between groups (NAWM controls, WMH controls, NAWM Alzheimer’s disease patients, and WMH Alzheimer’s disease patients) using Kruskal-Wallis tests and post hoc Mann-Whitney U-tests. To illustrate relationships between neuropathological measures mutually and with QMRI variables, univariate associations were assessed using Pearson’s correlation coefficients. Subsequently, to assess which neuropathological measures determined the QMRI parameters in Alzheimer’s disease patients, linear mixed model analyses were used. The QMRI parameters were the dependent variables (T1, FA and ADC) and the neuropathological measures (TmBodian, TmLFB, HLA-DR score and GFAP-score) the independent variables, whereas variables for both patient and slice were entered as repeated measures. Standardized Єs are reported. First, all analyses were performed for each neuropathological measure separately, corrected for age, gender and fixation time. Subsequently, a stepwise backward model was per-formed, in which first all neuropathological measures and covariates (age, gender, fixation time) were entered, and the neuropathological measures with the highest P-value were subsequently excluded until only significant predictors remained in the model.

RESULTS

Cases Thirty-three formalin-fixed, coronal hemispheric brain slices from 11 Alzheimer’s disease patients (mean age: 83±10 years, eight females, mean Braak stage: 5, post- mortem delay (h:min): 6:09±3:05) and 15 hemispheric slices from seven controls (mean age: 78±10 years, four females, mean Braak stage: 1, post-mortem delay:<24 h) were selected. The groups were age- and gender-matched. At slice level, the fixation time for T1-relaxation time measurements and neuropathological measures were comparable [fixation time (months): 2.6±1.2 for controls versus 2.3±0.9 for Alzheimer’s disease patients]. The fixation time for DTI scanning, however, was slightly longer in brain slices of control subjects than Alzheimer’s disease patients, because some slices were rescanned due to initial imaging artifacts [fixation time (months): 4.4±0.4 for controls versus 3.1±1.1 for Alzheimer’s disease patients, P<0.001].

188 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Figure 3: Boxplots of the quantitative MRI parameters per patient group and tissue type

Boxplots of quantitative MRI measures are shown by patient group and tissue type: normal appearing white matter (NAWM) of controls, white matter hyperintensities (WMH) of controls, NAWM of Alzheimer’s disease (AD) patients and WMH of AD patients. Group characteristics of the (A) fractional anisotropy (FA), (B) apparent diffusion coefficient (ADC,Ɖ m2/s) and (C)T1- Chapter relaxation time (ms) are depicted.

Quantitative MRI 6 To differentiate tissue characteristics of WMH and NAWM between Alzheimer’s disease patients and controls, the results of the QMRI parameters are listed per patient group and tissue type in Table 2 and illustrated as boxplots in Figure 3. Linear mixed models showed main effects for patient group, as Alzheimer’s disease patients had higher T1 P( = 0.04) and lower FA values (P<0.01) than controls in both tissue groups together. In addition, there were main effects for tissue type as WMH showed higher T1 (P<0.001) and lower FA values (P<0.01) than NAWM. A significant interaction between patient group and tissue type was found for the T1-relaxation time measurements (P = 0.04), indicating that the difference in T1-relaxation time between NAWM and WMH was larger in Alzheimer’s disease patients

189 Chapter 6 than in controls. For FA, there was no interaction between patient group and tissue type. No differences between patient groups or tissue types were found for ADC.

Neuropathology Group differences of the neuropathological characteristics are given inTable 2 and are illustrated as bargraphs and boxplots in Figure 4. Linear mixed models showed main effects for tissue type, as more axonal loss (TmBodian) and myelin loss (TmLFB) were found in WMH compared to NAWM of both patient groups together.

Figure 4: Group differences of the neuropathological characteristics

Neuropathological characteristics are depicted by patient group and tissue type: normal appearing white matter (NAWM) of controls, white matter hyperintensities (WMH) of controls, NAWM of Alzheimer’s disease (AD) patients and WMH of AD patients. Axonal loss (A) and myelin loss (B), represented by light transmittance of Bodian Silver stained sections (TmBodian) and

Luxol Fast Blue-Cresyl Violet stained sections (TmLFB), are illustrated as boxplots and microglial activation (C) and astrogliosis (D) are depicted as bargraphs with error bars.

190 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Table 2: Quantitative MRI and neuropathological characteristics per patient group and tissue type

Controls AD patients P-valuea

AD WMH Inter- NAWM WMH NAWM WMH versus versus action controls NAWM term

QMRI

T1 (ms) 285±74 389±98 318±47 464±83 0,04 <0.001 0,04

FA 0.69±0.07 0.57±0.07 0.61±0.11 0.44±0.10 <0.01 <0.01 0,47 ADC 189±60 203±28 233±59 239±50 0,06 0,99 0,98 (ѥm2/s) Neuro-pathology

Tm Bodian 147±11 167±15 146±16 161±15 0,38 <0.001 0,94

TMLFB 126±16 138±15 116±15 139±20 0,84 0,01 0,07 P-value: Between groups GFAP- 1 (0-2) 2 (1-3)** 1 (0-3) 2 (0-3)*** <0.001 scoreb

HLA-DR 0 (0-2) 0 (0-1) 1 (0-3) 2 (0-3)* <0.001 scoreb,c

AD = Alzheimer’s disease; WMH = white matter hyperintensities; NAWM = normal appearing white matter; QMRI = quantitative MRI; FA = fractional anisotropy; ADC = apparent diffusion coefficient; TmBodian = light transmittance Bodian staining, inversely related to axonal Chapter density; TmLFB = light transmittance LFB staining, inversely related to myelin density; GFAP- score = severity of astrogliosis, range (0-3); HLA-DR score = severity of microglial activation, range (0-3). aValues are means (SD): differences between patient groups (AD versus controls), tissue types 6 (WMH versus NAWM) and the interaction between patient group and tissue type were tested using linear mixed models. bValues are medians (range): overall differences between groups (NAWM of controls; WMH of controls; NAWM of AD patients; WMH of AD patients) were tested using Kruskal -Wallis tests. Post hoc Mann-Whitney U-tests were used: cP<0.001 for total score AD versus controls; *P<0.05, **P<0.01 and ***P<0.001 for WMH versus NAWM within patient groups.

However, for axonal loss (TmBodian) and myelin loss (TmLFB), there was no main effect for patient group and there were no significant interaction terms. Higher values of GFAP- scores were observed in WMH compared to NAWM of both patient groups together,

191 Chapter 6 indicating more astrogliosis. No differences between Alzheimer’s disease subjects and controls and no significant interaction between patient group and tissue type exist for severity of astrogliosis. Alzheimer’s disease patients showed more severe microglial activation than controls, as represented by higher HLA-DR scores. Moreover, more microglial activation (HLA-DR scores) was observed in the WMH than in the NAWM of Alzheimer’s disease patients, whereas this effect was not found in controls. Furthermore, a higher proportion of macrophages as the most predominant HLA-DR positive cell type was found in Alzheimer’s disease subjects compared to controls [N(%): 31(43%) for Alzheimer’s disease versus 1(4%) for controls, P<0.001]. Moreover, more macrophages were present in the WMH than in the NAWM of the Alzheimer’s disease group [N(%): 24(55%) in WMH versus 7 (25%) in NAWM, P = 0.014], whereas the proportion of macrophages was comparable in both tissue types of controls.

As expected, axonal loss (TmBodian) and myelin loss (TmLFB) were partially interrelated (Pearson’s r = 0.46, P<0.001). Furthermore, severity of astrogliosis (GFAP-scores) correlated with axonal and myelin loss (Pearson’s r = 0.25, P = 0.047 for astrogliosis— myelin loss; r = 0.26, P = 0.039 for astrogliosis—axonal loss), whereas microglial activation (HLA-DR score) was not correlated with any other histopathological measure (data not shown).

QMRI-pathology correlations Relationships of QMRI parameters with neuropathological measures in Alzheimer’s disease patients are illustrated as scatterplots in Figure 5. The scatterplots illustrate that WMH and NAWM do not have separate distributions, but partly overlap, implying a gradual difference between these tissue types. To assess which neuropathological measures were the strongest determinants of the QMRI parameters in Alzheimer’s disease patients, linear mixed models were built (Table 3). When neuropathological measures were entered separately, adjusted for age, gender and fixation time, axonal loss (TmBodian), myelin loss (TmLFB) and astrogliosis (GFAP-score) were determinants of FA. In the stepwise analysis, however, the only independent determinant of FA was axonal loss (TmBodian). All neuropathological measures were associated with T1 in the first model. The stepwise model revealed that axonal loss (TmBodian), myelin loss (TmLFB) and microglial activation (HLA-DR score) were independent determinants of T1-relaxation time, whereas the astrogliosis (GFAP- score) lost statistical significance. There were no associations between any of the neuropathological measures and ADC.

192 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Table 3: Neuropathological determinants of QMRI parameters

Model 1 Model 2

T1

TMBodian 0.72*** 0.47***

TMLFB 0.55*** 0.29** GFAP-score 0.32**

HLA-DR score 0.37** 0.35*** FA

TMBodian -0.62*** -0.62***

TMLFB -0.51*** GFAP-score -0.41***

HLA-DR score -0,12 ADC

TMBodian 0,13

TMLFB 0,002 GFAP-score -0,08

HLA-DR score -0,18

Values are standardized Єs, estimated with linear mixed models. The dependent variables were T1 (T1-relaxation time), FA (fractional anisotropy) and ADC (apparent diffusion coefficient) and the neuro-pathological measures were the independent va-riables. TmBodian= light transmittance Chapter Bodian staining, inversely related to axonal density; TmLFB= light transmittance LFB staining, inversely related to myelin density. Model 1: each neuropathological measure was separately entered, adjusted for age, gender and fixation time. 6 Model 2: stepwise backward model: first the co-variates (age, gender and fixation time) were entered. Subsequently the neuropathological measures were included in a stepwise backward manner. **P≤0.01; ***P≤0.001.

DISCUSSION

We found that QMRI techniques reveal differences between WMH of Alzheimer’s disease patients and WMH of controls and reflect the severity of underlying neuropathological changes.

193 Chapter 6

405,LQFUHDVHVVSHFLÀFLW\IRUQHXURSDWKRORJLFDOFKDQJHVZLWKLQ:0+ We hypothesized that the inconsistent and weak relation-ships between the presence and severity of WMH on conventional T2-weighted MRI and clinical symptoms in both demented and non-demented elderly subjects may be explained by heterogeneity in the neuropathological substrates of WMH. Several post-mortem MRI studies have demonstrated that pathological substrates of WMH are heterogeneous. Punctate WMH corresponded to mild perivascular tissue damage around lipohyalinotic arterioles with large perivascular spaces, atrophic neuropil and slight axonal loss (Fazekas et al., 1991). Confluent areas of WMH, however, reflect severe incomplete ischemic damage with extensive axonal and myelin loss, reactive astrogliosis and lipohyalinotic arterioles, partly developing towards complete infarctions (Fazekas et al., 1993). More recently, detailed immunohistochemical characterization of WMH in a large prospective neuropathological study (MRC CFAS, 2001) has contributed substantially to our understanding of the pathology and pathogenetic mechanisms of WMH, and has yet again confirmed the heterogeneous nature of WMH histopathology. Differences in microglial responses and the expression of hypoxia-related markers suggest that in the development of a proportion of WMH a hypoxic environment is involved, whereas other WMH are related more to immune activation resulting from disintegration of the ependyma or blood-brain barrier dysfunction (Fernando et al., 2006; Simpson et al., 2007a). In this study, we focused on QMRI techniques in an attempt to increase MRI specificity for WMH. It has been reasoned, though not verified, that QMRI measurements reflect histopathological changes in aging and dementia (Fernando et al., 2004). Only one previous post-mortem DTI study in demented subjects demonstrated that DTI is feasible in the post-mortem setting and correlated with myelin loss using one conventional myelin stain in two Alzheimer’s disease patients (Englund et al., 2004). In the current study, we demonstrated that, in Alzheimer’s disease, FA reflects the extent of astrogliosis and of myelin and axonal loss, and that the strongest predictor of the FA is axonal loss, whereas the relationships with myelin loss and astrogliosis are largely determined by their correlation with axonal density. T1-relaxation times independently reflected microglial activation, axonal loss and myelin loss. The FA was therefore more specific than T1-relaxation time measurements for the severity of axonal loss, whereas T1-relaxation time reflected a larger array of pathology.

194 Beyond multiple sclerosis: neurodegenerative and vascular diseases

Figure 5: Scatterplots of relationships between quantitative MRI parameters and neu- ropathological measures in Alzheimer’s disease patients.

Chapter 6

Relationships between quantitative MRI parameters [T1-relaxation time (ms; first column), FA 2 (second column) and ADC (çm /s; third column)] and neuropathological measures [TmBodian

(higher values represent more axonal loss; first row), TmLFB (higher values represent more myelin loss; second row), GFAP-score (astrogliosis; third row) and HLA-DR score (microglial activation; fourth row)] are illustrated in scatterplots. The coloured dots correspond to white matter hyperintensies (WMH), whereas the open dots represent NAWM. Univariate associations are assessed using Pearson’s correlation coefficient. The scatterplots show that WMH and NAWM have partly overlapping distributions, implying a gradual difference between these tissue types.

195 Chapter 6

QMRI demonstrates that WMH tissue change is more severe in $O]KHLPHU·VGLVHDVH Our data confirm the in vivo literature claiming that QMRI techniques show differences between WMH and NAWM (O’Sullivan et al., 2001; Shenkin et al., 2005). We further showed that Alzheimer’s disease patients had lower FA values and longer T1-relaxation times than controls. Additionally, we have found that QMRI parameters show differences between WMH of Alzheimer’s disease patients and WMH of controls as the difference in T1-relaxation time between WMH and NAWM was larger in Alzheimer’s disease patients than in controls. The T1-relaxation time difference between patient groups may be a reflection of the difference in microglia/macrophage activation, as we also found that WMH in Alzheimer’s disease patients had more severe microglial activation than NAWM, whereas in controls no difference was found. These findings are consistent with our correlation data showing that the degree of microglial activation was an independent predictor of T1-relaxation time. It is known that microglial activation plays a crucial role in Alzheimer’s disease as microglial cells become overactivated when they are engaged in the clearance of amyloid-Є (Block et al., 2007). Alternatively, amyloid-Є may directly recruit and activate microglial cells as suggested by studies of congophilic angiopathy (Eikelenboom et al., 2008). Microglial activation may subsequently lead to neuronal loss and to damage in the microvasculature (Block et al., 2007). We postulate that the T1- relaxation time prolongation in WMH of Alzheimer’s disease may be an expression of a diffuse inflammatory reaction due to microglial activation and accompanying increase of interstitial fluid due to blood-brain barrier leakage and vascular damage (Vrenken et al., 2006a). More extensive immunohistochemical stainings for inflammatory markers and leakage of blood-brain barrier proteins such as fibrinogen or collagen should be performed to further investigate this hypothesis (Vos et al., 2005). One previous study of the MRC CFAS could not confirm the difference in microglial activation between demented and subjects when using CD68-stainings (Fernando et al., 2006). The use of different antibodies for microglial staining could explain this difference (Block et al., 2007). An alternative explanation is the difference in study design. Our case- control design may reflect two ends of a spectrum and is therefore more likely to demonstrate actual differences, whereas the MRC CFAS reflects an epidemiologically based community sample. One could argue that several other factors such as agonal state or concomitant disease could also play a role, though our brain specimens were carefully controlled for other dementias and Braak stage. Moreover, comparable results were yielded when all analyses were repeated with and without subjects with a severe grade of hypoxic neurons (three Alzheimer’s disease subjects and one control subject; data not shown), which renders it highly likely that our findings are truly disease-specific tissue changes. Other neuropathological differences in WMH

196 Beyond multiple sclerosis: neurodegenerative and vascular diseases such as extensive denudation of the ventricular ependyma, gliosis and more severe axonal loss in Alzheimer’s disease patients as compared to controls were also reported (Scheltens et al., 1995). On the other hand, a recent study using markers for hypoxic injury concluded that WMH in demented and non-demented subjects are comparable and form part of a pathological continuum common to the aged brain (Fernando et al., 2006). In our study using basic measures for neuropathology, we found differences in inflammatory responses and in QMRI parameters that reflect underlying tissue changes between Alzheimer’s disease patients and controls.

Periventricular versus deep WMH Previous literature reported that PVL and DWMH areas differ with respect to several pathological measures (Fazekas et al., 1993; Fernando et al., 2006; Simpson et al., 2007b). We have performed comparison of QMRI and neuropathological measures between DWMH and PVL in our study (data not shown), but we did not find consistent differences and therefore pooled them into ‘WMH’ for further analyses. An explanation for the fact that we did not find differences between PVL and DWMH may lie in our definition of WMH. We only included WMH when sig-nificant PVL and/or (beginning) confluent DWMH were present. Whereas rim-like PVL or smooth halos and DWMH have been found to have different pathological correlates, we are probably comparing irregular large PVL with confluent DWMH, that have been described to be pathologically comparable (Fazekas et al., 1993).

Normal appearing white matter We have predominantly focused on changes that are visible on T2-weighted MRI, but it may be possible that pathological changes in the NAWM, i.e. WM that appears Chapter normal on MRI, also contribute to the clinical correlates of WMH (Bronge et al., 2002; O’Sullivan et al., 2004). Although our study could not adequately assess pathological and QMRI characteristics of NAWM, as this would have necessitated inclusion of WM 6 from non-diseased control brains, our data suggest that a fundamental segregation between WMH and NAWM could not be made on the basis of histopathology and QMRI data. We postulate that a dichotomization of the white matter into WMH and NAWM may be arbitrary and even inappropriate, as our correlation data between QMRI and histopathological measures suggest that a continuum may exist from neuropathologically normal to abnormal white matter. These changes could therefore also partly explain the weakness of the clinico-radiological correlation. Our findings should be reproduced or confirmed in studies that also include NAWM of non-lesional brains as this tissue may be pathologically different from NAWM of lesional brains and could therefore be separate from the suggested continuum in pathological changes.

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Methodological considerations and recommendations for future study In our study, the ADC was not correlated to any of the neuropathological measures and was not able to reveal differences between patient groups or tissue types. Possibly, the use of formalin-fixed brain specimens influenced the ADC, as death and fixation changes the cellular structure by dehydration, change in temperature, failure of energy dependent ion transport, lactate acidosis and formation of cross links. It has been shown that diffusivity, which largely depends on the free mobility of water molecules, decreases after death and declines further with longer fixation times (Pfefferbaum et al., 2004; Schmierer et al., 2008). On the contrary, FA was found not to be influenced by formalin fixation (Schmierer et al., 2008). Formalin fixation also shortens relaxation times, and post-mortem MRI values are therefore not directly comparable to in vivo MRI values. However, translation to clinically relevant values could still be achieved using formalin-fixed material (Schmierer et al., 2008). As it has been described that relaxation times stabilize after 3-4 weeks of fixation (Pfefferbaum et al., 2004), each brain specimen in this study was fixed for at least 4 weeks to minimize the variability of fixation effects within the study group. Additionally, linear mixed model analyses were used to correct for any remaining effects of fixation time. Furthermore, post-mortem delay until tissue fixation could be an other factor that causes structural damage due to autolysis and influences the brain’s diffusion properties (D’Arceuil and de Crespigny, 2007). Future analyses should therefore consider sampling fresh, unfixed tissue with a short post-mortem delay when studying post-mortem MRI characteristics of ageing and dementia. In addition, as the resolution of our FA and ADC maps were lower than the FLAIR and T1, which may have rendered it more difficult to match the ROIs with the stained sections. To avoid possible misclassification, we used FLAIR images with higher resolution for the definition of the ROIs and copied the ROIs to the QMRI-maps. We further took care to have at least a rim of WMH around each WMH ROI and to draw NAWM ROIs in areas at a distance of signal hyperintensities.

Understanding the clinical impact of WMH DTI and T1-relaxation time measurements were shown to be more specific than conventional T2-weighted MRI, as these techniques revealed differences between WMH of demented and WMH of non-demented subjects, and reflected the severity of the underlying neuropathological changes. Our findings should be reproduced in the in vivo setting, in order to determine the clinical correlates of WMH changes measured with QMRI in demented and non-demented subjects (O’Sullivan et al., 2004; Shenkin et al., 2005). Hence, these studies should lead to a better understanding of the clinical impact of WMH in demented and non-demented elderly.

198 Beyond multiple sclerosis: neurodegenerative and vascular diseases

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1. BARBER R, SCHELTENS P, GHOLKAR A, 10. EIKELENBOOM P, VEERHUIS R, FAMILIAN BALLARD C, MCKEITH I, INCE P, et al. White A, HOOZEMANS JJ, VAN GOOL WA, matter lesions on magnetic resonance ROZEMULLER AJ. Neuroinflammation imaging in dementia with Lewy bodies, in plaque and vascular beta-amyloid Alzheimer’s disease, vascular dementia, disorders: clinical and therapeutic and normal aging. J Neurol Neurosurg implications. Neurodegener Dis 2008; 5: Psychiatry 1999; 67: 66-72. 190-3. 2. BASSER PJ, MATTIELLO J, LEBIHAN D. 11. ENGLUND E, SJOBECK M, BROCKSTEDT MR diffusion tensor spectroscopy and S, LATT J, LARSSON EM. Diffusion tensor imaging. Biophys J 1994; 66: 259-67. MRI post mortem demonstrated cerebral 3. BASSER PJ, PIERPAOLI C. Microstructural white matter pathology. J Neurol 2004; and physiological features of tissues 251: 350-2. elucidated by quantitative-diffusion- 12. FAZEKAS F, CHAWLUK JB, ALAVI A, tensor MRI. J Magn Reson B 1996; 111: HURTIG HI, ZIMMERMAN RA. MR signal 209-19. abnormalities at 1.5T in Alzheimer’s 4. BLOCK ML, ZECCA L, HONG JS. Microglia- dementia and normal aging. AJR Am J mediated neurotoxicity: uncover-ing the Roentgenol 1987; 149: 351-6. molecular mechanisms. Nat Rev Neurosci 13. FAZEKAS F, KLEINERT R, OFFENBACHER 2007; 8: 57-69. H, PAYER F, SCHMIDT R, KLEINERT G, et al. 5. BRAAK H, BRAAK E. Neuropathological The morphologic correlate of incidental stageing of Alzheimer-related changes. punctate white matter hyperintensities Acta Neuropathol 1991; 82: 239-59. on MR images. AJNR Am J Neuroradiol 6. BRONGE L, BOGDANOVIC N, WAHLUND LO. 1991; 12: 915-21. Postmortem MRI and histo-pathology of 14. FAZEKAS F, KLEINERT R, OFFENBACHER white matter changes in Alzheimer brains. H, SCHMIDT R, KLEINERT G, PAYER F, et al. A quantitative, comparative study. Dement Pathologic correlates of incidental MRI Geriatr Cogn Disord 2002; 13: 205-12. white matter signal hyperintensities. 7. D’ARCEUIL H, DE CRESPIGNY A. The effects Neurology 1993; 43: 1683-9. of brain tissue decomposition on diffusion 15. FERNANDO MS, O’BRIEN JT, PERRY RH, Chapter tensor imaging and tractography. ENGLISH P, FORSTER G, MCMEEKIN W, et al. Neuroimage 2007; 36: 64-8. Comparison of the pathology of cerebral 8. DE GROOT JC, DE LEEUW FE, OUDKERK white matter with post-mortem magnetic 6 M, VAN GIJN J, HOFMAN A, JOLLES J, et resonance imaging (MRI) in the elderly al. Cerebral white matter lesions and brain. Neuropathol Appl Neurobiol 2004; cognitive function: the Rotterdam Scan 30: 385-95. Study. Ann Neurol 2000; 47: 145-51. 16. FERNANDO MS, SIMPSON JE, MATTHEWS F, 9. DE LEEUW FE, DE GROOT JC, ACHTEN E, BRAYNE C, LEWIS CE, BARBER R, et al. White OUDKERK M, RAMOS LM, HEIJBOER R, et matter lesions in an unselected cohort of al. Prevalence of cerebral white matter the elderly: molecular pathology suggests lesions in elderly people: a population origin from chronic hypoperfusion injury. based magnetic resonance imaging study. Stroke 2006; 37: 1391-8. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001; 70: 9-14.

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17. GEURTS JJ, BO L, POUWELS PJ, 25. O’SULLIVAN M, SUMMERS PE, JONES CASTELIJNS JA, POLMAN CH, BARKHOF DK, JAROSZ JM, WILLIAMS SC, MARKUS F. Cortical lesions in multiple sclerosis: HS. Normal-appearing white matter in combined postmortem MR imaging and ischemic leukoaraiosis: a diffusion tensor histopathology. AJNR - Am J Neuroradiol MRI study. Neurology 2001; 57: 2307-10. 2005; 26: 572-7. 26. PFEFFERBAUM A, SULLIVAN EV, 18. HIRONO N, KITAGAKI H, KAZUI H, ADALSTEINSSON E, GARRICK T, HARPER HASHIMOTO M, MORI E. Impact of white C. Postmortem MR imaging of formalin- matter changes on clinical manifestation fixed human brain. Neuroimage 2004; 21: of Alzheimer’s disease: a quantitative 1585-95. study. Stroke 2000; 31: 2182-8. 27. PIERPAOLI C, BARNETT A, PAJEVIC S, CHEN 19. JONES DK, LYTHGOE D, HORSFIELD MA, R, PENIX LR, VIRTA A, et al. Water diffusion SIMMONS A, WILLIAMS SC, MARKUS HS. changes in Wallerian degeneration Characterization of white matter damage and their dependence on white matter in ischemic leukoaraiosis with diffusion architecture. Neuroimage 2001; 13: 1174-85. tensor MRI. Stroke 1999; 30: 393-7. 28. SCHELTENS P, BARKHOF F, LEYS D, 20. MIRRA SS, HEYMAN A, MCKEEL D, SUMI WOLTERS EC, RAVID R, KAMPHORST W. SM, CRAIN BJ, BROWNLEE LM, et al. The Histopathologic correlates of white matter Consortium to Establish a Registry for changes on MRI in Alzheimer’s disease and Alzheimer’s Disease (CERAD). Part II. normal aging. Neurology 1995; 45: 883-8. Standardization of the neuropathologic 29. SCHELTENS P, BARKHOF F, VALK J, assessment of Alzheimer’s disease. ALGRA PR, VAN DER HOOP RG, NAUTA J, Neurology 1991; 41: 479-86. et al. White matter lesions on magnetic 21. MUNGAS D, JAGUST WJ, REED BR, KRAMER resonance imaging in clinically diagnosed JH, WEINER MW, SCHUFF N, et al. MRI Alzheimer’s disease. Evidence for predictors of cognition in subcortical heterogeneity. Brain 1992; 115 (Pt 3): ischemic vascular disease and Alzheimer’s 735-48. disease. Neurology 2001; 57: 2229-35. 30. SCHMIDT R, ROPELE S, ENZINGER C, 22. NEUROPATHOLOGY GROUP of the Medical PETROVIC K, SMITH S, SCHMIDT H, et al. Research Council Cognitive Function and White matter lesion progression, brain Ageing Study (MRC CFAS). Pathological atrophy, and cognitive decline: the correlates of late-onset dementia Austrian stroke prevention study. Ann in a multicentre, community-based Neurol 2005; 58: 610-6. population in England and Wales. Lancet 31. SCHMIERER K, WHEELER-KINGSHOTT 2001; 357: 169-75. CA, TOZER DJ, BOULBY PA, PARKES HG, 23. O’BRIEN JT, WISEMAN R, BURTON EJ, YOUSRY TA, et al. Quantitative magnetic BARBER B, WESNES K, SAXBY B, et al. resonance of postmortem multiple Cognitive associations of subcortical sclerosis brain before and after fixation. white matter lesions in older people. Ann Magn Reson Med 2008; 59: 268-77. N Y Acad Sci 2002; 977: 436-44. 32. SHENKIN SD, BASTIN ME, MACGILLIVRAY 24. O’SULLIVAN M, MORRIS RG, HUCKSTEP TJ, DEARY IJ, STARR JM, RIVERS CS, et B, JONES DK, WILLIAMS SC, MARKUS al. Cognitive correlates of cerebral white HS. Diffusion tensor MRI correlates matter lesions and water diffusion tensor with executive dysfunction in patients parameters in community-dwelling older with ischaemic leukoaraiosis. J Neurol people. Cerebrovasc Dis 2005; 20: 310-8. Neurosurg Psychiatry 2004; 75: 441-7.

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33. SIMPSON JE, FERNANDO MS, CLARK L, 41. VRENKEN H, POUWELS PJ, GEURTS JJ, INCE PG, MATTHEWS F, FORSTER G, et al. KNOL DL, POLMAN CH, BARKHOF F, et White matter lesions in an unselected al. Altered diffusion tensor in multiple cohort of the elderly: astrocytic, microglial sclerosis normal-appearing brain tissue: and oligodendrocyte precursor cell cortical diffusion changes seem related responses. Neuropathol Appl Neurobiol to clinical deterioration. J Magn Reson 2007a; 33: 410-9. Imaging 2006c; 23: 628-36. 34. SIMPSON JE, INCE PG, HIGHAM CE, GELSTHORPE CH, FERNANDO MS, MATTHEWS F, et al. Microglial activation in white matter lesions and nonlesional white matter of ageing brains. Neuropathol Appl Neurobiol 2007b; 33: 670-83. 35. SMITH CD, SNOWDON DA, WANG H, MARKESBERY WR. White matter volumes and periventricular white matter hyperintensities in aging and dementia. Neurology 2000; 54: 838-42. 36. STOUT JC, JERNIGAN TL, ARCHIBALD SL, SALMON DP. Association of dementia severity with cortical gray matter and abnormal white matter volumes in dementia of the Alzheimer type. Arch Neurol 1996; 53: 742-9. 37. VAN DER FLIER WM, VAN STRAATEN EC, BARKHOF F, VERDELHO A, MADUREIRA S, PANTONI L, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005; 36: 2116-20. 38. VOS CM, GEURTS JJ, MONTAGNE L, VAN Chapter HAASTERT ES, BO L, VAN DER VALK P, et al. Blood-brain barrier alterations in both focal and diffuse abnormalities on 6 postmortem MRI in multiple sclerosis. Neurobiol Dis 2005; 20: 953-60. 39. VRENKEN H, GEURTS JJ, KNOL DL, POLMAN CH, CASTELIJNS JA, POUWELS PJ, et al. Normal-appearing white matter changes vary with distance to lesions in multiple sclerosis. AJNR - Am J Neuroradiol 2006a; 27: 2005-11. 40. VRENKEN H, GEURTS JJ, KNOL DL, VAN DIJK LN, DATTOLA V, JASPERSE B, et al. Whole- brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology 2006b; 240: 811-20.

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7

SUMMARIZING DISCUSSION Chapter 7

In this thesis, histopathological, clinical and MRI findings were combined with the focus on grey matter damage, diffusely abnormal white matter and atypical white matter lesions. In addition, the same methods were applied to a different field, where differences between WM hyperintensities in Alzheimer and non-Alzheimer patients were studied. The method of combining histopathology and MRI as applied in this thesis was summarized in Chapter 2. This review focusses on the advances of knowledge that were gained with this method, as well as the challenges of this approach and gives recommendations for future research.

CHAPTER 3: THE GREY MATTER IN MULTIPLE SCLEROSIS

Grey matter (GM) lesions are one example of a research trend, which dramatically changed our understanding of MS within only a couple of years. While in the past, MS was seen as a demyelinating disease of the white matter, it is presently firmly acknowledged that GM damage is as least as abundant and important as WM damage. Histopathological studies have shown that grey matter demyelination can occupy up to 30% of the grey matter (1) which is twice as much as the demyelinated area in the white matter. Demyelination is not restricted to neocortical areas, paleo- and archicortical structures such as the insula and hippocampus, the basal ganglia, hypothalamus, the cerebellar cortex and spinal cord grey matter can also be affected (2, 3). However, the plethora of cortical lesions seen in pathology is in sharp contrast to the numbers detected in vivo by conventional MRI, where up to 95% of intracortical lesions are missed (4). Cortical GM lesion detection is hindered by the characteristics of the lesions itself -i.e. their small size, the sparse inflammatory cell infiltration and blood-brain barrier damage, and the low myelin density in the upper cortical layers - as well as several technical factors, including limited image resolution, and lower contrast between small cortical lesions and surrounding normal cortical GM due to intrinsically longer GM relaxation times (5). With the introduction of a new MRI sequence, double inversion recovery (DIR), intracortical lesion detection could be dramatically improved (6-8), and subsequently, this technique was applied in numerous (imaging) studies. These studies provide important information about the accumulation of cortical lesions, and the clinical consequences thereof. However, DIR applications have been criticized because this sequence is prone to image artefacts, which makes DIR- detected hyperintensities difficult to interpret. In

204 Summarizing discussion addition, DIR has regional variations in GM signal intensity (9) and an intrinsically low signal to noise ratio, which gives DIR images a “grainy” appearance (6, 8, 10). These properties might obscure the detection of cortical lesions on the one hand, but also lead to false positive ratings on the other hand (11). This, together with the fact that the number and volume of cortical lesions detected with DIR is much lower than those reported in histopathology studies, have fueled discussions about the validity of this technique. However, for an accurate correlation with clinical deficits, and inclusion of GM lesion burden in treatment studies, a reliable quantification of grey matter lesions is essential. Therefore, the post-mortem study described in Chapter 3.1 was performed to assess the sensitivity (percentage of lesions detected on MRI as compared to histopathology), and specificity (percentage of false positive lesions, which are seen on MRI, but lack histopathological correlation) of 3D-DIR for cortical lesions (CL). In this study, brain samples from patients with chronic MS were scanned with both 3D-DIR and 3D-FLAIR sequences at 1.5T. Cortical lesions were scored on both sequences twice, once blinded (prospective scoring), and once unblinded (retrospective scoring) to histopathology. This study could confirm the in vivo observations of the higher CL detection rate with 3D-DIR as compared to 3D-FLAIR. In addition, 3D-DIR showed a high pathologic specificity (90%), and therewith a low number of false positive scorings. Although the sensitivity of 3D-DIR for all cortical lesions was 1.5 -2.0-fold higher as when scored on 3D-FLAIR alone, the detection rate for purely intracortical lesions was still disappointingly low. In the best case, and with awareness of the histopathology (retrospective scoring), 29% of all intracortical lesions could be picked up, however, this number dropped to disillusioning 9% when the prospectively scored lesions only were considered. Especially, the detection of subpial type III lesions, which are the most common lesion type, was very low (7% detected in the prospective scoring). How should in vivo DIR studies be interpreted with these results in mind? Obviously, DIR at 1.5 Tesla detects only a small percentage of all cortical lesions. However, if lesions are detected, it can be confidently assumed that these are indeed true lesions rather than artefacts, as nearly all hyper-intensities seen on MRI Chapter corresponded to histopathologically proven lesions. It can be assumed that in vivo DIR studies have a similar sensitivity and specificity for cortical lesions, as comparable image contrasts were used in the post-mortem 7 3D-DIR and 3D-FLAIR sequences. However, DIR acquisition protocols and field strengths vary considerably between centers, which makes interpretation of DIR images difficult, and hampers comparison of(multicenter) in vivo data on cortical lesions. Geurts et al could show that even when standardized scoring criteria are used for cortical lesions, an international panel of experts agrees completely only on 19%

205 Chapter 7 of all scored cortical lesions (12). This underlines the necessity of a standard protocol for DIR scanning besides standardized scoring criteria. Especially “old” (multi- slab) 2D DIR sequences suffer from artefacts (incidental magnetic transfer effects and flow artefacts), whereas single-slab, 3D images are not affected by this phenomenon (13). Several studies have used DIR in vivo, so were cortical lesions described in up to 36% in CIS and in 64 to 81% of definite MS patients (14-17). If we interpret these studies with the numbers given in Chapter 3.1, it seems likely that these percentages are considerably higher in reality, but on the other hand it can be safely assumed that the described hyper-intensities truly correspond to lesions. Therefore, cortical lesions as imaged with DIR can be utilized for clinical correlations, but also to facilitate the diagnosis of MS. Filippi et al included cortical lesions as detected with DIR in the diagnostic criteria, which led to a significant improvement in the diagnostic accuracy (81% accuracy instead of 75 to 78%). Furthermore, the presence of cortical lesions are independent predictors of conversion to MS within 4 years after clinical onset (18). Most recently, CLs were incorporated in the dissemination in space criteria for MS, as an extension to the current inclusion of juxtacortical lesions (19). In addition, consideration of CLs could be helpful in the management of RIS, where cortical lesions were described in 40% (20). There, they could possibly identify patients at high risk for development of MS and could allow targeted follow up or even treatment. Furthermore, subpial cortical lesions represent a diagnostic hallmark of MS, as they are not seen in other chronic inflammatory or degenerative brain disease (21). In summary, consideration of cortical lesions improves the accuracy of diagnosis and prognosis in MS (22), but it has of yet to be established whether DIR is the MRI technique most suited to do so. Besides the described suboptimal sensitivity, the coefficient of variation between DIR detected grey matter lesion counts on a patient level is as large as 42% (12). Apart from DIR, several other sequences have been applied with the goal to increase the visibility of cortical lesions. These included the application of MRI techniques such as 3D-T1-based techniques (23), T2* weighted imaging (24) and phase-sensitive inversion recovery (PSIR) (25) either alone or in combination, and at various field strengths (results of selected studies are presented in Table 1). Higher field strengths, which have higher signal to noise ratio, can provide higher spatial resolution and therefore higher sensitivity for cortical lesions as compared to standard field strengths (26-29). So could for example, cortical lesion detection with DIR be improved when moving from 1.5 to 3T (30). Interestingly, DIR loses its benefits at 7T, and sequences that were suboptimal at 1.5 and 3T (such as FLAIR and T2) seem to perform better at ultra- high field strengths (Table 1).

206 Summarizing discussion

Table 1: Simplified comparison of MR sensitivities for overall cortical lesion detection (selected studies)

Author/year Sequence (Field strength) Histopathology

Geurts et al, 2005 (7) T2 (1.5T) < FLAIR (1.5T) < DIR (1.5T) no DIR (1.5T) < DIR (3T) T2 (1.5T) ≥ T2 (3T) Simon et al, 2010 (29) FLAIR (1.5T) ≤ FLAIR (3T) no T2 (1.5T) < FLAIR (1.5T) < DIR (1.5T) T2 (3T) < FLAIR (3T) < DIR (3T)

Nelson et al, 2007 (11) FLAIR (3T) < PSIR (3T) + DIR (3T) no

Wattjes et al, 2007 (31) aT2 (3T) < FLAIR (3T) < DIR (3T) no

Sethi et al, 2012 (25) DIR (3T) < PSIR (3T) no

Tallantyre et al, 2010 (32) FLAIR (3T) < MPRAGE (7T) < DIR (3T) no

T1 (3T) < T1 (7T) T2 (3T) < T2 (7T) De Graaf et al, 2012 (26) no FLAIR (3T) < FLAIR (7T) T1 (3&7T) < T2 (3&7T) < FLAIR (3&7T)

Nielsen et al, 2012 (24) DIR (3T) < T2* (7T) no

Pitt et al, 2010 (33) WHAT-TFE (7T) < T2* (7T) yes

Kilsdonk et al, 2013 (34) T1 (7T) < DIR (7T) < T2 (7T) < FLAIR (7T) no

bDIR (3T) < DIR (7T) FLAIR (3T) < FLAIR (7T) T2* (3T) < T2* (7T) Kilsdonk et al, 2016 (28) yes T1 (3T) < T1 (7T) T2 (3T) < T2 (7T) T1 = T2 = T2* = FLAIR = DIR (all 7T)

Jonkman et al, 2015 (27) T2* (7T) ≤ T2 (7T) yes

Beck et al, 2018 (35) T2* (7T) < MP2RAGE (7T) cyes Chapter Displayed results include intra-cortical lesions and mixed grey-white matter lesions (type I-IV) except stated otherwise. a: mixed grey-white matter lesions only 7 b: prospective scoring c: sensitivity data from in-vivo analysis, one post-mortem brain separately analysed for specificity. bold font: statistically significant differences

207 Chapter 7

So far, it is unknown which sequence or combination of sequences works best for cortical lesion detection (and various types of cortical lesions), and a comprehensive post mortem study comparing the various techniques is lacking at present. It seems that this question is of particular importance at lower field strengths, where the sensitivity of various sequences can vary significantly. At ultra high field on the other hand, differences between sequences seem to be smaller and non-significant with a smaller absolute difference. At 7T however, a combination of various techniques might help to characterize the pathologic features of MS lesions (36, 37). For a better interpretation of studies investigating CLs it would be helpful to know why some CLs are visible on MRI and others are not. In theory, various factors could influence the visibility of CLs, including size, inflammation or destructive properties. To assess the selective visibility of CLs, the comparative post-mortem study described in Chapter 3.2 was performed. In this study, MRI visible and invisible lesions (as determined by T2SE and FLAIR images on 1.5 Tesla), and non-lesional grey matter were compared by histopathology. In addition, cortical tissue was characterized by quantitative MRI measurements. This study demonstrates, that visible CLs are significantly larger than their invisible counterparts. Therefore, visibility of CLs is exclusively determined by size, and neither quantitative MRI measurements, nor histopathological parameters (such as inflammation, or neuroaxonal loss) could discriminate between visible and invisible lesions. Because our histopathological assessment was not exhaustive, it is possible that other factors than those measured might have contributed to the visibility of CLs. It can however be assumed that these factors were not significant contributors to visibility as the histopathological findings were confirmed by quantitative MRI measurements which also showed no difference between visible and invisible lesions. For the same reason, it can be argued that results determining visibility would have been comparable if assessments were performed with DIR instead of T2 weighted sequences. Unfortunately, no postmortem DIR protocol existed at the time this study was performed. None of our lesions showed macrophage or lymphocytic infiltration and it was therefore not possible to assess whether inflammation would lead to better visibility of even smaller lesions. The absence of acute inflammation in the cortex seems to be typical for autopsy studies (38, 39), as opposed to biopsies from patients with early MS (40, 41). While this has fueled the discussion whether CLs are neurodegenerative vs inflammatory, the findings from biopsy and autopsy may merely present the outer ends of a spectrum, starting with inflammation (as seen in biopsies), and ending with an “inactive” CL (42). Although CLs triple between early MS and SPMS (43), their inflammatory activity is rarely picked up by gadolinium enhanced scans (44). A milder disruption of the

208 Summarizing discussion

BBB and a shorter inflammatory phase than in WM lesions might be possible reasons for this phenomenon and an experimental autoimmune encephalomyelitis (EAE) rat model affirms that cortical inflammation resolves relatively rapidly (45). The second important finding of this study was that visible, and therefore larger lesions were associated with a higher cortical lesion number and higher percentage of demyelination. This indicates, that if cortical lesions are visible on MRI, they only represent the “tip of the iceberg”, as many more invisible lesions may be present. With this in mind, the presence of cortical lesions on DIR images could give a sufficient idea of the extent of GM damage in clinical routine. On the other hand, studies addressing the “normal appearing grey matter” have to be interpreted with the knowledge that most of the cortical lesions will fall within MRI defined extra-lesional GM and both lesions and extra-lesional GM may contribute to the changes measured (46). This could be an explanation for the only mild to moderate correlation of CLs with disability (15, 47). In addition, disability correlations of the current DIR studies are heavily distorted towards the most readily visible type I lesions and under-represent Type II and III lesions or -in the worst case - do not represent them at all. Even at 7T, approximately 70% of all cortical lesions and approximately 80% of type III lesions are missed in prospective scoring, whereas up to 100% of type I lesions could be detected (28). Cortical pathology extends well beyond areas of focal demyelination. Histopathological studies have demonstrated various heterogeneous processes including neuro-axonal loss, gliosis, loss of dendritic spines and various degrees of remyelination (48-51). For this reason, quantitative MRI measurements might be better suited for the overall assessment of damage in the GM. Our studies show, that MTR, and to a lesser extent T2 measurements reflect demyelination in the cortex. MTR did not differ between non-lesional GM and GM lesions, whereas T1 and T2 relaxation times showed a significant difference. Other studies at 3, 7 and 9.4T respectively, indicate that higher field strengths with consecutive better contrast and resolution are required to detect subtle differences within the cortical grey matter (52-54). Jonkman et al looked at the capacity to distinguish normal appearing GM from (subpial) lesions with quantitative Chapter MR imaging techniques at 7T and found lower mean MTR in subpial lesions compared with myelin-density matched normal appearing grey matter (55). This observation supports the notion that lower MTR in the outer cortex as measured in vivo is at 7 least partly related to demyelination and could reflect type III lesions which are underrepresented on conventional MRI (56). As far as our associations between T1- and T2 relaxation times and histopathology are concerned, our findings are similar to the results by Schmierer et al, who found that T2 is a predictor of demyelination and T1 is a predictor of neuronal density (53). Opposed to these findings, in a postmortem

209 Chapter 7 study by Tardif et al, T1 was strongly correlated to myelin content (54). This illustrates that it can be challenging to pin (changes of) quantitative MR measurements to a single underlying pathology. To give another example, the increased fractional anisotropy (FA) of demyelinated grey matter lesions has been attributed to either local activation of microglia or neuronal, dendritic or synaptic loss (57-59). Recently, Jonkmann et al have proposed an increased cellular density due to tissue compaction as possible alternative explanation (52). This shows that advanced MRI methods are sensitive to microstructural changes, as they reflect alterations in the physical characteristics of brain tissues, but lack specificity for pathological processes (60). This problem can be partly overcome by another technique, positron emission tomography (PET), which measures the distribution of specific ligands, therewith allowing for the highest possible specificity at cellular and tissue level (61). Only a couple of studies have explored the cortical grey matter with PET (for a comprehensive overview see Table 2A in addendum, a summary of findings for the cortical grey matter is presented in Table 2) and most studies used PET to visualize activated microglia. Activated microglia form a central aspect of neuroinflammation and are a key element of neurodegeneration, but cannot be visualized with conventional MRI (84- 86). Histopathological studies focusing on cortical GM show an increased number of activated microglia in patients with subpial lesions in early disease stages (87), and presence of rims of activated microglia is associated with younger time at death and a shorter disease course, however this is not uniformly observed (50). PET studies on activated microglia with results for cortical grey matter show a similar heterogeneous picture, but technical aspects, small sample sizes and genetic polymorphisms influencing the binding affinity of radioligands have to be taken into account when interpreting these results. Overall, it seems, that at least in a subset of patients, more cortical microglia activation is present as compared to controls (66, 69, 73). Within MS patients, microglia activation in the cortex is more prominent in progressive MS and correlates with physical and cognitive disability (66, 69). So far, only four longitudinal treatment studies with observation times between 6 months and 1 year and small participant numbers have been performed.

210 Summarizing discussion

Table 2: Cellular processes measured in the MS cortex with PET (human in-vivo studies only)

Cortical GM Cortical GM Pathophysiologic Clinical Radioligand findings in findings within Mechanism correlations MS vs HC MS group

Inflammation [11C]PK11195 MS > HC MS BL > MS FU EDSS (66) (66,63*, 68*) (+T) (67) MS = HC (62, MS BL = MS FU 64, 65) (+T) (62,63)

[11C]PBR28 MS > HC (69) RR < SP (69) EDSS, cognition MS = HC (69) (70,71)

[18F]FEDAA1106 MS = HC (72)

[11C]DPA713 MS > HC (73) MS BL < MS FU (+T) (73)

[11C]TMSX MS = HC (74)

Demyelination [11C]PiB MS = HC (75)

Astrocyte activation [11C]acetate MS > HC (76)

Neuronal Damage [11C]FMZ MS < HC (77) cognitive performance (77)

Neuronal Integrity/ [18F]FDG MS < HC (79, MS BL < MS FU fatigue, Inflammation 82) (+T) (78) depression (78, MS = HC (78) MS (+C) < MS (-C) 80) walking speed (82) (79)

Cholinergic [11C]MP4A MS = HC (83) cognitive metabolic profile performance (83)

MS: Multiple Sclerosis, HC: Healthy control, BL: Baseline, FU: Follow-Up, (+T): with treatment, RR: relapsing remitting MS, SP: secondary progressive MS, (+C): with cognitive impairment, (-C): without cognitive impairment, *: data not significant

Chapter

Two studies exploring the effects of natalizumab (62) and fingolimod (63) showed no difference in microglial activation between baseline and follow up, while treatment 7 with glatiramer acetate led to a decrease (67) and a group treated with IFN or Fingolimod showed an increase (73) in cortical microglia activation. Interpretation of these results is challenging, as at the moment it is not possible to distinguish between functional states of microglial cells (pro-inflammatory vs protective) with TSPO PET

211 Chapter 7

(60), but might also indicate that some medications might be more suited to influence microglial activation than others. Besides inflammation, remyelination is an important process in the grey matter, which is even more extensive in cortical- than in white matter lesions, but is heterogeneous between patients (51, 88). MRI techniques are sensitive to alterations in myelin density, but cannot distinguish between “good” (i.e. remyelination) and “bad” (i.e. demyelination). Therefore, an imaging marker for remyelination would be of interest for measuring treatment effects. 11[ C]PIB is a radioligand which allows observations of positive and negative changes over a follow- up period in white matter lesions, corresponding to dynamic re- and demyelination (89). So far, only one cross- sectional study has compared cortical [11C]PIB uptake between MS and controls, showing no difference between the groups (75). Similarly, no difference was found when the whole grey matter (deep grey matter and cortex) was compared between MS and controls (89). The low spatial resolution of PET systems (above 4 mm for most), together with the low thickness of the cortical ribbon and the low myelin density of the cortex may explain this result (60). PET could also contribute to our understanding of neurodegeneration by selectively targeting neuronal damage. Neurodegenerative changes within the GM range from axonal transection to synapto-dendritic pathology and neuronal loss, occur independent from GM demyelination (48, 49) and are thought to be a major cause of neurological decline. Neuronal damage can be indirectly measured by glucose metabolism with [18F]FDG PET, and reduced uptake in the cortex has been correlated to cognitive disability, fatigue and depression (78, 80, 82). While [18F]FDG is not a pure neuronal marker (as glucose is metabolised by other than neuronal cells as well) other radioligands which are specific for neurons have been developed. For example, [11C] FMZ reflects axo-somatic and axo-dendritic synapses which have been shown to be reduced in the MS cortex and reduced uptake of this radioligand correlates with cognitive performance (77). This mirrors a histopathological study by Jürgens et al, showing pronounced loss of dendritic spines in the MS cortex independent of cortical demyelination and axon loss (48). In summary, visualization of “hidden pathology” such as microglial activation, neurodegeneration and remyelination contributes to our understanding of the pathogenesis of cortical pathology. Further efforts should be made to validate the above mentioned techniques so they can find application in treatment studies. In addition, future attempts should be made to improve visualization of cortical lesions, especially type III lesions.

212 Summarizing discussion

Unsolved questions and suggestions for further research: A focus on the visualization of hidden inflammation (microglia) and remyelination and repair will help to explain pathological processes leading to cortical lesion formation and could serve as therapeutic targets. Further efforts to increase the visibility of cortical lesions will allow for more accurate correlations with MRI and clinical measures. • What is the independent contribution of cortical lesions to cortical atrophy? • Do all subpial lesions arise from leptomeningeal inflammation? Which role does microglia play in cortical lesion development and remyelination? • Which (combination of) MRI sequences are best suited to image and characterize cortical lesions? • Are diffuse changes in the NAGM predictors of cortical lesion development?

CHAPTER 4: THE WHITE MATTER IN MULTIPLE SCLEROSIS

The white matter harbors not only the most prominent feature of MS- the white matter lesion- but also hidden, diffuse damage, outside lesions. Damage in this so- called NAWM has been extensively studied with quantitative MRI measurements, and has been shown to be a significant contributor to disability (90-92). Invisible on conventional MR images, this damage defies the eye of the assessing clinician and contributes to the so called clinico- radiological paradox (93). However, one aspect of diffuse damage is readily visible on conventional MRI: “Diffusely abnormal white matter”, or DAWM, which presents as a subtle signal hyper- intensity with dispersed borders on T2-weighted images (94). Although extensive at times, these changes are disregarded in conventional measures of the lesion burden, because their underlying histopathology and clinical impact is unclear. Several processes have been claimed to contribute to DAWM, including inflammation, newly Chapter forming lesions, blood brain barrier disruption, demyelination and axonal loss. A clear definition of DAWM could lead to its inclusion in routine assessments and bring about a better picture of disease burden. 7 We therefore aimed to define DAWM in terms of histopathology and quantitative MRI measurements. In the post-mortem study presented in Chapter 4.1, DAWM was compared to NAWM and focal WM lesions in a region- of- interest approach. DAWM differed significantly between NAWM and WM lesions, and formed an intermediate between NAWM and lesions in the histopathological and quantitative

213 Chapter 7

MRI measurements. DAWM was found to consist of extensive axonal loss, decreased myelin density, and chronic fibrillary gliosis. Acute axonal damage, acute inflammation or blood brain barrier disruption was absent in DAWM. As such, DAWM is likely to reflect chronic, axonal loss and might therefore serve as imaging marker for disease progression. Therefore, monitoring DAWM in vivo might give important information if one wants to focus on the neurodegenerative aspect of the disease. To determine whether the findings of Chapter 4.1 can be reproduced and reliably measured in vivo, the study presented in Chapter 4.2 was performed. In this study, DAWM, NAWM and lesions were characterized by four quantitative MRI measurements, and compared between PPMS and SPMS patients. DAWM differed significantly between lesions and NAWM, and formed an intermediate between these two types of pathology. This shows that the results from Chapter 4.1 can be reproduced in vivo. In addition, DAWM was found to vary in severity between SPMS and PPMS patients, suggesting a more severe tissue damage in patients with SPMS. These findings pose two essential questions. First, is DAWM related to disability and if so, can DAWM be measured with a more practical method, not requiring sophisticated MRI techniques? And second, how does DAWM relate to white and grey matter lesions and atrophy? If DAWM truly represents neurodegeneration, one would expect DAWM to correlate with disease severity. Therewith, DAWM could serve as an important marker for disease status and progression in addition to WM lesions, especially in patients which are defined to have “not- active” disease (95). So far, the clinical consequences of DAWM have only been addressed in two studies, and surprisingly, both fail to show a correlation between DAWM and disability (96, 97). Neither the presence or absence of DAWM (96), nor the severity of DAWM as measured in ROI’s by quantitative MRI (97) seem to have an influence on the EDSS or the Multiple Sclerosis Severity Score (MSSS). One explanation for these findings could be the method of assessment of DAWM: The volume and extent of DAWM might be more predictive of disability than quantitative MRI measures of single ROIs, which are placed in allegedly minuscule areas of DAWM. DAWM can be extensive and extend throughout the white matter; we and others (94, 97, 98) have seen DAWM as well in periventricular as in deep white matter regions of the occipital, parietal, temporal and frontal lobes. Furthermore, the anatomical location of DAWM may be an important contributor to disability. Especially in the corticospinal tract, in which axonal loss is known to underpin the spastic paraparesis that typifies progressive MS, DAWM could be an important marker for disability. Based on the above, the inclusion of both volume and location in the assessment of DAWM might lead to a more accurate correlation with clinical parameters. We

214 Summarizing discussion have therefore proposed a semi-quantitative scale to quantify the extent of DAWM, in similarity to the scales used to quantify white matter hyper-intensities in vascular disease (99). This rating scale provides three sum scores per hemisphere in a semi- quantitative way (see figure 1 - 3 and table 1) and is rated on axial PD and T2- weighted images. To quantify the inter-observer agreement between four raters, intra-class correlation coefficients (ICCs) were calculated. ICCs were 0.57 between all four raters, varying between pairs from 0.43 to 0.92. Consecutively, the DAWM rating scale was applied in a pilot study, which aimed to explore DAWM in RR and SP patients and its relation to T2 lesion load and clinical parameters. For this retrospective study, the MRI scans of 20 secondary progressive MS patients before and after their conversion were compared to those of relapsing remitting MS patients and healthy controls. Lesion counts, lesion volumes and DAWM was assessed by two raters blinded to clinical information. DAWM scores of SP- MS patients were significantly higher after their conversion, and also significantly higher than those of RR- MS patients with a comparable disease duration. Significant correlations were found between DAWM scores and T2- and black hole counts- and loads. Expanded disability status scale (EDSS) scores positively correlated with frontal DAWM scores, but no other correlations between DAWM scores and clinical measures (including results of the multiple sclerosis functional composite and Guy’s Neurological disability Scale) were found (data not shown). However, these data have to be interpreted with care, as the effects of disease duration and age were only partly accounted for in the matched groups and selection bias might have occurred. We therefore aimed to verify these data in a bigger study of 51 MS patients who converted from RR- to SPMS. Furthermore, the development of DAWM over time and its relation with disease progression was addressed. Preliminary results show that DAWM is extremely variable between patients and that a certain score is not tied to the relapsing or remitting phase of the disease. DAWM does increase over time in the majority of patients, but unexpectedly, also the opposite was observed in a subset of patients. This phenomenon could be a result of the increasing difficulty to detect DAWM at later stages of the disease, due to the accumulation of lesions and signal related Chapter changes, as well as brain atrophy. In other words, DAWM might well be present in those progressive patients, but may be hidden by the higher lesion load and the reduced brain volume, which would translate in lower DAWM scores. Also, the differentiation between 7 peritrigonal zones and DAWM can be challenging (100). Unfortunately, this finding defers DAWM as a tool to distinguish between progressing and relapsing patients. Most recently, Vertisky et al published similar findings with regards to DAWM progression and EDSS. While DAWM could either decrease, increase or stay the same

215 Chapter 7 over an eight year period, DAWM increase did not predict EDSS progression in a group of RR patients but did correlate with brain atrophy (101). It has yet to be determined if the presence of DAWM at disease onset is a predictor for long term disability. Furthermore, it might be worthwhile to determine which factors contribute to a decrease in DAWM and whether DAWM correlates better with other than physical measures of disability, for example cognitive decline. The second question that emerges from the studies in Chapter 4, is the relationship between DAWM and other MS related pathology. Upon consideration of the relationship between WM lesion load and DAWM, it was observed that the severity of DAWM measurements did not differ between patients with many and few WM lesions (97). This is not surprising, as it is well known that there is only a marginal correlation between focal white matter lesions and diffuse white matter injury in the brain and spinal cord in general (102-104), although variable results exist when an influence of focal white matter lesions on certain tracts is sought (105-108). This supports the argument that DAWM is not exclusively due to Wallerian degeneration from WM lesions, but develops by mechanisms at least partly independent from WM demyelination. Cortical lesions are functionally connected to remote white matter just as WM lesions are, and their contribution to DAWM could probably be even more important than those of WM lesions. A recent study has shown that cortical lesion volumes and counts strongly correlate with changes in NAWM (109). The contribution of unseen cortical lesions to the development of DAWM, and more destructive WM lesions in SPMS, could explain the observed differences in DAWM between SP- and PPMS patients in Chapter 4.2. However, the temporal and causal relationship between inflammation (white and grey matter lesions) and neurodegeneration (DAWM and atrophy) is complex and only partly understood (110). Neuropathological observations indicate a close association between inflammation and ongoing axo-glial degeneration, also in late progressive disease (111). The very close relationship between inflammation and neurodegeneration at all stages of the disease makes answering the question whether inflammation or neurodegeneration comes first impossible. So far, most authors interpret this relation as evidence that inflammation drives neuro-degeneration throughout the stages of MS. However, also the opposite could be true: Ongoing axo- glial degeneration which is driven by an unknown factor, could elicit a continuous inflammatory response by degenerating cellular elements. Laule et al argues that DAWM reflects a primary lipid degeneration in the myelin- and axonal bilipid membranes (112). Progression in MS may then occur independently of radiologically visible lesion load, but as a consequence of microglial mediated axonal destruction that may in part be triggered by diffuse white matter lipid abnormalities and modified

216 Summarizing discussion by Wallerian degeneration caused by lesions. As such, DAWM could represent the “most pure” form of MS pathology and the visible “core” problem of the disease (113). Therefore, studying the relationship of DAWM to grey and white matter atrophy and lesions might provide an important piece of information in the puzzle of MS pathology.

Unsolved questions and suggestions for further research: • The temporo-spatial relationship between the development of cortical lesions, cortical/deep grey matter atrophy and DAWM is unclear. In this respect it would be interesting to assess the relationship between the development of (tract related) DAWM, cortical atrophy and cortical lesion development in a longitudinal study. • Can DAWM mirror unseen GM damage and is it the counterpart of cortical atrophy?

Chapter 7

217 Chapter 7

Figure 1: Rating of frontal DAWM

1a: frontal DAWM grade 1 (F1): diffuse abnormalities are seen around the ventricles, not exceeding 5mm Ø. 1b: frontal DAWM grade 2 (F2): Diffuse abnormalities stay confined to the deep white matter, reaching to the basis of the frontal gyri. The U-fibers and NAWM can be clearly distinguished from DAWM. 1c: frontal DAWM grade 3 (F3): DAWM extends in frontal gyri, not affecting the U fibers. 1d: frontal DAWM grade 4 (F4): Diffuse abnormalities affect the deep white matter and frontal gyri including U-fibers.

218 Summarizing discussion

Figure 2: Rating of periventricular DAWM

2a: periventricular DAWM grade 1 (PV1): Diffuse incoherent abnormalities periventricular and in the deep white matter, not extending to the gyri. NAWM and U-fibers are visible. 2b: periventricular DAWM grade 2 (PV2): DAWM reaches from periventricular to the basis of the gyri. U fibers and gyri are free of DAWM. 2c: periventricular DAWM grade 3 (PV3): Diffuse abnormalities affect the entire white matter including U-fibers. Chapter 7

219 Chapter 7

Figure 3: Rating of parietooccipital DAWM

3a: parietoocipital DAWM grade 1 (PO1): diffuse abnormalities around the ventricles, not exceeding 5mm Ø. 3b: parietoocipital DAWM grade 2 (PO2): Diffuse abnormalities stay confined to the deep white matter, spreading to the basis of the parietooccipital gyri. The U-fibers and NAWM can be clearly distinguished from DAWM. 3c: parietoocipital DAWM grade 3 (PO3): DAWM extends in the gyri, not affecting the U-fibers. 3d: parietoocipital DAWM grade 4 (PO4): Diffuse abnormalities affect the deep white matter and parietoocipital gyri including U-fibers.

220 Summarizing discussion

Table 1: Visual rating of DAWM

Frontal 0/1/2/3/4 0= absent (F 0-8)* 1= periventricular, ≤ 5mm 2= deep white matter, 5mm, U-fibers visible 3= reaching in frontal gyri: “fingers”, U-fibers visible 4= deep white matter and gyri, U-fibers not or barely visible X= not applicable

Periventricular 0/1/2/3 0= absent (PV 0-6)* 1= present, incoherent areas periventricular 2= periventricular and deep white matter, extending to basis of gyri, U fibers visible 3 deep white matter and frontoparietal gyri, also involving U-fibers X= not applicable

Parietooccipital 0/1/2/3/4 0= absent (PO 0-8)* 1= periventricular, ≤ 5mm 2= deep white matter, 5mm, U-fibers visible 3= reaching in frontal gyri: “fingers”, U-fibers visible 4= deep white matter and gyri, U-fibers not or barely visible X= not applicable

Semiquantitative rating of DAWM is performed on T2 and Pd weighted images in three brain regions on each side. * The range of the scale for both hemispheres.

The periventricular DAWM is rated on an axial section through the corpus callosum/ cella media of the lateral ventricle. The frontal and parietooccipital DAWM is rated on an axial section through the basal ganglia/ insular cortex.

CHAPTER 5: ATYPICAL LESIONS IN MULTIPLE SCLEROSIS

The big bulk of MS lesions present as described in Chapter 1, but rarely (approximately Chapter 0.3/100000 people or in 1-2/ 1000 MS patients (114, 115)), lesions can deviate from their normal characteristics and present as large (>2 cm), isolated masses. Referred to as “atypical”, “tumefactive”, or atypical IIDLs (idiopathic inflammatory demyelinating 7 lesions), their diagnosis is challenging because they can mimic tumors and abscesses, and because they can be found in a heterogeneous group of demyelinating disorders (116). Once a lesion is diagnosed as atypical IIDL, the further disease course is undefined and warrants a “wait and see” strategy in most cases. While some atypical IIDLs occur only once and remain the sole clinical event, some can recur. Furthermore, they can

221 Chapter 7 present at the beginning of, or even during a relapsing remitting disease, suggesting a relation with “classical” MS. The frequency of this relation is unclear and it is unknown which patients are finally affected from relapses. Due to their various morphological presentations and various overlap with other demyelinating diseases (117, 118), an internationally approved classification of atypical demyelinating lesions is lacking. In Chapter 5.1, we attempted to classify atypical IIDLs by MRI morphological patterns while bearing in mind that these patterns should be suited for a prospective registry of atypical IIDL cases. After a literature review, 69 cases were included in the study and five MRI patterns of atypical lesions were identified: A megacystic type, a Balo-like type, a ring like type, and an infiltrative type. The fifth “unclassified” group consisted of imaging patterns which were not representative of either of the groups. The inter-observer agreement varied for the defined groups, and ranged from substantial (for the Balo- and megacystic groups) to almost no agreement (for the unclassified group). Subsequently, we linked the identified patterns to demographical, clinical and para-clinical data. Although consisting of relatively small numbers, the five groups seemed to differentiate in terms of relapses and recurrence, death, and of the development of lesions typical for MS. But how reliable and applicable are these data in a real- life clinical setting? As already mentioned in the discussion of Chapter 5.1., clinical information provided has to be interpreted with care, as data were insufficient and likelihood of bias is big. This might lead to the concern whether the proposed classification of atypical IIDLs is reproducible and clinically meaningful. It is therefore of interest to compare the present work to the findings of longitudinal studies of atypical IIDLs with long term clinical and radiological evaluation. To date, the largest case series comes from a study from Lucchinetti et al, which reports the clinical course and radiological features of 168 biopsy confirmed cases of atypical IIDLs (119). In this study, and in subsequently published smaller case series, MRI enhancement patterns were used to describe the radiological features of IIDLs. Specifically, the presence of open- and closed rings, homogeneous, heterogeneous, or nodular enhancement was used to classify atypical lesions, in addition to the presence or absence of T2W hypointense rims (120-123). While these studies give an impression about the various morphological aspects of atypical IIDLs and their overall clinical course, none could attribute MRI characteristics to outcome parameters. The second largest published case series, which includes 90 patients with a mean follow up of 4 years, has used the classification proposed in Chapter 5.1 to investigate MRI-clinical relationships in patients with atypical IIDLs. This study confirms not only the occurrence of the in Chapter 5.1. proposed lesion types throughout eight centers, but also validates their clinical correlations (124).

222 Summarizing discussion

In the study by Wallner et al, infiltrative lesions were described as the most frequent lesion type (49%), which is higher than one would expect from the data in chapter 5.1. The inclusion of different populations, especially Asian cases in the review data of Chapter 5.1, and the relative exclusion of ring- like lesions in the follow up study, which are presently accepted as part of the classical MS spectrum, could be a possible explanation for this difference (125-132). Other studies also suggest that ring like enhancement patterns are the most common presentations of IIDLs (119, 120, 122, 123), but these most likely include both “ring-like” and “megacystic” lesion types, which typically present with ring enhancement. The mean age and age range of patients presenting with atypical IIDLs was similar in both review and follow up study (124, 133). The data presented in Chapter 5.1 and the follow- up study by Wallner-Blazek et al (124) contradict the historical notion that AIIDLs are associated with a malignant disease course. This seems to be true both in terms of recovery of the atypical attack, and in terms of the further disease course. Other recent studies have reported similar observations (119, 122, 134, 135). With regards to recovery from atypical demyelination, half of the patients of Kuan et al achieved almost total recovery or only mild residual impairment (EDSS less than 1) after 2 years (122). Lesion size and location were not associated with the clinical course (76) as in other reports (119, 121). However, a more detailed prognosis might be possible: Both the data in Chapter 5.1, and the study by Wallner et al suggest, that the clinical outcome is lesion type dependent. Overall, outcome seems to be worst for patients with infiltrative lesions (between 10% and 20% good recovery), whereas the majority (>80%) of Balo- like cases show marked improvement during the follow up period. In contrast, half of the patients with megacystic- and ring- like lesions recover well from the attack (124). With regards to the further disease course, Luchinetti et al. and Altintas et al. found similar rates of conversion to clinically definite MS of 66-70% (119, 120). However, this differs from both the review data and the results of Wallner et al, where only roughly one third of patients experienced a second attack (124), although the average follow up was shorter than in the other two studies. Here also, lesion type seems to be of Chapter importance: Patients with ring like and infiltrative atypical IIDLs show further attacks in 62% and 35% respectively. Therefore, the differences in reported conversion rates might be based on the different distribution of lesion types within the studied case 7 series, in most of which ring like patterns dominate. In addition, most patients who relapse will have lesions typical of MS, with only a minority relapsing in a tumefactive lesion (119, 124). In this line, Wallner et al could show that new MS lesions at follow up develop in more than half of patients with lesions at baseline, vs in only 28% of patients without MS typical lesions.

223 Chapter 7

The presence of MS typical lesions is reported in up to 70% (119), and could be a helpful hint in the differential diagnosis of atypical lesions, especially given the fact that atypical IIDLs are associated with a first clinical attack in most instances (119, 122, 124). Interestingly, the presence of grey matter lesions has been described in nearly 40% in association with atypical IIDLs (40). As this finding is solely based on histopathological analysis of cortical tissue obtained in passing during biopsy sampling of white matter lesions, cortical lesions might be found even more frequently on imaging and should be actively looked for. Infiltrative- and megacystic lesions most likely present as solitary lesions, and awareness of these IIDL variants in addition to the presence of MS typical lesions could be helpful in the diagnostic process. However, one has to be aware that the presence of MS typical lesions (or a pre-existing diagnosis of MS) does not exclude the possibility of a coexisting tumor or other pathology (136-138). Gliomas in patients with MS may have a different appearance than gliomas in other patients, with a higher incidence of a diffusely infiltrative or multi-centric appearance (30% compared to 2.5-5%) (138). In retrospect, some of the described cases might well fall in the spectrum of neuromyelitis spectrum disorder (NMOSD), which is accepted as a separate entity of demyelinating disease. The articles used for the review were published before 2004, i.e. before the aquaporin-4 (AQP4) antibody era. During this time, a relative paucity of brain involvement was considered characteristic for NMOSD (139). With the availability of AQP4-IgG assays however, it became clear that brain abnormalities are common (up to 89% in patients with NMOSD), and that these are not only located in areas with high AQP4 expression but also occur in brain areas where AQP4 expression is not particularly high (140-142). Furthermore, brain abnormalities are present at onset in 43%-70% in patients with NMOSD (141, 143, 144), and might be even responsible for the presenting symptom (145). im et al have grouped the MRI characteristics of 78 AQP4+ NMOSD patients into five patterns (146), of which two are of specific interest in the light of the similarity to the described atypical MS lesions. The first are lesions in the hemispheric white matter, which are described as extensive and confluent and were found in 29%. Interestingly, most of these lesions are tumefactive (>3cm in diameter) or configured as long spindle-like or radial-shaped signal changes and are following white matter tracts. Some tended to shrink or disappear over time, whereas others revealed cystic-like or cavitary changes. It is quite likely that some of the described atypical MS lesions which we have classified as “infiltrative” might fall into this category of NMOSD- lesions. This suspicion is supported by the behaviour of gadolinium uptake, which was described as “patchy” or “cloud- like” in NMOSD and matches our descriptions of “inhomogeneous” uptake for the “infiltrative” group.

224 Summarizing discussion

The second group described by Kim et al is characterised by peri-ependymal lesions which surround the lateral ventricles and were found in 40%. These can involve the entire thickness of the corpus callosum and may also extent in cerebral hemispheres forming an extensive and confluent white matter lesion. Again, some of our “infiltrative” cases, especially those which expand throughout the splenium and to the occipital lobes might be part of this group. Whether infiltrative lesions per se could be a feature of a separate subtype in the demyelinating diseases spectrum has yet to be determined, as well as the degree of overlap of this radiological feature with other syndromes. The use of biomarkers, for example anti myelin-oligodendrocyte glycoprotein, which is found in a subset of patients with NMOSD (147), might render helpful. Turning to CSF, provided data are limited in the literature and firm conclusions about the frequency of oligoclonal bands in atypical IIDLs as compared to prototypic CIS and their relation to risk of conversion to MS cannot be drawn. It seems however, that oligoclonal bands are overall less common than in classical MS (119-121). In our review material, oligoclonal bands were most frequently found in the infiltrative (54%) and ring-like (30%) types and interestingly, these types are overall most common to lead to relapses on follow up (124). The histopathologic findings in our review data did not differ between the lesion types, apart from Balo-like lesions. This is not surprising as the prime reason for histopathology was the exclusion of pathology other than demyelination and therefore, selection of staining techniques and interpretation of results might have served only this purpose. The pathology of atypical IIDLs is similar to that of typical MS lesions with areas of confluent demyelination and relative axonal sparing, although areas of widespread axonal damage might be sometimes seen. Additionally, inflammatory infiltrates of foamy macrophages are admixed with reactive astrocytes, and perivascular and parenchymal lymphocytic infiltrates are common. Previous studies have described four immunopathological patterns of demyelination in early multiple sclerosis lesions (128). Of those, the antibody/complement-mediated pattern II has gained most attention as it seems responsive to B-cell directed therapy (148, 149). Therefore, evaluation of the underlying immunopathology of atypical IIDLs could Chapter have major implications for their treatment and could offer perspectives for pathology directed immunotherapy in the future. The antibody- mediated pattern of MS has also been tentatively linked with ring enhancement patterns (128, 150). Further evidence 7 for a possible correlation between radiological and pathological features comes from a more recent study, which suggests that atypical IIDLs with different contrast enhancement patterns on MRI have different underlying histopathology (151). In conclusion, characterization of atypical IIDLs by MRI patterns might render helpful for prognosis, diagnosis and treatment decisions. In the future, disease registries with

225 Chapter 7 documentation of clinical, radiological and pathological data should further advance our understanding of atypical IIDLs and hopefully lead to a standardized classification and common nomenclature.

Unsolved questions and suggestions for further research: • Do the described lesion types represent distinct subtypes of demyelinating diseases? Under which circumstances are they part of the continuum of MS? • The use of disease registries and documentation of clinical, histopathological and radiological data and long term follow up is necessary to advance our understanding of these presentations. • The development of new biomarkers and the use of advanced MRI techniques might allow further classification of atypical presentations.

&+$3(51(852'(*(1(5$7,9($1'9$6&8/$5 DISEASE

Similar to the clinico-radiological dissociation observed in MS, the clinical expression of MRI defined small vessel disease is generally moderate and heterogeneous. This can be partly explained by the pluriformity of underlying pathological changes and the relative inability of clinically applied MRI sequences to differentiate between these changes. The review presented in Chapter 6.1 summarizes studies that directly correlate post- mortem MRI and histopathology of white matter hyper-intensities (WMH), lacunes and microbleeds with the aim to better characterize the pathological substrates of small vessel disease and to better explain their clinical manifestations. More specifically, the study in Chapter 6.2 addresses WMH in patients with Alzheimer’s disease and in controls without dementia. WMH can be found in a significant proportion of Alzheimer patients, but whether or not WMH have an effect on cognitive decline in dementia is unclear (152-155). The heterogeneity of the neuropathological substrates underlying WMH and the lack of specificity of T2 weighted images to differentiate between these substrates might be the reason for inconsistent results in studies correlating cognitive decline with WMH. Quantitative MRI is claimed to be more specific to the presence of structural brain damage in vivo, however, the neuropathological substrates that define the changes in quantitative MRI parameters in WMH are not well defined (156-159). Consequently, the study presented in Chapter 6.2 was designed to explore differences between WMH of Alzheimer patients and controls without dementia and to assess whether quantitative MRI can reflect these differences. In this post-

226 Summarizing discussion mortem study, brain slices containing WMH from 11 Alzheimer patients and 7 non- demented controls were scanned at 1.5 Tesla using qualitative and quantitative MRI. Neuropathological examination included assessment of axonal- and myelin density, astrogliosis and microglial activation. Normal appearing white matter (NAWM) and white matter hyper intensities (WMH) were compared with respect to quantitative MRI- and neuropathological measures in a region of interest approach. This study shows, that tissue changes measured by quantitative MRI are more severe in the WMH of Alzheimer patients than in the elderly without dementia. Also, quantitative MRI is able to reflect the type and severity of the neuropathological changes involved. These findings, - if verified in in vivo studies- could help understand the clinical impact of WMH in the elderly with and without dementia. This is of particular interest since recent epidemiological and clinico-pathological data indicated a considerable overlap between cerebrovascular- and Alzheimer’s disease. These studies imply, that not only small but also large vessel disease and cardiovascular risk factors are important components contributing to cognitive decline. In a study by Yarchoan et al, the severity of atherosclerosis in the circle of Willis was highly correlated with Alzheimer’s disease type pathology (i.e. neuritic plaques, paired helical filament tau neurofibrillary tangles and cerebral amyloid angiopathy) (160). In this line, Zhu et al could show that intracranial arterial stenosis increases the risk of developing Alzheimer’s dementia in patients diagnosed with mild cognitive impairment (161). It is speculated that cerebral atherosclerosis could directly contribute to Alzheimer’s disease pathology through its effects on cerebral blood flow. In animal models, chronic cerebral hypoperfusion leads to a cascade of cellular and molecular changes that initiate cognitive deficits and eventually progression to Alzheimers disease. This is caused by impaired clearance of amyloid beta and hyper-phosphorylation of tau protein, resulting in the formation of amyloid beta plaques and neurofibrillary tangles (162-164). These findings suggest that Alzheimer’s disease and vascular disease are interrelated and suggest that common etiologic or reciprocally synergistic pathophysiological mechanisms promote both vascular pathology and plaque and tangle pathology. Chapter The combination of histopathology, imaging and clinical findings could also render useful to improve our understanding of neurodegeneration by comparing different neurodegenerative diseases. 7 In Alzheimer’s disease, it is firmly established, that synaptic dysfunction (i.e. decreased synaptic density, compromised synaptic transmission, and defected synaptic plasticity) is the pathological basis of cognitive impairment, and precedes neuronal death (165). In MS, significant and widespread synaptic pathology (reduction of spine density) which is independent from demyelination and axonal degeneration

227 Chapter 7 has recently been described (48). The mechanisms that lead to the elimination of synapses in neurodegenerative diseases have yet to be determined, but it might be possible that MS and Alzheimer’s disease share common pathways, for example via microglial activation or mitochondrial dysfunction (166, 167).

Unsolved questions and suggestions for further research: • The postmortem method could be used as source for comparative studies in other neurodegenerative diseases. • It would be interesting to compare patterns of neurodegeneration between diseases (for example between multiple sclerosis, Alzheimer’s disease, Parkinson’s disease) • Is the neurodegeneration in MS the same as in Alzheimer’s disease?

SUMMARY OF THE FINDINGS OF THIS THESIS:

1. Cortical lesions can be reliably imaged with MRI, although we only see the “tip of the iceberg”. 2. Diffusely abnormal white matter is a unique pathological entity, which can also be reliably imaged in vivo. Whether DAWM is a useful tool for monitoring or predicting the disease course in everyday practice, has to be determined in a further study. 3. The categorization of “big”, “atypical” MS lesions on MRI can help predicting the future disease course in the individual patient. Further combined MRI- histopathological studies could give insight in the immunological processes leading to different types of lesions and therewith lead to a better understanding of the disease itself and might even influence treatment choices in the future. 4. The method applied in the MS postmortem studies can be successfully used to investigate other diseases as shown in the study comparing NAWH in Alzheimer and non-Alzheimer patients. This is an example of how knowledge and principles can be transferred form one field to the other.

228 Summarizing discussion

ADDENDUM

Table 2a: Human in-vivo PET studies with results for the cortical grey matter

Pathophysiologic mechanism: Inflammation Radioligand: [11C]PK11195 Binding site: TSPO receptor/ activated microglia Author/year Study design n Main findings in cortical GM Kaunzner et Longitudinal HC: 6 At baseline no differences in cortical 11[ ] al, 2017 (62) (baseline, 3 and 6 RRMS: 16 CPK11195 binding between MS and HC. months) SPMS: 2 Within MS, no longitudinal changes in cortical GM binding after treatment with Treatment study: natalizumab. Natalizumab Sucksdorff et Longitudinal HC: 8 At baseline higher uptake of [11]CPK11195 al, 2017 (63) (baseline, 2 and 6 RR: 10 in cortical GM ROIs of MS vs HC, but not months) statistically significant. In first 2 months, slight increase of cortical [11]CPK11195 Treatment study: binding. After 6 months, no changes in Fingolimod cortical GM binding after treatment with fingolimod. Gianetti et al, Cross- sectional HC: 8 No difference in cortical [11]CPK11195 2015 (64) CIS: 18 uptake between CIS and HC. Rissanen et Cross-sectional HC: 8 No difference in cortical [11]CPK11195 al, 2014 (65) SP: 10 uptake between MS and HC. Politis et al, Cross-sectional HC: 8 Higher uptake of [11]CPK11195 in GM of 2012 (66) RR: 10 MS vs HC. Within MS group higher uptake SP: 8 and wider cortical areas affected in SP vs RR. Total cortical [11]CPK11195 binding correlates with EDSS, particularly in SP. Ratchford et Longitudinal, RR: 9 Significant decrease in global cortical al, 2012 (67) (baseline, 1 y) GM uptake after 1 y of treatment with Treatment study: glatiramer acetate. Glatiramer actetate Debruyne et Cross-sectional HC: 7 Higher uptake of [11]CPK11195 in GM of MS al, 2003 (68) RR: 13 vs HC, but not statistically significant. Chapter PP: 2 SP: 7 7

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Table 2a: Continued Radioligand: [11C]PBR28 Binding site: TSPO receptor/ activated microglia Author/year Study design n Main findings in cortical GM Herranz et al, Cross-sectional HC: 14 Higher cortical [11]CPBR28 binding in MS 2016 (69) RR: 12 vs HC. Increased binding correlates with SP: 15 disability. Highest uptake in occipital & temporal cortex in RR, more widespread uptake frontal and parietal in SP. Negative correlation between cognitive function and [11]CPBR28 uptake in cortical GM. Cross-sectional HC: 4 No difference in global and regional Park et al, and test-retest RR: 4 cortical [11]CPBR28 uptake between MS 2015 (70) and HC. Oh et al, 2010 Cross-sectional, HC: 7 No difference in cortical 11[ ]CPBR28 binding (71) subset longitudinal MS: 11 between MS and HC. (baseline, 4 (disease months) type not specified, mostly RR) Pathophysiologic mechanism: Inflammation Radioligand: [18F]FEDAA1106 Binding site: TSPO receptor/ activated microglia Author/year Study design n Main findings in cortical GM Takano et al, Cross- sectional HC: 5 No difference in cortical 18[ ]FFEDAA1106 2013 (72) RR: 9 binding between MS and HC. Radioligand: [11C]DPA713 Binding site: TSPO receptor/ activated microglia Author/year Study design n Main findings in cortical GM Bunai et al, Longitudinal HC: 6 At baseline and after 1y higher cortical 11[ ] 2018 (73) (baseline, 1y) RR: 6 CDPA713 binding in MS vs HC. After 1 y, cortical [11]CDPA713 binding is seen in more Treatment study cortical areas as compared to baseline. (Fingolimod, IFN 1b) Radioligand: [11C]TMSX Binding site: A2AR receptor/ activated microglia Author/year Study design n Main findings in cortical GM Rissanen et Cross-sectional HC: 7 No difference in cortical (ROI-specific) al, 2013 (74) SP: 8 binding of 11CTMSX between SP and HC.

230 Summarizing discussion

Table 2a: Continued Pathophysiologic mechanism: Demyelination Radioligand: [11C]PiB Binding site: Fibrillary Є-Amyloid/ myelin Author/year Study design n Main findings in cortical GM Zeydan et al, Cross- sectional HC: 60 No difference in global cortical 11[ ]CPiB 2018 (75) MS: 12 uptake between MS and HC. (disease type not specified, likely SPMS) Pathophysiologic mechanism: Astrocyte activation Radioligand: [11C]acetate Binding site: monocarboxylate transporter/ astrocytes Author/year Study design n Main findings in cortical GM Takata et al, Cross-sectional HC: 6 Increased uptake in both deep and some 2014 (76) RR: 6 regions of cortical GM in MS as compared to HC. Pathophysiologic mechanism: Neuronal damage Radioligand: [11C]FMZ

Binding site: central benzodiazepine receptor (component of GABAA Receptor) Author/year Study design n Main findings in cortical GM Freeman et Cross- sectional HC: 8 Reduced [11]CFMZ binding in the cortical al, 2015 (77) RR: 9 GM of MS vs HC. [11]CFMZ cortical binding SP: 9 correlates with cognitive performance. Pathophysiologic mechanism: Neuronal integrity, Inflammation Radioligand: [18F]FDG Binding site: glucose transport and uptake by neurons and astrocytes Author/year Study design n Main findings in cortical GM Baumgartner Longitudinal HC: 10 No difference in cortical glucose et al, 2018 (baseline, 6 RR: 15 metabolism between MS vs HC. Within (78) months) MS, increased glucose metabolism in some neocortical areas after treatment with IFN. Treatment study Lower glucose metabolism in cortical Chapter (IFN ) areas correlated with higher fatigue and depression in untreated MS. Kindred et al, Cross- sectional HC: 8 Overall, lower [18]FDG uptake in MS brain 7 2015 (79) MS: 8 (no specific numbers for GM given). In MS (disease negative correlation between walking type not speed and [18]FDG uptake in insula, specified, hippocampus and calcarine sulcus. likely RR)

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Table 2a: Continued Pathophysiologic mechanism: Neuronal integrity, Inflammation Radioligand: [18F]FDG Binding site: glucose transport and uptake by neurons and astrocytes Author/year Study design n Main findings in cortical GM Derache et Cross- sectional RR: 17 Negative correlation between physical al, 2013 (80) score of EMIF-SEP (fatigue questionnaire) and cortical glucose metabolism in L parietal R frontal and R temporal regions. Blinkenberg Cross-sectional RR: 20 No correlation between global cortical et al, 2012 glucose metabolism and cognitive (81) dysfunction. Cortical glucose metabolism correlates with cortical NAA. Paulesu et al, Cross-sectional HC: 10 Reduced cortical glucose metabolism 1996 (82) RR: 16 in MS vs HC. Within MS, more cortical SP: 12 areas affected in patients with memory impairment vs unimpaired patients. Reduced glucose metabolism affects different cortical areas in patients with cognitive disturbance vs patients with additional frontal dysfunction. Pathophysiologic mechanism: cholinergic metabolic profile Radioligand: [11C]MP4A Binding site: AChE Author/year Study design n Main findings in cortical GM Virta et al, Cross-sectional HC: 10 No differences in cortical AChE activity 2011 (83) SP: 10 between MS and HC. Within MS, higher AChE activity correlated with more pronounced cognitive symptoms.

TSPO: translocator protein (former benzodiazepine receptor, PBR), 18-kDa macromolecular complex expressed in the outer mitochondrial membrane of activated microglia and macrophages. A2AR: adenosine A2A receptor: up regulation on microglial cells under inflammatory stimuli; Adenosine binding on receptor leads to modulation of inflammation. AChE: Acetylcholinesterase. [11C]MP4A acts as acetylcholine analogue that is selectively metabolised by AChE and thus the rate of tracer accumulation represents the local AChE activity. MS: Multiple sclerosis CIS: Clinically isolated syndrome HC: Healthy control RR: Relapsing remitting MS PP: Primary progressive MS SP: Secondary progressive MS GM: Grey matter Y: Year ROI: Region of interest L: Left R: Right IFN: Interferon NAA: N-acetyl-aspartate 232 Summarizing discussion

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8 Summary in Dutch/ Nederlandse samenvatting Bibliography Curriculum vitae Acknowledgements/ Dankwoord Summary in Dutch

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Multipele sclerose (MS) is een ziekte van het centrale zenuwstelsel, wat bestaat uit de hersenen en het ruggenmerg, die vooral jonge mensen treft en tot een veelvoud van klachten kan leiden. Wereldwijd zijn er ongeveer 2.5 miljoen MS patiënten en de ziekte komt twee keer zoveel bij vrouwen als bij mannen voor. MS is gekenmerkt door het ontstaan van ontstekingshaarden aan de myelineschede, de bescherm- en isolatielaag rondom zenuwuitlopers. Dit leidt tot het verdwijnen van deze huls en wordt demyelinisatie genoemd. De gebieden zonder myeline worden ook wel aangeduid als MS plaques of laesies. Zonder de bescherming van de myelineschede degenereren de zenuwuitlopers. Zij kunnen hun signalen niet meer effectief doorgeven en dat veroorzaakt neurologische klachten. Deze klachten variëren afhankelijk van de locatie van het aangedane gebied en de hoeveelheid aangedane gebieden in de hersenen en het ruggenmerg. Veel voorkomende verschijnselen zijn balans en coördinatie stoornissen, een verminderd gevoel, incontinentie, concentratie en geheugen problemen. Het ziektebeloop kan sterk verschillen tussen individuele MS patiënten wat betreft de ernst en het soort klachten dat wordt ervaren. Op dit moment is het niet goed te voorspellen hoe het beloop zal zijn. In het algemeen worden er drie vormen van MS beschreven. In ongeveer 85% van de gevallen begint de ziekte met een relapsing- remitting (RR) beloop. Hierbij ontstaan nieuwe klachten die samengaan met het ontstaan van nieuwe laesies in de hersenen of het ruggenmerg, die naar enige tijd weer (deels) overgaan. Na enkele jaren zal een deel van deze patiënten overgaan tot de secundair progressieve fase (SPMS), waarbij ze geleidelijk achteruitgaan en niet meer herstellen. In ongeveer 10% van de gevallen is het ziektebeloop progressief vanaf het begin. Dit MS type wordt primair-progressief (PPMS) genoemd. De oorzaak van MS is niet bekend, wel is duidelijk dat een combinatie van omgevingsinvloeden en genetische factoren een rol spelen. Er bestaat tot op heden geen enkelvoudige test om de diagnose MS met zekerheid te stellen. Door het in kaart brengen van klachten in combinatie met bevindingen van bepaalde testuitslagen kan de diagnose MS worden vastgesteld. MS laesies in de hersenen en het ruggenmerg, vooral die laesies die gelegen zijn in de witte stof, kunnen goed met magnetic resonance imaging (MRI) worden afgebeeld. Op de MRI beelden zijn de laesies als witte plekjes te zien met een voor MS typische vorm en locatie. Soms zie je ongewone laesies, zoals grote, op tumoren lijkende (‘tumefactive’) laesies. Zo’n bevinding maakt het stellen van de diagnose en het inschatten van het ziektebeloop soms bijzonder lastig. Opvallend is dat het aantal MS laesies in de hersenen niet goed correleert met de ernst van de ervaren symptomen. Vooral geheugenstoornissen kunnen hiermee niet goed worden verklaard. We denken dat de gebruikelijke of “gewone” MRI technieken

246 Nederlandse samenvatting niet alle beschadigingen die bij MS optreden kunnen laten zien. De laesies in de grijze stof (corticale laesies) worden bijvoorbeeld nauwelijks opgepikt, dit in tegenstelling tot laesies in de witte stof. De afwijkingen in de grijze stof kunnen zeer uitgebreid zijn, vooral in de progressieve fase van de ziekte en zijn voor een belangrijk deel verantwoordelijk voor het ontwikkelen van beperkingen (invaliditeit). Nieuwe MRI technieken zoals double inversion recovery (DIR) maken laesies in de grijze stof beter zichtbaar dan de “oudere” MRI technieken. MS laesies zijn niet de enige pathologische uiting van MS die aandacht verdient. Weefselbeschadiging treedt niet alleen op in laesies maar ook daarbuiten. Zowel in gebieden die op het oog normaal lijken (‘normal appearing matter’) als in gebieden met diffuse afwijkingen zijn pathologische veranderingen te vinden. Ook deze veranderingen zijn met gewone MRI technieken niet zichtbaar of verder te onderzoeken. De zogenoemde kwantitatieve MRI technieken kunnen deze veranderingen wel zichtbaar maken en geven daarom een completer beeld van de weefselbeschadiging bij MS. Het onderzoek beschreven in dit proefschrift had tot doel het beschrijven van zowel grijze stof laesies als de zogenoemde diffuse afwijkingen in de hersenen van MS patiënten. Daarnaast wordt het klinische beloop van patiënten met tumor-achtige laesies bestudeerd. In het onderzoek is gebruik gemaakt van MRI bij leven (in vivo) en na het overlijden (post mortem) en van histopathologische technieken (door hersenweefsel te bekijken onder de microscoop). Hoofdstuk 2 geeft een algemene inleiding hoe de hersenen van overleden MS patiënten met MRI en onder de microscoop in beeld kunnen worden gebracht door middel van een overzichtsartikel. Het beschrijft een gestandaardiseerd protocol om de hersenen van MS patiënten post mortum te beschrijven, zowel microscopisch als met verschillende MRI technieken. Het is dan mogelijk te bepalen welk MRI techniek het meest geschikt is om MS laesies op te pikken. Ook kan worden verklaard welke weefselverandering op MRI als laesie te zien is. De onderzoeken in hoofdstuk 3.1, 3.2, 4.1, en 6.2 maken van deze methode gebruik. In Hoodfstuk 3 worden twee studies beschreven die de detecteerbaarheid van grijze stof laesies bespreken. Hoofstuk 3.1 behandelt welke MRI sequentie -3D FLAIR of 3D DIR - het meest gevoelig is om grijze stof laesies te detecteren in een post-mortem setting. Door middel van histologie werd het aantal laesies in de grijze stof van 14 MS patiënten bepaald. Dit aantal werd vergeleken met de gescoorde aantallen laesies op 3D DIR en 3D FLAIR MRI. De met dit onderzoek bepaalde gevoeligheid (sensitiviteit) wordt besproken, evenals de specificiteit van deze MRI sequenties, met andere woorden: Chapter hoeveel MRI laesies geen histopathologische veranderingen laat zien. Uit de resultaten bleek dat er met 3D DIR in vergelijking met 3D FLAIR ongeveer twee keer zo veel laesies zichtbaar werden. Ook heeft deze sequentie een hoge specificiteit. Desondanks werd 8

247 Summary in Dutch het grootste aantal grijze stof laesies met 3D DIR nog steeds gemist. In hoofdstuk 3.2 wordt onderzocht welke grijze stof laesies met MRI zichtbaar zijn en welke niet. Om dit te doen werden alle laesies met MRI en door middel van histopathologie gescoord en in twee groepen verdeeld: MRI zichtbaar of MRI onzichtbaar. Vervolgens werden de laesies met zogeheten kwantitatieve MRI technieken en histopathologische technieken onderzocht. Er werd met MRI en histopathologie geen verschil tussen de groepen aangetoond, maar het bleek dat de MRI zichtbare laesies veel groter waren dan de MRI onzichtbare laesies. Bovendien werd vastgesteld dat hersenen met grote zichtbare laesies meer (kleine onzichtbare) grijze stof laesies vertonen. Men ziet dus met de MRI alleen maar het “topje van de ijsberg”. In hoofdstuk 4 worden twee onderzoeken beschreven die diffuse afwijkingen (DAWM) in de witte stof van MS patiënten onderzoeken. DAWM is vaak zichtbaar op de MRI scan als vage, slecht afgegrensde gebieden van afwijkend signaal. In hoofdstuk 4.1. werd er in een post-mortem setting onderzocht welke weefselveranderingen voor DAWM verantwoordelijk zijn. Uit de resultaten bleek dat vooral schade aan zenuwuitlopers en myeline verantwoordelijk is voor DAWM. Het werd geconstateerd dat deze veranderingen een uiting is van het accumulerende schade in de MS hersenen en waarschijnlijk bijdraagt tot progressie van de ziekte. Het leek daarom belangrijk om DAWM ook bij levende MS patiënten betrouwbaar te kunnen meten. In hoofdstuk 4.2 werd DAWM in progressieve MS patiënten met kwantitatieve MRI technieken gemeten en vergeleken met de veranderingen die werden gezien binnen laesies en normaal ogende witte stof. Deze studie bevestigt dat DAWM ook in-vivo betrouwbaar gemeten kan worden. De resultaten suggereren dat er een verschil is tussen de mate van DAWM in primair progressieve ten opzichte van secundair progressieve MS patiënten. In hoofdstuk 5 werden grote, atypische MS laesies bestudeerd. Atypische MS laesies kunnen zich op verschillende manieren presenteren en kunnen bijvoorbeeld op tumoren lijken. Het stellen van een diagnose en het voorspellen van de verdere beloop is dan bijzonder moeilijk. In hoofdstuk 5.1 werd een poging gedaan om atypische laesies op basis van uiterlijke kenmerken op MRI te rangschikken. Dit is gedaan door 69 verschillende atypische laesies te bekijken en te beschrijven. De op elkaar lijkende laesies zijn vervolgens gegroepeerd in vier groepen en werden gecorreleerd met klinische gegevens. Er werd vastgesteld dat de aanwezigheid van bepaalde grote laesies niet altijd met een ernstig ziektebeloop geassocieerd is. De gekozen groepering werd vervolgens toegepast bij een prospectieve verzameling van atypische laesies. In hoofdstuk 6 werd het post-mortem protocol, zoals beschreven in hoofdstuk 2, op andere hersenaandoeningen toegepast, zoals dementie en vasculaire veranderingen in de hersenen. Hoofdstuk 6.1 geeft een samenvatting van eerdere publicaties, die post-mortem MRI en histopathologie aan elkaar hebben gecorreleerd

248 Nederlandse samenvatting en met name naar witte stof afwijkingen in de hersenen kijken. Witte stof afwijkingen zijn een uiting van schade aan de kleine hersenvaten (‘small vessel disease’), maar de onderliggende oorzaak voor deze veranderingen in de hersenen is heterogeen. De review laat zien dat vooral schade aan het vezelnetwerk, reacties van gliacellen, en microvasculaire veranderingen voor de witte stof afwijkingen verantwoordelijk zijn. De gevonden heterogeniteit in onderliggende weefselveranderingen is waarschijnlijk de verklaring voor de zwakke klinische-radiologische associatie bij small vessel disease. Ten slotte wordt in hoofdstuk 6.2 onderzocht of er een verschil is in de ernst en het soort witte stof afwijkingen bij Alzheimer patiënten ten opzichte van niet-demente controles. Hiervoor zijn 48 hersencoupes met witte stof afwijkingen post mortem gescand waarbij ook gebruik werd gemaakt van kwantitatieve MRI technieken. Vervolgens zijn pathologische kleuringen uitgevoerd om axon densiteit, myeline densiteit, astrogliose and microglia activatie zichtbaar te maken. De resultaten van zowel het histopathologisch onderzoek en de MRI metingen tonen verschillen aan tussen de witte stof afwijkingen van Alzheimer patiënten en die van niet- demente controles. De voornaamste bevinding is dat de witte stof afwijkingen meer uitgebreid waren bij patiënten met de ziekte van Alzheimer.

Chapter 8

249 Bibliograpgy

BIBLIOGRAPHY

1. Fabis-Pedrini MJ, James I, Seewann A, Yau WY, van de Bovenkamp AA et al. Natural history of benign multiple sclerosis: Clinical and HLA correlates in a Western Australian cohort. J Neurol Sci. 2018;388:12-18 2. Pedrini M, Seewann A, Bennett K, Wood A, James I, Burton J, et al. Helicobacter pylori infection as a protective factor against multiple sclerosis risk in females. J Neurol Neurosurg Psychiatry. 2015;86(6):603-7. 3. Wallner-Blazek M, Rovira A, Fillipp M, Rocca MA, Miller DH, Schmierer K, et al. Atypical idiopathic inflammatory demyelinating lesions: prognostic implications and relation to multiple sclerosis. J Neurol. 2013;260(8):2016-22. 4. Vennegoor A, Rispens T, Strijbis EM, Seewann A, Uitdehaag BM, Balk LJ, et al. Clinical relevance of serum natalizumab concentration and anti-natalizumab antibodies in multiple sclerosis. Mult Scler. 2013;19(5):593-600. 5. Seewann A; Kermode, A. Practical management of multiple sclerosis. MedicineToday. 2013;14(11):16-26. 6. Seewann A, Kooi EJ, Roosendaal SD, Pouwels PJ, Wattjes MP, van der Valk P, et al. Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology. 2012;78(5):302-8. 7. Wattjes MP, van Oosten BW, de Graaf WL, Seewann A, Bot JC, van den Berg R, et al. No association of abnormal cranial venous drainage with multiple sclerosis: a magnetic resonance venography and flow-quantification study. J Neurol Neurosurg Psychiatry. 2011;82(4):429-35. 8. Seewann A, Vrenken H, Kooi EJ, van der Valk P, Knol DL, Polman CH, et al. Imaging the tip of the iceberg: visualization of cortical lesions in multiple sclerosis. Mult Scler. 2011;17(10):1202-10. 9. Gouw AA, Seewann A, van der Flier WM, Barkhof F, Rozemuller AM, Scheltens P, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J Neurol Neurosurg Psychiatry. 2011;82(2):126-35. 10. Vrenken H, Seewann A, Knol DL, Polman CH, Barkhof F, Geurts JJ. Diffusely abnormal white matter in progressive multiple sclerosis: in vivo quantitative MR imaging characterization and comparison between disease types. AJNR Am J Neuroradiol. 2010;31(3):541-8. 11. Killestein J, Vennegoor A, Strijbis EM, Seewann A, van Oosten BW, Uitdehaag BM, et al. Natalizumab drug holiday in multiple sclerosis: poorly tolerated. Ann Neurol. 2010;68(3):392-5.

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12. Seewann A, Vrenken H, van der Valk P, Blezer EL, Knol DL, Castelijns JA, et al. Diffusely abnormal white matter in chronic multiple sclerosis: imaging and histopathologic analysis. Arch Neurol. 2009;66(5):601-9. 13. Seewann A, Kooi EJ, Roosendaal SD, Barkhof F, van der Valk P, Geurts JJ. Translating pathology in multiple sclerosis: the combination of postmortem imaging, histopathology and clinical findings. Acta Neurol Scand. 2009;119(6):349-55. 14. Ropele S, Seewann A, Gouw AA, van der Flier WM, Schmidt R, Pantoni L, et al. Quantitation of brain tissue changes associated with white matter hyperintensities by diffusion-weighted and magnetization transfer imaging: the LADIS (Leukoaraiosis and Disability in the Elderly) study. J Magn Reson Imaging. 2009;29(2):268-74. 15. Killestein J, Jasperse B, Liedorp M, Seewann A, Polman C. Very late delayed- allergic reaction to natalizumab not associated with neutralizing antibodies. Mult Scler. 2009;15(4):525-6. 16. Vellinga MM, Oude Engberink RD, Seewann A, Pouwels PJ, Wattjes MP, van der Pol SM, et al. Pluriformity of inflammation in multiple sclerosis shown by ultra-small iron oxide particle enhancement. Brain. 2008;131(Pt 3):800-7. 17. Seewann A, Enzinger C, Filippi M, Barkhof F, Rovira A, Gass A, et al. MRI characteristics of atypical idiopathic inflammatory demyelinating lesions of the brain : A review of reported findings. J Neurol. 2008;255(1):1-10. 18. Gouw AA, Seewann A, Vrenken H, van der Flier WM, Rozemuller JM, Barkhof F, et al. Heterogeneity of white matter hyperintensities in Alzheimer’s disease: post- mortem quantitative MRI and neuropathology. Brain. 2008;131(Pt 12):3286-98. 19. Fazekas F, Ropele S, Enzinger C, Gorani F, Seewann A, Petrovic K, et al. MTI of white matter hyperintensities. Brain. 2005;128(Pt 12):2926-32. 20. Petru E, Benedicic C, Seewann A, Pickel H. Palliative cytostatic treatment of cervical carcinoma. Eur J Gynaecol Oncol. 2003;24(6):473-4.

Chapter 8

251 Curriculum vitae

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Alexandra Marion Elisabeth Seewann-Gaitatzis was born in Graz, Austria on the 4th of August 1976. She studied Medicine at the Medical University Graz and obtained her medical degree (cum laude) in 2004. In Medical school, she held a teaching assistant position in Anatomy and in Histology for several years with particular interest in brain anatomy. After qualifying she started residency in Neurology in Graz (prof. dr. F. Fazekas). In 2006 she obtained a MS fellowship in the MS Center of the Free University, Amsterdam (prof.dr. C.H. Polman), which eventually led to this thesis. She continued her residency in Neurology in Australia and the Netherlands. During her studies, she developed an interest in the effects of nutrition, exercise and lifestyle on health and disease. She is currently pursuing a career in lifestyle Medicine in Australia. Alexandra is married to Athanasios Gaitatzis. They have one son, Thomas (2012).

252 Curriculum vitae

Chapter 8

253 Acknowledgements

ACKNOWLEDGEMENTS/ DANKWOORD

Graag wil ik iedereen bedanken die heeft bijgedragen tot het stand komen van dit proefschrift.

Promotoren en co-promotoren Prof. dr. J.J. Geurts, Prof. dr. F. Barkhof, Prof. dr. P. Van der Valk. Prof. dr. J.J. Geurts, beste Jeroen, ik had geen betere supervisor kunnen bedenken. Jouw creativiteit en jouw “outside the box” denken zullen altijd invloed op mij hebben. Het was erg leuk om met jou te werken en van jou te kunnen leren. Prof. dr. F. Barkhof, beste Frederik, dank voor jouw begeleiding en het kritisch lezen van mijn proefschrift. Ik heb veel geleerd van je ideeën, je verhelderende commentaren op mijn werk en jouw enorme kennis van de neuroradiologie. Prof. dr. P. van der Valk, beste Paul, ik ben heel blij dat ik soms met jou achter de microscoop kun zitten, daar heb ik veel van geleerd. Jouw ontzettend mooie preparatie techniek van de hersenplakken zijn onvergetelijk. Prof. dr. C.H. Polman, beste Chris, dank dat je me de mogelijkheid gaf om onderzoek te doen in het MS-centrum. Helaas heb ik jou niet meer als promotor aan tafel zitten. Je bent mijn idool als wetenschapper en arts. Jouw scherpe blik en de manier hoe je artikelen wist te analyseren heb ik altijd erg bewonderd. Prof. F. Fazekas, Sie sind mein Promotor in Gedanken! Ohne Ihre Unterstützung und großes Vertrauen hätte diese Arbeit nie stattgefunden. Ich bin sehr dankbar, daß ich ein Teil der Neurologie Graz sein durfte.

De leden van de promotiecommissie en co-auteurs Prof. dr. L. Reneman, Prof. dr. I. Huitinga, Prof. dr. H.E. De Vries, Prof. dr. J. Killestein, Dr. J.F. Meilof, Dr. B. Moraal: Bedankt voor de bereidheid om zitting te nemen in de promotiecommissie. I would like to thank all Dutch and international co-authors for their work and collaboration. Dr. Hugo Vrenken, beste Hugo, dank voor het uitleggen van alle soorten ingewikkelde technische dingen die ik zonder jou nooit had begrepen! Ook dank aan Dirk Knol, zonder jou was de statistiek niet gelukt. Bijzonder dank aan Evert-Jan Kooi en Alida Gouw, mijn mede-postmortem collega’s. Bedankt voor jullie bijdrage en vooral de prettige samenwerking. Veronica, Mirja and Christian, many thanks for your efforts collecting atypical lesions. Lieber Mike, auch Dir herzlichen Dank für Dein unermüdliches scoren von DAWM, schade, dass wir kein klinisches Korrelat finden konnten. Sylvie en Laurens, groot dank ook aan jullie voor jullie werk aan dit project.

254 Dankwoord

&ROOHJD·V Mijn lieve mede-MS- onderzoekers en mede-MS-fellows: Beste Libertje, Laura, Machteld, Eva, Judith, Jessica, Bas, Marieke, Anke, Femke, Joe en Veronica: dankzij jullie voelde ik me meteen thuis in Amsterdam. (En ik voel me nog steeds erg Nederlands). Jullie hebben mijn onderzoeks-tijd gemaakt tot een geweldige tijd die ik nooit had willen missen! Het is niet overdreven als ik zeg dat jullie mijn leven op z’n kop hebben gezet! (Bedankt Lauraxel voor een lunch met consequenties! And Joe, you had far-reaching influence! Wat had ik zonder Dutch slang cursus gedaan, Bas? En zonder jouw organisatietalent was het niet gelukt, Judith! En zonder Madeleine - en Lars- was ik nooit echt ingeburgerd, zoals op een rijdend fiets gesprongen… En Machteld, dankzij jou wist ik altijd wat er hip was in Amsterdam! Eva, jouw fotoboekjes hebben en ereplaats op ons rek! Laura erg bedankt voor je hulp bij het vertalen van het Nederlandse gedeelte van mijn proefschrift!) MS- onderzoekers van de radiologie en pathologie - en vooral de leden van het nachtelijke obductieteam - Stefan, Ivo, Bastiaan, Wolter, Hanneke, Menno, Veronica, en Evert-Jan, dank voor al die gezelligheid. Wolter, jij stond altijd klaar als ik die scanner al weer niet aan wist te zetten, jij bent zeker de meest behulpzame mens die ik tegen ben gekomen! MS- Neurologen en stafleden Neurologie van “de overkant” dank voor jullie steun en bereidheid me als AIOS op te nemen - en voor het corrigeren van mijn nooit vlekkeloze Nederlandse brieven. Helaas is dan toch alles anders gegaan. Ook wil ik alle medewerkers van de poli neurologie, het secretariaat neurologie, secretariaat radiologie, de MRI- en pathologie afdeling bedanken voor de ondersteuning en samenwerking. In het bijzonder wil ik Petra Pouwels, Tabe Kooistra, Ton Schweigmann en Karin Barbiers bedanken voor jullie hulp bij het scannen, inladen van MRIs en uitleg. Regina Wijhenke-Rakim, bedankt voor je hulp bij de laatste loodjes. Tenslotte nog alle collega’s met wie ik nooit daadwerkelijk heb samengewerkt – de Alzheimer-, Parkinson-, slaap-, en neuro-onco onderzoekers - jullie hebben zeker een hele hoop aan de gezelligheid en werkplezier bijgedragen! Finally, I would like to thank my Australian mentors Prof. Allan Kermode and Prof. Bill Carroll fort their ongoing interest and support- even if I decided to head in a slightly different direction.

Familie en vrienden Dear friends from all over the world- thanks for your interest, support and above all Chapter your friendship. I hope to see many of you during my Europe trip! Lieve paranimfen Libertje en Mike, dank dat jullie naast mij willen staan! Lieve Libertje, mijn kamergenootje. Wat hebben wij gelachen tijdens onze onderzoekstijd! 8

255 Acknowledgements

Grappig dat nu ook ik een andere toekomstrichting heb gekozen! Helaas zijn ons plannen elkaar vaker te zien niet goed gekomen, maar ik kijk heel vaak naar de leuke bakfiets foto’s met ons kindjes daarin! Lieber Mike, meine Zeit wäre ein Stück weniger unterhaltsam gewesen ohne unseren Kaffeeklatsch und Tratsch! Danke dass Du immer en offenes Ohr für mein Geraunze hattest wenn wieder mal was nicht geklappt hat. Ich bin jedenfalls sehr geehrt dass so ein berühmter Professor neben mir steht ř My loving thanks to my dear family, Mama and Papa and my extended Greek family. Thanks for your tireless support and interest. Thanasis and Thomas. You are the most important people in my world. Thank you for being who you are. ’ 
. Thomas, I see the world from a different perspective thanks to you. I have dedicated this thesis to you.

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