Medicine, Faculty of
Medicine, Department of
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UBCV
Lee, Eun Young (Lisa)
Master of Science - MSc
University of British Columbia
While conventional magnetic resonance imaging (MRI) is qualitatively useful for the diagnosis and clinical management of multiple sclerosis (MS), it has limitations in terms of detecting specific myelin loss and diffuse damage in the normal-appearing white matter. A non-conventional MRI technique, called myelin water imaging (MWI) can be achieved using multicomponent T₂ relaxation to provide a quantitative in vivo measurement of myelin, termed myelin water fraction (MWF). MWF has been proposed as a candidate MR marker of myelin content in the central nervous system. In this study, I present the application of MWI to gain a deeper insight into the diffuse white matter damage in MS. First, we found lower myelin content and higher myelin heterogeneity in brain and cervical spinal cord, as well as correlations between myelin heterogeneity and clinical disability in cervical spinal cord in progressive MS compared to healthy controls. We also found myelin abnormalities in the regional and global white matter in progressive solitary sclerosis, which has recently been proposed as a potential variant of MS. Finally, we demonstrated good global white matter MWF reproducibility (coefficient of variation = 2.77 %; Pearson’s r = 0.91, p < 0.001; mean bias = 0.002) between two sites using different scanner vendors. Together, our findings support that MWI is a useful quantitative imaging technique that may be used to improve our understanding of the pathological processes in MS. Furthermore, inter-vendor reproducibility takes MWI a step closer to routine use for multicenter studies and clinical applications.
APPLICATION OF MYELIN WATER IMAGING TO DETECT DIFFUSE WHITE MATTER DAMAGE IN MULTIPLE SCLEROSIS by Eun Young (Lisa) Lee B.Sc., The University of British Columbia, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2019 © Eun Young (Lisa) Lee, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Application of myelin water imaging to detect diffuse white matter damage in multiple sclerosis submitted by Eun Young (Lisa) Lee in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine Examining Committee: Dr. Shannon Kolind, Medicine (Neurology) Supervisor Dr. Anthony Traboulsee, Medicine (Neurology) Supervisor Dr. Robert Carruthers, Medicine (Neurology) Supervisory Committee Member Dr. Carles Vilarino-Guell, Medicine (Medical Genetics) Supervisory Committee Member Dr. Joanne Weinberg, Cellular and Physiological Sciences Additional Examiner iii Abstract While conventional magnetic resonance imaging (MRI) is qualitatively useful for the diagnosis and clinical management of multiple sclerosis (MS), it has limitations in terms of detecting specific myelin loss and diffuse damage in the normal-appearing white matter. A non-conventional MRI technique, called myelin water imaging (MWI) can be achieved using multicomponent T2 relaxation to provide a quantitative in vivo measurement of myelin, termed myelin water fraction (MWF). MWF has been proposed as a candidate MR marker of myelin content in the central nervous system. In this study, I present the application of MWI to gain a deeper insight into the diffuse white matter damage in MS. First, we found lower myelin content and higher myelin heterogeneity in brain and cervical spinal cord, as well as correlations between myelin heterogeneity and clinical disability in cervical spinal cord in progressive MS compared to healthy controls. We also found myelin abnormalities in the regional and global white matter in progressive solitary sclerosis, which has recently been proposed as a potential variant of MS. Finally, we demonstrated good global white matter MWF reproducibility (coefficient of variation = 2.77 %; Pearson’s r = 0.91, p < 0.001; mean bias = 0.002) between two sites using different scanner vendors. Together, our findings support that MWI is a useful quantitative imaging technique that may be used to improve our understanding of the pathological processes in MS. Furthermore, inter-vendor reproducibility takes MWI a step closer to routine use for multicenter studies and clinical applications. iv Lay Summary Magnetic Resonance Imaging (MRI) safely images the human brain and spinal cord. However, some patients with substantial disability have minimal findings on the MRI. Myelin, the protective layer coating the nerve fibers in the brain and spinal cord, is damaged in multiple sclerosis (MS). Myelin water imaging (MWI) is an MRI technique that measures the concentration of myelin. MWI detects myelin abnormality in brain and spinal cord, not seen with routine clinical MRI. MWI findings were associated with physical disability in progressive MS and showed that progressive solitary sclerosis may be a rare form of MS. MWI can also be used on different MRI machines, which vastly increases the number of research centres that can perform MWI. MWI is a valuable technique to improve our understanding of MS and to better assess treatments that prevent new damage or help repair myelin. v Preface Chapter 2 This work was conducted at the MRI Research Centre at the University of British Columbia (UBC). This work was approved by the UBC Clinical Research Ethics Board (H17-00866, “Establishing an imaging biomarker for disease progression in multiple sclerosis”). Dr. Shannon Kolind designed and conceptualized the study, supervised the overall study and obtained funding. I helped with the recruitment by presenting about the study to the patient population at the MS Society Self Help Group. The neurologists at the UBC Multiple Sclerosis Clinic helped with the majority of the participant recruitment. Shawna Abel, Emilie Nevill and Poljanka Johnson helped with clinical and MRI data collection at baseline. I performed the data analysis, including data transfer, conversion to appropriate file format, creating and smoothing myelin water fraction maps, performing registration, creating and editing regions of interest masks, calculating myelin water fraction values and performing statistical analysis. I created a data analysis pipeline that can be used in the future for further baseline and follow-up data with help from Dr. Irene Vavasour, Adam Dvorak and Hanwen Liu. The co-authors helped with interpretation of the results and provided valuable feedback. I am preparing a manuscript based on this work that will be submitted for peer review. A portion of this work has been accepted for presentation at the Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) conference and at the North American Imaging in Multiple Sclerosis (NAIMS) workshop in February 2019 in Dallas, Texas, United States. vi Publication • Lee LE, Dvorak A, Liu H, Abel S, Johnson P, Vavasour I, Laule C, Tam R, Li D, Chan J, Carruthers R, Traboulsee A, Kolind S. Associations between myelin damage in the cervical spinal cord and disability in relapsing and progressive multiple sclerosis. Proceedings of the ACTRIMS Annual Meeting. Dallas, TX, United States. 2019 (Accepted for presentation). Chapter 3 This work was conducted at the MRI Research Centre at UBC. This work was approved by the UBC Clinical Research Ethics Board (H14-01083, “New magnetic resonance techniques for neuroimaging”; H11-00600, “A phase 3, multicentre, randomized, parallel-group, double-blinded, placebo controlled study to evaluate the efficacy and safety of ocrelizumab in adults with primary-progressive multiple sclerosis”; H11-02278, “A randomized, double-blind, double-dummy, parallel-group study to evaluate the efficacy and safety of ocrelizumab in comparison to interferon beta-1a (Rebif ®) in patients with relapsing multiple sclerosis”). Dr. Robert Carruthers designed and conceptualized the study, as well as, obtained funding to scan progressive solitary sclerosis cases. I coordinated the study, collected MRI data for progressive solitary sclerosis cases and analyzed myelin water imaging data for all participants in the study. I also drafted and revised the manuscript. Dr. Jillian Chan (co-first-author) recruited progressive solitary sclerosis participants and helped drafting and revising the manuscript. Emilie Nevill, Adam Soares and Dr. Erin MacMillan performed magnetic resonance spectroscopy analysis. Drs. Anthony Traboulsee and Shannon Kolind led and obtained funding for clinical trials, where we obtained comparative MRI data of multiple sclerosis and healthy control participants used in this study. vii Multiple sclerosis lesion identification and segmentation were performed by Dr. Roger Tam and the UBC MS/MRI Research Group. Dr. Irene Vavasour contributed to parts of the image analysis, particularly for multiple sclerosis and healthy control data. All co-authors helped interpreting the results and revised the manuscript for intellectual content. This work (or portions thereof) has been accepted and presented at the Pan-Asian Committee for Treatment and Research in Multiple Sclerosis (PACTRIMS), American Academy of Neurology (AAN) and International Society for Magnetic Resonance in Medicine (ISMRM). A manuscript has been accepted for publication in Multiple Sclerosis Journal – Experimental, Translational and Clinical (MSJ ETC). Publication • Chan JK, Lee LE, Traboulsee A, Kolind S, Carruthers R. Myelin water imaging in a progressive solitary sclerosis case. Proceedings of the 10th PACTRIMS Annual Meeting. Ho Chi Minh City, Vietnam. 2017. • Chan JK, Lee LE, Vavasour IM, Tam R, Traboulsee AL, Kolind SH, Carruthers RL. Quantitative assessment of myelin content in normal-appearing brains of rare progressive solitary sclerosis compared to multiple sclerosis and healthy control participants. Proceedings of the 70th AAN Annual Meeting. Los Angeles, CA, United States. 2018. • Lee LE, Chan JK, Vavasour IM, Tam R, Traboulsee AL, Carruthers RL, Kolind SH. Multicomponent relaxation analysis of myelin in the brains of rare progressive solitary sclerosis compared to multiple sclerosis and healthy control participants. Joint Annual Meeting ISMRM-ESMRMB. Paris, France. 2018. viii • Lee LE, Chan JK, Nevill E, Soares A, Vavasour IM, MacMillan EL, Garren H, Clayton D, Keegan BM, Tam R, Traboulsee AL, Kolind SH, Carruthers RL. Advanced imaging findings in progressive solitary sclerosis – a single lesion or a global disease? Accepted in MSJ ETC. 2018. Chapter 4 This work was conducted at the MRI Research Centre at UBC in collaboration with Dr. Jongho Lee’s group at the Seoul National University (SNU), Seoul, Republic of Korea. This work was approved by the UBC Clinical Research Ethics Board (H03-70237, “Magnetic Resonance Measurements on Normal Volunteers”). Drs. Shannon Kolind and Jongho Lee, designed and conceptualized the study and obtained funding. The preliminary work, which I was involved in prior to the start of my MSc, has been presented at PACTRIMS, AAN and ISMRM. We extended our collaboration with Dr. Jongho Lee’s group at SNU, collected new data and improved data analysis method as part of my MSc scope. Emil Ljungberg and I coordinated the study, collected data at UBC, performed data analysis, and drafted and revised the manuscript. I recruited participants to travel to both sites to be scanned. Dongmyung Shin collected data at SNU. Drs. Carolyn Taylor and Biljana Stojkova provided statistical advice. All co-authors helped interpreting the results and revised the manuscript for intellectual content. A manuscript has been published in Frontiers in Neuroscience. Publication • Lee LE, Ljungberg E, MacKay AL, Rauscher A, Li DKB, Traboulsee A, Figley CR, Kolind S. Examining the reproducibility of myelin-specific magnetic resonance imaging ix across sites and scanner manufacturers. Proceedings of the 68th AAN Annual Meeting. Vancouver, B.C., Canada. 2016. • Lee LE, Ljungberg E, MacKay AL, Rauscher A, Li DKB, Traboulsee A, Figley CR, Kolind S. Examining the reproducibility of myelin-specific magnetic resonance imaging across sites and scanner manufacturers. Proceedings of the 9th PACTRIMS Annual Meeting. Bangkok, Thailand. 2016. • Ljungberg E, Cohen-Adad J, Lee LE, Rauscher A, Li DKB, Traboulsee A, MacKay A, Figley CR, Lee J, Koind S. Making myelin water imaging mainstream: multi-site and multi-vendor reproducibility. Proceedings of the 25th ISMRM Annual Meeting. Honolulu, HI, USA. 2017. • Lee LE, Ljungberg E, Shin D, Figley CR, Vavasour IM, Rauscher A, Cohen-Adad J, Li DKB, Traboulsee AL, MacKay AL, Lee J, Kolind SH. Inter-vendor reproducibility of myelin water imaging using a 3D gradient and spin echo sequence. Front Neurosci. 2018; doi: 10.3389/fnins.2018.00854. x Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .............................................................................................................................. iv Preface .............................................................................................................................................v Table of Contents ...........................................................................................................................x List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ................................................................................................................ xvii List of Symbols ........................................................................................................................... xix Acknowledgments ........................................................................................................................xx Dedication .................................................................................................................................. xxii 1. Introduction ................................................................................................................................1 1.1 Central Nervous System ........................................................................................................2 1.2 Multiple Sclerosis ...................................................................................................................4 1.2.1 General Overview of MS ............................................................................................4 1.2.2 The Pathogenesis of MS .............................................................................................7 1.3 Magnetic Resonance Imaging ..............................................................................................10 1.3.1 The Basic Principles of MRI.....................................................................................10 1.3.2 Limitations of Conventional (Clinical) MRI ............................................................12 1.3.3 Myelin Water Imaging ..............................................................................................12 1.3.3.1 Multi-Echo T2 Relaxation ..........................................................................14 1.3.3.2 Multicomponent-driven Equilibrium Single Pulse Observation of T1 and T2 (mcDESPOT) ....................................................................................................14 1.4 Application of MWI in MS ..................................................................................................15 1.5 Overview of Thesis ..............................................................................................................17 2. Associations between myelin damage and disability in relapsing and progressive multiple sclerosis .........................................................................................................................................18 2.1 Introduction .........................................................................................................................18 2.2 Objectives .............................................................................................................................19 2.3 Hypothesis ...........................................................................................................................19 2.4 Methods ................................................................................................................................19 2.4.1 Participant Information .............................................................................................19 xi 2.4.2 Data Acquisition .......................................................................................................19 2.4.3 Data Analysis ............................................................................................................21 2.4.4 Statistical Analysis ....................................................................................................22 2.5 Results ..................................................................................................................................22 2.5.1 Brain Data Results ....................................................................................................23 2.5.2 Spinal Cord Data Results ..........................................................................................30 2.6 Discussion ...........................................................................................................................36 2.7 Conclusion ...........................................................................................................................38 3. Advanced imaging findings in progressive solitary sclerosis – a single lesion or a global disease?..........................................................................................................................................40 3.1 Introduction .........................................................................................................................40 3.2 Objectives .............................................................................................................................41 3.3 Hypothesis ...........................................................................................................................41 3.4 Methods ................................................................................................................................41 3.4.1 Participant Information .............................................................................................41 3.4.2 Data Acquisition .......................................................................................................42 3.4.3 Image Analysis..........................................................................................................43 3.4.4 Statistical Analysis ....................................................................................................43 3.5 Results ..................................................................................................................................44 3.6 Discussion ...........................................................................................................................46 3.7 Conclusion ...........................................................................................................................47 4. Inter-vendor reproducibility of myelin water imaging using a 3D gradient and spin echo sequence ........................................................................................................................................48 4.1 Introduction .........................................................................................................................48 4.2 Objectives .............................................................................................................................48 4.3 Hypothesis ...........................................................................................................................49 4.4 Methods ................................................................................................................................49 4.4.1 Participant Information .............................................................................................49 4.4.2 Data Acquisition .......................................................................................................49 4.4.3 T2 Decay Curve Analysis ..........................................................................................50 4.4.4 Image Analysis..........................................................................................................51 4.4.5 Statistical Analysis ....................................................................................................52 4.5 Results ..................................................................................................................................53 xii 4.6 Discussion ...........................................................................................................................56 4.7 Conclusion ...........................................................................................................................59 5. Conclusion ................................................................................................................................61 Bibliography .................................................................................................................................62 xiii List of Tables Table 3.1: Clinical and demographic information of the participants at the time of MRI scan ....42 Table 4.1: Intersite MWF COVs in the global WM for each participant ......................................54 Table 4.2: Summary of mean intersite MWF COVs in the global WM and ROI averaged across 10 participants ................................................................................................................................54 xiv List of Figures Figure 1.1: General overview of the CNS .......................................................................................3 Figure 1.2: Biological events involved in MS pathogenesis ...........................................................6 Figure 1.3: Subtypes of MS .............................................................................................................6 Figure 1.4: Kurtzke EDSS score in MS ...........................................................................................7 Figure 1.5: Mechanisms of tissue injury and repair in MS ..............................................................9 Figure 1.6: The basic principles of MRI ........................................................................................11 Figure 1.7: Two relaxation processes – longitudinal (spin-lattice) relaxation (T1) and transverse (spin-spin) relaxation (T2) ..............................................................................................................12 Figure 1.8: MWF is derived from the fraction of MR signals originating in water pool trapped between myelin sheaths ................................................................................................................14 Figure 1.9: mcDESPOT uses a series of rapid steady state images over a range of flip angles using SPGR and bSSFP signal models ..........................................................................................15 Figure 2.1: MWF distribution in the global WM in brain and cervical spinal cord in HC, RRMS and ProgMS ...................................................................................................................................23 Figure 2.2: Mean MWF in brain of HC, RRMS and ProgMS .......................................................24 Figure 2.3: MWF SD in brain of HC, RRMS and ProgMS ...........................................................25 Figure 2.4: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 14 RRMS participants ...................................................................26 Figure 2.5: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 17 ProgMS participants .................................................................26 Figure 2.6: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 14 RRMS participants ...................................................................27 Figure 2.7: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 16 ProgMS participants .................................................................27 Figure 2.8: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 15 RRMS participants ...................................................................28 xv Figure 2.9: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 18 ProgMS participants. ................................................................28 Figure 2.10: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 15 RRMS participants ...................................................................29 Figure 2.11: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 17 ProgMS participants .................................................................29 Figure 2.12: MWF mean and SD in the cervical spinal cord at the C2/C3 cord level of HC, RRMS and ProgMS ......................................................................................................................31 Figure 2.13: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants.......................................................................32 Figure 2.14: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 16 ProgMS participants ....................................................................32 Figure 2.15: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 12 RRMS participants.......................................................................33 Figure 2.16: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 15 ProgMS participants ....................................................................33 Figure 2.17: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants .....................................................34 Figure 2.18: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 17 ProgMS participants ...................................................34 Figure 2.19: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants.......................................................................35 Figure 2.20: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 16 ProgMS participants ....................................................................35 Figure 3.1: Example slices through the MWF Z-score maps of two PSS cases ...........................45 xvi Figure 3.2: MWF and NAA/tCr in normal-appearing brain .........................................................46 Figure 4.1: Key steps in the quantitative analysis of myelin (T2 decay curve) ............................51 Figure 4.2: MWF maps from 10 healthy participants from site 1 and site 2 ................................55 Figure 4.3 Correlation plot of mean MWF and Bland-Altman plot comparing MWF measurements between site 1 and site 2 in global WM mask across 10 healthy participants .......55 Figure 4.4: Mean MWF measurements between site 1 and site 2 in genu and splenium of the corpus callosum, major forceps, minor forceps and superior longitudinal fasciculus across 10 healthy participants .......................................................................................................................56 xvii List of Abbreviations 9HPT nine-hole peg test bSSFP balanced steady state free precession COV coefficient of variation CNS central nervous system DTI diffusion tensor imaging EDSS expanded disability status scale FAST FMRIB’s automated segmentation tool FLAIR fluid-attenuated inversion recovery FLIRT FMRIB’s linear image registration tool FNIRT FMRIB’s non-linear image registration tool GRASE gradient and spin echo HC healthy control HLA human leukocyte antigen IgG immunoglobulin G mcDESPOT multicomponent-driven equilibrium single pulse observation of T1 and T2 MHC major histocompatibility complex MNI-152 Montreal Neurological Institute (anatomical standard template for brain) MPRAGE magnetization-prepared rapid gradient-echo MRI magnetic resonance imaging MRS magnetic resonance spectroscopy MS multiple sclerosis MSSS multiple sclerosis severity score MT magnetization transfer MWF myelin water fraction MWI myelin water imaging NAA N-acetyl-aspartate NADPH nicotinamide adenine dinucleotide phosphate hydrogen NAWM normal-appearing white matter NNLS non-negative least squares PAM50 anatomical standard template for the spinal cord PD/T2 proton density T2-weighted PPMS primary progressive multiple sclerosis PRESS point resolved spectroscopy xviii ProgMS progressive MS PSS progressive solitary sclerosis RF radiofrequency ROI region of interest RRMS relapsing remitting multiple sclerosis SENSE sensitivity encoding SNR signal to noise ratio SPGR spoiled gradient recalled echo SPMS secondary progressive multiple sclerosis T1 longitudinal (spin-lattice) relaxation time T2 transverse (spin-spin) relaxation time T25W timed 25-foot walk TE echo time TI inversion time TR repetition time tCr total creatine WM white matter xix List of Symbols r correlation coefficient (Spearman’s rho in Chapter 1, Pearson’s r in Chapter 4) a flip angle µ population mean s population standard deviation c sample mean T Tesla xx Acknowledgments First and foremost, I offer my heartfelt thanks to my supervisors, Dr. Shannon Kolind and Dr. Anthony Traboulsee, for their excellent mentorship, guidance and support. They provided me with valuable opportunities that allowed me to develop as a scientist and taught me more than I could ever have learned in a classroom. I am very grateful to my supervisory committee members, Dr. Robert Carruthers and Dr. Carles Vilarino-Guell for their teaching, support and insightful comments that were fundamental to my scientific growth. Special thanks to Dr. Irene Vavasour for answering my endless questions and brightening up a day at the office with her humour, which I will miss very much. Additional special thanks to Emil Ljungberg for patiently teaching me from the very basics when I first started MRI research and Dr. Jillian Chan for her clinical insight, teachings and encouragement. I sincerely thank Drs. Joanne Weinberg, Alex MacKay, Alexander Rauscher, David Li, Erin MacMillan, Cornelia Laule, Roger Tam, Jacqueline Quandt, Carolyn Taylor and Biljana Stojkova for their valuable feedback during my thesis. I thank Carina Graf, Karina Antonenko, Jayoung Lee, Michelle Eisner, Bretta Russell-Schulz, Kevin Hanwen Liu, Adam Dvorak, Shawna Abel, Poljanka Johnson, Emilie Nevill, Heather Yong, Dayna Ortner, Sarah Morris, Vanessa Wiggermann, Andrew Riddehough, Ken Bigelow, Kevin Lam, Laura Barlow, Jen Procter, Alex Mazur, Fraser Muir, Sumeet Mutti, Jared Splinter, Linda Chandler, Dr. Shelly Au, Dr. Nathalie Ackermans, clinical trials team, collaborators and participants for their wonderful research help and support. I acknowledge Canadian Institutes of Health Research for awarding me a Frederick Banting and Charles Best Canada Graduate Scholarship – Master’s. I also acknowledge Vancouver Korean-Canadian Scholarship Foundation for awarding me a scholarship. Finally, I express my deepest gratitude to my family and friends, who have always provided me with unconditional love, support and care xxi that enabled me to reach the biggest milestones in my life thus far. I am forever grateful to have worked with collaborators, researchers and participants, especially patients, who have constantly inspired me and changed my purpose of research from a simple scientific curiosity to my desire of providing answers to people that were hopeful for scientific discovery. xxii Dedication To the patients that have shared powerful stories that have left me speechless and inspired me to continue contributing to advance medicine. 1 Chapter 1 Introduction “At inception and into the future, our goal remains: To find the cause and cure of MS through research, training and multidisciplinary clinical care.” Donald W. Paty, UBC Professor and former Director of the UBC MS Research Programs. In 1980, Professor Paty arrived at UBC to develop the multiple sclerosis (MS) clinic with a leading neuroimaging unit that would integrate research and care to advance medicine in MS. He greatly contributed to bringing the British Columbia’s first magnetic resonance imaging (MRI) scanner to UBC. He recognized the value of MRI for improving MS diagnosis, management and research. Since then, landmark studies have established MRI outcome measures as a key clinical trials endpoint that are pivotal to regulatory approval of disease modifying drugs, such as interferon beta-1b, for MS1. Thus, the use of MRI has led to the important milestones in better understanding and improving the management of MS. In this thesis, I focus on an MRI technique, called myelin water imaging (MWI), that can quantify the myelin content in the central nervous system (CNS). To understand the application of MWI in MS, I will begin by reviewing the pathology of MS and how MWI quantifies myelin damage using T2 relaxation. Then, I will present my MWI findings in MS and progressive solitary sclerosis (PSS), which has recently been proposed as a potential rare variant of MS. Finally, I will present how MWI can be used for multicenter studies and clinical applications. 2 1.1 Central Nervous System The CNS consists of the brain and spinal cord. A key anatomical and physiological feature of the CNS is a highly selective semi-permeable membrane, called the blood brain barrier, which is comprised of specialized endothelial cells held together by tight junctions2,3. This maintains the CNS homeostasis by strictly regulating the transfer of ions, molecules and cells between the peripheral blood and the CNS; thereby, protecting the CNS from harmful substances, such as toxins and pathogens in blood2,3. Furthermore, pericytes embedded in the vascular basement membrane and astrocytes help to sustain the blood-brain barrier2,3. In MS, the blood-brain barrier may become disrupted when the endothelium is exposed to a high level of pro-inflammatory cytokines (i.e. tumor necrosis factor a, interleukin-6/12 and interferon g), thereby, disorganizing tight junctions between the cells, expressing adhesion molecules on the endothelial surface that enables activated leukocyte adhesion and migration into the CNS4. The two main tissue types of the CNS include gray matter (GM) and white matter (WM), predominantly composed of neuronal cell bodies and myelinated axons, respectively (Fig. 1.1)5. The functional unit of the CNS is a neuron, which is divided into the soma (cell body), axon (elongated extension that propagates an electrical impulse to send messages), and dendrites (branches at the end of the neuron to receive messages from other neurons) (Fig. 1.1). Neurons enable transmission of information through electrical (the action potential along the axon) and chemical (neurotransmitter release and uptake at the synapses) signals5. Most mature neurons are post-mitotic and unable to undergo cell division; thus, neuronal loss can result in a permanent functional impairment in a patient6. In addition, glial cells, including oligodendrocytes, astrocytes and microglia, are important support cells of the CNS5. Oligodendrocytes produce myelin, which is a fatty insulating substance that envelops the axons enabling saltatory 3 conduction of action potentials and directly supplying energy metabolites to the axons7–9. A single oligodendrocyte can myelinate multiple axons; thus, a loss of one oligodendrocyte may demyelinate many axons and make the CNS vulnerable to further damage5,10. Astrocytes provide physical and biochemical support for neurons and maintain homeostasis5. At the site of the injury, astrocytes may become reactive and form astroglial scars to limit the spread of inflammation11. Microglia are the resident macrophages of the CNS that phagocytose cellular debris and pathogenic substances5. For example, in MS, microglia clear myelin debris and degenerating axons12,13. However, microglial activation may also contribute to disease progression in MS (Fig. 1.3)14,15. For example, activated microglia produce neurotoxic molecules, such as reactive oxygen species, that induce oxidative damage and contributes to axonal injury 15–18. Therefore, the CNS is a complex communication system that has limited ability for repair. Figure 1.1: General overview of the CNS. The brain and spinal cord consists of gray matter (appears gray due to mainly neuronal cell bodies) and white matter (appears white due to mainly 4 fatty myelin around the axons) (A). The functional unit of the CNS is a neuron, which can be divided into myelinated axon, cell body and dendrites (B). (Image sources: (A) Ref. 19 (B) Ref. 20) 1.2 Multiple Sclerosis A possible case of MS was first described in 1421 by Count of Holland, Jan Van Bieren, about a 16-year-old girl, Lidwina, who developed an acute illness and fell while skating on a frozen canal21. She later experienced facial pain, blindness in one eye, weakness and numbness in both legs and eventually died in 1433 at the age of 5321. Many years later, in 1868, Dr. Jean-Martin Charcot first defined MS after observing the correlations between the clinical characteristics of MS and pathological changes postmortem, particularly the sclerotic scars in the brain and spinal cord22. His findings sparked much interest for further study in MS. 1.2.1 General Overview of MS MS is a chronic inflammatory disease of the CNS characterized primarily by demyelination (Fig. 1.2)23. In MS, the myelin, a fatty protective layer ensheathing the axon enabling saltatory conduction of action potentials and direct supply of energy metabolites to the axons, become damaged7,8. This may lead to subsequent axonal loss and neurodegeneration, resulting in irreversible neurological disability (Fig. 1.2)23. Depending on the site of the injury (lesion), the symptoms may vary. A lesion refers to scarring in the CNS due to inflammation triggered by the immune system attacking myelin. Symptoms can include limb weakness, vision loss, Lhermitte’s sign (paresthesia in trunk and extremities when the neck is flexed forward), cognitive impairment, fatigue, neurogenic bladder/bowel, sensory disturbances, spasticity, sexual dysfunction and depression24,25. The MRI findings, particularly dissemination of lesions over space and time, and laboratory findings, such as the presence of oligoclonal bands and elevated IgG index in the cerebrospinal fluid, support an early diagnoses of MS25. There is a female 5 preponderance in relapsing MS26. MS typically presents in adults between 20 to 45 years of age27. The exact etiology of MS remains unclear; however, a combination of genetic (i.e. risk genes, such as HLA-DRB1) and environmental (i.e. Epstein-Barr virus, vitamin D deficiency, obesity, tobacco exposure, toxins) factors are thought to contribute to the development of MS23,25,27,28. Most MS cases (85%) start with a relapsing-remitting course (RRMS) marked by episodes of relapses (MS attacks) followed by a period of remission with either full recovery or residual deficits (Fig. 1.3)27,29. Between 60-70% of the RRMS patients transition into a secondary progressive phase (SPMS) within 20-25 years and gradually accumulate disability (Fig. 1.3)27,29. About 10% of MS cases undergo continuous neurological decline from the disease onset independent of relapses, termed primary progressive MS (PPMS) (Fig. 1.3)27,29. The Kurtzke Expanded Disability Status Scale (EDSS) is used as a common clinical score to evaluate neurological impairment in MS; however, EDSS puts much emphasis on mobility function (Fig. 1.4)30. The nine-hole peg test (9HPT) and timed 25-foot walk (T25W) are also used to evaluate upper extremity function and ambulation, respectively. Although, there are disease modifying therapies available for RRMS and one recently developed drug for progressive MS, there is no known cure23,31. 6 Figure 1.2: Biological events involved in MS pathogenesis. Lymphocytic infiltration across the blood-brain barrier may drive inflammation leading to demyelination and subsequent axonal transection. This prevents a nerve impulse from propagating through the axon and presents as an acute lesion on an MRI. Activated microglia or redistribution of sodium channels along demyelinated axons may promote axonal degeneration. Astrocytic gliosis (proliferation) may inhibit repair (Image source: Ref. 14) Figure 1.3: Subtypes of MS. Most MS cases start with a relapsing-remitting course, which may then transition into a secondary-progressive phase. Some MS cases may start with a primary-progressive phase, in which the patients gradually accumulate disability from the disease onset independent of relapses (MS attacks presented as sharp peaks). (Image source: Ref. 32) 7 Figure 1.4: Kurtzke EDSS score in MS. A score of 0.0 indicates no disability and a score of 10.0 indicates death due to MS. (Image adapted from: Ref. 30) 1.2.2 The Pathogenesis of MS Autoreactive peripheral B and CD4+ T cells (helper T cells) cross the compromised blood-brain barrier and enter the CNS25,33. In the CNS, these cells recognize the self-antigens and become reactive to myelin peptides (i.e. myelin basic protein, myelin oligodendrocyte glycoprotein, myelin-associated glycoprotein) (Fig. 1.5) 25,34,35. This amplifies immune response by producing autoantibodies, chemokines and pro-inflammatory cytokines (i.e. tumor necrosis factor a, interleukin-6/12 and interferon g) resulting in further migration of peripheral immune cells (macrophages, CD4+ T cells, CD8+ T cells and B cells) into the CNS, as well as activation of macrophages and microglia in the CNS25,34,35. Activated macrophages and microglia in the CNS produce more pro-inflammatory cytokines, generate reactive oxygen and nitrogen species, release excitotoxins (glutamate), activate complements and release proteolytic and lipolytic enzymes25. Cytotoxic CD8+ T cells target antigens presented on MHC class I on oligodendrocytes and neurons to directly induce tissue injury (Fig. 1.5)25,36,37. Although MS used to be known as a T-cell mediated disease, it is now widely recognized that B cells also contribute to the pathogenesis of MS. In addition to producing cytokines and presenting antigens to T cells, B cells can also differentiate into plasma cells to secrete autoantibodies that target the antigens on myelin and oligodendrocytes34,38. The autoantibodies activate complements leading to 8 complement-mediated cytotoxicity resulting in cell lysis (Fig. 1.5)25. The importance of B cells in the pathogenesis of MS was supported by the clinical efficacy of anti-CD20 therapies, rituximab and ocrelizumab, which deplete B cells in MS patients31,39,40. Together, these processes lead to demyelination, a pathological hallmark of MS, with variable amounts of axonal loss in both lesions and normal-appearing white matter (NAWM; abnormal white matter region that can qualitatively appear normal on a conventional (clinical) MRI)25,41,42. Macrophages and microglia may engulf myelin debris and degenerated axons12. Oligodendrocyte precursor cells can mature into myelin-forming oligodendrocytes to repair injury25. However, when the tissue injury exceeds the compensatory capacity of the CNS, MS patients develop worsening symptoms33. Some other processes driving progression in MS include microglial activation, which produces chronic oxidative stress that promotes mitochondrial dysfunction, thereby, contributing to axonal injury14,43. Furthermore, glutamate excitotoxity and astrocytic gliosis may contribute to irreversible axonal injury and neurodegeneration (Fig. 1.2)14,43. 9 Figure 1.5: Mechanisms of tissue injury and repair in MS. (Image source: Ref. 25) 10 1.3 Magnetic Resonance Imaging Some limitations to pathological assessment include limited availability of biopsy tissue and evaluation of a single snapshot of the disease, thus, missing information about the evolution of the disease over time. MRI, which uses magnetic field and radiofrequency (RF) pulse to produce an image, is a powerful tool that can be used to improve our understanding about the pathophysiology of MS in vivo. 1.3.1 The Basic Principles of MRI The human body mainly consists of water. Hydrogen protons found in water molecules have a magnetic property called nuclear spin (Fig. 1.6)44,45. To obtain an MR image, the subject is placed in an MRI scanner, then a strong external magnetic field (B0) is applied to align the hydrogen proton spins (often simply called “spins”) parallel or anti-parallel to the direction of the magnetic field (Fig. 1.6)44,45. A slightly greater number of spins align parallel to the field than anti-parallel, leading to a net magnetization, which is what is measured using MRI. Spins precess around the direction of B0 at a frequency proportional to the strength of B0 (Fig. 1.6)44,45. A magnetic pulse applied at the precession frequency can be used to tip the spin away from B0 (Fig. 1.6)44,45. For magnetic field strengths used for clinical applications, the frequency required falls in the RF range, thus this process is referred to as applying an RF pulse. Once the RF pulse stops, the spin releases energy to return to its alignment with B0 (Fig. 1.6)44,45. This is known as relaxation. MR image contrast depends on two relaxation times: longitudinal (spin-lattice) relaxation time (T1) and transverse (spin-spin) relaxation time (T2) (Fig. 1.7)44,45. T1 measures the time required to recover 63% of longitudinal magnetization (Fig. 1.7)44. T2 measures the time required to decay 37% of transverse magnetization (Fig. 1.7)44. T2 characterizes the decay of the 11 magnetization within the transverse plane due to random interactions between spins that cause an irreversible loss of phase coherence and overall loss of signal. Both T1 and T2 depend on tissue composition and structure. This relaxation produces a signal detected by the receiver coil and sends to the computer, which then uses a multidimensional Fourier transform to generate an MR image44,45. Figure 1.6: The basic principles of MRI. MRI is based on the excitation and relaxation of hydrogen protons (H+) from water molecules that possess magnetic properties called nuclear spin. (Image source: L. E. Lee) 12 Figure 1.7: Two relaxation processes - longitudinal (spin-lattice) relaxation (T1) and transverse (spin-spin) relaxation (T2). T1 measures the time required to recover 63% of longitudinal magnetization. T2 measures the time required to decay 37% of transverse magnetization. (Image source: Ref. 44) 1.3.2 Limitations of Conventional (Clinical) MRI While conventional (clinical) MRI, such as T1 and T2-weighted images, has the benefit of qualitatively demonstrating lesion distribution over space and time, it has limitations. Progress in MS research is hampered by low specificity to pathology and low sensitivity to diffuse damage in the NAWM. Cross-sectional and longitudinal findings on conventional MRI may not correlate with clinical disability, representing clinical-radiographic dissociation. To overcome these issues, non-conventional (advanced) MRI techniques can be used. In particular, I will focus on myelin water imaging in this thesis because of its potential value in the field of MS research. 1.3.3 Myelin Water Imaging Previously, magnetization transfer (MT) imaging and diffusion tensor imaging (DTI) have been commonly used to study myelin in vivo due to wide availability46,47. MT refers to the exchange of magnetization between mobile (free) hydrogen nuclei and immobile (restricted) hydrogen nuclei bound to large macromolecular proteins and lipids (i.e. in myelin)47. DTI is based on the 13 concept of diffusion of water molecules. Depending on the tissue type, water molecules can diffuse more or less freely in a particular direction, which provides information about orientation of neurons. The perpendicular component of the diffusion tensor, termed radial diffusivity, has been shown to inversely correlate with myelination47. However, neither MT nor DTI provide metrics specific to myelin. MT effects have shown to be strongly affected by inflammation and edema48,49. DTI measures have been shown to reflect fibre coherence, axonal density and membrane permeability, in addition to myelin46,50,51. Fortunately, multicomponent T2 relaxation measures can be used to provide more specific and sensitive information about myelin content. Quantitative measurement of in vivo multicomponent T2 relaxation in the CNS can provide information about pathophysiology based on different water environments in tissues52. MWI was developed and pioneered by Dr. Alex MacKay at the University of British Columbia52. It uses multi-component T2 relaxation analysis to quantify the MR signals from different water compartments within a voxel52–54. Healthy tissue in the CNS typically contains intra- and extra-axonal water (intermediate T2 component of ~ 70 ms) and myelin water in between myelin bilayers (short T2 component of ~15 ms) (Fig. 1.8)52–54. The myelin water fraction (MWF), the ratio of the MR signal from the short T2 component (myelin water) to the total MR signal, has been proposed as an in vivo marker of myelin content in the CNS (Fig. 1.8)52–54. MWF has been shown to strongly correlate with histological measures using myelin-specific staining in rats55–57, guinea pigs48,58 and postmortem human brains59,60. MWI has been widely used to study WM abnormalities in MS46,61,62. In this thesis, we used two different multicomponent T2 relaxation methods to acquire MWF data. 14 Figure 1.8: Myelin water fraction (MWF) is derived from the fraction of MR signals originating in water pool trapped between myelin sheaths. (Image adapted from: Ref. 63) 1.3.3.1 Multi-Echo T2 Relaxation Multi-echo T2 relaxation is a widely-used and histopathologically validated technique to study myelin pathology in MS41,64,65. A combined gradient and spin echo (GRASE) sequence was developed to accelerate acquisition of a traditional multi spin-echo pulse sequence (i.e. Carr-Purcell-Meiboom-Gill) to perform MWI66,67. GRASE MWI can be acquired in under 15 minutes with full cerebral coverage67. It typically consists of a 90° excitation pulse followed by a train of 180° refocusing pulses, each of which generates an echo as the spins re-phase. This produces a T2 decay curve, which is decomposed into multiple T2 components to create a T2 distribution52,53,67. Then, MWF can be calculated. Further details about T2 decay curve analysis using multi-echo T2 relaxation will be discussed in Chapter 4. 1.3.3.2 Multicomponent-driven Equilibrium Single Pulse Observation of T1 and T2 (mcDESPOT) An alternative approach to multi-echo based T2 mapping is the multicomponent-driven equilibrium single pulse observation of T1 and T2 (mcDESPOT)68,69. This method also estimates 15 MWF using the same concepts as multi-echo T2 relaxation; however, data acquisition is fundamentally different. Instead of using multiple spin echoes, mcDESPOT uses a series of spoiled gradient recalled echo (SPGR) and balanced steady state free precession (bSSFP) across a range of flip angles to extract myelin water and intra/extra-axonal water components in the CNS (Fig. 1.9)68,69. An advantage of mcDESPOT includes whole brain imaging under 10 minutes with high spatial resolution69,70. It can also produce T1 maps and estimate other information, such as myelin water residence time (proposed to indicate myelin thickness)71. However, mcDESPOT still lacks histological validation. Figure 1.9: mcDESPOT uses a series of rapid steady state images over a range of flip angles using spoiled gradient recalled echo (SPGR) and balanced steady state free precession (bSSFP) signal models. The red curve indicates the overall observed cumulative signal. Using this information, a fitting algorithm computes the optimal set of parameters to fit fast (more restricted myelin water in green) and slow (less restricted intra/extra-axonal water in blue) relaxing water pools to estimate MWF. (Image courtesy of Dr. Shannon Kolind) 1.4 Application of MWI in MS There has been much evidence showing a significant reduction of MWF in MS lesions compared to the WM of healthy control (HC) participants in the brain61,72. For example, Faizy et al found a mean MWF difference in contrast-enhancing (active) lesions (70.2 %), black holes (WM lesions 16 that appear hypointense on T1-weighted image and is concordant with a hyperintense lesion on a T2-weighted image that may indicate axonal and neuronal destruction) (89.3 %) and T2 lesions (defined as non-contrast-enhancing and non-black hole lesions) (78.0 %) of RRMS participants compared to the WM of HC participants61. More interestingly, MWF has shown to be sensitive, not only to the changes in MS lesions, but also more subtle diffuse changes in brain NAWM46,61,72–75. The mean reduction of MWF in brain NAWM of MS compared to WM of HC participants has ranged from 16 to 37 %61,74,76. The wide range of MWF levels may be due to differences in MWI techniques or study protocol, participant selection and heterogeneity of MS. Furthermore, there have been variable results regarding the correlation between the MWF in brain NAWM and clinical scores. For example, Kolind et al found a significant correlation between lower MWF in brain NAWM and higher EDSS indicating clinical disability; however, Faizy et al could not find this correlation61,73. The spinal cord is inherently more difficult to image than the brain resulting in fewer MRI studies in MS participants. The spinal cord is much smaller (approximately 1.5 cm across at the widest point in cervical enlargement in adult humans) than the brain77. Further, spinal cord imaging is more prone to artefacts due to physiological motion (cardiac, vascular, respiratory, cerebrospinal fluid flow)77. In addition, inhomogeneous magnetic field in the spinal cord may result in a loss of signal intensity and image distortion77. Kolind et al found that lower MWF in the cervical spinal cord had a significant correlation with higher EDSS and T25W indicating decline in ambulatory function and a trend with lower 9HPT indicating reduced manual dexterity in PPMS participants46. They also found a 19 % MWF reduction in the cervical spinal cord of PPMS compared to HC participants46. Further, Combes et al found a significantly lower MWF in 17 lesions and normal-appearing cervical spinal cord in RRMS compared to HC participants cross-sectionally78. They also found a decreased MWF in normal-appearing cervical spinal cord over one year in RRMS participants78. A key clinical importance of MWI in MS is to evaluate treatment efficacy. Vavasour et al have shown a steady 4 % increase in MWF in brain NAWM of RRMS participants treated with alemtuzumab compared to 10 % decrease in RRMS participants without treatment over 5 years79. Therefore, these evidences suggest that MWI is feasible to assess pathological changes in MS. 1.5 Overview of Thesis The goal of this thesis is to present the utility of MWI to study diffuse myelin damage in MS. In Chapter 2, I aim to compare MWF in the brain and spinal cord of RRMS, progressive MS and HC participants, then, correlate MWF with clinical scores in MS cohorts. I hypothesized that the MWF would be lower in MS cohorts compared to HC participants in both brain and spinal cord, which would correlate with increased disability scores. In Chapter 3, I aim to use MWF to evaluate whether in PSS, a potential variant of MS, pathology is restricted to a solitary lesion or if diffuse brain pathology beyond the focal demyelinating lesion is also evident. I hypothesized that there would be an extensive myelin abnormality in the NAWM of PSS cases. Finally, in Chapter 4, I aim to bring MWI a step closer to clinical application and multicentre studies by examining the reproducibility of MWI between two sites and two MRI scanner vendors. I hypothesized that the MWF in the white matter would be reproducible across sites and scanner vendors. 18 Chapter 2 Associations between myelin damage and disability in relapsing and progressive multiple sclerosis 2.1 Introduction The prevalence of MS in Canada is among the highest in the world, affecting over 100,000 individuals80,81. The total annual health sector cost for MS is predicted to exceed $2 billion by 2031 in Canada81. Although over 50 % of people with MS have a progressive form of the disease leading to permanent disability and unemployment, there is only one approved drug option available in Canada for progressive MS, which does not yet have public drug coverage31. Past clinical trials have largely been unsuccessful, likely, in part, due to lack of imaging biomarkers. For example, new lesions on MRI have served as a meaningful marker in development of relapsing MS therapies; however, new lesions are less common in progressive MS and may not directly reflect disease progression82. Similar imaging outcomes have not yet been established for progressive MS, hindering development of new therapies. As myelin is damaged in the brain and spinal cord of MS, MWI could improve understanding of the biological changes that may drive progression and increase disability46,72,83. An imaging marker for progressive MS is in urgent need to better understand the pathogenesis, develop effective therapies and improve quality of life of people with MS. In this chapter, I analyzed cross-sectional MWF data collected for a subset of RRMS, progressive MS (ProgMS) and HC cohorts. MWF mean (myelin content) and standard deviation (SD; myelin heterogeneity) can be used to study myelin abnormality. This study will continue after the completion of my thesis and the same protocol will be used to collect and analyze longitudinal data and compare the results over 2 years to find potential imaging markers for disease progression in MS. 19 2.2 Objectives (1) To assess myelin damage in the brain and cervical spinal cord WM in relapsing and progressive MS compared to HC participants. (2) To evaluate the correlation between MWF and disability as measured by 9HPT, T25W and EDSS. 2.3 Hypothesis We hypothesized that, using MWI, we could reveal a greater myelin damage in the brain and cervical spinal cord of progressive MS participants compared to relapsing MS and HC, which would correlate with higher clinical disability scores. 2.4 Methods 2.4.1 Participant Information Fifteen RRMS (10 females and 5 males; mean age 41 years (range 26-57 years); median EDSS 2.25 (0.0-4.0); mean disease duration 5 years (0-11 years)), 18 ProgMS (13 females and 5 males; mean age 58 years (48-65 years); median EDSS 4.0 (2.0-8.5); mean disease duration 20 years (1-42 years)) and 14 HC (12 females and 2 males; mean age 40 years (22-63 years)) participants were included in the study. MS participants were diagnosed accordingly to the 2010 revised McDonald diagnostic criteria84. Our ProgMS cohort included 12 SPMS and 6 PPMS participants. HC participants had no previously known neurological disorders or brain or spinal cord abnormalities. All participants provided written informed consent prior to participation. The study was approved by the Clinical Research Ethics Board at UBC. 2.4.2 Data Acquisition 20 All MR imaging was performed on a Philips Achieva 3 T (Philips Medical Systems, Best, The Netherlands) using an 8-channel head coil and a 6-channel spine coil to obtain brain and cervical spinal cord data, respectively. MWI data were acquired using a 3D GRASE sequence in the brain and cervical spinal cord at the C2/C3 cord level. Additionally, we acquired a high-resolution 3DT1-weighted image and multi-echo fast gradient echo (mFFE) image of the brain and cervical spinal cord, respectively, for tissue segmentation and spatial normalization. The MRI protocols used to obtain MWI data were as follows: • Brain GRASE: 48 echoes, TE = 8 ms x n, n = 1 … 48, TR = 1073 ms, acquired at 1 x 2 x 5 mm3, 20 slices (reconstructed at 1 x 1 x 2.5 mm3, 40 slices), SENSE factor of 2, acquisition time = 7.5 min. • Cervical Spine GRASE: 32 echoes, TE = 10 ms x n, n = 1 … 32, TR = 1501 ms, acquired at 0.75 x 0.75 x 5.0 mm3, 8 slices (reconstructed at 0.63 x 0.63 x 2.5 mm3, 16 slices), SENSE factor of 2, field of view = 180 x 150 x 40 mm3, acquisition time = 8.5 min. Furthermore, clinical data, including EDSS, T25W, 9HPT and disease duration were collected at the UBC MS Clinic. EDSS was evaluated by a neurologist. T25W measured the time it required for the MS participant to reach the 25-foot mark. A second trial immediately followed repeating this procedure. MS participants were able to use assistive devices if needed. T25W score used in the study was the average time between the two trials. 9HPT measured the time it required for the MS participants to individually place each peg into the holes on the board as quickly as possible using the dominant hand. This process was immediately repeated the second time. 9HPT score used in the study was the average time between the two trials. Disease duration was defined as the period of the disease course since the onset of first symptoms. 21 2.4.3 Data Analysis Brain: All analyses were performed on FMRIB Software Library (FSL)85. A global WM mask was obtained from the 3DT1 using FMRIB’s Automated Segmentation Tool (FAST), which was then transformed into the native space of GRASE data using the FMRIB’s Linear Image Registration Tool (FLIRT)85,86. As we were interested in the global disease pathology rather than pathology in the lesion or NAWM alone, we manually re-inserted any lesions that had been automatically excluded during the FAST process back into the WM mask. Then, the WM mask was thresholded and binarized. Additionally, ROI, including the superior longitudinal fasciculus, inferior longitudinal fasciculus and corpus callosum, were obtained from JHU atlases in MNI-152 space and transformed to GRASE space using FSL’s combined FLIRT and FMRIB’s Non-Linear Image Registration (FNIRT)87–89. We further extracted substructures of the corpus callosum, including the genu, body and splenium of the corpus callosum. All ROI were then multiplied by the global WM mask, thresholded and binarized to create more conservative ROI masks. The MWF mean and SD within global WM, superior longitudinal fasciculus, inferior longitudinal fasciculus and corpus callosum ROI for the 32 middle slices were calculated then, correlated with EDSS, T25W, 9HPT and disease duration. Spine: Data from 2 RRMS and 1 ProgMS participants were excluded due to substantial flow artefact throughout the cervical spinal cord or ghosting artefact. Registration and segmentation were performed using the Spinal Cord Toolbox90. MWF calculation within each ROI was performed using FSL. Global cervical spinal cord and global WM masks at the C2/C3 cord level were extracted on mFFE image. Three ROI, including dorsal column, lateral funiculi and ventral funiculi were obtained from the PAM50 spinal cord template. Global cervical cord, global WM 22 and the three ROI masks were then transformed into the GRASE space and the MWF mean and SD were extracted using FSL85. The MWF mean and SD within global cervical cord, global WM and each ROI for the 10 middle slices were calculated, then correlated with EDSS, T25W, 9HPT and disease duration. All MWF maps were obtained from the multi-echo T2 relaxation data acquired by GRASE using a regularized non-negative least squares (NNLS) algorithm with stimulated echo correction67,91. We applied non-local spatial regularization (nlsrNNLS) to create brain MWF maps92. Due to unavailability of clinical data, 1 ProgMS was excluded from correlation analysis with EDSS; 1 RRMS and 2ProgMS from correlation with T25W, and 1ProgMS from correlation with 9HPT. 2.4.4 Statistical Analysis Non-parametric statistics, Mann-Whitney U test, were performed to compare MWF mean and SD in the brain and cervical spinal cord between cohorts. Spearman’s rank correlation coefficient (r) was used to correlate MWF mean and SD with T25W, EDSS, 9HPT and disease duration. 2.5 Results The MWF distribution histogram illustrates higher voxel count at lower MWF values and lower voxel count at higher MWF values for ProgMS compared to RRMS and HC, which is as expected due to disease pathology (Fig. 2.1). 23 Figure 2.1: MWF distribution in the global WM in brain (left) and cervical spinal cord (right) in HC (blue), RRMS (orange) and ProgMS (green). The voxel count presented on y-axis was normalized by dividing the voxel count within each ROI by the total voxel count. 2.5.1 Brain Data Results Mean MWF in the global WM was significantly lower in ProgMS compared to HC (-10.03 %, p = 0.008; Fig. 2.2). No significant correlation was found between mean MWF and EDSS, T25W, 9HPT or disease duration in RRMS or ProgMS in any of the ROI (p ≥ 0.07; Fig. 2.4 – 2.11). MWF SD in the global WM was significantly higher in RRMS than HC (+12.89 %, p < 0.001) and also higher in ProgMS compared to HC (+23.50 %, p < 0.001) and RRMS (+9.40 %, p = 0.04; Fig. 2.3). MWF SD in the superior longitudinal fasciculus was significantly higher in ProgMS compared to HC (+18.39 %, p = 0.005) and RRMS (+15.49 %, p = 0.01; Fig. 2.3). MWF SD in the splenium of the corpus callosum was significantly higher in ProgMS than HC (+16.08 %, p = 0.01; Fig. 2.3). There was a low to moderate correlation between MWF SD and EDSS in the corpus callosum for RRMS (r = -0.57, p = 0.03; Fig. 2.4) and between MWF SD and T25W in the inferior longitudinal fasciculus for ProgMS (r = -0.50, p = 0.05; Fig. 2.7). 24 Figure 2.2: Mean MWF in brain of HC, RRMS and ProgMS. Mean MWF in the global WM was significantly lower in ProgMS compared to HC (p = 0.008). 25 Figure 2.3: MWF SD in brain of HC, RRMS and ProgMS. There was a significant difference in the global WM between all cohorts (p £ 0.04). MWF SD in the superior longitudinal fasciculus was significantly higher in ProgMS than HC (p = 0.005) and RRMS (p = 0.01). MWF SD in the splenium of the corpus callosum was significantly higher in ProgMS than HC (p = 0.01). 26 Brain MWF vs. EDSS in RRMS and ProgMS Figure 2.4: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 14 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). Figure 2.5: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 17 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. 27 Brain MWF vs. T25W in RRMS and ProgMS Figure 2.6: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 14 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.7: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 16 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). 28 Brain MWF vs. disease duration in RRMS and ProgMS Figure 2.8: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 15 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.9: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 18 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. 29 Brain MWF vs. 9HPT in RRMS and ProgMS Figure 2.10: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 15 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.11: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in the global WM, corpus callosum, superior longitudinal fasciculus and inferior longitudinal fasciculus across 17 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. 30 2.5.2 Spinal Cord Data Results RRMS and ProgMS had a pattern of lower mean MWF in global cervical cord, global WM, dorsal column and lateral funiculi compared to HC although not reaching significance (p ≥ 0.06; Fig. 2.12). There was a significant but low to moderate correlation between mean MWF and T25W (r = -0.62, p = 0.03; Fig. 2.15) in RRMS ventral funiculi and mean MWF and disease duration (r = 0.49, p = 0.05; Fig. 2.18) in ProgMS ventral funiculi. Compared to HC, MWF SD was significantly higher in ProgMS global cervical cord (+8.77 %, p = 0.03; Fig. 2.12), global WM (+13.61 %, p = 0.01; Fig. 2.12), dorsal column (+10.37 %, p = 0.03; Fig. 2.12), lateral funiculi (+16.43 %, p = 0.01; Fig. 2.12) and ventral funiculi (+13.06 %, p = 0.05; Fig. 2.12). Compared to HC, MWF SD was significantly higher in RRMS lateral funiculi (+15.53 %, p = 0.02; Fig. 2.12). MWF SD was significantly correlated with EDSS in global cervical cord (r = 0.62, p = 0.01), global WM (r = 0.62, p = 0.01) and lateral funiculi (r = 0.73, p = 0.001) for ProgMS (Fig. 2.14). Lower mean MWF was significantly correlated with higher T25W in ventral funiculi (r = -0.62, p = 0.03; Fig. 2.15). MWF SD was significantly correlated with disease duration in global cervical cord (r = 0.56, p = 0.02) and global WM (r = 0.49, p = 0.05) for ProgMS (Fig. 2.18). Finally, MWF SD was significantly correlated with 9HPT in dorsal column (r = 0.65, p = 0.01) for ProgMS (Fig. 2.20). 31 Figure 2.12: MWF mean (top) and SD (bottom) in the cervical spinal cord at the C2/C3 cord level of HC, RRMS and ProgMS. MWF SD in the lateral funiculi was significantly higher in RRMS compared to HC (p = 0.02). MWF SD in the global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi were significantly higher in ProgMS compared to HC (p £ 0.05). 32 Spine MWF vs. EDSS in RRMS and ProgMS Figure 2.13: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.14: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and EDSS in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 16 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). 33 Spine MWF vs. T25W in RRMS and ProgMS Figure 2.15: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 12 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). Figure 2.16: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and T25W in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 15 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. 34 Spine MWF vs. disease duration in RRMS and ProgMS Figure 2.17: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.18: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and disease duration in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 17 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). 35 Spine MWF vs. 9HPT in RRMS and ProgMS Figure 2.19: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 13 RRMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant. Figure 2.20: Spearman’s rank correlation between mean MWF (top) or MWF SD (bottom) and 9HPT in global cervical cord, global WM, dorsal column, lateral funiculi and ventral funiculi at the C2/C3 cord level across 16 ProgMS participants. The solid line represents the slope of the MWF data. p £ 0.05 was considered statistically significant (marked in red). 36 2.6 Discussion Our findings suggest lower mean MWF (myelin content) and higher MWF SD (myelin heterogeneity) in brain and cervical spinal cord for RRMS and ProgMS compared to HC. More differences of significance were found between MS and HC using MWF SD than mean MWF alone in both brain and cervical spinal cord; the discussion is hence focused on myelin heterogeneity (MWF SD) rather than mean MWF. Similarly to previous findings by Kolind et al46, there was a stronger correlation between myelin abnormality and disability score in cervical spinal cord than in brain of MS participants. In cervical spinal cord of ProgMS, there was moderate to high correlations between myelin heterogeneity and clinical disability, as measured by EDSS, in global cervical cord (r = 0.62, p = 0.01), global WM (r = 0.62, p = 0.01) and lateral funiculi (r = 0.73, p = 0.001), as well as a moderate correlation between myelin heterogeneity and disease duration in global cervical cord (r = 0.56, p = 0.02). A low correlation was also found between myelin heterogeneity and disease duration in global cervical WM (r = 0.49, p = 0.05); while sitting against the cut-off for statistical significance, the consistency with other measures supports the result. We further evaluated the correlation between MWF and multiple sclerosis severity score (MSSS; disease severity measured using EDSS and disease duration)93. We found that the correlations were consistent with what was seen between myelin abnormalities and EDSS in brain for both RRMS and ProgMS cohorts. In cervical spinal cord, only the correlation between MWF SD and MSSS in lateral funiculi for ProgMS was significant (r = 0.60, p = 0.01). Lateral funiculi contain the lateral corticospinal tract (descending pathway) and the spinothalamic tract (ascending pathway), which are important for voluntary motor function and pain and temperature sensations, respectively94,95. Interestingly, there was a moderate correlation between higher myelin heterogeneity and reduced dexterity, as measured by 9HPT, in the dorsal 37 column (r = 0.65, p = 0.01). Similar result was reported by Kolind et al, where they observed a trend of correlation between MWF in the spinal cord and 9HPT46. This may be explained by the cuneate fasciculus, a tract of nerves in the dorsal column, functioning to regulate finger positioning and fine motor control of the digits96,97. Two low to moderate correlations between myelin heterogeneity and mobility impairment had the opposite direction from what was expected. Higher myelin heterogeneity was associated with lower EDSS in RRMS corpus callosum (r = -0.57, p = 0.03), and with shorter T25W in ProgMS inferior longitudinal fasciculus (r = -0.50, p = 0.05). These correlations could hint at compensation, or as they are near the cut-off for significance, may be due to chance. This will further be investigated with follow-up data. Some pathological processes that might explain myelin heterogeneity in MS include microglial activation, chronic oxidative injury, astrocytic gliosis and age-dependent iron accumulation14,15. Microglial activation occurs in both NAWM and lesions, particularly in the rim of smoldering (“slowly-expanding”) lesions, which are almost exclusively found in ProgMS cohort15,98. Activated microglia express NADPH oxidase and myeloperoxidase, which produce reactive oxygen species15. This creates a chronic oxidative environment, which could directly damage myelin or promote mitochondrial dysfunction by disrupting oxidative phosphorylation for energy production; thereby, contributing to axonal injury15. Astrocytic gliosis (formation of glial scars) limits the spread of inflammation; however, it also creates a physical and chemical barrier that could prevent remyelination of axons14. Finally, iron is stored in myelin, oligodendrocytes and mitochondria in non-toxic ferric (Fe3+) form bound to ferritin, which accumulates with age15. In 38 MS, iron is liberated from damaged myelin and oligodendrocytes and converts into toxic ferrous (Fe2+) form, which increases the toxicity of reactive oxygen species15. This may further contribute to myelin damage. A limitation of the study is a relatively small sample size, which could have prevented presenting significant differences that would otherwise have been detectable in larger cohorts. Although an effort was made to match for age and gender in our cohorts, ProgMS participants were older (mean age 58 years, range 48-65 years) than HC (mean age 40 years, range 22-63 years). Therefore, it could be argued that the myelin damage may be driven by age rather than the disease in ProgMS. However, no significant correlations were found between age and mean MWF or MWF SD in global WM in both brain and cervical spinal cord in any of the cohorts. Also, Flynn et al found that MWF correlated more with education level than age, which was also matched for where possible99. As our goal was to investigate the global pathology of MS that may contribute to clinical disability, including damage in both lesions and NAWM, the lesions were not excluded from our analysis. Future study using longitudinal data to investigate whether MWF changes over time to detect disease progression in MS is warranted. 2.7 Conclusion Low mean MWF (myelin content) and high MWF SD (myelin heterogeneity) in brain and cervical spinal cord, as well as correlations between myelin heterogeneity and clinical disability in ProgMS, suggest that myelin abnormality (in lesions and NAWM) in cervical spinal cord is associated with disability in ProgMS. Myelin heterogeneity (MWF SD) may detect subtle 39 alterations in brain and cervical spinal cord myelin that may not be evident with mean MWF measures alone. 40 Chapter 3 Advanced imaging findings in progressive solitary sclerosis – a single lesion or a global disease? 3.1 Introduction PSS is a unifocal demyelinating disease involving the corticospinal tract with downstream progressive weakness, which does not currently meet the diagnostic criteria of MS due to an absence of dissemination of lesions over space32,100–103. However, this entity was recently proposed as a possible MS variant100. Previously, Keegan et al found a solitary lesion in the cervical cord (60 %), cervicomedullary junction (17 %), thoracic cord (13 %), subcortical WM (7 %) or pons-mesencephalic junction (3 %) in 30 PSS cases100. They also found abnormal cerebrospinal fluid findings of either elevated IgG index or oligoclonal bands in 50 % of the PSS cases and an overall failure of MS disease modifying or immunosuppressive therapy, as expected in progressive MS100. The current study gathers hypothesis-generating data from two PSS cases to evaluate whether pathology is restricted to a single chronically demyelinated lesion with relative sparing of NAWM or if diffuse brain pathology, similarly to MS, beyond the focal lesion is present41. Quantitative analysis of myelin content and brain metabolite ratio could provide an improved understanding of the extent of the underlying pathologic involvement of NAWM in PSS and MS. mcDESPOT is a rapid, whole-brain imaging method that can determine the MWF. Proton magnetic resonance spectroscopy (1H-MRS) can be used to quantify brain metabolite ratio, such as N-acetyl-aspartate (NAA) to total creatine (tCr) ratio (associated with axonal and neuronal integrity)104. Apart from a case study, which showed an increased choline and myo-inositol level 41 in the NAWM indicating a demyelinating process in one PSS case, advanced imaging studies have not been reported102. Improved understanding of PSS may provide further insights into the pathology of progression in MS by learning more about the NAWM and the impact non-lesional disease has on progression15. 3.2 Objectives To qualitatively compare the myelin content and brain metabolite ratio in the normal-appearing brain tissue in PSS compared to RRMS, PPMS and HC participants. 3.3 Hypothesis We hypothesized that there would be diffuse abnormalities in the brain NAWM of PSS similar to MS. 3.4 Methods 3.4.1 Participant Information Two PSS cases, who met the proposed inclusion criteria for the diagnosis as described by Keegan et al100, were included in the study. A third case, initially thought to have PSS, underwent MRI; however, he was excluded after a second spinal cord lesion, not reported on the initial MR, was later discovered. PSS data were compared qualitatively to historical data collected for RRMS, PPMS and HC participants (Table 3.1). MS participants were diagnosed accordingly to the 2010 revised McDonald diagnostic criteria84. All participants provided written informed consent prior to participation. The study was approved by the UBC Clinical Research Ethics Board. 42 Table 3.1: Clinical and demographic information of the participants at the time of MRI scan. PSS cases were included in MWF and NAA/tCr analyses. All RRMS, PPMS and HC participants were included in the MWF analysis. A subset of RRMS and HC cohorts were included in the NAA/tCr analysis due to data availability. Group Gender Solitary Lesion Location PSS Clinical Finding Median EDSS (range) Mean Age (range) (years) Mean Disease Duration (range) (years) Individual PSS participant included in both MWF and NAA/tCr analyses PSS1 F C1 cervical cord Left hemiparesis EDSS 5.0 60 9 PSS2 F Cervico-medullary junction Quadriparesis EDSS 7.0 49 11 RRMS, PPMS and HC participants included in the MWF analysis RRMS (n=57) 37 F: 20 M N/A 2.0 (0.0-4.5) 37 (20-56) 6 (0.25-23) PPMS (n=7) 2 F: 5 M N/A 5.0 (2.5-6.5) 49 (43-55) 7 (2-20) HC (n=41) 27 F: 14 M N/A N/A 35 (20-56) N/A RRMS and HC participants included in the NAA/tCr analysis RRMS (n=23) 13 F: 10 M N/A 2.0 (0.0-4.5) 37 (20-54) 6 (0.7-18) HC (n=25) 15 F: 10 M N/A N/A 38 (22-56) N/A 3.4.2 Data Acquisition All MR imaging was performed on a Philips Achieva 3 T (Philips Medical Systems, Best, The Netherlands) using an 8-channel head coil. Whole-brain mcDESPOT data (voxel size 1.7 x 1.7 x 1.7 mm3) was acquired using a series of sagittal SPGR (TR/TE = 6.5/3.6 ms, a = [2,3,4,6,9,13,18]°) and bSSFP (TR/TE = 5.8/2.9 ms, a = [7,11,15,19,24,30,47]°) acquisitions across a range of flip angles (a)68. 1H-MRS protocol used a proton density T2-weighted image (PD/T2) to prescribe a 6.5 x 4.5 x 1.8 cm3 primarily NAWM voxel above the lateral ventricles. Point resolved spectroscopy (PRESS) was used to acquire single voxel spectra with TE/TR = 36/4000 ms, 56 water-suppressed and 8 non-water-suppressed acquisitions. Second order 43 shimming was performed on the same region as the voxel. Additionally, conventional anatomical images were also acquired. These included a 3DT1-weighted image for tissue segmentation and spatial normalization, and PD/T2-weighted images for lesion identification and segmentation. Further, clinical history of PSS was obtained by retrospective chart review. 3.4.3 Image Analysis A whole-brain voxelwise analysis of Z-scores was computed comparing the MWF of individual PSS case to the HC (n = 41) group distribution. The voxels with a Z-score of £ -2 were qualitatively presented as abnormal to indicate significantly lower MWF (p £ 0.05). Z = !" µs where 𝑥 = sample mean, µ = population mean and s = population standard deviation Three NAWM tracts, including left and right corticospinal tracts and corpus callosum, were obtained from standard atlases in MNI-152 2 mm space and transformed to SPGR space using FSL’s combined FLIRT and FNIRT87,89,105. The mean MWF was calculated within each ROI and compared between individual PSS case and RRMS, PPMS and HC groups. Further, spectra were fit using LCModel version 6.3 to calculate the NAA/tCr. This ratio was compared between each PSS case and a subset of the RRMS and HC groups with available data. 3.4.4 Statistical Analysis Non-parametric statistics, Mann-Whitney U test, were performed to compare MWF between RRMS and HC, PPMS and HC, and RRMS and PPMS, as well as, NAA/tCr between RRMS and HC on R software. Statistical tests between individual PSS cases and the comparison groups 44 were not performed. Instead, individual PSS case was compared qualitatively to MS and HC groups due to the small number of PSS cases available. 3.5 Results The clinical and demographic information of all participants are found in Table 3.1. PSS1 presented at age 51 with progressive left hemiparesis evolving over 8 years and a solitary lesion in the C1 cervical cord (Fig. 3.1). PSS2 presented at age 38 with rapidly progressive quadriparesis resulting in severe disability requiring a wheelchair over 1 year and a solitary lesion in the cervico-medullary junction (Fig. 3.1). Neither of the PSS cases had further development of new neurological deficits or new demyelinating lesions on MRI. Visual evoked potentials were available for PSS1 and showed a normal response. Cerebrospinal fluid analysis was available for PSS2 and did not show oligoclonal bands or elevated IgG index. The clinical investigations did not reveal alternative diagnoses, including neuromyelitis optica, transverse myelitis, neurosarcoidosis or alternative myelopathies (neoplastic, compressive, metabolic, nutritional, toxic, or infectious). The Z-score map of individual PSS cases showed global diffuse reduction of myelin in the brain indicated by the number of voxels that were £ -2 standard deviations away from the mean MWF constructed from 41 HC participants (p £ 0.05; Fig. 3.1). Both PSS cases had lower MWF in all NAWM tracts than the average across each of the RRMS, PPMS and HC groups (Fig. 3.2). PSS2 had a higher disability score measured by the EDSS (7.0) and longer disease duration (11 years) with concomitant greater abnormalities seen on MWI in the whole brain and ROI compared to 45 PSS1 (EDSS = 5.0; disease duration = 9 years) (Table 1). Further, both PSS cases showed a lower NAA/tCr than the median NAA/tCr of the RRMS and HC groups (Fig. 3.2). MWF differences were statistically significant between HC and RRMS and HC and PPMS in corpus callosum (p £ 0.02; Fig. 3.2). NAA/tCr was statistically significant between HC and RRMS (p = 0.04; Fig. 3.2). Statistical tests between individual PSS cases and the comparison groups were not performed. 46 Figure 3.1: Example slices through the MWF Z-score maps of two PSS cases (A, B). Areas marked in blue correspond to regions with significantly lower MWF (p £ 0.05), where Z £ -2, when compared to an MWF atlas constructed from 41 HC participants. The Z-score map of the oldest HC (56 years) is illustrated (C) to show that the abnormalities seen in PSS cases are likely due to the disease rather than age-dependent myelin changes. The Z-score maps were overlaid on a standard MNI-152 2 mm template displayed axially (left) and sagitally (middle). Solitary lesion in each PSS case is marked in red arrow on fluid-attenuated inversion recovery (FLAIR) images (right). Figure 3.2: MWF and NAA/tCr in normal-appearing brain. MWF (a marker for myelin) in left corticospinal tract (A), right corticospinal tract (B) and corpus callosum (C) was compared between individual PSS case and HC, RRMS and PPMS cohorts. NAA/tCr (associated with axonal and neuronal integrity) in the voxel above the ventricles (D) was compared between individual PSS case and HC and RRMS cohorts. PSS1 had the EDSS of 5.0 and disease duration of 9 years. PSS2 had the EDSS of 7.0 and disease duration of 11 years. 3.6 Discussion The MWF and NAA/tCr data show abnormal myelin and metabolic dysfunction in non-lesional tissue of two PSS cases. This suggests that PSS pathology may be similar to that seen in MS demonstrating diffuse NAWM abnormality rather than damage restricted to a single white matter lesion in the CNS. PSS cases appear to have greater diffuse myelin damage in NAWM as measured by MWF than RRMS, PPMS and HC groups. Moreover, PSS cases were found to have low NAA/tCr, which may indicate axonal or neuronal loss (low NAA) and/or inflammation (high 47 tCr)104,106. Wallerian degeneration (process of degeneration in the axon distal to the site of injury) along the pathway of the focal lesion along the corticospinal tract or trans-synaptic degeneration (degeneration in the axons due to loss of trophic support resulting from an injury to a nearby axon with which they synapse) may contribute to diffuse pathology in PSS. Further, it could be argued that myelin changes may be age-dependent; however, we did not find any significant correlations between age and MWF in HC or MS cohorts. To illustrate this point, Figure 3.1C shows a Z-score map of the oldest HC data available (56 years, in the range between the ages of PSS1 (60 years) and PSS2 (49 years)), which does not show myelin abnormality. Limitations of the study include a small sample size of PSS cases, which reflects the low prevalence of this variant and prevents group-level statistical analyses. There was a large variation in MWF and NAA/tCr for all groups, which is expected when analyzing these measures in heterogeneous groups. The age of one PSS case was outside the range of our comparison groups, which were obtained from retrospective studies, by 4 to 6 years. Further study involving longitudinal advanced MRI metrics in a larger PSS cohort is warranted to better understand the pathological mechanisms of PSS and to provide evidence to include PSS as a rare variant of MS in the future MS diagnostic criteria. 3.7 Conclusion Advanced MRI findings demonstrate extensive abnormalities in the normal-appearing brain of two PSS cases despite a solitary demyelinating lesion along the corticospinal tract, which may in part contribute to the gradual progression of disability. CNS injury in PSS may not be isolated to a solitary lesion. 48 Chapter 4 Inter-vendor reproducibility of myelin water imaging using a 3D gradient and spin echo sequence 4.1 Introduction So far in this thesis, we have shown that MWI can be a valuable tool to better understand the underlying WM pathology in MS and PSS that may not be detectable on a conventional MRI. However, to effectively apply MWI in multicenter studies and clinically, MWF measurements must be reproducible across sites and scanner vendors. Recently, a combined GRASE sequence was adopted for MWI, reducing the acquisition time to less than 15 minutes for full cerebral coverage (20 slices at 5 mm thickness)67. Previously, Meyers et al assessed the reproducibility of MWI with partial brain coverage (7 slices at 5 mm thickness, acquisition time = 18.5 min) in five healthy participants across six sites using a 3D spin echo sequence acquired on 3 T Philips scanners107. They demonstrated a good reproducibility of MWF in the global WM (intersite coefficient of variation (COV) = 4.68 %)107. Our study follows the methodology of Meyers et al but improves on the acquisition technique by using the recently developed rapid whole cerebrum GRASE MWI sequence and including 3 T MRI scanners from different vendors67,107. The three major MRI scanner vendors include Philips, Siemens and General Electric. Demonstrating reproducibility across scanner vendors with a rapid whole brain acquisition technique in a clinically feasible time will bring MWI a step closer to routine use for multisite studies and clinical applications. 4.2 Objectives 49 To assess the reproducibility of MWF measurements in regional and global brain WM across two sites with different scanner vendors using a 3D GRASE sequence. 4.3 Hypothesis GRASE-derived MWF in the brain WM acquired from two different sites and scanner vendors would be highly correlated. 4.4 Methods 4.4.1 Participant Information Ten healthy participants (4 females and 6 males; mean age 36.5 years, range 21-53 years) were scanned across two sites with different 3 T MRI scanner vendors. The mean time between the two scans was 26 days (range 5-62 days). All participants had no previously known neurological disorders or brain abnormalities. All participants provided written informed consent prior to participation. The study was approved by the Research Ethics Boards at both institutions. 4.4.2 Data Acquisition MRI data were acquired from each participant on a Siemens Magnetom Trio 3 T (Siemens Medical Solutions, Erlangen, Germany) with a 32-channel head coil at Seoul National University, Republic of Korea (site 1) and Philips Achieva 3 T (Philips Medical Systems, Best, The Netherlands) with an 8-channel head coil at UBC, Canada (site 2). T1-weighted anatomical images were acquired at each site using a whole-brain 3D magnetization-prepared rapid gradient-echo (MP-RAGE) sequence to facilitate automated tissue segmentation and spatial normalization. MWI data were acquired using a whole-cerebrum, multi-echo 3D GRASE sequence67. We were not able to exactly match acquisition parameters at each site due to 50 differences in sequence implementation between the two sites and vendors. Therefore, we chose to use protocols that were standard practice for each site (i.e. that were the most commonly used for ongoing research studies or clinical examinations within that center and which would be representative sample for a future multicenter study), which were as follows: • Site 1 GRASE: 32 echoes, TE = 10, 20, 30 … 320 ms, TR = 1000 ms, 5 mm slice thickness, 30 slices, slice partial Fourier factor = 5/8, acquisition time = 12 min, 1.5 x 1.5 mm2 in-plane resolution. • Site 1 MP-RAGE: TR = 2400 ms, TE = 2.12 ms, TI = 1000 ms, 1 x 1 x 1 mm3, α = 8°. • Site 2 GRASE: 32 echoes, TE = 10, 20, 30 … 320 ms, TR = 1000 ms, 5 mm slice thickness, 20 slices (40 slices reconstructed at 2.5 mm slice thickness), SENSE factor of 2, acquisition time = 14 min, 1 x 1 mm2 in-plane resolution. • Site 2 MP-RAGE: TR = 3000 ms, TE = 3.00 ms, TI = 820 ms, 1 x 1 x 1.6 mm3, α = 8°. 4.4.3 T2 Decay Curve Analysis Figure 4.1 presents the summary of T2 decay curve analysis. The 32-echo GRASE sequence produced a T2 decay curve in each voxel, which was analyzed using a regularized non-negative least squares (NNLS) algorithm with stimulated echo correction to obtain the T2 distribution (T2 = 15 to 200 ms) for each voxel67,91. The stimulated echo correction adjusts for errors in the refocusing flip angle due to B1 inhomogeneity. The extended phase graph algorithm was used to calculate a theoretical T2 decay curve for non-ideal refocusing pulse flip angle91. It then estimated the true refocusing flip angle by comparing theoretical decay curves with eight potential refocusing pulse flip angles linear spaced from 50 to 180 degrees, to the experimental decay. Using the optimal refocusing pulse flip angle, a T2 distribution (40 logarithmically spaced 51 T2 values from 15 to 200 ms) was obtained using NNLS for each voxel. The short T2 component attributed to myelin water was identified as 15 to 40 ms. T2 analysis was performed using in-house software code (MATLAB R2013b, The Mathworks, Inc.) developed at UBC. Figure 4.1: Key steps in the quantitative analysis of myelin (T2 decay curve). (Image adapted from: Ref. 63) 4.4.4 Image Analysis A global WM mask was obtained from the MP-RAGE using FAST and subsequently transformed to the native space of the GRASE data using the linear transformation obtained from the first step in the registration process described above85,86. The WM mask was then thresholded, eroded, binarized and edited when necessary to ensure that non-WM voxels were removed and that the coverage was the same for all participants. To obtain ROI masks, the MP-RAGE image was linearly registered and transformed to the first echo of the GRASE data from the same site using FMRIB’s FLIRT89,105. Second, the transformed MP-RAGE image was registered to the MNI-152 2 mm template using a non-linear registration process implemented in FMRIB’s FNIRT to obtain the non-linear warp-field between GRASE and MNI-152 space87. 52 Five WM ROI, including the genu and splenium of the corpus callosum, major and minor forceps, and superior longitudinal fasciculus, were obtained from JHU DTI-based white-matter atlases in MNI-152 space and transformed to GRASE space using the inverse of the previously obtained non-linear warp-field108,109. All ROI were then multiplied by the global WM mask, thresholded and binarized to generate more conservative ROI masks. They were further manually edited when necessary to remove non-WM voxels and to ensure same coverage between the two scans for all participants. 4.4.5 Statistical Analysis To assess reproducibility, Pearson’s correlation coefficient (r) was calculated using the mean MWF from the global WM of each participant at site 1 and site 2. A p-value was calculated from the Pearson’s r to determine if the correlation was significant. Statistical significance for all comparisons were defined as p < 0.05. A paired t-test was performed to determine if there was a difference in mean MWF between two sites. In addition, an equivalence test, two one-sided test (TOST), was performed to determine whether mean MWF between two sites were statistically equivalent. TOST prevents potential misinterpretation of non-significant p-values obtained from the paired t-test as the absence of a practically important effect. The 95% confidence interval for the estimated difference between the sites is also used to indicate the smallest difference that would have been detectable. The COV was calculated by dividing the standard deviation by the mean, of the two sites, for each ROI and global WM per participant. The COV is biased to lower values when the number of points (n) used to calculate COV is small. To correct for this, the sample COV was multiplied by (1+1/(4*n)) as suggested by Meyers et al 107. Here, n = 2 so the sample COV was multiplied by 1.13. The corrected COVs were averaged across all participants. 53 Finally, Bland-Altman analysis was used to assess the agreement in MWF between the two sites. All statistical analyses were performed using the R software package. 4.5 Results MWF maps of 10 healthy participants from the two sites are illustrated in Fig. 4.2. Tables 1 and 2 display the mean intersite MWF COVs in the global WM and ROI for each participant. The mean intersite MWF COV was the highest in the genu (17.89 %) and lowest in the splenium (4.47 %) of the corpus callosum (Table 4.2). A strong correlation was observed for the mean MWF in the global WM at the two sites (Pearson’s r = 0.91, p < 0.001) (Fig. 4.3). The estimated difference between sites for the global WM was -0.002. A paired t-test was not statistically significant (t = -1.480, p = 0.173) with a 95% confidence interval of -0.005 to 0.001 indicating that the smallest detectable difference would have been 0.003. Based on the equivalence test, the observed estimated effect of -0.002 was statistically equivalent to zero (p = 0.03). This test was based on equivalence bounds of -0.005 and 0.005, an alpha of 0.05 and the 90% confidence interval of -0.0047 to 0.0005. The variation in the MWF measurements in the global WM between the two sites was not associated with a longer time between the two scans (mean time between the scans = 26 days, range 5 – 62 days; r = 0.14, p = 0.70). The mean MWF for the individual ROI are shown in Fig. 4.4 as a scatter plot. There was a high correlation between the mean MWF in the genu (r = 0.75, p = 0.01) and splenium (r = 0.97, p < 0.001) of the corpus callosum, major forceps (r = 0.73, p = 0.01), minor forceps (r = 0.82, p = 0.003) and superior longitudinal fasciculus (r = 0.76, p = 0.01) at each site (Fig. 4.4). 54 Bland-Altman analysis showed good agreement in the MWF measurements in the global WM between the two sites (Fig. 4.3). There was a very small bias of 0.002 with the upper limit of agreement (+1.96 standard deviation) of 0.011 and lower limit of agreement (-1.96 standard deviation) of -0.007. The data points were distributed evenly around the bias, as indicated by the non-significant slope (p = 0.96) (Fig. 4.3). Table 4.1: Intersite MWF COVs in the global WM for each participant. Participant MWF COV (%) 1 0.03 2 1.01 3 1.03 4 1.04 5 1.98 6 2.51 7 2.53 8 3.46 9 6.11 10 8.00 Mean 2.77 Table 4.2: Summary of mean intersite MWF COVs in the global WM and ROI averaged across 10 participants. ROI Mean intersite MWF COV (%) Splenium of the corpus callosum 4.47 Superior longitudinal fasciculus 10.86 Minor forceps 12.71 Major forceps 6.63 Genu of the corpus callosum 17.89 55 Global white matter 2.77 Figure 4.2: MWF maps from 10 healthy participants from site 1 (top) and site 2 (bottom). Figure 4.3: Correlation plot of mean MWF (A) and Bland-Altman plot comparing MWF measurements (B) between site 1 and site 2 in the global WM mask across 10 healthy participants. The solid line represents the slope of the MWF data and the dashed line represents y = x, which indicates 1:1 agreement, on the correlation plot (A). The black solid line represents the average difference (bias), the black dashed lines indicate the limits of agreement (±1.96 standard deviation) and the orange dashed line indicates the linear fit to the data point (B). 56 Figure 4.4: Mean MWF measurements between site 1 and site 2 in genu (A) and splenium (B) of the corpus callosum, major forceps (C), minor forceps (D) and superior longitudinal fasciculus (E) across 10 healthy participants. 4.6 Discussion In this study, we extended previous findings from a multisite and single-vendor study of MWF reproducibility107. We assessed the reproducibility of MWF at two different sites using MR scanners of different vendors (Siemens and Philips) and found good intersite global WM mean MWF reproducibility indicated by a low COV (mean 2.77 %, range 0.03 – 8.00 %), high Pearson’s correlation coefficient (r = 0.91, p < 0.001) and very small bias (mean bias = 0.002). The COV remained low when comparing the 25th (6.04 %) and 75th percentile (4.27 %) of MWF in global WM across all participants between sites. In comparison, Meyers et al found an intersite mean MWF COV (mean 4.68 %, range 2.86 – 8.14 %) in the global WM for five healthy participants scanned at six sites using the same MR scanner vendor107. Meyers et al also showed wider intervals between the mean bias and ± 1.96 standard deviation (-0.014 to 0.016) on Bland-Altman plot, which indicates a greater uncertainty compared to our study (-0.007 to 0.011). Assuming the true mean difference is the worst case of -0.005, the lower boundary of the 95% confidence interval from a paired t-test, this gives a ± 4.74 % change on average based on our MWF data for global WM. Based on our equivalence bounds, this is equivalent to worst case ± 4.74 % change between sites. To put into perspective of expected changes in diseases, previous 57 studies have reported that mean MWF was 16 to 37 % lower in the NAWM of MS compared to WM of healthy controls61,74,76. Flynn et al found that mean MWF was 12 % lower in the overall WM in schizophrenia compared to healthy controls99. Given the small sample size, the magnitude of difference is uncertain, so we recommend taking the effect of center or machine into account when doing the analysis in multicenter studies. While the intersite MWF reproducibility from regional ROI remained high (COV range 4.47 – 17.89 %), frontal brain regions, including genu (17.89 %) and minor forceps (12.71 %), showed higher variability than others, possibly due to artefact caused by susceptibility, flow and motion. As a comparison, Meyers et al reported intersite MWF COV of 15.67 % and 17.18 % in genu and minor forceps, respectively, using the same MR scanner vendor107. The air-filled sinuses produce differences in the tissue magnetic susceptibility resulting in local magnetic field inhomogeneities; it is still unclear how this will affect our quantitative measurements. Furthermore, the genu is located near the frontal horn of the lateral ventricle containing cerebrospinal fluid, which pulsates with the cardiac cycle, which again could introduce artefacts in our measurements. A power analysis using the G* Power 3.1 program indicated that based on our pilot study results, for a larger trial, a total sample of 46 people would be needed to detect medium effect size (Cohen’s d = 0.49) with 90 % power using a paired t-test between means with alpha at 0.05. A total sample of 35 people would be needed to achieve 80% power with the same effect size and alpha. 58 Previous multi-echo T2 reproducibility studies using a single vendor scanner have shown good to moderate reproducibility of myelin content measurements107,110,111. The data acquired for our study differs from the previous MWF reproducibility studies in several aspects. Our study was performed on 3 T scanners, which improved signal-to-noise in the decay curve by almost 100 % compared to 1.5 T112. The GRASE sequence used in this study can be acquired in a clinically feasible time (under 15 minutes) compared to a single-slice multi-echo spin echo acquisition (1 slice in 26 minutes)52,67. Another advantage of the GRASE acquisition is that data are collected in 3D. This avoids potential magnetization transfer effects between slices that could occur in 2D multi-slice acquisition, which can affect the MWF113. To account for B1 inhomogeneity, stimulated echo correction was applied to correct for errors in refocusing flip angle, which will improve the robustness of the analysis91. We also studied both regional and global WM. Finally, and most importantly, we studied reproducibility across two scanner vendors located at two sites. In this study, we are interested in studying the reproducibility of the MWF across two different sites, each using their standard of practice MWI sequence. We believe this best reflects a future multicenter study where the inevitable slight differences between vendors make it impractical to match all sequence parameters as well as hardware. As a consequence of this, different in-plane resolution and imaging acceleration were used at the two sites in this study. Both of these parameters are likely to affect the signal to noise ratio (SNR) of the acquisition. Furthermore, the use of partial k-space acceleration on the Siemens scanner in comparison to parallel imaging on the Philips scanner can also affect the image quality. Another factor that will contribute to differences in SNR between the two sites is the use of different RF receiver coils. The Siemens site used a 32-channel coil compared to the 8-channel coil at the Philips site. A larger number of 59 channels in the coil will intrinsically lead to higher SNR and lower g-factor for parallel imaging; however, we would not expect any drastic differences considering that parallel imaging was not used on the Siemens scanner and acceleration factor of 2 was used on the Philips scanner114. Previous studies have investigated the effect of SNR on the MWF through simulations and found that the MWF may be underestimated at low SNR115. In this study, we do not find any results that would support the hypothesis that differences in SNR between the two sites, due to differences in hardware and acquisition, cause significant differences in the MWF. This is an important result, indicating that the MWF estimates are reasonably robust to differences in both hardware and acquisition parameters. Another limitation of the present study is the small sample size of only 10 participants. Also, scan-rescan data at each site were not collected in this study. However, previous studies that acquired a multi-echo spin echo sequence in brain (mean intrasite MWF COV = 3.99 %)107 and GRASE-derived MWI in spinal cord116 using Philips Achieva 3 T reported good scan-rescan intrasite repeatability, suggesting robustness of the technique. Further, another study that acquired a GRASE-derived MWI in brain using a Siemens Magnetom Trio 3 T also reported good scan-rescan intrasite repeatability (mean correlation coefficient = 0.88 ± 0.03). 4.7 Conclusion This study showed good reproducibility of MWF measurements between two sites with MRI scanners from different vendors. The intersite variability was comparable to previous results using a single MRI scanner vendor without significant bias between sites107. Thus, our results 60 support the future use of MWI in studies involving multiple centers and different scanner vendors (Philips and Siemens). Greater availability of MWI enables acquiring myelin data across larger groups and populations, which will allow us to gain deeper insight into pathological processes due to underlying disease progression in demyelinating diseases like MS, which may not be possible with conventional MRI. 61 Chapter 5 Conclusion Chapters 2 and 3 demonstrated the application of MWI in looking at the relationship with disability in MS and in refining the hypothesis that PSS is not a pathologically restricted form of MS. Myelin heterogeneity (MWF SD) may be a more sensitive tool to detect subtle alterations in brain and cervical spinal cord myelin in MS patients than mean MWF alone. Finally, we showed good reproducibility of MWF measurements between two sites with different MRI scanner vendors. Thus, our results support the future use of MWI in studies involving multiple centers with different scanner vendors. 62 Bibliography 1. Paty DW, Li DKB. 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Application of myelin water imaging to detect diffuse white matter damage in multiple sclerosis
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