A DIFFUSION TENSOR IMAGING INVESTIGATION OF WHITE MATTER IN
PEDIATRIC MULTIPLE SCLEROSIS PATIENTS
by
Allison J. Bethune
A thesis submitted in conformity with the requirements
for the degree of Master’s of Science, Program in Neuroscience
Graduate Department: Institute of Medical Sciences
University of Toronto
© by Allison J. Bethune (2009)
A DIFFUSION TENSOR IMAGING INVESTIGATION OF WHITE MATTER IN PEDIATRIC MULTIPLE SCLEROSIS PATIENTS Masters of Science, University of Toronto, 2009 Institute of Medical Sciences, Program in Neuroscience Allison Jane Bethune
Background: To explore normal-appearing white matter (NAWM) in pediatric-onset multiple sclerosis (MS) patients, using diffusion tensor imaging (DTI). DTI study provides measures of WM integrity in adult MS patients. Pediatric MS patients provide a uniquely early window for exploring pathological components of myelin disruption. Methods: DTI data were obtained for 23 pediatric MS patients and 17 healthy children. Images were acquired using GE LX1.5T scanner (DTI parameters: 25 directions, 5mm slice thickness, b=1000s/mm2). Fractional anisotropy (FA) and apparent diffusion co-efficient (ADC) were analyzed in lesions and NAWM throughout corpus callosum (CC) and hemispheres. Results: Altered NAWM integrity in MS patients relative to controls is demonstrated by: reduced FA values (p<0.0001) and elevated ADC values (p<0.05) throughout CC and hemispheres. Conclusions: DTI measures show widespread disruption of WM integrity in children with MS extending beyond visible lesions. These findings implicate diffuse and potentially very early WM degeneration in MS pathobiology.
ii
Acknowledgments Many thanks to my supervisor, Dr. Brenda Banwell, and committee members, Dr. Donald Mabbott and Dr. John G. Sled for the expertise, guidance and patience shown by you all. I feel privileged to have learned from such a talented team of people. The contributions of many people’s talents and efforts have been essential in the completion of this work. With sincere gratitude for time and teaching, I wish to thank the following people for their involvement in this project.
Conrad Rockel - Image processing techniques and teaching Hospital for Sick Children, Department of Psychology, Image Analyst Rezwan Ghassemi – Lesion map creation Montreal Neurological Institute, McGill University, Research Assistant Noor Kabani, PhD – Anatomical brain atlas Department of Medical Biophysics, Associate Professor, University of Toronto Sandra Magalhaes – Statistical support Hospital for Sick Children, Neurosciences & Mental Health, Clinical Research Project Manager Ruth-Ann Marrie, MD – Statistical support Winnipeg Regional Health Center, University of Manitoba, Neurologist, Assistant Professor
Thank you also to:
• Dr. Doug Arnold and Dr. Sridar Narayanan, of the Montreal Neurological Institute, for feedback and input throughout.
• Dr. Manohar Shroff for clarifying clinical image artifacts. Studentship Funding Sources:
• MS Society of Canada • University of Toronto Awards • Restracomp Foundation, Hospital for Sick Children
Project Funding Sources:
• MS Society Scientific Research Foundation • CIHR
iii
Thank you to my wonderful husband, Ian, who has been extremely supportive and patient
throughout this experience.
Thank you to my parents- who have been role models throughout my life and education-
and instilled in me a love of learning.
iv
Table of Contents
1. Introduction 1 2. Background Literature 4 i) Overview of Multiple Sclerosis 4 Fundamentals of Immune Function in Multiple Sclerosis 4 White Matter (WM) Biology 7 Developmental Changes in Myelin Structure 8 Variable Mechanisms for White Matter Injury in Multiple Sclerosis 11 Clinical Manifestation 12 Pediatric Disease Progression 13 Determinants of MS Risk 14 Genetic Factors 14 Environmental Factors 16 Epidemiology 17 Childhood MS Epidemiology 17 ii) Magnetic Resonance Imaging in Pediatric Multiple Sclerosis 18 Physics of Magnetic Resonance Imaging 20 Signal generation in conventional Imaging 20 T2 MRI 21 FLAIR 22 Lesion Enhancement 22 T1 Hypointensities 23 iii) Quantitative Diffusion Tensor Imaging 24 Signal Generation in Diffusion Imaging 24 Diffusion Metrics 26 Axial and Radial Diffusivities: Eigenvalues and Eigenvectors 27 ADC 28 FA 28
iv) DTI in MS 30 Pathobiological Basis for Diffusion Abnormalities 30
Clinical correlates of DTI 31 DTI study in childhood onset MS 33 DTI study of MS pathology in the Corpus Callosum 34 Studying Maturation and Myelin Development with DTI 37
v) Rationale 40 3. Current Project 41 Study goal 41 Objectives 41 Hypotheses 41 Methods 43 Participants 43 Image Acquisition and Post-processing 44 MS Participants 44 Control Participants 45
v
Post-processing 45 Region-of-Interest (ROI) Definition 46 Corpus Callosum ROI Definition 46 Hemispheric ROI Definition 48 Lesion Definition 49 Image Processing Sequence 50 Statistical Analyses 55 Results 57 Participants 57 Corpus Callosum Analyses 57 Total CC Tissue DTI Metrics in MS Patients and HC 57 Comparison of CC NAWM in MS patients to the CC of HC 57 Comparing Lesional and Non-Lesional Tissue in the CC of MS
patients 58 Regional CC Differences in Volumes and FA Values of MS and
HC 59 Hemispheric WM Analyses 59 Participants 59
Comparing DTI metrics of Total Hemispheric WM in MS Patients and HC 59 Comparing Hemispheric NAWM in MS patients to the Hemispheric WM of HC 60 Comparing Lesional and Non-Lesional Tissue in Hemispheric WM of MS Patients 61
Axial and Radial Diffusivities of MS Patient NAWM and Lesions 61 CC Diffusivities 61 Hemispheric Diffusivities 61 Factors Influencing FA values 62 Inter- and Intra-subject variability 62 Lesion location and FA variability 63 Relationship of Lesion FA to Lesion Volume 63 Relation of DTI metrics and Clinical Outcomes 63 CC Lesion FA and Clinical Disability 63 CC Lesion Volume and Clinical Disability 65 Tables and Figures 66
Discussion 80 Key Findings 80
Comparing WM of Pediatric MS patients to WM of Healthy Children 81 Corpus Callosum Analyses 82 Regional Heterogeneity of the CC 84 CC Lesion Analyses 86 Hemispheric NAWM Analyses 87 Hemispheric Lesion Analyses 88 Comparing Lesional and Non-Lesional Tissue within MS Patients 88
Inter and Intra-Individual Variability in Lesions 89
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Proposed Pathophysiologic Mechanisms for Altered Diffusion in Lesion Tissue 90 Eigenvalues 93
Axial and Radial Diffusivities in NAWM and Lesion Tissue 93 Study Strengths and Potential Limitations 94 Clinical Implications and Future Directions 97
Summary 99 References 100
Appendix A 111 Appendix B 112 Appendix C 115
Appendix D 116 Appendix E 117
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List of Figures: Figure 1: Schematic depiction of peripheral and central immune cell sites of activation
in MS Figure 2: Schematic and electron micrograph views of myelinated axons Figure 3: T2 Weighted -FLAIR image representing supratentorial lesions in pediatric
MS Figure 4: Eigenvalue components of diffusion ellipsoids in anisotropic and isotropic
diffusion Figure 5: Cross-sections of the human corpus callosum Figure 6: Trends in CC midsagittal area and diffusion metrics across the lifespan Figure 7: Manual subdivision of CC performed on a T1 weighted image Figure 8: Editing the CC segments in the axial plane Figure 9: Representative FA maps of two MS patients with lesions in CC and
hemispheric ROIs Figure 10: Overview of acquired images and registrations used in processing of MS
patient CC data Figure 11: FA values for CC white matter (WM) of healthy children and normal appearing
white matter (NAWM) of MS participants Figure 12: Regional CC white matter (WM) volumes in MS patients and healthy children
(HC) Figure 13: Hemispheric FA values for healthy control (HC) white matter (WM) and MS
participant normal appearing white matter (NAWM) Figure 14: Mean hemispheric white matter (WM), normal appearing WM and lesion FA
values in healthy children and MS Participants Figure 15: Axial and average radial diffusivities in CC lesions and normal appearing white
matter (NAWM) of MS patients Figure 16: Whole brain normal appearing white matter (NAWM) and lesion FA values
within individual MS patients Figure 17: FA of CC lesions relative to Expanded Disability Status Scale Scores (EDSS)
in pediatric MS patients Figure 18: Volume of CC lesions relative to Expanded Disability Status Scale Scores
(EDSS) in pediatric MS patients Figure 19: Reductions in regional CC volumes and FA values Figure 20: Schematic representation of diffusion abnormalities with changing structural
neuron integrity at varying tissue phases in MS
viii
List of Tables Table 1: Typical values for DTI metrics in Healthy WM versus Normal-Appearing and
Lesional WM in Children with MS Table 2: Region of Interest (ROI) Definition and Rotation with Diffusion Tensor Maps Table 3: MS and Control Participant Demographics Table 4: Total CC White Matter FA and ADC values in HC and MS Participants Table 5: DTI Metrics of NAWM and HC WM in CC Regions Table 6: Mean FA, ADC, and Volumes for CC NAWM and Lesion Tissue in MS patients Table 7: Mean FA values for Total WM in MS and HC Participant Hemispheres Table 8: Mean Hemispheric FA and ADC Values (SD) for HC and MS NAWM
Participants Table 9: Mean Hemispheric FA and ADC Values (SD) and Regional Volumes Table 10: Axial and Average Radial Diffusivities for NAWM and Lesions in MS Patients Table 11: Summary Table of Hemispheric FA and ADC values for HC, NAWM and Lesion
Tissue Table 12: Axial and Average Radial Diffusivities for CC Regions Table 13: Axial and Average Radial Diffusivities Hemispheric WM
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List of Appendices
Appendix A: Methods of Corpus Callosal ROI Definition Appendix B: Hemispheric WM Region Definition Appendix C: Summary of Hemispheric HC WM, MS NAWM and Lesion FA and ADC
Values Appendix D: MS Patient Eigenvalues in NAWM and Lesions Appendix E: Participant Consent and Assent Forms
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List of Abbreviations ACPC: Anterior Commissure-Posterior Commissure alignment (of a T1 image) ADC: Apparent Diffusion Co-efficient ADEM: Acute Disseminated Encephalomyelitis ANOVA: Analysis of Variance BBB: Blood-brain-barrier CAP: Compound Action Potential CC: Corpus Callosum CNS: Central Nervous System CSF: Cerebral Spinal Fluid CV: Conduction Velocity DTI: Diffusion Tensor Imaging EDSS: Expanded Disability Status Scale EBV: Epstein-Barr Virus EPI: Echo Planar Imaging FA: Fractional Anisotropy FLAIR: Fluid Attenuated Inversion Recovery Gd: Gadolinium GM: Grey Matter HC: Healthy Control(s) NIH: National Institutes of Health NMR: Nuclear Magnetic Resonance MD: Mean Diffusivity MRI: Magnetic Resonance Imaging MS: Multiple Sclerosis MTR: Magnetization Transfer Ratio NACC: Normal Appearing Corpus Callosum NAGM: Normal Appearing Gray Matter NAWM: Normal Appearing White Matter NMO: Neuromyelitis Optica OG: Oligodendrocytes PD: Proton-Density Weighted Scan RF: Radiofrequency ROI: Region of Interest RRMS: Relapsing-Remitting MS SNR: Signal to Noise Ratio SPGR: Spoiled Gradient Recalled Echo TE: Echo Time TR: Repetition Time WM: White Matter WD: Wallerian Degeneration
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1
1) Introduction:
Multiple Sclerosis (MS) is a chronic autoimmune disorder, characterized by
inflammatory attacks on central nervous system white matter (WM).
Demyelinating lesions and progressive neurodegenerative changes are the
pathobiological hallmarks of the disease.1 In Canada, MS is the most common
cause of neurological disability among young adults, and is being increasingly
recognized in children. Accurate diagnosis and timely therapeutic intervention is
a challenge that physicians treating patients with pediatric MS commonly face.
The clinical and radiological features of an initial MS attack must be distinguished
from other demyelinating disorders, such as neuromyelitis optica (NMO) or acute
disseminated encephalomyelitis (ADEM). To date, no single diagnostic measure
reliably predicts a future diagnosis of MS in children presenting with an acute
demyelinating attack. Understanding the earliest pathobiological features of MS
will aid clinicians and scientists in identifying those individuals for whom an initial
immune attack represents the first attack of MS. Advanced magnetic resonance
imaging (MRI) techniques, such as diffusion tensor imaging (DTI) interrogate WM
tissue microstructure not visible on conventional MRI, and thus have potential for
improving early diagnostic accuracy.
Diffusion Tensor Imaging (DTI) is a quantitative MR method that probes WM
integrity on a micro-structural tissue level, with metrics assessing the magnitude
and direction of hindrance to water diffusion by axons and myelin membranes.
Two measures of water diffusion through neural tissue are commonly reported:
fractional anisotropy (FA) and apparent diffusion co-efficient (ADC). The average
magnitude of water diffusion is described by ADC. In this scalar quantity, values
frequently increase in lesional tissue relative to healthy WM as surrounding
myelin and axonal membranes are degraded, enabling increased water diffusion.
The shape and orientation of the diffusion tensor is described by the magnitudes
and directions of component vectors, which determine the FA value. Highly
anisotropic water diffusion (FA values approaching 1) is depicted by an
ellipsoidal shaped distribution describing water displacement within a diffusion
time.2 High FA values are generally observed in healthy, well myelinated WM,
where structural boundaries, such as myelin membranes and axonal walls
restrict diffusion. In contrast, low anisotropy values (approaching 0) indicate
isotropic diffusion. Isotropic diffusion is characterized by relatively unrestricted
diffusion in all directions, and is depicted by a spherical distribution of water
displacement where water diffuses more freely in multiple directions. Low FA
values are seen in regions with little restriction to water diffusion, such ventricular
cerebral spinal fluid.
The present study applies quantitative DTI to interrogate WM microstructure in
children with MS. We hypothesize that FA will be reduced in the WM of children
with MS relative to healthy control (HC) participants owing to the presence of
MS-related WM disruption, with further FA reductions and ADC increases in MRI-
visible lesions.
Within MS patients, we evaluated DTI metrics to compare lesional WM and
normal-appearing white-matter (NAWM). The corpus callosum (CC) is the
2
largest inter-hemispheric WM trajectory, and one that is particularly afflicted with
lesions in MS.3,4 Furthermore, damage to the CC has been linked to cognitive
disability.5,6 We therefore specifically explored DTI measures in the CC. We also
explored DTI measures in anatomically defined regions of extra-callosal
hemispheric WM in order to obtain a view of global WM integrity. Comparison of
DTI measures in all regions were assessed between children with MS and
healthy participants, and were also evaluated for intra- and inter-patient
variability. DTI results were correlated with clinical physical disability scores in
the MS patients, and our future work will evaluate associations between DTI and
cognitive performance.
3
2) Background Literature:
i) Overview of Multiple Sclerosis:
Multiple Sclerosis (MS) is a chronic auto-immune disorder characterized by
inflammatory attacks on the central nervous system (CNS). The relapsing-
remitting form of MS predominates in children and is characterized by acute
(relapse-related) areas of demyelination and axonal injury with clinical features
reflective of the area of acute inflammation. Common clinical MS attack features
include visual loss due to inflammatory attack of the optic nerve, paraplegia and
sensory deficits due to inflammatory attack in the spinal cord, diplopia and
impaired co-ordination related to lesions in the brainstem, as well as a variety of
other manifestations related to lesions in supra- and infra-tentorial regions.7
Myelinated axons in the peripheral nervous system are not targeted by the
aberrant immune responses involved in MS.
In addition to clinical deficits attributable to inflammatory lesions, progressive
physical disability, cognitive impairment, and pervasive fatigue reflect the
neurodegenerative component of MS.1 It remains to be determined whether the
neurodegenerative processes occurs independent of inflammation or as a
consequence of the inflammatory CNS milieu in MS.
Fundamentals of Immune Function in Multiple Sclerosis:
It is generally accepted that the pathological immune dysfunction in MS is
initiated by antigen recognition and activation of immune cells (T cells, B cells) in
4
the peripheral blood or lymph nodes. The inciting antigen(s) remain to be
defined, but epidemiological studies implicate infectious agents, such as Epstein-
Barr Virus.8,9
It has been hypothesized that the inciting antigens are protein or polysaccharide
components of invading bacteria or viruses. Pathogen invasion of host tissues
stimulates phagocytosis of the pathogen by macrophages and dendritic cells,
externalization of pathogenic antigens by these cells (now acting as antigen
presenting cells, APCs). Interaction of APCs with B cells leads to B cell
activation, and subsequent B cell synthesis of antibodies specific to the pathogen
(humeral immunity). In a normal immune response, these antibodies bind to the
pathogenic antigen on the virus or bacterium, targeting these infectious agents
for recognition and destruction by cytotoxic T cells and macrophages. In
autoimmune disease, antibodies not only identify pathogenic antigens, but also
cross-react with host antigens that bear homology to the pathogenic antigens..10
In addition to humoral immunity, the immune response in MS is also driven by T
cells (cell-mediated immunity). The site of T cell maturation is the thymus.
Presentation of foreign or stimulating antigens to thymic T cells leads to T cell
receptor activation, creation of reactive T cell clones and subsequent secretion of
cytokines (which aid in further T cell recruitment).
Immune cell activation also leads to production of immune cell complexes
capable of interacting with endothelial cells of the blood-brain barrier (BBB). This
inflammatory dialogue leads to enhanced entry of activated immune cells into the
5
CNS compartment. Recognition of CNS antigens homologous to the antigens
against which these immune cells were initially primed leads to autoimmune-
mediated damage to CNS myelin and axons. Additional injury in the CNS results
from cytokine-related toxicity to neighboring CNS cells .7 Figure 1 provides a
schematic representation of this process.
Figure 1: Schematic depiction of peripheral and central immune cell activation in MS. Foreign antigens invade the peripheral circulation (A). Macrophages internalize antigens (B), and present them to immune cells in the context of HLA (human leukocyte antigen) (C). In the presence of co-stimulatory molecules (D), the combination of C and D lead to T cell activation. The activated T-cells gain access to the CNS via the blood-brain barrier (E). Complex interactions of immune cell and BBB endothelial cells mediate immune cell entry into the CNS (not shown). In the CNS, T cells become reactivated upon exposure to CNS antigens homologous to the antigens previously encountered in the periphery (F). These reactivated T cells can then target CNS structures (eg: myelinated axons) (G). Figure courtesy of Dr. A.Bar-Or, Montreal Neurological Institute (not published).
Immune Cell Activation: Peripheral Circulation
T
Immune Cell Reactivation: Central Nervous System
BBBBBB
TT
AA
BB
CC
DD
EE
FF
GG
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White Matter (WM) Biology:
The primary target of immune attack in MS has been long believed to be WM of
the CNS. However, even the earliest pathological descriptions of MS identified
neuronal cell loss and axonal degeneration. Interest in the neuronal degenerative
processes in MS has re-emerged, owing to the elegant immunofluorescence
studies that have quantified the extent of neuronal and axonal injury in both WM
and grey matter (GM) in individuals with MS.11
Despite the emerging interest in GM pathology, the integrity of WM remains a
very key area of research, and loss of WM integrity is felt to underlie many of the
symptoms of the disease (see also section below on MS clinical features). Axons
in the CNS are ensheathed in an electrically insulating material, known as
myelin, which serves to potentiate fast conduction of action potentials along the
nerve. Action potential transmission occurs through saltatory conduction.
Depolarization at nodes of Ranvier that is sufficient to elevate voltage at a
neighboring node leads to depolarization and action potential propagation. In the
case of demyelination, loss of the insulating myelin leads to failure of saltatory
conduction, overall impulse conduction becomes impaired and neurological
dysfunction results. Figure 2 illustrates the structure of myelin.
7
Figure 2: Schematic and electron micrograph views of myelinated axons. Myelinated axons: A simplified version of a myelinated axon, with its neurofilament and microtubule components (right).12 Cross sectional view of transmission electron micrograph of a myelinated axon. Evidence of the multi-layered myelin membranes are depicted (left). Image generated and deposited into the public domain by the Electron Microscopy Facility at Trinity College.*
As illustrated, the myelin sheath is a multi-layered phospholipid structure, which,
in healthy situations is relatively impermeable to water. The dry-weight of the
CNS is composed of 20-25% myelin protein, with the remaining 75-80% being
composed of glycolipids.13 Myelin-basic protein (MBP), hydrophobic proteolipid
proteins (PLP), myelin oligodendrocyte protein (MOG) are the major myelin
proteins.14
Developmental Changes in Myelin Structure:
Myelin in the CNS is synthesized by oligodendroglial cells, with each cell
myelinating multiple axons. Myelination of the CNS begins in utero, although
completion of myelination progresses into early adulthood.15-18 The age-
*Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License".
8
dependent state of primary myelination must therefore be considered when
studying disorders affecting myelin in childhood.
A core component of myelin maturation relates to biochemical post-translational
modifications of the chemical or conformational composition of proteins or lipids
in the myelin membrane. Much of the work on post-translational myelin
modification during maturation has focused on the alterations of MBP, for which
several isoforms have been identified. During maturation, MBP is modified by the
presence of citrulline - a neutral molecule that takes the place of cationic arginine
residues at specific locations in a MBP molecule.19 In infancy and until age 2
years, the major isoform of MBP contains eight citrulline residues (termed the “C-
8 isoform”), while in mature myelin, citrulline residues are replaced by arginine,
leading to a “C1 isoform” containing only one citrulline residue. The C-8 isoform
is positively charged, which inhibits the compact folding of myelin membranes.
Compact myelin is formed by the more mature C1 isoform. Throughout typical
human development the proportion of C-8 in MBP reduces with increasing age.
Interestingly, pathological study of the citrullination state of MBP in the brain of a
45 year-old MS patient revealed a C-8 (citrulline) content similar to that expected
in two year olds.13 It was proposed that C8-rich MBP may be more susceptible to
degradation since it is less compact than mature myelin and cannot form as
highly a structured myelin sheath. Pathological studies of the citrullination state of
MBP in pediatric demyelination have not been studied, in part owing to the
relatively rare occurrence of brain biopsy in children with acute demyelination. It
could be speculated that myelin maturation in children with MS is delayed leading
9
to persistence of immature, highly citrullinated, C8-isoform of MBP, and thus a
myelin membrane that is less compact and potentially more vulnerable to
immune-mediated degeneration. To date, non-invasive imaging techniques to
measure the citrulline content of myelin have not been developed. As discussed
below, DTI studies can detect differences in the fractional anisotropy as a
function of age,20 suggesting that the degree of compaction of myelin may be
assessable using DTI metrics.
The biological processes involved in WM maturation have a physiological
correlate, and a relationship between the progressive (eg: myelination) and
regressive (eg: synaptic pruning) events in cellular maturation exists.17 Spatial
mapping of brain growth demonstrates continued WM development in both the
frontal and posterior-temporal lobes in participants aged 7-30 years.17 Anatomical
regions within the frontal lobes indicated areas of prominent brain growth (ie:
myelination) which corresponded exactly with robust accelerated gray matter
density loss on MRI (suggesting synaptic pruning). In contrast, the period of
occipital and parietal myelination and cortical grey matter pruning occurred
earlier than in frontal lobe regions. Neuropsychological studies also suggest that
parietal lobe functions (such as visuo-spatial and sensory processing) are
functionally more mature at younger ages, while frontal lobe-related executive
functions develop later.17 Increased efficiency (i.e. reduced reaction times) might
also occur concurrent with the increased myelination observed with maturity. It
has been hypothesized that improved accuracy on cognitive task performance
may result from regressive changes such as synaptic pruning, given that unused
10
or less efficient synaptic connections are eliminated and only functionally efficient
connections remain.21
In the frontal lobes, WM maturation is correlated with the development of working
memory. Throughout childhood and adolescence, working memory capacity
increases and is important for the development of a wide range of cognitive
abilities, including complex reasoning. Older children and adolescents, with
higher visuospatial memory function, specifically, display more brain activity in
the intra-parietal cortex and in the posterior part of the superior frontal sulcus,
during the performance of working memory tasks. The degree of white matter
maturation is positively correlated with the degree of cortical activation in both
frontal and parietal regions. This work suggests that during childhood and
adolescence cortical networks associated with specific cognitive functions
develop, in addition to the WM tracts connecting them.18
The onset of MS during childhood occurs at a time when primary myelination has
yet to be completed and the architecture of WM pathways is not yet fully
mature.22-24 This important issue frames a significant aspect of the rationale for
studying WM integrity in pediatric MS, as there may be key differences from the
WM integrity of adult-onset MS.
Variable Mechanisms for White Matter Injury in Multiple Sclerosis:
A range of plausible pathogenic mechanisms have been proposed in MS. The
primary target of the immune response in MS includes mature oligodendrocytes
11
(OG), and potentially the oligodendroglial progenitor cells. Four distinct patterns
of lesion pathology (defined by myelin protein loss, pattern of lesion borders, OG
destruction, and immunoreactivity for proteins involved in the complement
cascade) were identified in a large sample of actively demyelinating lesions
obtained through biopsy and autopsy.25 Whether these four patterns are also
detected in pediatric MS patients is unknown.
During an MS attack, the initial symptoms are thought to be due to acute
inflammation, edema, and disruption of myelin. Symptom regression is believed
to be caused by resolution of inflammatory edema and by remyelination.7
Inflammatory cytokines inhibit axonal function, and recovery of function has been
linked to redistribution of sodium channels along demyelinated regions of
axon.1,26 Following the relapsing-remitting phase of MS, patients may enter a
secondary progressive phase characterized by progressive loss of neurological
function without clinical relapses. Secondary disease progression is associated
with irreversible axonal injury, gliotic scarring, and eventual exhaustion of the
oligodendrocyte progenitor pool. Importantly, although the degree of
neurodegeneration becomes more evident with increasing disease duration,
axonal injury is also detectable in both the early and late stages of MS, although
the precise pathogenic mechanisms of early axonal injury remain unclear.1
Clinical Manifestation:
Physical Symptoms: The clinical manifestations of relapses are defined on
clinical examination. Polyfocal or polysymptomatic presentations, with clinical
12
features localized to multiple CNS sites, occur in 50-70% of children who are
eventually diagnosed with MS. Monofocal attacks, such as optic neuritis, motor
dysfunction, sensory symptoms, ataxia and brainstem symptoms are also
common.27 Children who present at less than 10 years of age more commonly
display ataxic gait, fever, and confusion.28
Cognitive function: MS creates local regions of demyelination in WM while
concurrent primary myelination is ongoing in developing pathways. Cognitive and
learning impairments have been identified in at least 60% of childhood MS
patients, and are particularly notable in higher order processing.6 Deficits in
general cognition, visuo-motor integration and memory are also significant with a
major impact on academic performance. 6 Demyelination also disrupts WM
processes in adults with MS-–with the difference being demyelinating lesions
disrupt mature, fully myelinated WM pathways. Functionally redundant or
‘secondary’ pathways previously formed may mask the severity of cognitive
impairment in adult MS patients.
Pediatric Disease Progression:
Following an initial demyelinating event, the time to a second attack (relapse) is
variable. Generally younger children experience a longer time interval between
initial and second attack, while adolescents are more likely to experience a
relapse within one year.27 Overall, children and adolescents tend to have a
higher number of relapses in the first two years relative to adult MS patients.29-31
13
In a prospective study of 296 children with acute demyelination, 196 were
diagnosed with MS after being followed for 2.9 ± 3 years. Relative to adult onset
MS patients (16-65 years) a pediatric MS cohort (<16 years) had longer median
times to reach an Expanded Disability Status Scale (EDSS) score of 4
(significant impairments). The slower rates of disease progression characteristic
of pediatric MS patients suggest more plasticity to recover within the developing
CNS.29 Despite higher potential for plasticity, enhanced repair capacities and
slower disease progression, the age at which severe disability scores are
reached appears much younger in pediatric MS patients (age 30-40 years)
relative to adult MS populations in whom disability is more commonly noted at
age 50-60 years.
Determinants of MS Risk:
A complex interplay of genetic and environmental factors influence MS
susceptibility.
i) Genetic factors:
The influence of nature versus nurture in MS was questioned as early as the
1890s when familial aggregation patterns were first identified. The presence of
family history of MS has been associated with increased MS incidence in
relatives of affected individuals.32,33 Although familial genetics are clearly
implicated, the majority of individuals with MS do not have an affected family
member. Recurrence risks for relatives of individuals with MS indicate that first,
second and third degree relatives of affected individuals were at increased risk of
developing MS, relative to the general population.34 Studies of adopted children
14
who had been raised from infancy with an MS patient were no more at risk of
developing MS than were the general population.35 Examining the recurrence-
risks in half-siblings raised in a ‘shared family environment’ with an index MS
patient, relative to those raised separately from an index MS patient, gives further
support that genetic sharing, more than family environment is essential for
familial disease aggregation. 36 The role of genetic factors is further highlighted
by studies showing that MS risk increases with the degree of genetic sharing. If
both parents are affected with MS, their offspring have a markedly higher risk of
developing MS relative to the general population to the general population. When
only one parent has MS, the risk of the offspring being affected is 2.5%.
Offspring from a consanguineous relationship (with one parent affected) are also
at higher risk for MS. The risk of MS in identical twins is 30%.37 A family history
of other immune-mediated diseases (systemic lupus erthyematosus or
rheumatoid arthritis) is also associated with an increased risk of MS.38
Despite the genetic associations in MS, no single gene has been identified as
causal. The identification of a genetic association of MS with human leukocyte
antigen (HLA) class I antigens was first discovered in 1970.37 Recent
observations have shown that 55-60% of MS patients have the HLA phenotype,
HLA DRB1*15,37 Several genome-wide searches have identified potential
candidate genes, although these genetic associations account for only a small
component of MS risk. These genes include genes encoding T cell receptor beta,
interleukin-7, and TNF-α to MS. 39, 32 The association with TNF-α is of particular
interest, as this pro-inflammatory cytokine has many promoter region single
15
nucleotide polymorphisms (SNP). Two SNP (TNF-376, and TNF -238) were
investigated in a Sardinian population, and TNF-376 was elevated by 51% in the
Sardinian sample relative to the general population. Sardinia has one of the
highest MS prevalence rates worldwide.40
ii) Environmental factors
Given that genetics alone can not explain MS prevalence, several environmental
factors have also been studied. MS prevalence increases by increasing distance
from the equator. Environmental contributions are also influenced by the age of
the individual when they reside in that environment; with place of residence in
childhood showing the strongest association with MS risk.41,42
Exposure to viruses in childhood, particularly Epstein-Barr virus (EBV) infection
experienced before the age of 15 years is associated with elevated risk of MS in
children43 and adults.44 A multinational observational study comparing 137
children with MS and 96 healthy children, showed seropositivity for remote EBV
infection in 86% of affected children, while only 64% of healthy matched controls
were seropositive for EBV.28 Not all children with MS, however, are seropositive
for EBV, thus other infectious organisms may contribute to MS pathogenesis. A
study of childhood onset demyelinating diseases -ADEM, MDEM (multi-phasic
disseminated encephalomyelitis) and MS - indicated 74% of ADEM/MDEM, and
38% of MS patients had illnesses one month prior to neurological symptom
onset. Upper respiratory tract infections, influenza and fever of unknown origin
were the preceding illnesses in the MS patients.
16
Evidence for the importance of place of residence during childhood as a
determinant of MS risk is supported by recent Canadian studies that show that
the demographic features of the pediatric MS population closely mirror recent
immigration patterns.42 Two proposed explanations were outlined: (1) childhood
residency in areas of high MS prevalence (eg: Canada, Sardinia), with ancestors
originating from regions in which MS is rare (Caribbean, Asian or Middle Eastern
countries) can lead to childhood MS onset, and (2) the place of residency during
childhood, regardless of ancestry, determines lifetime MS risk -- a fact that will
become increasingly evident as Canadian-raised children of recent immigrants
reach the typical age of adult-onset MS.42
Epidemiology:
The overall annual incidence rate of MS in Canada is 3.6 cases per 100,000, with
regional prevalence rates as high as 130/100,000 (http://www.mssociety.ca). It is
estimated that there are 2 million individuals living with MS worldwide. Females
are reported to outnumber males approximately 2:1. The predominant form of
MS is relapsing-remitting and this type affects 80 percent of adults,7 while 97% of
pediatric MS patients have relapsing-remitting MS.27
Childhood MS Epidemiology:
The onset of MS during childhood is receiving increasing attention, due to
increasing recognition of the disease in the pediatric population, and the potential
for pediatric-onset patients to provide pivotal insights to the earliest disease
17
biology. Onset before the age of 18 occurs in 3-10% of all MS patients, with the
majority of childhood MS patients presenting between 10 and 16 years.27,45 MS
onset under age 10 years occurs far less commonly. While female
preponderance is notable in pediatric MS, as it is in adults, a male
preponderance exists in children with MS onset under age 10 years.27 In the
adolescent populations, females far outnumber males (female: male 2.6:1).
ii) Magnetic Resonance Imaging in Pediatric Multiple Sclerosis:
Magnetic resonance imaging (MRI) plays a critical role in the diagnosis and
monitoring of disease progress in MS in both children and adults. Confirming
lesion distribution and excluding neurological disorders mimicking MS has been
greatly enabled by MRI. Serial MRI in suspected-MS patients should indicate
accrual of new lesions over time.30 The “McDonald criteria” for diagnosing MS in
adults requires MRI confirmation of lesion dissemination in time and space.46-48
McDonald and co-workers indicate these criteria should not be used in children
younger than 10 years of age.48 These diagnostic criteria appear less
appropriate in both early childhood and adolescent onset MS –as studies have
indicated sensitivity as low as 52-54% when these criteria are applied to MRI
images obtained at the time of an initial demyelinating even in children.30,49 MRI
diagnostic criteria specific for pediatric MS have been proposed after evaluation
of a large Canadian pediatric MS population.50 The presence of at least two of
the following -five or more lesions, two or more periventricular lesions, or one
brainstem lesion - was shown to distinguish MS from other pediatric non-
18
demyelinating diseases with 85% sensitivity and 98% specificity. Figure 3
illustrates the typical pattern of lesions seen in pediatric MS patients.
Deep White
Internal Capsule
Cortical Grey
Corpus Callosal
Deep Grey Nuclei
Periventricular White
Juxtacortical White
Figure 3: T2 Weighted -FLAIR image representing supratentorial lesions in pediatric MS. Axial image at the level of the decussation of the genu of the corpus callosum, showing the typical lesion distribution seen in pediatric MS (Callen, Neurology 2008 50, Permission granted from the journal, Neurology). Infra-tentorial lesion categories and sizes are not shown.
19
Physics of Magnetic Resonance Imaging
The production of varying signal intensities through excitation of protons is
fundamental to all nuclear magnetic resonance (NMR) imaging. Key features of
conventional NMR images will be briefly outlined.
Signal generation in conventional imaging:
Magnetic resonance generates signal intensity through the excitation of hydrogen
nuclei (protons). In the presence of a magnetic field water becomes polarized
such that a small majority of dipole moments associated with the hydrogen nuclei
are oriented in the direction of the applied magnetic field. This net magnetization
of the water can be exploited to produce MR images by taking advantage of the
fact that the polarized (water) protons absorb or emit radiation in the form of RF
magnetic fields at a resonant frequency which is dependent on the strength of
the polarizing magnetic field. Image contrast varies among tissues due to
differences in: i) the densities of the excited nuclei and ii) relaxation times
following the applied excitation pulse.2 In the presence of an external magnetic
field, the weak radio frequency field (or excitation pulse) is applied to excite water
protons to a higher energy state within the tissue causing them precess
simultaneously and generate a precessing magnetic moment overall. The T1
relaxation time (termed longitudinal relaxation) describes the time constant for
restoring the direction of the nuclear magnetic moment to the direction parallel to
the applied field; throughout the T1 relaxation time, the excited protons return to
the original lower energy state.
20
It is important to note that although signal generation from water protons is the
focus in diffusion imaging, protons associated with bone, WM, GM and other
CNS structures also produce an NMR signal.
T2-weighted images emphasize the loss of synchrony between protons of water
molecules due to, a concept termed "spin-spin-" (or T2-) relaxation. A T2 time
constant represents decay of the magnetization component perpendicular to the
external magnetic field.2
T2 weighted and FLAIR (Fluid-Attenuated Inversion Recovery) sequences
provide the best view of inflammatory lesions, while lesions identified on T1-
weighted sequences may reflect hyper-acute or chronic “black holes” (regions of
tissue destruction). The key features of each imaging sequence include:
T2 MRI: Conventional spin-echo images have been commonly acquired in MS
clinical trials. Typically this pulse sequence yields 2 different image contrasts per
repetition time (TR). The image generated with a shorter echo time (TE) is a
proton-density (PD) weighted image while the image with the longer TE is the T2
weighted image.51 To improve lesion detection for MS scans, increasing T2
weighting is desirable.51 On a heavily T2-weighted image, lesions appear bright
relative to surrounding grey or WM. T2 weighting is maximized when TE is
approximately equal to T2 for the tissues of interest. A faster version of a
conventional T2 weighted image can be acquired with a fast-spin echo (FSE). In
FSE, multiple phase encodings within each TR are performed. FSE images are
advantageous over conventional spin-echo in MS because they allow thinner
21
contiguous slice collection, while high signal-to-noise ratios (SNR) similar to that
of spin-echo images are maintained; these important features enable detection of
small lesions in MS, as less tissue averaging occurs within a volume.51
FLAIR: Fluid-Attenuated Inversion Recovery (FLAIR) is a widely used MR
technique in MS imaging. The image produced is heavily T2-weighted with the
CSF signal suppressed. Nulling of bright CSF signal is advantageous in MS
imaging for discriminating similarly bright (hyperintense) lesions commonly found
in periventricular or cortical/subcortical tissue. The limited contrast between CSF
and lesion signal on T2 images increases the risk of lesions going undetected.2
The reported improvement in lesion detection and lesion conspicuity, and CSF-
lesion contrast make FLAIR a highly valued tool by MS clinicians.52
Lesion enhancement: The presence of Gadolinium (Gd) enhancing lesions on
T1-weighted images is an additional method for confirming inflammatory CNS
activity. Disruption of the blood-brain barrier permits entry of gadolinium into the
inflamed lesion site. The proximity of the paramagnetic gadolinium to water
protons, has a shortening effect on the T1-relaxation time of water. The shorter
T1 relaxation time produces brighter signal on a T1 weighted image.2 Although a
sensitive marker of early lesion activity, Gd- enhancement requires the presence
of a ‘leaky’ BBB, the ability of contrast to reach the region, a sufficient bolus
concentration, the timing of the scan in relation to contrast injection, and the
exact MR pulse sequence.
22
T1 Hypointensities: T1 hypointensities, or “black holes” have been linked to
worsening Expanded Disability Status Scores (EDSS) in adult MS patients
moving from the relapsing-remitting disease into secondary progressive (SP)
MS.53 Acute, edematous lesions may also appear as T1 hypointensities- and
therefore have the potential to resolve. For the chronic black holes, histological
studies have suggested the degree of hypointensity may relate to ‘matrix
destruction’ or axonal loss.54
iii) Quantitative Diffusion Tensor Imaging
Diffusion Tensor Imaging (DTI) has proven utility in describing microstructural
changes in WM integrity. The signal measured in DTI is generated from water
diffusion through neural tissue.2,55,56 DTI evaluates microstructural tissue
changes impeding or altering water diffusion patterns and thus provides disease
related insight beyond that of traditional imaging methods. DTI technology can
also construct three-dimensional illustrations of an individual’s WM fiber tracts for
surgical planning or functional imaging investigations.57
Although the T1 and T2 weighting characteristics do not account for the signal
generated in quantitative DTI, the principles of excitation and relaxation of water
protons are fundamental for signal production in conventional imaging techniques
also apply in quantitative imaging techniques.
23
Signal Generation in Diffusion Imaging:
The goal of DTI is to capture and quantify water movement through tissues that
impose various degrees of restriction. Diffusion describes random molecular
movements that are thermally driven by Brownian motion. In vivo measures of
diffusion are influenced by surrounding tissue microstructure, viscosity, molecular
weight and temperature.56 In a purely isotropic environment, in which no
boundaries to water diffusion exist and diffusion is equal in all directions, the
approximation of the probability, P, of water molecule displacement X, at time t,
is equivalent to the Gaussian distribution:
⎟⎟⎠
⎞⎜⎜⎝
⎛ −
⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛=
DtX
DtXP
4
2
4
1),(π
where D represents the diffusion coefficient (mm2s-1).2
Diffusion in a restricted environment can be quantitatively described by a
distribution predicting the probable location of water protons after a particular
diffusion time. This distribution is often approximated as being ellipsoidal in
shape. For this ellipsoid to be defined, the lengths of the major and minor
diffusion axes are required. Mathematically, the lengths and directions describing
the ellipsoid comprise a tensor. Hence the term, diffusion tensor imaging.
Diffusion weighting can be detected by applying a pair of consecutive magnetic
gradients serving to dephase and rephase the moment created by proton spin in
the main magnetic field. The MR sequence used to detect diffusion in vivo was
designed by Stejskal and Tanner in 1965.58 The degree of signal loss is
24
dependent on the diffusion of water molecules in the interval between the two
gradients. Additionally, both gradient length and duration affect diffusion
sensitivity. In clinical scanners, a typical diffusion time of approximately 40-50ms
leads to a free diffusion distance of approximately 12μ m (based on a D-value of
1.5x10-9 mm2s-1).
An important parameter in diffusion imaging is the choice of b-value. The value, b
(reported in s/mm2) measures the sensitivity to diffusion, and represents the
product of the gradient strength and timing constants.59 In an environment where
diffusion is not occurring, the applied magnetic dephasing and rephasing
gradients cause no loss of signal coherence. In contrast, diffusion prevents
complete rephasing of the dephased proton spins because the extent of
dephasing and rephasing that occurs varies with position along the direction of
the magnetic field gradient. Signal attenuation resulting from diffusion is
described by the equation below (where S is the signal intensity after a particular
diffusion time, and S0 is the signal acquired with no diffusion gradient).47
bDeSS −=
0
In measuring diffusion, attenuation of signal intensity must be determined. One
scan with diffusion weighting and one without is required for calculating the mean
diffusivity (MD) or apparent diffusion co-efficient (ADC). In human biological
studies, b-values of 0 and 1000s/mm2 are frequently used. A b-value of 0
produces the reference scan with no diffusion weighting and is termed the B-0,
pronounced “beta-not”. Images acquired at the larger b-value, have a degree of
25
diffusion weighting proportional to the product of the strength of the gradient and
the duration of gradient application. This provides a diffusion signal for each of
the applied gradient directions. A larger b value will detect bound (or more
restricted water) with a higher degree of sensitivity.
Tensor determination requires a minimum of two b-values (eg: b=0 and
b=1000s/mm2) combined with gradient applications in 6 or more directions at the
higher b-value. Six gradient directions are necessary and sufficient to calculate
the tensor (or describe the ellipsoid size and orientation) in 3D space, although
recent human studies employ a minimum 20-30 directions for more complete
depiction of diffusion.60 Various combinations of the diffusion tensor elements
allow determination of diffusivity and anisotropy measures.
Diffusion Metrics:
The most commonly reported DTI metrics are the apparent diffusion co-efficient
(ADC) and fractional anisotropy (FA). The outcome measures of FA and ADC are
each comprised of smaller components, known as eigenvalues and eigenvectors
which describe diffusion magnitudes and orientations respectively.
i) Axial and Radial Diffusivities: Eigenvalues and Eigenvectors
Eigenvectors represent the predominant diffusion direction within a voxel or
ellipsoid. The associated eigenvalues ( 1λ , ,2λ 3λ ) are the scalar components that
simply reflect the magnitude of each corresponding vector. The largest, primary
eigenvalue, 1λ , (also termed axial diffusivity) is associated with the primary
26
eigenvector and the major direction of diffusion within the voxel. It is directed
along the long axis of the diffusion ellipsoid, and often in parallel with healthy
axons. Similarly, the second and third largest eigenvalues, ,2λ and ,3λ are the
secondary and tertiary eigenvectors respectively. These are orthogonally
directed from the primary eigenvector and each other. The secondary and tertiary
eigenvalues are often averaged, and represent radial (or perpendicular)
diffusivity. A diagonalized diffusion tensor is required to determine eigenvalues;
combinations of these provide the scalar quantities of ADC and FA. A highly
anisotropic environment would have large 1λ relative to 2λ and 3λ . In a completely
isotropic environment ( 1λ = 2λ = 3λ ). Figure 4 depicts sample of diffusion ellipsoids
and how eigenvectors could contribute.
Anisotropic Diffusion Isotropic Diffusion
Figure 4: Eigenvalue components of diffusion ellipsoids in anisotropic and isotropic diffusion. Primary, secondary and tertiary eigenvectors are shown respectively by: 1λ , 2λ and 3λ . Anisotropic diffusion (left) is depicted with an elongated diffusion ellipsoid. The longer λ1, represents the dominant orientation of diffusion. In tightly packed neural tissue, λ1, is oriented parallel to structural boundaries (eg: along the length of nerve fibres). Isotropic diffusion (right) is depicted with a spherical probability, having all eigenvectors of equal magnitudes. In contrast, isotropic diffusion is most obvious in regions with limited restriction to water diffusion (eg: within ventricular CSF).
λ2 λ2
λ1 λ1
λ3 λ3
27
Many studies in mouse models of MS are investigating the biological meaning of
axial and radial diffusivities.61,62 Findings to date suggest that axial diffusivity
provides a measure of axonal integrity, with water movement in parallel to the
axon. Radial diffusivity, which measures the hindrance to water diffusion
perpendicular to the axon, is influenced predominantly by myelin membranes,
and is thus influenced by loss of myelin integrity in MS. Further discussion of the
directional diffusivities and their biological interpretation with respect to MS is
discussed in the context of our work.
ADC:
The average amount of water diffusion occurring within a voxel or region is
measured by ADC or mean diffusivity (MD). These terms are used
interchangeably and are approximately equivalent. 2 MD is calculated as an
average of the eigenvalues of the diffusion tensor within a voxel. In contrast, the
mean ADC is the average of the ADC measures obtained from any three
orthogonal directions.47 This scalar quantity provides no indication of diffusion
direction; it is purely a measure of magnitude. The Mean Diffusivity is then given
by:
3321 λλλ ++
=≅ MDADC
FA:
Anisotropy measures can describe the degree of eccentricity of the diffusion
ellipsoid.63 The surrounding microstructural tissue integrity is thought to be
reflected with the shape and orientation of the diffusion ellipsoid. Fractional
28
anisotropy (FA) is the most commonly reported measure of anisotropy- and as
implied by its name, measures the proportion of the overall mean diffusivity that
is ascribed to anisotropic diffusion. The degree of anisotropy varies with the
relative magnitude of the eigenvalues (described below). The calculation of FA
would be determined as follows.63
)(2
)()()(32
32
22
1
23
22
21
λλλ
λλλλλλ
++
−+−+−=FA
FA values range from 0 to 1. An FA value approaching zero reflects an isotropic
environment, and a diffusion ellipsoid that is spherical in shape. This would be
seen in free water, or CSF, where few boundaries exist to hinder diffusion.
Higher FA values describe an eccentric ellipsoid in which water flow is more
uniformly directed. In neural tissue the highest anisotropy is found in regions of
densely packed and highly structured WM tissue, such as the CC. The degree to
which anisotropy is an accurate depiction of axonal integrity and myelination
remains to be determined. Anisotropy is also seen in GM however findings of
fewer myelinated fibres result in decreased anisotropy relative to WM.64
29
iv) DTI in MS
Recent studies of adult MS patients highlight the ability of DTI to interrogate WM
change of lesional and NAWM.65
Pathobiological Basis for Diffusion Abnormalities
Several biological processes operative in MS may alter diffusion measurements
(FA and ADC). Properties of NAWM in MS patients were outlined by Allen and
colleagues in 1979. They found 72% of post-mortem non-lesional WM samples
from MS patients were histologically abnormal despite having normal
macroscopic appearance. Abnormal features included: diffuse astrocytic
hyperplasia; patchy edema; perivascular infiltration; gliosis; abnormally thin
myelin or axonal loss.66,67 All of these pathologies are potential substrates of
diffusion abnormality.
Inflammatory changes, following local tissue injury can result in microvasculature
dilatation, increased endothelial permeability, exudation of fluids or leukocyte
migration - all of which can alter diffusion patterns dramatically in neural tissue.
Edema, with an increased quantity of abnormal fluid in intracellular spaces will
decrease intracellular anisotropy and increase mean diffusivity. Cell swelling
results in reduced extracellular spaces, and typically reduces anisotropy and
increases mean diffusivity.2.
The key neurobiological processes of relevance to MS interrogated by DTI
studies are demyelination and axonal loss. Demyelination, which results in
30
destruction to the insulating myelin sheath surrounding axons, will remove (or
impair) the structural barrier to diffusion in the planes perpendicular to the axon.
In healthy WM, myelin restricts diffusion in these transverse planes. Several adult
studies have shown reductions in anisotropy, as well as increases in mean
diffusivity in the WM of MS patients relative to healthy controls.65,68-71 Axonal
loss, which reflects the neurodegenerative component of MS, likely impacts
diffusion metrics as well. Wallerian degeneration (WD), a process in which
axonal transaction leads to an anterograde degeneration of the neuron cell body,
is prominent in both lesional and NAWM in MS.69 Marked diffusion abnormalities
in the hemisphere contralateral to MS lesions (n=51) suggesting that WD of
ipsilateral axons traversing the lesion site and projecting contralaterally contribute
to altered diffusion in the contralateral NAWM.69 Tractography – a DTI analysis
technique that illustrates statistically probable fiber connectivity based on
anisotropies of neighboring voxels 72,73 – may provide insights into the
contribution of WD.
Elevated ADC scores were observed in the ipsilateral NAWM six months
preceding the development of Gd-enhancing acute lesions.74 This raises the
possibility that very early loss of BBB integrity with influx of inflammatory cells
may lead to micro-structural WM abnormality prior to visible lesion detection.
Clinical correlates of DTI:
Clinical correlations with DTI metrics have recently been identified in studies of
adult MS patients with a broad range of disabilities.75 Differentiation of lesion
31
types has been identified in DTI studies of adult MS patients.68 The largest
change in ADC was observed in the more destructive T1 hypo-intense lesions. In
contrast, substantial changes in FA were observed in the inflammatory,
gadolinium-enhancing lesions. The severity of anisotropy in T2-visible lesions
has been moderately correlated with clinical disability in several studies.70,74,76
Interestingly, a relation between changes in MD and FA was revealed for
cerebral peduncles and pyramidal functional scores; this suggests that NAWM
disruption, detected by DTI in eloquent neural tissue may also contribute to
disability in MS.74 A multi-modality imaging study found the combined the MR
metrics of Mean Diffusivity, T1-hypointense lesions and the average N-acetyl-
aspartate/creatine ratio accounted for almost half of the variance in a measure of
clinical disability (EDSS) scores of MS patients.77
Patients who have entered the secondary progressive phase of MS, typically
associated with greater physical disability and defined by the accrual of disability
independent of discrete relapses, have been shown to have whole-brain,78
NAGM,79 and T2-lesion increases in ADC values that are of greater magnitude
than that of relapsing-remitting MS patients.
Preliminary investigations of cognitive function in adult MS patients suggest DTI
can provide insight into language, attention and memory deficits. Moderate
correlations were observed on an index of global cognitive impairment between
MD of NAGM.80
32
DTI study in childhood onset MS
Studies of DTI and magnetization transfer ratios (MTR) were performed on 13
pediatric-onset MS patients and 10 healthy children. Mezzapesa and colleagues
compared the MD (mean and peak height) within normal-appearing brain tissue
and the cervical cord.81 The volume of T2-weighted brain lesions was 11.1mL,
and the average lesion MD was 1.01 x10-3 mm2s -1. Mean diffusivity was mildly
increased in the MS patients relative to controls (1.02 versus 0.97 x10-3 mm2s -1
respectively). This finding seems to be a smaller difference than similar studies in
adult MS have shown. The minimally elevated MD in normal-appearing brain
tissue suggested to the authors that the normal-appearing tissue in pediatric MS
patients is spared. However, many technical limitations existed. The DTI scans in
the above study were collected with only 10 axial slices, each 5mm-thick, with
diffusion gradients applied in only eight directions. There was interpolation of the
diffusion images to match the matrix size of the T2 scan. Therefore rotation of the
diffusion image would alter the underlying MD data and increase error in the
outcome measure. Including anisotropy measures and segmenting brain tissue
into GM and WM would have enhanced their analyses. The authors also
postulated that the limited DTI findings were reflective of short disease durations
in these children. However, DTI metrics in adults studied within the first two years
of MS onset have demonstrated abnormalities, suggesting that a lengthy time
from disease onset may not be required for DTI abnormalities to be detected.82
The same lab extended the above DTI study by exploring GM and WM in 23
pediatric MS patients (mean age14.1 years), and compared the DTI metrics to 16
33
age and sex matched controls 83 Measures of DTI (FA, MD) and MTR were
assessed in NAWM and GM independently. DTI of GM did not differ between
children with MS and controls, suggesting that GM is relatively spared in early-
onset MS patients. The NAWM of this population showed large reductions in FA
and moderate increases in ADC relative to controls. Typical values for WM in
children with MS, relative to healthy controls are outlined in Table 1. It should be
noted that only eight diffusion gradients were applied in collection of the DTI
data, and 5mm slice thickness was used.
Table 1: Typical values for DTI metrics in Healthy WM versus Normal-Appearing and Lesional WM in Children with MS
White Matter (WM) Tissue ADC ( x10-3 mm s -1) (SD) FA (SD)
Healthy Hemispheric WM 0.98 (0.02) 0.29 (0.01)
MS Patient Hemispheric NAWM 1.0 (0.04) a 0.26 (0.02)
MS Patient Hemispheric Lesion WM 1.2(0.08) a 0.26 (0.03)
aData from Tortorella et.al,200683
DTI study of MS pathology in the Corpus Callosum
As the CC is largest inter-hemispheric WM trajectory, the CC has been the focus
of several quantitative MRI studies in adult-onset MS. It is a region commonly
targeted by MS lesions. Adult histological studies report the CC to be comprised
of approximately 200 million ordered fibres.84 The CC has varying regional fiber
densities and myelination levels reflecting functional specializations and cortical
topography.84 The central (mid-sagittal) CC provides a very homogeneous WM
sample with many uniformly oriented fibres. The well defined borders (at midline)
34
permit very accurate delineation of the CC border. Defining the CC border on the
most lateral slices (in a sagittal plane) is more challenging, as the margins are
indistinguishable, particularly when atrophy is present.
DTI analysis was performed on the CC in a group of adult MS patients with a
wide range of disease duration and MRI evidence of variable lesion burdens and
brain volume loss. MS patients displayed strong intracallosal T2 lesion volume
correlations with measures of MD and FA in the CC, NAWM and abnormal-
appearing WM (AAWM) (r=|0.73-0.95|).4 The NAWM fraction of CC tissue had
14.2% higher MD, and a 10.3% lower FA, relative to healthy control WM. Callosal
T2-hyperintense lesion volume was correlated with CC area (r=-0.78, p=<0.01).
Similarly, whole brain T2-hyperintense lesion volume was negatively correlated
with CC area (r=-0.81, p=<0.01). Other DTI work by Ciccarelli et al, showed
strong correlations of frontal, parietal and occipital T2 lesion loads with diffusivity
and anisotropy measures in the genu, body and splenium of the CC.85 A similar
study of DTI compared regional hemispheric ADC and FA values with those in
the CC. The transcallosal fibers project in a pattern reflective of cortical
topography. The CC fibres in the genu connect the frontal lobes, while the
midbody CC fibres connect the temporal lobes, and the splenium of the CC
contains fibres of parietal and occipital origin.86 Figure 5 represents topographic
projections of the midsagittal CC and the corresponding fibre diameters.87
35
Figure 5: Cross-sections of the human corpus callosum (CC). A topographical representation of myelinated cortical fibres transecting the CC (left). Regional distributions of axonal density and diameter of trans-callosal fibres (larger circles indicate larger fiber diameters) (right). A, auditory fibers; F, frontal fibers; M, motor cortex fibers; Ss, somatosensory fibers; T/P, temporoparietal fibers; V, visual fibers. (Adapted from Aboitiz)87
The normal appearing corpus callosum (NACC) and NAWM of adult MS patients
with short disease duration (mean = 2.7 years) were compared with healthy
controls. Due to the size, compact organization and multiple connections of the
CC, the authors expect MS related damage in the CC NAWM, to occur earlier
and be most severe relative to other brain regions.88 The NACC showed marked
reductions in FA and increases in MD in all CC regions. In contrast, no
differences between MS patients and controls were noted in frontal or occipital
NAWM on any DTI metric. As the disease duration was relatively short in these
patients, the author’s suggest that the CC is an early target in MS.
An additional finding of increased NACC diffusivity correlating with T2 cerebral
lesion volume suggests the effect of tract degeneration or axonal loss within the
CC.88
Interpretation of DTI measures in the CC must consider the known regional
heterogeneity of anisotropy values across different CC regions.71,89-91 Higher
36
anisotropy in the splenium relative to the genu is consistent across sex and age.
Future studies exploring the relative contributions of axonal geometry, packing,
microstructure and myelination within each CC region are required to fully
appreciate DTI values in the CC in both health and disease.12,84
Studying Maturation and Myelin Development with DTI
Age related increases in anisotropy have been well documented.18,24,92,93 Neil
and colleagues outline two phases of age-related increases in anisotropy in
developmental animal work.94 First, a ‘pre-myelinating’ phase exists where
changes in anisotropy are observed prior to a histologic appearance of myelin in
rat pups-ages 5 days to 8 weeks,95,96 suggesting that structural changes in
axonal non-myelin membranes are sufficient to provide some degree of
restriction of water diffusion. Non-myelinated fibers in the CC were detected by
diffusion tensor MRI as early as 28 weeks.97 Numerous structural changes
characterize the ‘pre-myelinating’ phase which could lead to increased FA,
including a larger number of micro-tubule associated proteins, a change in axon
caliber, and a significant increase in oligodendrocyte number.95 Axonal
membrane changes, creating increased conduction velocity and altered Na+/K+-
ATPase activity have also been suggested, although their mechanism of FA
increases remains unclear. In this pre-myelinating phase radiological evidence of
increased signal on T2-weighted image changes corresponding with increased
anisotropy, although precedes the presence of histological staining of myelin.
The second phase of myelin-associated increases in anisotropy occurs in parallel
with the histological presence of myelin. This produces more prolonged and
37
dramatic increases in FA. The earliest changes in anisotropy that occur with
primary myelination have been studied in rat models 95 and full and pre-term
infants. 97
Figure 6 illustrates typical human CC diffusivity and anisotropy values, based on
cross sectional data representing different age groups and CC regions. Periods
of myelination progressively increase into young adulthood and regress in late
adulthood. This describes an inverted-U trend in anisotropy across the lifespan,
and a U-shaped trend in mean and radial diffusivity.91
38
Figure 6: Trends in CC midsagittal area and diffusion metrics across the lifespan. DTI corpus callosal segmentation was based on Witelson seven segments geometric approach.86 (CC1–CC7: CC1 = rostrum; CC2 = genu = gCC; CC3 = rostral body; CC4 = anterior midbody = bCC; CC5 = posterior midbody; CC6 = isthmus = iCC; CC7 = splenium = sCC). The red trace, eCC represents the midsagittal callosal area, manually delineated on conventional imaging. Of note are the rapid changes in slope from 10-25 years of age the mid-sagittal CC area (a), fractional anisotropy (b), and both radial and mean diffusivity curve (c, d respectively). The highest values for FA are achieved at approximately 30 years of age, which corresponds to the lowest values for MD. (Reproduced with permission from Hasan, Brain Res 2008 91).
Similarly, Mukherjee and colleagues describe bi-exponential decay of diffusivity
with increasing (developmental) age;92 in gray and white matter regions, except
in the genu where monoexponential decay occurred. A steep nonlinear increase
in anisotropy of white matter tracts paralleled the time course of the decline in
diffusivity. Of note, thalamus and basal ganglia ROIs exhibited significant linear
39
anisotropy increases with age, although they were of smaller magnitude as was
seen in the CC ROI.
As mentioned above, throughout typical development, myelination continues into
the third decade of life15. The expected decreases in mean and radial diffusivity
during childhood and increases in adulthood with advancing age are believed to
reflect regional dynamics of myelination. As noted in the above paragraphs,
myelination alone is not essential for measurable anisotropy to exist, as
anisotropy has been observed in non-myelinated fibres.12 For example, the optic
nerve of rats display diffusion anisotropy prior to the earliest histological stain of
myelin in WM.95,98 Various developmental structural changes may contribute to
anisotropy: neurofilament development; axon alignment, a growing number of
membrane barriers, greater cohesiveness and compactness of fibre tracts,
reduced extra-axonal space (or greater packing), changing extra and intra-axonal
matrices, and finally myelin formation in maturing WM.12
v) Rationale:
Studying children with MS provides a window into the earliest aspects of the
disease, a window that can be frequently missed when MS detection occurs in
adulthood. DTI provides a non-invasive technique capable of determining the
integrity of WM, particularly, WM that appears normal on conventional imaging.
The insights gained may provide enhanced understanding of MS biology.
40
3) Current Project
a) Study goal: This study investigates WM microstructure in children with MS
using quantitative DTI. We will specifically investigate the i) WM of children with
MS relative to healthy children, and ii) contribution of lesions to WM integrity.
b) Objectives:
i) To compare quantitative DTI measures of FA and ADC in the Corpus Callosum
(CC) and regions of hemispheric WM between children with confirmed MS and
healthy control (HC) children;
ii) To describe contributions of lesion tissue to any observed differences in CC
and hemispheric WM between groups;
iii) To describe diffusion metrics in the NAWM of CC and hemispheres of MS
patients relative to HC;
iv)To probe any observed differences in anisotropy by assessing primary and
secondary eigenvalues in NAWM and lesion tissue of MS patients.
c) Hypotheses:
i) Children with MS will show reductions in FA and increases in ADC relative to
healthy children in both CC regions and hemispheric WM, due to the disease
related regions of demyelination and axonal injury.
ii) Lesion tissue of MS patients will show reduced FA and increased ADC values
relative to NAWM of MS patients and the WM of healthy controls, as the most
41
severe tissue damage is thought to occur in lesions. These trends will exist in both
hemispheric and CC analyses.
iii) Reductions in the primary eigenvalues are expected within lesion tissue of MS
patients. Increases in the radial diffusivity are expected to occur in lesion tissue
of MS patients relative to the NAWM, and would be expected to be relatively
larger than the axial diffusivity increases, due to loss of myelin membranes.
Increases in radial diffusivity are also expected in the NAWM of MS patients in
contrast to healthy controls.
42
d) Methods
i) Participants
Children with MS, defined by two or more relapses,99 cared for in the Hospital for
Sick Children Demyelinating Disease Clinic were offered enrollment in our MRI
and neurocognitive study .All patients were examined on the day of MRI
scanning, relapse history was obtained and an Expanded Disability Status Scale
(EDSS) score was assigned by one examiner (BB). Children with MS
experiencing new neurological deficits (active relapses), or patients receiving
corticosteroid therapy within 30 days prior to the MRI were excluded. A history of
learning disability did not result in exclusion, as cognitive impairment can be an
early feature of MS. Consent and Assent forms for MS patients are included in
Appendix E.
Previously published, archived DTI images from 17 healthy control (HC)
participants were available for comparison.20 Healthy control participants were
recruited through community newspapers and parent networks. Children were
excluded on the basis of a history of prior acquired brain injury, developmental
delay, or learning disability. Informed consent was obtained from the parents
prior to participation, and verbal assent was obtained from the participants.
Consent and Assent forms for HC participants are archived in the Sick Kids
Research Ethics Board Office.
The Research Ethics Board at The Hospital for Sick Children approved all
studies.
43
ii) Image Acquisition and Post-processing:
All imaging data was acquired on the same GE LX 1.5T MRI scanner (General
Electric Healthcare, Milwaukee, WI) using a single channel quadrature headcoil
for all MS and control participants. MRI technicians were trained to ensure
standardization of imaging protocols. MRI scan times ranged from 60-90 minutes
with intermittent breaks for participant comfort. All children were offered
headphones and the option to watch a movie during their scan. None of the
participants were sedated.
MS participants:
The following scan parameters were collected: sagittal T1-weighted 3D spoiled
gradient-recalled echo (SPGR) sequence (TR/TE = 22/8 ms, 1 signal average,
256 x 256, 250 mm FOV, 1.5mm slice thickness, 30° flip angle) for region of
interest (ROI) definition; dual-contrast proton-density/T2 weighted interleaved
sequence (TR/TEPD/TET2= 3500/15/63 ms, 256 x 256 matrix, 90 axial slices, 1
signal average, 250 mm FOV, 2mm thick contiguous slices, 90° flip angle) was
acquired to facilitate the image registration. Diffusion tensor data were acquired
with a single shot spin echo DTI sequence and EPI readout (25 directions, TR/TE
8300/79 ms, 32 contiguous axial slices, 5mm thick, 128x128 matrix, and diffusion
weighted images with b=0 and b=1000s/mm2).
44
Control participants:
The HC participant images were collected with the following parameters: sagittal
3D T1 SPGR (TR/TE=8.6/4.2ms, 122 contiguous axial slices, 1.5 mm slice
thickness, 256 x 192 matrix); and a dual contrast proton-density/T2 weighted
interleaved sequence (TR/TEPD/TE2=2800/30/90ms, 54 axial slices with 2.5 mm
gaps, 5mm slice thickness, 256x 192 matrix). Diffusion tensor data were acquired
with a single shot spin echo DTI sequence with and EPI readout of (25 directions,
TR/TE 8300/79 ms, 32 contiguous axial slices, 260 mm FOV, 3mm thick,
128x128 matrix, with one b=0 image and b=1000s/mm2). Processing of the HC
scans was performed for a prior study.20
Post-processing:
Eddy-current correction was performed on raw diffusion weighted images to
remove artifacts associated with switching on and off of diffusion gradients.
Visual inspection for artifacts due to additional sources (eg: head movements,
braces) was also performed. Two MS patient scans were not included in the
analysis due to movement artifacts. Subsequent calculation of ADC and FA
maps and was performed using DTI Studio.72,100 Background principles for
determination ADC, FA and eigenvalue maps were outlined previously in the
introduction.
45
iii) Region-of-Interest (ROI) Definition:
T1 anatomical scans were used for region of interest (ROI) definition. The ROI
approach to the analysis was selected, rather than tractography to address our
primary goal of evaluating the global integrity of WM in children with MS, rather
than the selective impairment of specific pathways. In preparation for manual
tracing of the ROI, (and to remain consistent with the HC processing) the MS
patients’ sagittally acquired T1 SPGR scans were resized with axial image
dimensions, and re-sampled to create isotropic voxels. Manual positioning of
each subject’s T1 image was performed, using Analyze Software (Brain
Research Institute, Mayo Clinic) to standardize the image position such that
anterior and posterior-comissures were visible on a single slice (termed ‘T1ac-pc’
alignment). The CC and hemispheric regions of interest (ROIs) were then
identified.
Corpus Callosum ROI Definition:
The method of manually tracing the CC ROIs on a T1ac-pc image, along with the
post processing methods were published by Mabbott and colleagues.20 A four-
segment CC object was manually traced on all participants’ T1ac-pc image,
using Analyze software (Brain Research Institute, Mayo Clinic). The mid-sagittal
slice was consistently located to begin CC tracing. The methodology for CC
segmentation into the genu, splenium, anterior and posterior midbodies is
outlined in detail in Appendix A, and shown in Figure 7. To ensure coverage of
the entire CC, the object traced on the mid-sagittal slice was then copied on 12
slices bilaterally. Editing the CC margins was performed on each slice in axial
46
and coronal planes, to reduce partial volume effects- and avoid inclusion of
surrounding non-WM tissue into the ROI (Figure 8a,b). Editing also permitted
delineation of CSF and peri-ventricular lesions. To minimize partial volume
effects, the image boundaries were maintained one voxel inside the CC margin.
Posterior
Inferior
Figure 7: Manual subdivision of CC performed on a T1 weighted image. Genu-anterior body boundary (red), is determined by locating the anterior-most inferior margin of the CC border, and tracing vertically. The posterior-body-splenium division (green), is then determined by tracing vertically from the second groove on the inferior CC margin, near the fornix junction. The anterior-posterior body division (yellow) is located at the voxel equidistant from the genu and splenium boundaries.
47
partial volume edited in axial
plane
Figure 8: Editing of CC segments in the axial plane. Editing the CC segments in the axial plane is required to determine lateral CC boundaries (left) and minimize partial volume effects (Before (middle) and after (right) editing). (Figures 7 and 8 are pediatric MS patient images; tracing methods outlined in Mabbott lab ROI Manual).
Hemispheric ROI Definition:
An anatomical hemispheric mask was edited locally to accommodate for pediatric
brain size, and registered to a brain of representative size and shape*. Each
participant’s T1ac-pc aligned image could then be individually registered with
hemispheric mask. The registration used in this step was an affine registration,
which describes alignment between two distinct objects, rather than a less
complex rigid body alignment between two images from the same object (as
performed for all CC ROI rotations). Affine registrations employ 12 degrees of
freedom (3 translations, 3 rotations, 3 scales and 3 shears) while rigid body
registrations require only 6 degrees of freedom (3 translations and 3 rotations).102 * The hemispheric analysis initially performed on the HC and first 15 MS patients employed a hemispheric lobe template that was used in previously published control data.20 The results of this preliminary comparison between groups are outlined in Table 7. The anatomically based hemispheric mask was selected for used part-way through the analysis, so all MS and control patients were re-analyzed using this method.101
48
An affine registration was required for the hemispheric ROI processing, because
the brain atlas is defined on a ‘universal pediatric brain template’ rather than the
subject’s own brain. For detailed explanation of the processing steps, please
refer to Appendix B.
Lesion Definition:
Lesion segmentation was conducted at the Montreal Neurological Institute,
McConnell Brain Imaging Center. T2-weighted lesion segmentation was
performed in a semi-automated manner. An initial segmentation was performed
using MNI developed automated software (based on the theses work of Francis,
SJ.103) that employs a Bayesian algorithm, using multi-modal tissue intensity
class-conditional posterior probability, and an anatomical and tissue class spatial
probability to generate a probability map for each tissue class. Briefly this
segmentation algorithm determines the output tissue classification in each voxel
after considering both: i) identical voxel locations (via co-registration) on T1-
weighted, T2-weighted and PD-weighted images, and ii) the anatomical
likelihood of a particular tissue (lesion/CSF/WM/GM) existing in the given brain
location of that voxel.
The maximum a posteriori probability from each voxel is used to create a tissue
classification image from which the primary lesion maps are extracted. Using
DISPLAY (McConnell Brain Imaging Centre, MNI), visualization software, the T2-
weighted lesion labels output by the initial segmentation were superimposed on
the T1-, T2-, and PD-weighted images, carefully reviewed and, manually
49
corrected, if necessary.
Lesion maps were converted to a format readable by Analyze using MNI Tools.
The overall transformation enabling re-sampling of each subject’s lesion map
from T2 into DTI image space was performed. After lesion maps were combined
with CC and hemispheric ROI objects (Figures 9ab), computation of FA, ADC
and eigenvalues for all subjects and tissue types (HC WM, NAWM or lesion) was
performed. Analyze software was used to sample all DTI data. Volume
measurements for lesions and each ROI were also sampled simultaneously.
9a) 9b)
Figure 9: Representative FA maps of two MS patients with lesions in CC and hemispheric ROIs. a) CC ROI indicating lesion (blue) in the splenium (green). The genu (red) does not display lesions. Extra callosal lesions are purple. CC ROIs were defined manually, and lesions were located using semi-automated segmentation techniques. b) A second patient showing hemispheric WM ROI: frontal WM (orange), temporal WM (purple), parietal WM (turquoise), occipital WM (red) with a lesion (green) in the right frontal-temporal WM. This image depicts hemispheric ROIs identified with a brain atlas optimized for pediatric brains.
iv) Image Processing Sequence:
The processing stream involves re-sampling or co-registration among several
scans of each subject’s own MR images. Combining the individual transformation
50
algorithms sequentially enables CC or hemispheric ROIs and lesions to be
superimposed onto a non-native image space. Table 2 outlines the processing
stream used for the ROI analysis. Automated Image Registration software (AIR
3.0) was used for all image registrations.104 Prior to co-registration, the skull was
removed from all images using ‘Brain Extraction Tool’ in MRIcro Software.105,106
The number of slices acquired in each patient’s T1, PD/T2 and DTI scans varies
with head size, and operator inputs, therefore the steps outlined in Table 2 (2a,b,
and c) the registration were performed on each subject individually.
Table 2: Region of Interest (ROI) Definition and Registration with Diffusion Tensor Maps Table 2a: CC Definition Table 2b: Hemisphere Definition
ROI Object Definition
Software AND ROI Object Definition
Software
CC tracing on T1ac-pc image
Analyze Hemispheric Atlas Analyze for editing
Atlas aligned with Representative Brain
AIR, Affine
T1ac-pc aligned with Representative Brain
AIR, Affine
Segment WM, GM and CSF from each
region
FAST' (FMRIB, Oxford)
Following CC and Hemispheric ROIs definition, objects are rotated into DTI image space using AIR software. Each
component registration output file is described in Table 2c and sequentially
combined to provide an overall transformation from ac-pc to DTI image
space.
51
Table 2c: Image Registrations
REGISTRATIONIMAGES
CO-REGISTERED
RATIONALE
1 Linear T1ac-pc to PD-weighted
Image intensities on T1 and PD weighted scans are more similar than those of T1 and T2 weighted scans; therefore, these scans more accurately co-register. This initial linear registration is an intermediate step towards aligning the anatomically defined ROIs to the target DTI acquisition space.
2 Proton density / T2 weighted
PD/T2 interleaved scans are acquired with identical image dimensions; and are thus aligned.
3a Linear T2 to B0 (“T2 pre-warp”)
This linear registration is used to minimize any translational differences between T2 and B0 images prior to non-linear registration. The B0 image volume is collected as a baseline image.
3b Non-linear (warp)
T2pre-warp to B0 (DTI space)
The output image from step 3a is then non-linearly registered into the diffusion acquisition space. A non-linear registration is utilized to account for differing spatial distortions induced by EPI on the diffusion image. Comparison of the warp output image, with actual B0 image (created in 3a) confirms that the non-linear registration was successful in aligning the scans.
4 Manual adjustment
T2-pre (for warp correction if needed)
If required, manual adjustments are applied to the output image of step 3a. The adjustments are user defined and include: rotations in yaw/pitch/roll planes, or translations in x, y, z directions. The goal of these minor adjustments is to best approximate the image position in the destination space prior to a non-linear registration (warp). The manually adjusted image is then warped to the B0.
52
The overall transformation algorithm for each subject results from sequentially
combining output files (AIR format) from each registration step until the target
image space is reached.104 Rotation of the CC and hemispheric WM ROIs from
T1 AC-PC image space to DTI space was achieved. Superimposing the CC
object (using nearest neighbor interpolation) with DTI data maps yields the ADC
and FA values for each ROI. Similar processing is employed to rotate lesions
defined on T2-weighted images to DTI, with the difference being an overall
transformation file rotates lesion objects only from T2 image space into DTI
image space. Sampling of DTI data occurs in its native space to preserve the
dependent variables’ underlying values (FA, ADC, eigenvalue intensities).20,72,100
A summary of the image processing pathway and object identification is outlined
in Figure 10.
53
Figure 10: Overview of acquired images and registrations used in processing of MS patient CC data. The acquired scans used for DTI post-processing are outlined in the left column. Registration of the acquired T1-weighted image to the Proton-Density (PD)/T2 weighted image space occurs via linear registration (following positioning of the T1 image with anterior and posterior commissure alignment (T1AC-PC)). Non-linear registrations are used to align T2-weighted images with diffusion images (first to a b-0 (non-diffusion weighted) image; not shown). The CC ROI, (defined on the T1AC-PC image) and the lesions (identified on T2 images), can then be aligned to the diffusion images using the required sequence of registrations. All co-registrations are performed using AIR 3.0 software.
Object Definition
Image Acquired Images
T1
FA & ADC Maps
Segment lesions from ROI and WM mask Diffusion
Images
T1 AC-PC
Generate MS lesion maps
CC ROI traced
LINEAR
LINEAR&
NON-LINEAR
LINEAR
PD / T2
Ideally, re-sampling of images from a native space into any non-native (target)
space ensures maximal preservation of the underlying intensity values. Thus,
registrations performed in the above sequence (Table 2) employ a trilinear
interpolation model. The output intensity determined via trilinear interpolation is
54
based on a weighted average of surrounding voxel intensities (at the vertices of a
cube surrounding the point of interest in the target image.107 In contrast, when
resampling objects (ROIs or lesion maps) which consist of binary values
indicating only present or absent intensity, into a non-native (DTI) space, the
interpolation model applied is known as ‘nearest neighbour’. This output is simply
intensity of the nearest voxel in the target image.107
v) Statistical Analyses
Assessment of statistically significant differences between HC and MS patients’
(combined WM) DTI metrics, was performed using analysis of covariance
(ANCOVA) for two independent samples, using SAS software. Group differences
in FA or ADC were first assessed for each region of the brain independently. The
effect of age and sex on FA and/or ADC, were then assessed as covariates,
using ANCOVA. Age and sex did not alter the significance of the regional
comparisons. The analyses of segmented lesions and NAWM within each MS
patient used a within-subjects repeated measures ANOVA. Therefore, only
patients displaying lesions in a particular region had their NAWM included in the
analysis. This eliminated the risk of comparing lesions from an older patient
having potentially higher FA values with the mean NAWM of a younger sample.
For each dependent variable, the regional mean across all voxels and slices was
calculated and used in the analyses. To adjust the alpha level for multiple
comparisons, Bonferroni correction was performed. In the CC region the alpha
level was calculated as 0.05/4 ~ 0.0125 (as 4 CC segments were assessed). The
hemispheric analyses were also corrected for multiple comparisons using
55
Bonferroni correction, and the alpha level was adjusted with the following
calculation: <0.05/8 ~ 0.00625. To investigate differences in intra-patient
variability, and inter-patient variability, an intra-class correlation was assessed,
with a linear correction for age. Spearman correlation coefficients were
calculated and used to assess the relationships between MS lesion
characteristics and measure of clinical disability (EDSS).
56
e) Results:
Participants:
Table 3 summarizes the clinical and demographic features of the 21 pediatric MS
and 17 HC participants. Children with MS were more likely female (p<0.0001),
and were significantly older (p<0.0001) than were HC participants.
Corpus Callosum Analyses:
i) Total CC Tissue DTI Metrics in MS Patients and HC:
Reductions in the fractional anisotropy (FA) of the CC tissue were evident in the
anterior and posterior bodies and splenium (each p<0.0001) in MS patients
relative to controls (Table 4). The largest differences in FA were observed in the
anterior and posterior bodies. Healthy control WM and MS patient WM in the
genu was not different on measures of FA.
Increases in ADC values were observed in the anterior and posterior bodies and
splenium in MS patients relative to the HC participants (p<0.05). ADC values
were increased in MS patients relative to HC participants in the genu, despite the
absence of FA reduction in this region.
i) Comparison of CC NAWM in MS patients to the CC of HC:
Segmentation of lesions from the total CC WM enabled investigation of the
relative contribution of lesions and NAWM to whole tissue FA and ADC metrics.
57
Table 5 and Figure 11 contrast the CC FA and ADC values of NAWM to the HC
WM.
Robust reductions in the FA values of NAWM were observed relative to HC WM,
in the anterior body, posterior body and splenium of the CC. The FA differences
between HC WM and the NAWM of MS participants are similar in magnitude to
the observed differences between HC WM and total CC WM (NAWM plus
lesions) of MS patients (Tables 4 and 5), confirming that lesions had relatively
little contribution to the reduced FA and increased ADC values noted. The
increase in mean ADC (of approximately 60%) was observed in all CC NAWM
regions relative to HC WM (p<.0001). Of note, the genu NAWM was observed to
have an elevated ADC relative to the HC WM, despite the FA values in this
region being similar in HC and MS patients.
iii) Comparing Lesional and Non-Lesional Tissue in the CC of MS patients:
As highlighted in Table 6, FA values within lesions were reduced relative to
NAWM in the genu, anterior body and splenium. FA values were increased in the
posterior body relative to the NAWM in the region. When total CC lesions were
considered irrespective of region, the mean lesion and NAWM FA values were
0.44(SD=0.14, range: 0.25-0.73) and 0.54(SD=0.12, range: 0.18-0.80)
respectively (p<0.008). ADC values of lesions and NAWM did not significantly
differ.
58
iv) Regional CC Differences in Volumes and FA Values of MS and HC
As Figure 11 and Tables 4, 5 illustrate, comparisons of both total WM, and
NAWM of MS patients relative to HC WM, demonstrate statistical differences in
FA and ADC values in the splenium, anterior and posterior mid-bodies. The FA
values in the genu did not differ between HC and MS patient NAWM. As outlined
in Table 6, lesions comprised a maximum of 5.2% of any region volume. The
small volume of lesion tissue within each CC region indicates a minimal
contribution of lesions to DTI measures in the combined WM, and supports the
robust abnormality in the NAWM of children with MS, relative to HC WM. Figure
12 compares the CC regional volumes and FA values between MS patients and
controls, as well as the volume of lesions within each region for the MS patients.
Hemispheric WM Analyses:
i) Participants: Hemispheric WM analyses were performed in 19 of the MS
subjects, and all 17 controls. The two MS patients excluded demonstrated
susceptibility artifact on DTI images at the bone-air interface of the sinuses which
led to registration error in the frontal poles.
ii) Comparing DTI metrics of Total Hemispheric WM in MS Patients and HC:
Total hemispheric WM (prior to lesion segmentation) of MS patients was
compared to HC WM. Mean FA values are reported in Table 7. These data were
generated using a lobar mask in which anatomical regions were delineated by
brain proportions (as published in Mabbott, 2006).20
59
Significant group differences were found for all hemispheric regions (p<0.0001).
The MS patient WM FA values were reduced by as much as 40% relative to
values obtained from the same region in controls. Aside from bilateral parietal
lobes, all regions of MS WM demonstrated increased ADC relative to HC WM
(p<0.05 corrected for multiple comparisons).
iii) Comparing Hemispheric NAWM in MS patients to the Hemispheric WM
of HC
FA values of NAWM were lower in the frontal (p=0.012), temporal (p<0.0001)
and occipital (p<0.0001) regions relative to corresponding regions in HC WM.
Even following segmentation of lesion tissue, up to 30% lower FA values existed
in occipital NAWM relative to the HC occipital WM bilaterally (Figure 13, and
Tables 7, 8). The parietal lobes did not reveal differences between MS patients
and controls. In HC participants, FA values did not differ greatly by hemispheric
region. In MS patients, the occipital FA values were significantly lower than the
frontal, temporal or parietal regions (p< 0.0001 for all comparisons). Further
details are illustrated in Figures 13 and 14. ADC was elevated in all hemispheric
regions in the MS patient NAWM, relative to HC (p<0.0001 corrected for multiple
comparisons). Although sex and age differences contributed to the initial
hemispheric ADC findings, the marked elevation in ADC remained after both sex
and age were evaluated as covariates. The effect of laterality was evaluated and
was non-significant for both ADC and FA. A summary of Hemispheric DTI values
can be found in Appendix C.
60
iv) Comparing Lesional and Non-Lesional Tissue in Hemispheric WM of MS
Patients:
Figure 14 and Table 9 display mean regional FA values for hemispheric lesions,
NAWM and HC WM. It was of interest to note that in certain hemispheric regions
the FA values in lesions were higher than the NAWM in the same region (left
parietal, left temporal and bilateral occipital lobes), although these observations
did not reach statistical significance. Reduced rather than the anticipated
increased ADC values were found in lesion tissue of the frontal (p<0.0001) and
temporal lobes (p<0.005) relative to the corresponding NAWM.
Axial and Radial Diffusivities of MS Patient NAWM and Lesions
i) CC Diffusivities:
With the aim of further interrogating lesion and NAWM micro-structural tissue
properties, axial and radial diffusivities were compared (Figure 15, Appendix D
Table 12). The axial diffusivity was markedly reduced in lesion tissue relative to
NAWM among MS patients’ CC (p=0.0083). Average radial diffusivities were not
significantly different between tissue types. No significant regional effects were
observed for the CC axial or radial diffusivities.
ii) Hemispheric Diffusivities: Further examination of NAWM and lesion tissue
was possible through generation of the primary and averaged secondary and
tertiary eigenvalue maps. Interestingly, elevated axial and radial diffusivities in
lesions relative to NAWM were observed in all hemispheric regions. The mean
61
values for axial and average radial diffusivities are reported in Table 10 (and
Appendix D, Table 13). The axial diffusivity findings were markedly different
between lesions and NAWM in temporal (p=0.0000), parietal (p=0.003), and
occipital (p=0.001) regions after correcting for multiple comparisons. The average
radial diffusivity values were elevated in lesions in frontal (p=0.004) and temporal
(p=0.001) regions after correction for multiple comparisons.
Factors Influencing FA values:
i) Inter- and Intra-subject variability: (i) within the NAWM of the corpus
callosum: FA values of NAWM in the CC of MS patients exhibited greater
variability than was seen in FA values of healthy CC WM in controls, as
illustrated by the whiskers of the box plot graph (Figure 11); (ii) within CC
lesions: Intra-class correlation was performed to compare the intra- and inter-
patient FA variability. No significant differences were found. (iii) within the
hemispheric NAWM: FA values of NAWM in the hemispheres of MS patients
exhibited larger FA ranges than did FA values of HC hemispheric WM, as
illustrated by their respective ranges (sd), 0.26(0.12) and 0.08(0.01). (iv) within
the hemispheric lesions: The range of lesion FA values was 0.21(sd=0.12). (v)
whole brain FA: Nineteen of 21 MS patients had lesions. In 15 of the19 patients
with lesions, variability in NAWM FA values was higher than lesional FA
variability. Within patient differences in whole brain FA values between NAWM
and lesions are displayed in Figure 16.
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ii) Lesion location and FA variability: Higher FA values were reported in CC
lesions relative to hemispheric lesions. The range of hemispheric lesion FA
values was 0.29-0.34, while CC lesion FA values ranged from 0.41-0.56. Within
the CC, the mid-body sections had lower mean lesion FA values relative to the
genu or splenium as reported in Table 6. Trends in hemispheric lesion location
suggest occipital lobe lesions have the lowest FA relative to other areas (Figure
14).
ii) Relationship of Lesion FA to Lesion Volume: Mean lesion FA values
were weakly correlated with whole brain lesion volumes (Pearson r= -0.169,
p=0.039). However, when analyzed independently, no relation was found
between CC lesion FA values and the mean volume of CC lesions, nor was a
relation found between the mean lesional FA of hemispheric NAWM and
corresponding hemispheric lesion volumes.
Relation of DTI metrics and Clinical Outcomes:
Spearman correlation coefficients were calculated and used to assess the
relationship between MS lesions and measure of clinical disability (EDSS). Full
regression analyses were not feasible with this sample size.
i) CC Lesion FA and Clinical Disability: The relation between individual lesion
FA values and disability was assessed. Correlations of whole brain lesion FA and
EDSS scores were not related (r=0.06, p=0.4370). The assessment was
narrowed to include only the FA of CC lesions and EDSS. Although this was not
statistically significant the observed trend is illustrated (Figure 17).
63
ii) CC Lesion Volume and Clinical Disability: CC lesion volume did not
correlate with EDSS (p=0.0729). Figure 18 suggests the effect of outliers
exaggerates the mild trend observed, and a larger patient sample would likely
indicate no relation between inflammatory lesion burden and EDSS.
64
f) Tables and Figures
Table 3: MS and Control Participant Demographics Patient
Demographics Sex
(F,M) Mean Age at Scan
(SD) Age
Range MS Participants: 17, 4 15.2 (2.3) 10-17.8 HC Participants: 4, 13 11.7 (6.3) 6.2-17.8
Table 4: Total CC White Matter FA and ADC values in HC and MS Participants
Healthy Controls
Mean FA (SD) Mean ADC (x10-6)mm2 s -1
(SD)
MS CC Tissue Mean FA (SD)
Mean ADC(x10-6) mm2 s -1 (SD)
% Difference
0.643 (0.06) 0.656(0.16) 0.47% nsdGenu
800(50) 2265 (1400)** 64.50% 0.683 (0.04) 0.517 (0.15)* 32.11% Anterior
Body 770 (50) 2066(1400)** 62.50% 0.646 (0.07) 0.496 (0.16)* 30.24% Posterior
Body 810(70) 2269(1300)** 64.30% 0.728 (0.03) 0.642 (0.16)* 13.40% Splenium
730(40) 2293(1500)** 68.36% MS patient CC tissue was significantly different relative to HC WM at: *p<.0001, **=p<005. (Bonferroni corrected for multiple comparisons). NSD: non-significant difference. Main effect of FA and ADC differences were first assessed for each region. The effects of age and sex were included as covariates in the ANOVA model and did not alter regions with significant main effect differences.
65
Table 5: DTI Metrics of NAWM and HC WM in CC Regions
Healthy Controls Mean FA (SD)
Mean ADC(x10-6) mm2 s -1 (SD)
MS CC NAWM Mean FA (SD)
Mean ADC(x10-6) mm2 s -1 (SD)
% Difference
0.643 (0.06) 0.636 (0.09) -1.09% nsd Genu 800(50) 2090 (1100)* 61.25% 0.683 (0.04) 0.488 (0.10)a -28.55% Anterior
Body 770 (50) 2050(1100)* 62.43% 0.646 (0.07) 0.423 (0.124)b -34.52% Posterior
Body 810(70) 2200 (1100)* 63.18 % 0.728 (0.03) 0.647 (0.096)c -11.13% Splenium 730(40) 1880 (900)* 61.17%
FA reductions significant at: aanterior body, p<.0001,b posterior body p=.012, csplenium, p=.003. Mean ADC increases were significant at: * p<0.0001. (Bonferroni corrected for multiple comparisons). Main effect of FA and ADC differences were first assessed for each region. The effects of age and sex were included as covariates in the ANOVA model. Age and sex did not account for the differences noted between MS and HC participants.
66
Table 6: Mean FA, ADC, and Volumes for CC NAWM and Lesion Tissue in MS patients
MS NAWMa MS Lesionsb % Difference
No. Pts withLesions
Mean FA (SD) Mean FA (SD) FA
Mean Lesion Volume (mm3)
CC Region
Mean ADC(x10-6)mm2 s -1
(SD) Mean ADC (x10-6)mm2 s -1
(SD) ADC
Mean NAWMVolume(mm3)
Lesion % ofRegion
7 0.636 (0.09) 0.450 (0.12)* -29.25% 64.10 Genu
20900 (1100) 1900 (1300) -90.91%
1968.33
3.26% 10
0.488 (0.10) 0.416 (0.12) -14.75% 93.35 Anterior
Body 2050 (1100) 2000 (1000) -2.44%
1796.07
5.20% 5
0.423 (0.124) 0.436(0.08) 3.07% 41.26 Posterior
Body 2200 (1100) 3355(280) 52.50%
1447.43
2.85% 4
0.647 (0.096) 0.514(0.15) -20.56% 82.52 Splenium 1880 (9000) 15200 (800) 708.51%
2338.89
3.53% MS patient NAWM was statistically different from the MS lesion tissue at: **p<.005 (Bonferroni corrected for multiple comparisons). a The MS NAWM FA and ADC values were calculated based on mean results of 21 participants. b MS lesion FA and ADC values were calculated based on all subjects presenting with lesions in a particular region; the number of patients with lesions identified in each region is identified in the far right column. Main effect of FA and ADC differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants.
67
Table 7: Mean FA values for Total WM in MS and HC Participant Hemispheres
Healthy Controls MS Total WM HEMISPHERIC REGION Mean FA (SD) Mean FA
% Reduction
Inferior Frontal 0.48 (0.06) 0.33 (0.03) -31.25% Frontal 0.53 (0.04) 0.32 (0.03) -39.62%
Temporal 0.51 (0.04) 0.33 (0.03) -35.29% Frontal-Parietal 0.53 (0.04) 0.33 (0.03) -37.74%
Occipital 0.49 (0.04) 0.31 (0.03) -36.73% Parietal-Occipital 0.48 (0.04) 0.29 (0.03) -39.58%
Differences in HC WM and Total MS WM were significant in all regions, p<0.0001. Note: Results generated with proportional brain segmentation mask. Main effects of FA differences were first assessed in each region. Age and sex did not account for the differences noted between MS and HC participants
68
Table 8: Mean Hemispheric FA and ADC Values (SD) for HC and MS NAWM Participants
HEMISPHERIC REGION
Healthy Controls Mean FA (SD)
Mean ADC x10-3 mm2 s -1 (SD)
MS NAWM Mean FA (SD)
Mean ADC x10-3 mm2 s -1
(SD)
Percent Difference
0.351 (0.05) 0.328(0.04)* -6.55% Frontal
0.00076 (.00003) 0.00163 (.00082)* 114.47%
0.375(0.04) 0.323(0.04)* -13.87% Temporal
0.00071 (.00003) 0.00212(.00085)* 198.59%
0.323 (0.04) 0.316(0.04) -2.17% Parietal
0.00074(.00003) 0.0016(.00086)* 116.22%
0.385 (0.04) 0.271(0.04)* -29.61% Occipital
0.00075 (.00003) 0.0017(.00087)* 126.67%
Differences in HC WM and NAWM were significant by: * p<0.05 (Bonferroni corrected for multiple comparisons). Main effect of FA and ADC differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants Note: Results generated using anatomically defined hemispheric mask.
69
Table 9: Mean Hemispheric FA and ADC Values (SD) and Regional Volumes
HEMISPHERIC REGION
MS NAWM Mean FA (SD)
Mean ADC x10-3 mm2 s -1
(SD)
MS Lesions Mean FA (SD)
Mean ADC x10-3 mm2 s -1
(SD)
Mean Region Volume
mm3
No. Pts with
Lesions Mean Lesion Volume Lesion % of
Region 19
784.50Frontal 0.328(0.04) 0.00163 (.00082)
0.315(0.08) 0.00102 (.0008)** 68511.73
1.15% 19
826.89Temporal 0.323(0.04) 0.00212(.00085)
0.318(0.06) 0.00169(.0011)* 34310.57
2.41% 19
473.03Parietal 0.316(0.04) 0.0016(.00086)
0.309(0.08) 0.0018(.0008) 33983.30
1.39% 19
249.40Occipital 0.271(0.04) 0.0017(.00087)
0.291(0.10) 0.0020 (.00098) 9610.94
1.27%Within subject evaluations (repeated measures ANOVA) were performed regions containing both NAWM and lesion tissue. *p=0.02, **p<0.000 for ADC differences between NAWM and lesions. (Bonferroni corrected for multiple comparisons). Main effect of FA and ADC differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants.
70
Table 10: Axial and Average Radial Diffusivities for NAWM and Lesions in MS Patients
MEAN BILATERAL DIFFUSIVITIES Axial Diffusivity mm2 s -1
Average Radial Diffusivity mm2 s -1 HEMISPHERIC
REGION NAWM LESION
1.07E-03 1.12E-03 Frontal 7.03E-04 7.82E-04 1.10E-03 1.36E-03* Temporal 7.24E-04 8.63E-04** 1.05E-03 1.16E-03** Parietal 7.06E-04 7.43E-04 1.05E-03 1.22E-03* Occipital 7.58E-04 8.25E-04
Temporal, parietal and occipital lesions had significantly higher axial diffusivity relative to NAWM *p<.0001, p=0.003, p=0.001 respectively. Frontal and temporal regions had significantly higher radial diffusivity values in lesions **p=0.004 and p=0.001 respectively. (Bonferroni corrected for multiple comparisons). Main effect of FA and ADC differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants.
71
Figure 11: Mean and Range FA Values in CC of HC and Children with MS * p<0.05
* *
Figure 11: Mean FA values for CC white matter (WM) of healthy children and normal appearing white matter (NAWM) of MS participants. Results indicate significant reductions in the NAWM of children with MS through three CC regions, with relative sparing of the genu. *p<0.0001 (Bonferroni corrected for multiple comparisons). Median values, first and third quartiles, and maximum and minimum data value are represented by the black line, box height, and T-bar extremes respectively. Main effect of FA and ADC differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants.
* * * * 0.80
0.60
0.40
0.20
FA
MS NAWM
HEALTHYCONTROL
TISSUE
GENU POSTERIOR BODY ANTERIOR BODY REGION
SPLENIUM
72
Figure 12: Regional CC white matter (WM) volumes in MS patients and healthy children (HC). *Volume reductions in MS patient CC relative to HC: Genu (red) p<0.001, Anterior Body (yellow) p<0.0018, Posterior Body (blue) p<0.037, Splenium (green) p<0.001. Significant reductions in total CC volume are observed in all CC regions of MS patients. (p values are Bonferroni corrected for multiple comparisons). Main effect of volume differences were first assessed regionally. Age and sex did not account for the differences noted between MS and HC participants.
% VOLUME DIFFERENCE: MS CC vs. HC
3044.73 3169.79 2156.51 1989.29HC WM mm3
2032.43 * 64.10
1488.69* 93.35
1889.42* 41.26
2421.41* 82.52
-31%
-33%
- 5%
-23%
MS WM mm3
MS LESION mm3
73
0.50
0.40
0.30
0.
FA
L Frontal
R Frontal
L
TemporalR
Temporal L
ParietalR
Parietal L
Occipital R
Occipital
MS NAWM
TISSUE HEALTHY WM
20
REGION
Figure 13: Hemispheric FA values for healthy control (HC) white matter (WM) and MS participant normal appearing white matter (NAWM). Median values, first and third quartiles, and maximum and minimum data value are represented by the black line, box height, and T-bar extremes respectively. The largest reductions in NAWM integrity are evident in the bilateral occipital regions. Relative preservation of parietal WM was also of interest. *Indicates significant difference between healthy control tissue and NAWM p<0.05. (Bonferroni corrected for multiple comparisons). Main effects of FA differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participants.
74
00.020.040.060.08
10.120.140.160.18
20.220.240.260.28
30.320.340.360.38
40.420.44
0.
Figure 14: Mean hemispheric white matter (WM), normal appearing WM and lesion FA values in healthy children and MS Participants. *Indicates significant difference between healthy control tissue and NAWM p<0.05. (Bonferroni corrected for multiple comparisons). Main effects of FA differences were first assessed for each region. Age and sex did not account for the differences noted between MS and HC participant. ** Indicates significant differences between lesion tissue and NAWM p<0.05. Error bars indicate two standard deviations above and below the mean FA.
0.
0.
0.
LESIONS
R Frontal
L Frontal
R Parietal
L Parietal
R Occipital
L Occipital
R Temporal
L Temporal
Region of Interest
Mean FA NAWM
HC
TISSUE
* * * * ***
75
1.80E-03
2.00E-03 *
1.40E-03
1.60E-03
Figure 15: Axial and average radial diffusivities in CC lesions and normal appearing white matter (NAWM) of MS patients. *p=0.0083 for NAWM and lesion tissue across all CC regions. **Radial diffusivity values are the average of the secondary and tertiary eigenvalues. Error bars indicate standard error of the mean. Across all CC regions, a significant reduction in average axial diffusivity was observed in lesion tissue. Radial diffusivity values did not reach significant differences in the two tissue types, despite larger values in all CC regions.
0.00E+00
2.00E-04
4.00E-04
6.00E-04
8.00E-04
1.00E-03
1.2DIFFUSIVITY
0E-03
NAWM LESION NAWM LESIONAXIAL DIFFUSIVITY AVERAGE RADIAL DIFFUSIVITY
mm2s-1
GENUANTERIOR BODYPOSTERIOR BODYSPLENIUMAVERAGE
76
0.1
0.3
0.5
0.7
MEAN NAWM FA MEAN LESION FA
COMBINED CC AND HEMISPHERIC MEAN FA
Figure 16: Whole brain normal appearing white matter (NAWM) and lesion FA values within individual MS patients. Combined corpus callosal and hemispheric NAWM and lesion FA values were compared in all patients displaying lesions (n=19). Two patients did not display lesions and are therefore not included. Variability within patients appears to exist, as a few patients with the highest NAWM FA values display the lowest lesion FA values. Intra-patient variability in FA values may be linked to lesion acuity or lesion size. Error bars indicate standard error of the mean for each patient.
77
Lesion FA
Figure 17: FA of CC lesions relative to Expanded Disability Status Scale Scores (EDSS) in pediatric MS patients. The observed CC FA values are displayed for each lesion and the corresponding EDSS of the patient. Therefore only patients who had CC lesions are displayed (n=12). The relatively narrow distribution of EDSS scores in the MS patients precludes assessment of a link between lesion FA and disability. Larger sample size would be required to investigate this potential relation more closely. Dotted lines represent the 95% confidence interval.
78
LESION
VOLUME (mm3)
Figure 18: Volume of CC lesions relative to Expanded Disability Status Scale Scores (EDSS) in pediatric MS patients. The observed volumes are displayed for each CC lesion and the corresponding EDSS of the patient. Therefore only patients who had CC lesions are displayed (n=12). No statistically significant relation was observed.
79
g) Discussion: Key Findings:
Analyses of DTI in children with MS demonstrate wide-spread loss of integrity not
only within lesion tissue, but also in normal-appearing white matter (NAWM).
These differences were particularly notable in analysis of the corpus callosum
(CC), the major inter-hemispheric WM trajectory. Our findings implicate wide-
spread disruption of white matter in MS, detectable in young MS patients.
Diffusion tensor imaging assesses WM structural integrity and provides distinct
measures from conventional imaging.12 FA values measure a dominant
orientation or shape of water diffusion imposed by structural barriers restricting
water flow. The ADC values are scalar co-efficients reflecting the average
magnitude of water diffusion, with no directional component. Reductions in FA
and increases in ADC values indicate alterations in WM integrity; the former
believed to more strongly reflective of loss of axonal integrity, while the latter is
more indicative of myelin disruption, 12 although the assessment of axial and
radial diffusivities will provide more detailed biological insights.
Previous study of DTI in pediatric MS patients has been limited. Mean diffusivity
was assessed in NAWM of the brain and cervical spinal cord for 13 pediatric MS
patients.81 Tortorella extended this work by examining NAWM and GM in
pediatric MS patients. Similar to the findings presented here, Tortorella observed
reductions in the pediatric MS NAWM FA values relative to controls, although
GM FA values of pediatric MS patients did not differ relative to controls.83 We
80
focused on DTI evaluation of WM, and did not evaluate GM for several reasons.
Firstly, analysis of GM is hampered by the thin cortical mantle, making partial
volume errors likely, as adjacent subcortical WM could easily contaminate GM
samples. Secondly, conventional T2-weighted MRI reliably identifies lesions in
WM, but is relatively insensitive to lesions in GM. As such, delineation of GM
lesions using DTI would lack reliability. Imaging of lesions in GM is enhanced
with higher Tesla strength magnets (3T or 7T), which were not available for
patients enrolled in our work. Future studies exploring DTI of lesional and NAGM
will be of interest.
Comparing WM of Pediatric MS patients to WM of Healthy Children:
A key question in MS pathobiology relates to whether the WM of individuals with
MS is structurally intact prior to visible lesion formation. Loss of NAWM integrity
may occur as a primary determinant of MS, through genetically-determined or
post-translational modifications of myelin proteins that produce fundamental
alterations in WM integrity, which in turn may render the WM prone to
recognition by host immune cells.108-110 Alternatively, the inflammatory CNS
milieu in MS may lead to widespread disruption of previously intact WM, even
prior to the creation of visible lesions on T2-weighted images.111 In the first
scenario, abnormal NAWM would be expected to be independent of age at MS
onset or disease duration, assuming the WM microstructure to be congenitally
abnormal. Furthermore, the NAWM integrity would be expected to uniformly
impaired throughout the brain. In the second scenario, the inflammatory
environment leads to loss of previously normal WM integrity, a process which
81
might require a minimum period of subclinical and/or clinical disease. DTI
abnormalities have been found in NAWM of adult-onset MS patients, however,
as the sub-clinical phase of MS likely precedes the diagnosis of MS by several
years, it is difficult to ascribe these widespread DTI abnormalities as primary or
secondary.70,71,88,112
While our finding of diffuse DTI abnormalities in NAWM indicate that WM
disruption occurs very early in the MS disease process, we did find regions of
WM that appear to have DTI features similar to those of healthy children. Thus,
while we suggest that the MS disease process leads to a WM insult that far
exceeds visible lesions, the intact WM regions (genu and parietal lobes) argues
against a congenital (primary) WM abnormality in MS.
Corpus Callosum Analyses:
As the largest inter-hemispheric WM trajectory, the CC is a known target of CNS
lesions in MS patients.3,4 Evidence of CC damage in pediatric MS patients is
highlighted by our findings of reduced CC volumes relative to HC (Figure 9), and
by the presence of at least one or more CC lesion(s) in 15 of 19 MS patients. In
children with lesions visible in the CC, the mean lesion volume comprised only
5% volume of a CC region- and thus, lesions alone do not explain the marked
volume loss and widespread DTI abnormalities in this brain region.
To our knowledge, a focused DTI evaluation of the CC has not been performed
in children with MS. DTI measures of CC integrity in adult-onset MS patients
82
show dramatic reductions in FA and increased ADC values in the CC NAWM.71,88
Ge and colleagues reported on analyses using the same four CC regions as
were studied in our work. These authors found significant reductions in NAWM
FA values of 10-17% relative to HC.88 Hasan and colleagues used a seven
segment analysis of CC regions, and found the FA values in the genu were
relatively intact compared to HC participants, despite significant reductions in
NAWM of all other CC segments.71 As shown in Figure 8 and Table 5, we also
found the genu to be spared, despite the differences in DTI metrics noted in the
anterior body, posterior body and splenium between children with MS and
controls,. In contrast, ADC values in the genu were significantly increased
relative to HC. This paradoxical increase in the genu ADC with relatively
unchanged FA values was also evident in the seven-segment CC analysis of
adult MS NAWM (K.Hasan, personal communication), and requires further study
to define the mechanisms underlying this observation.
FA and ADC values are believed to measure independent tissue properties. As
discussed above, FA reflects a restriction of water diffusion, and ADC the
average magnitude of water diffusion; in many biological situations they are
inversely related. For example, during healthy development, elevated FA values
commonly correspond with lower ADC values. The elevated FA values are often
attributed to increasing myelination during development.18 Reduced ADC values
in healthy development may indicate reductions in brain water.94 Our finding of
no differences in genu FA values between participant groups, despite increased
genu ADC values in MS patients, suggest that FA and ADC measure two
83
pathologically distinct parameters. Furthermore, consistent ADC increases of
60% across all CC regions were observed in NAWM relative to HC CC WM. As
indicated in Table 5, a greater percent difference exists in ADC comparisons of
NAWM and HC WM, than are observed for FA values. The large increase in ADC
values through all CC regions may be reflective of increased total brain water
due to global inflammatory processes in MS; while the FA values may be more
sensitive to restricted axonal impairments, or focal WM changes.
Regional Heterogeneity of the CC
Reduced axonal densities and axonal counts in adult MS patients have been
shown by pathobiological study across the anterior, middle and posterior CC
regions relative to controls.113 Aboitiz and colleagues have suggested the
regional distribution of large and small diameter fibres have a functional
significance. 87 For example, fibre composition in CC regions connecting primary
motor and sensory areas contain higher proportions of large-diameter, thickly
myelinated fibres used in efficient signal transmission, relative to areas
associated with higher-order cognitive processing. Fibres connecting primary
somatosensory cortices traverse the CC midbody. As displayed in Figure 16 and
the corresponding table, pediatric MS patients display FA declines in the
midbody sections and relatively smaller declines in the splenium indicating
variable susceptibility to WM damage throughout the CC. Speculating on the
variable effects of demyelination may suggest a loss of myelin from larger fibres
would result in more substantial increases in the existing inter-axonal spaces
compared to a loss of myelin in a region populated with smaller diameter fibres.
84
On the other hand, one might expect larger fibres to have more resilience to
immune attack than would a thinly myelinated fibre. Changes in CC regional
volumes are outlined in Figures 9 and 16 also indicate a range of tissue damage
across CC segments.
CC Measures: MS vs. HC GENU ANTERIOR
BODY POSTERIOR
BODY SPLENIUM
% FA Reduction 0.00% -27.94% -35.38% -10.96% % Volume Reduction 33.00% 31.00% -5.00% -23.00%
Figure 19: Reductions in regional CC volumes and FA values. Cross-sections of the human corpus callosum (CC) indicating the representation of transcallosal of projections to different cortical regions (left). Regional differences in fiber diameter and density (larger circles indicate larger fiber diameters) (right). The table indicates reductions in FA and volumes in pediatric MS CC WM relative to HC (as previously shown in Figures 11 and 12). No relation between FA reductions and volume loss was evident. A, auditory fibers; F, frontal fibers; M, motor cortex fibers; Ss, somatosensory fibers; T/P, temporoparietal fibers; V, visual fibers. (Adapted from Aboitiz)87
Interestingly, the volume loss occurring in the posterior body was minimal,
although this region is associated with large FA reductions in MS patients relative
to HC. In contrast, the volume loss in the genu was marked, despite relatively
preserved FA values. These findings suggest that volume loss, while presumably
representative of loss of tissue in this region, does not necessarily occur
concurrently with loss of integrity of the remaining fibres.
85
CC Lesion Analyses:
DTI comparisons of NAWM and lesion tissue in adult MS patients, have shown
FA reductions and ADC increases in lesional WM relative to NAWM and HC
WM.68,70 White matter integrity in mean lesion FA values was reduced across the
CC, relative to NAWM (0.44 versus 0.54) in our pediatric MS patients. This
finding supports the expectation that the greatest degree of tissue disruption
occurs in areas that appear as visible lesions on conventional MRI.
However, some inconsistencies in lesional FA values were detected, as
paradoxical elevations in lesion FA (relative to the surrounding NAWM) were
observed in the posterior body of the CC (Table 9). This discrepancy could
simply reflect sampling error, as the number of patients was small and not all
patients had lesions in this region. Sufficiently investigating regional CC lesion
loads with DTI will require a larger sample size (Table 9 indicates the number of
patients with lesions in individual regions).
Clearly, the CC is a region particularly targeted in pediatric MS patients, as
demonstrated by regional volume loss, reduced FA in CC lesions, and reduced
FA in NAWM CC tissue.
86
Hemispheric NAWM Analyses:
DTI studies in adult MS patients have shown widespread increases in ADC
values across multiple hemispheric regions, although regional FA values in
NAWM did not differ from controls.114
In contrast, we did find differences in FA values of hemispheric WM in children
with MS relative to controls, with the FA reduction of 30% in occipital lobe NAWM
(Figure 10 and Table 8) being the most notable. Smaller yet significant
reductions in temporal and frontal NAWM, and non-significant FA reductions
were observed in the parietal lobes relative to HC (Figures 10, 11 and Tables
7,8). It is unclear why the FA values in the parietal lobes are preserved, as
parietal WM integrity is not known to be preferentially preserved in pathological
studies of the MS brain. Of interest, the parietal lobe contains large diameter,
heavily myelinated projection fibres to the primary somatosensory cortex, 87
which could possibly be more resilient to immune insult. Similar to the parietal
lobe in the pediatric population, adult MS hemispheric regional analysis of
NAWM showed unchanged FA values concurrent with increased ADC values.114
Important to our studies in pediatric MS is the consideration of age-dependent
maturation of WM. During normative healthy development increases in the
structural development of myelin and WM pathway organization across the
hemispheres are known to elevate FA values, and reduce ADC values.24
Specifically, with maturity, increased axonal packing, myelination, and fiber
diameter contribute to larger FA values with age, while reduced brain water
87
depresses ADC values.12 DTI investigations of adult MS patients demonstrate
ADC values for NAWM are increased relative to hemispheric WM of age-
matched controls (NAWM: 0.843 x10-3 mm2/s; 115 HC WM ADC: 0.726 x 10-3
mm2/s116,117). The ADC values from NAWM in our pediatric MS patients (ranging
from 1.60 – 2.1 x10-3 across hemispheres) are higher than those of pediatric
controls (Table 8), suggesting either disruption of white matter integrity or failure
of normative maturational processes. Of note, the mean ADC values of adult MS
NAWM data are higher than the mean ADC of even the youngest HC children in
our study, highlighting the degree of ADC abnormality.
Hemispheric Lesion Analyses:
As shown for lesions in the CC, mean reductions in lesion FA values relative to
the NAWM occurred in bilateral frontal, right parietal and left temporal
hemispheres regions. This finding may again reflect a discrepancy attributable to
the small size of our population.
Comparing Lesional and Non-Lesional Tissue within MS Patients:
The analysis described in the preceding section assesses group differences
between the NAWM of MS patients and the WM of HC. We have also considered
within-subjects repeated measures analyses to assess differences in DTI metrics
between the NAWM and lesion tissue within an individual MS patient. Due to the
inter-patient variation in lesion distribution within patient measures were
assessed.
88
Inter and Intra-Individual Variability in Lesions
Lucchinetti et. al. studied pathological specimens from adults with MS and
demonstrated considerable inter-individual heterogeneity in lesion appearance,
but a remarkable intra-individual homogeneity in patterns of lesion pathology.118
Classification of early active lesions into four possible categories based on
patterns of myelin destruction was proposed: type I) macrophage associated;
type II) antibody/complement mediated; type III)distal oligodendrogliopathy; and
type IV) oligodendrocyte degeneration in periplaque WM.118
We were therefore particularly interested to explore whether the degree of FA
reduction was a consistent feature of all lesions within a patient. As shown in
Figure 13, the distribution of NAWM and lesion FA values illustrates reduced
anisotropy within each patient’s lesion tissue relative to the NAWM (whole brain
values displayed). Similar to Lucchinetti, a pattern of inter-individual
heterogeneity exists across the pediatric MS patient group. However, the
pediatric MS population also displayed intra-individual heterogeneity in lesion
and NAWM tissue –suggesting that at least with DTI measures, lesions within a
given patient are not homogenous (as illustrated in Figure 13). Lesion acuity and
lesion size are variable within individual patients and likely contribute to the
observed intra-individual heterogeneity. Lesion size may be an important
consideration. In all lesions, the MRI voxels at the edge of the lesion may contain
tissue representative of both lesion and surrounding NAWM. In large lesions,
these voxels account for a very small percentage of the total number of voxels of
the large lesion. In very small lesions, however, the peri-lesional border may be
89
account for a greater representation of the DTI voxels attributed to the lesion,
consequently diluting the pure lesional DTI measures, and altering neighboring
NAWM DTI measures.
Interestingly, DTI studies in adult MS patients for whom brain biopsy was
subsequently performed, demonstrated that patients with pattern III lesions
showed marked increases in the ADC of the NAWM, where the ADC of pattern I
and II lesions did not differ from normative controls.118 In the case of pattern III
patients, Wallerian degeneration is a likely contributor to the elevated NAWM
ADC values.71 However, it may only be a secondary explanation of the
pathogenic cause. Assuming CC lesions contribute entirely to degeneration in
hemispheric WM tracts downstream requires caution, as axons varying in lengths
and diameters and would be differentially effected by the distance from the
lesion.119
Proposed Pathophysiologic Mechanisms for Altered Diffusion in Lesion Tissue
A key variable affecting diffusion within MS lesions is the inflammatory milieu of
the tissue. Differing inflammatory states within regions or individual lesions may
exist. Additional effects of lesion chronicity and degree of WM damage would
also impact local diffusion characteristics. Potential changes in FA and ADC
values as related to immunopathologic events are proposed in Figure 20.
90
Figure 20: Schematic representation of diffusion abnormalities with changing structural neuron integrity at varying tissue phases in MS. Relative to a healthy nerve, edema, causing intra-axonal swelling reduces FA values, and would increase ADC due to the increased water bulk. Similarly, loss of myelin boundaries at sites of demyelination reduces anisotropy values, and increases water flow in multiple directions thus increasing ADC. Axonal degeneration would have similar effects on FA values, as fibre diameters are reduced, enabling un-hindered diffusion; metabolic debris following axonal injury would potentially alter a previously anisotropic diffusion pathway. Interestingly, gliosis would likely increase FA, as new cells are laid down with a different and potentially more restrictive morphology. The beginning of remyelination would likely cause small reductions in FA relative to a healthy nerve; however, ongoing repair processes would likely produce FA values higher than in the previous demyelinated axon, but lower than those seen in areas where no demyelination has occurred (given that remyelination produces thinner myelin membranes).
As outlined above, a lesion in the acute, edematous phase would be expected to
exhibit reductions in FA due to less directional intra-cellular water diffusion, and a
smaller magnitude of inter-cellular water diffusing between adjacent axons.
91
Transient increases in ADC paralleling the time-frame for gadolinium
enhancement, are followed by reduced ADC extra-cellularly as swelling reduces
the bulk water diffusion.120 In a region of demyelination in which the acute
inflammatory response is resolving , the reduction in FA values may now reflect
the consequences of inflammation which lead to structural breakdown of the
myelin membranes (as depicted in the third scenario of Figure 20). In a
demyelinated region, water can flow more freely in multiple directions, increasing
the overall magnitude of diffusion or ADC. In the following scenario, depicting
axonal degeneration, (which is associated most strongly with T1-hypointense
lesions) reduced FA values could be attributed to both: i) partial loss of structural
boundaries directing water flow and ii) cellular debris or metabolic byproducts
that accumulate around the damaged and alter water flow unpredictably.2 The
elevated ADC values expected in axonal degeneration may relate to an overall
increase in inter-axonal water content. The diffusion in a remyelinated lesion
would likely parallel those of a demyelinated lesion (relative to NAWM). In the
case of gliotic scar tissue, (the final scenario in Figure 20) in which infiltration of
glial cells into areas of damaged tissue may have a similar orientation to
previously existing myelin, FA may increase,121 and ADC values would be
expected to decrease owing to restriction of water diffusion imposed by tightly
approximated glial processes.
92
Eigenvalues
Axial and Radial Diffusivities in NAWM and Lesion Tissue:
Analyzing the component diffusivities contributing to FA and ADC results may
permit further insight into microstructural tissue damage in MS lesions and
specifically aid in distinguishing myelin loss from axonal damage.61,62,122 Studies
in animal models have correlated reduced axial diffusivity with axonal injury and
increased radial diffusivity with demyelination.61,62 Axonal damage has been
shown to occur early in disease and lesion formation processes of adult MS
patients.123 Therefore, we assessed axial and radial diffusivities for their potential
insights into axonal and myelin integrity in the children with MS. As indicated in
Figure 15, average axial CC diffusivity was reduced in lesion tissue, while
average radial diffusivities were not significantly altered in lesions relative to the
NAWM. Of further interest was the concomitant increase in posterior body lesion
FA values with the reduced axial diffusivity in that region. Assuming the
hypothesis that axonal injury is reflected by axial diffusivity, our CC findings
would suggest axonal injury is occurring in CC lesion tissue of children with MS,
as evidenced by marked lesion declines in axial diffusivity.
However, biological correlates of axial and radial diffusivities remain to be fully
defined. In DTI studies performed prior to death and then correlated with post-
mortem analyses, both axial and radial diffusivities were predictive of myelin
content in adult MS WM.124 In contrast to our observed CC findings, the
hemispheric lesions illustrated elevated axial diffusivity relative to NAWM. Radial
diffusivity was elevated in hemispheric lesion tissue relative to the NAWM in the
93
pediatric MS participants, as expected, although significance was reached only in
the temporal lobes for this measure. This observation could imply the effect of
altered radial diffusivity may not be large enough to detect the variations in the
lesion chronicity or severity of demyelination.
Study Strengths and Potential Limitations:
This study employed a standardized analytical technique, previously optimized in
the pediatric brain.20 The image processing techniques utilize both automated as
well operator-dependent methods. As such, substantial operator expertise was
required, and the initial phase of my thesis work involved comprehensive training
on the tools for DTI analysis. Application of these methods also necessitated a
thorough understanding of the processing methods and the concepts behind their
use.
The DTI acquisition protocols used for our study were carefully selected, but may
not have been completely optimal. Partial volume effects are a known potential
confounder in DTI studies; particularly in those acquired at coarser resolutions,
as more tissue is averaged per voxel. In future studies, it would be recommended
that 2 mm3 isotropic voxels be collected, rather than the 5 mm slice thickness
used for the present DTI scans. At the expense of scan time, collecting finer
resolution scans reduces co-registration error during image post-processing. This
would reduce the likelihood of any misclassification of tissue. For example, peri-
ventricular lesions can occasionally appear continuous with CSF at the margin of
the posterolateral ventricle horns. In our future work, a consideration might be
94
excluding a one voxel margin surrounding lesions to prevent misclassification of
CSF or NAWM tissue at ill-defined lesion borders.
The HC participants were collected as part of prior work.20 As such, the DTI
protocol was not identical to that selected for the MS study population. The DTI
images collected for the HC have 3mm slices. In contrast, the MS patient DTI
images have 5mm thick slices. Therefore, inherent differences in the signal-to-
noise ratios (SNR) exist between groups. However, SNR values for two sample
patients for HC and MS patients were 13.8 and 12.9 respectively. This
calculation is based on the mean signal intensity of the B=0 image, divided by the
standard deviation of the noise. Larger slice thickness is known to increase SNR,
as the signal from random background noise is averaged over the larger volume,
and the effect of noise is subsequently minimized. Although this sample
calculation shows a larger SNR for HC (with thinner 3mm slices), the small
difference may be due to the calculation used. While enrollment of healthy
children in research studies poses practical challenges, we are now in the
process of obtaining DTI studies of healthy children using the DTI slice thickness
that will match that of our MS patients.
Our HC and MS groups were not matched for sex. The prevalence of MS in
females made the patient sample predominantly female (17:4), where the HC
group consisted of 13 males and 4 females. All results reported were adjusted for
the effects of sex differences, and none of the analyses were altered when sex
was evaluated as a covariate.
95
Age differences existed between our MS and HC groups (MS mean =15.25
years, s.d.=2.3, range: 10-17.8 versus HC mean age =11.68 years, s.d.=6.3,
range: 6.2-17.8). We accounted for age as a cofactor (generalized linear model
ANOVA) in all analyses. However, it is important to note that the younger age of
the HC group actually strengthened the magnitude of our findings. As FA values
are known to increase with age,94 we would have expected our MS patients to
have higher FA values in NAWM when compared the normal WM of the control
group. Our findings of reduced FA in the NAWM of children with MS, despite their
older age, further support the significance of our findings.
Analysis of DTI metrics in the developing brain must also consider the potential
contribution of myelin maturation, a process that is not normally complete until
early adulthood. Normal myelination proceeds in a caudal-rostral manner, and as
such, frontal WM regions are the last to fully myelinate.15,18,93 The control patients
included in our work were previously evaluated specifically with respect to
maturation.20 The rate of FA increase in corpus callosal WM is most evident in
the ages between 8-12 years, while subcortical hemispheric WM continued to
show FA increases into late adolescence.20,24,125,126 Our sample of MS patients,
supports this work by showing significant correlations between frontal lobe
NAWM FA values and age (r=0.682, p<0.01). However, as our youngest
participant was 10 years old at the time of imaging, and given our mean age of
15.2 years, we were not poised to fully explore CC FA values and age across the
period of 8 and 12 years.
96
Clinical Implications and Future Directions:
Traditionally, T2 lesion burden and gadolinium enhancement have been used as
surrogates of MS disease severity.127 However, the term “clinical-radiographic
paradox” has been coined to emphasize the relatively poor correlation between
traditional MRI measures and MS clinical severity.128,129 We explored whether a
measure of physical disease severity, the Expanded Disability Status Scale score
(EDSS), correlated with reductions in lesional and NAWM FA. No correlations
were found, (Figures 14,15) however, it is noteworthy that few children develop
physical disability early in their MS disease course, and as a consequence the
range of EDSS scores is very restricted to the low end of the scale.
We also explored whether the ‘inflammatory lesion burden’, as determined by the
total T2 lesion volume, influenced the degree of tissue destruction as measured
by DTI. A weak correlation was observed when whole brain lesion FA and lesion
volume measures were compared. When analyzed for the CC alone, CC lesion
volumes did not correlate with the degree of FA reduction within these lesions. A
larger sample or longitudinal follow-up may more accurately describe a
relationship between DTI and clinical disability.
This cohort of pediatric MS patients included children with disease durations as
short as 0.15 years, and children as young as age 10 years. All of these children
demonstrated FA reductions in NAWM of the CC and hemispheric NAWM -
97
suggesting DTI is able to identify loss of tissue integrity even at the earliest
phases of the MS disease process.
While DTI and conventional T2 measures of MS disease in children do not
correlate with EDSS, a more relevant comparison is to evaluate MRI and
cognitive outcomes, as cognitive impairment is very important aspect of MS in
evident even early in the MS disease in children.6 The present DTI study is linked
with a comprehensive parallel project examining the neuropsychological features
of this cohort. Analyses of these data are now underway.
Lateralization of certain cognitive functions is identified in adults. Specifically,
right-sided (frontal-temporal-parietal) pathways exist which have known function
in visual-spatial information processing have been correlated with FA.130 This
work employed tests of visual self-paced choice reaction time (CRT) in an adult
population, and identified CRT and FA as correlating in the right optic radiation,
right posterior thalamus, and right medial precuneus WM. The projection and
association pathways from these regions are known to support visuospatial
attention. Interestingly, preliminary analysis of the cognitive assessments in the
children enrolled in our present study also showed impaired in visuo-spatial
function (Till, Bethune et al, presented as a poster, WCTRIMS Sept 2008). We
did not observe significant hemispheric lateralization in MS patients.
Our further work will explore the influence of tissue integrity, as measured by
DTI, on cognitive performance. We will evaluate these measures both at baseline
98
and in a longitudinal manner to evaluate whether loss of brain tissue integrity
occurs over time, and what impact this has on the developing neural substrates
of cognition. We will also apply DTI analyses across the now over 100 children
with an acute, initial demyelinating event for whom DTI has been obtained as
part of our national study of MS risk in children. In these studies, we will be
poised to address the important question of whether DTI evidence of loss of
structural integrity in NAWM predicts MS outcome.
Summary:
Impaired tissue microstructure in the NAWM of children with MS relative to
healthy children has been identified. Investigations reveal significant CC and
hemispheric disruptions, as evidenced by altered DTI metrics in both the lesion
tissue and NAWM of these regions. Indications of regionally different WM
damage in children with MS are highlighted by relative sparing of WM integrity
within the genu, as well as the robust diffusion changes localized to the occipital
lobe. Longitudinal study currently underway may clarify a relationship between
DTI findings and scores of physical and cognitive disability.
We have demonstrated widespread loss of tissue integrity in NAWM of children
with MS, highlighting the need for further research in the mechanisms
responsible, and for strategies that might serve to protect or repair the WM of MS
patients.
99
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Appendix A: Methods of Corpus Callosal ROI Definition
Tracing CC ROI Segments:
• Locate the midslice on the T1 sagittal image, corresponding to the inter-
hemispheric fissure (Shown in Figure 7).
• Segmentation of the CC into 4 regions: genu, anterior body, posterior body
and splenium.
o Anatomical landmarks define tracing start points and intra-callosal
boundaries for the genu and splenium.
1) Genu: begin tracing in the superior-inferior direction from the anterior-
most inferior margin of the CC border and include all CC WM anterior and inferior
to this line.
2) Splenium: begin tracing in the z-direction superiorly- from the edge of the
second groove on the inferior CC margin (at the fornix) and include all CC WM
tissue posterior to this point.
3) The CC midbody tissue divided into the anterior and posterior sections at
the voxel equidistant between the initial genu and splenium boundaries (Figure 7)
4) Copy this CC object onto 12 slices bilaterally to encompass the entire
CC.
5) Editing CC boundaries on each of 25 slices in the sagittal plane is
required. Particular caution is used as borders become ill-defined when moving
laterally and appear continuous with cingulate cortex. Further manual correction
is performed when viewing in the axial and coronal planes.
Editing aims to reduce partial volume effects- or the inclusion of surrounding
non-WM tissues (CSF, peri-ventricular lesions, gray matter) into the ROI. To
minimize partial volume effects, the image boundary was maintained one voxel
inside the CC pure WM margin Figure 8.
Manual delineation of CC tissue can present challenges, particularly in MS
patients. Several changes in CC morphology must be considered for accurate
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measurement: thinning (in the mid-body regions), intra-callosal T1-hypointense
(chronic) lesions, and peri-ventricular lesions which are commonly distributed
around the lateral horns of the ventricles. To limit the effects of the CC structural
differences, the following considerations were used. At the superior lateral
margin, CC thinning can make differentiating the adjacent cingulate gyrus a
challenge; tracing one-voxel inside the visible CC tissue to limit the potential
inclusion of non-callosal surrounding WM tissue. Additionally, superior CC
margins were designated on the initial mid-sagittal trace and maintained on all
lateral slices of the CC object –again to limit partial volume effects. Inter-rater
reliability was compared for CC tracings on 5 HC and 5 MS patients. FA values
were strongly correlated between raters (Pearson r=0.963, p<0.01).
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Appendix B: Hemispheric WM Region Definition
Processing Sequence of Hemispheric WM:
• Affine registrations employ a linear alignment between two distinct objects (of
different shapes), contrary to the CC processing where image registrations were
intra-subject.16,104 Twelve degrees of freedom rather than six (as with simple 3D
transformations) are employed in affine registrations; in addition to three
translations and three rotations, affine registrations also use three shearing and
three scales to minimize inter-subject scan differences. Affine registration was
required to combine individual subjects’ T1-ACPC images to a representative
pediatric brain, containing an anatomical brain atlas.101
• A brain image of representative size and shape was previously selected from
HC participants. This was used for generating a hemispheric WM template that
approximates the size of the pediatric cohort. The following steps are required
prior to fitting a subject specific brain atlas into DTI space.
• The accuracy of each subject’s registration was checked using Analyze
software. If further manual adjustments were necessary, they were performed
with AIR.
• A .warp file was created by successively adding .air file transformations to
obtain the overall transformation algorithm. This enabled co-registration of the
representative brain image, with the brain atlas to each subject’s diffusion
acquisition space.
• The original hemispheric masks used in previous publication included the
cerebellum**. Thus removal of the cerebellum and brainstem was performed on
the MS patient data by creating a ‘posterior fossa stamp’. The brainstem,
cerebellum and cerebellar peduncles were traced on the T1 image and included
in the stamp object.
• The posterior fossa object was subtracted from the acquired T1 image.
• Tissue segmentation of GM, WM and CSF was performed on the T1acquisition
image (without cerebellum) using an FSL Tool: FAST (FMRIB’s Automated
Tissue Segmentation Tool).
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• Should any registration error exist, a CSF stamp was overlaid to avoid inclusion
of ventricular CSF with CC ROI or hemispheric regions, and minimize partial
volume effects.
• FA, ADC, and average radial and axial diffusivity maps were loaded into
Analyze
• The anatomical atlas76 was combined with the segmented WM of each
individual subject. Lesion maps were then overlaid for regional definition of
NAWM and lesion objects (Figure 9a,b).
• Average radial diffusivity maps were obtained from averaging the secondary
and tertiary eigenvalue maps (using Analyze Software, Image Calculator). Axial
diffusivity maps were derived purely from the primary eigenvalues, calculated in
DTI studio.
• Sampling of all DTI measures (FA, ADC, axial and radial diffusivities) were
performed for each region and tissue type.
*A proportional hemispheric template was used for initial processing of the HC
previously published data. Identical processing was used for the current study
for the preliminary MS patient data.
**An anatomically defined brain atlas was used, and subsequent adaptations for
pediatric patients were made. The brain atlas was applied to both MS and HC
participants for the final analysis of this population.
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Appendix C: Summary of Hemispheric HC WM, MS NAWM and Lesion FA and ADC
Values Table 11: Summary Table of Hemispheric FA and ADC (mm2 s -1) values for HC WM NAWM and Lesions
RIGHT HEMISPHERE
Healthy Controls Mean FA (SD)
Mean ADCx10-3 (SD)
MS NAWM Mean FA (SD) Mean ADCx10-3
(SD)
MS Lesions Mean FA (SD)
Mean ADC (SD)
Mean Region Volume
mm3 (MS
Only)
Lesion Number,
Mean Lesion Volume (mm3), % of
Region
0.380 (0.04) 0.327(0.04)** 0.315(0.08) 16
751.70 Frontal 0.00076(.00003) 0.0016 (.00082)* 0.0017 (.0008)
70434.92 1.07%
0.379(0.04) 0.320(0.04)** 0.318(0.06) 16
805.20 Temporal 0.00071(.00003) 0.0017(.00085)* 0.00214(.0011)
34822.38 2.31%
0.324 (0.04) 0.313(0.05) 0.309(0.08) 18
358.73 Parietal 0.00074(.00003) 0.0016(.00086)* 0.0018(.0008)
32734.11 1.10%
0.383 (0.04) 0.268(0.04)** 0.291(0.10) 14
360.28 Occipital 0.00075(.00003) 0.0017(.00087)* 0.0020 (.00098)
20174.18 1.79%
LEFT
HEMISPHERE
0.322 (0.04) 0.329 (0.04) 0.302 (0.08) 17
867.74 Frontal 0.00076(.00003) 0.001617(.00080)** 0.0004 (.00043)
66588.55 1.30%
0.371 (0.04) 0.328 (0.04)** 0.366(0.09) 15
848.57 Temporal 0.00071(.00004) 0.0017(.00085)* 0.0021(.00102)
33798.76 2.51%
0.322 (0.04) 0.313(0.05) 0.385(0.11)*** 17
587.33 Parietal 0.00073(.00003) 0.0017(.00087)* 0.0018(.0008)
35232.48 1.67%
0.388 (0.04) 0.273(0.04)* 0.291(0.10) 14
138.51 Occipital 0.00075(.00003) 0.0017(.00085)* 0.0020(.0011)
19047.71 0.73%
MS patient NAWM was significantly different when compared to HC WM at: *p<.0001, **=p<.001 MS lesions and NAWM were different on ADC measures, ***p=0.0247 (not significant after Bonferroni correction multiple comparisons).
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Appendix D: MS Patient Eigenvalues in NAWM and Lesions Table 12: Axial and Average Radial Diffusivities for CC Regions
Corpus Callosum Region
MS Patient NAWM Axial Diffusivity(mm2s -1)
Average Radial Diffusivity (mm2s -1)
MS Patient Lesions Axial Diffusivity(mm2s -1)
Average Radial Diffusivity (mm2s -1)1.63E-03 1.55E-03 Genu: 5.74E-04 8.98E-04 1.42E-03 1.22E-03 Anterior
Body: 6.98E-04 7.14E-04 1.39E-03 1.20E-03 Posterior
Body: 7.27E-04 6.54E-04 1.52E-03 1.34E-03 Splenium: 4.95E-04 5.71E-04
*Increased radial diffusivity in lesions was relative to NAWM (p<0.05). p values Bonferroni corrected for multiple comparisons. Table 13: Axial and Average Radial Diffusivities Hemispheric WM
RIGHT HEMISPHERE Axial Diffusivity (mm2s -1)
Average Radial Diffusivity (mm2s -1)
LEFT HEMISPHERE Axial Diffusivity(mm s -1)
Average Radial Diffusivity(mm2s -1) NAWM LESION NAWM LESION
Frontal 1.07E-03 1.14E-03 1.06E-03 1.09E-03 7.08E-04 7.78E-04 6.97E-04 7.85E-04
Temporal 1.09E-03 1.33E-03 1.11E-03 1.39E-03 7.26E-04 8.93E-04 7.21E-04 8.32E-04
Parietal 1.05E-03 1.15E-03 1.05E-03 1.17E-03 7.12E-04 7.72E-04 7.00E-04 7.13E-04
Occipital 1.05E-03 1.25E-03 1.05E-03 1.18E-03 7.62E-04 8.69E-04 7.54E-04 7.80E-04
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Appendix E: Consent and Assent Form
Research Consent Form For parents/legal guardians of children with MS
Title of Research Project: A longitudinal study of the neuropsychological and MRI characteristics in children and adolescents with multiple sclerosis (MS) Investigators: Principal Investigator, Dr. Brenda Banwell, Staff Neurologist, (416) 813-6660 Co-investigators: Dr. Christine Till, Psychologist, (416) 597-3422 ext. 7870 Dr. Donald Mabbott, Psychologist, (416) 813-8875 Allison Bethune, Graduate student, University of Toronto, (416) 813-1500 ext. 1738 Dr. Manohar Shroff, Staff Neurophysicist Dr. John Sled, MRI Physicist Purpose of the Research: The purpose of this study is to determine whether or not children and adolescents with Multiple Sclerosis (MS) have difficulties with learning, attention, memory, verbal and nonverbal abilities, and whether these abilities change over time. Results of neuropsychological testing will be compared with children and adolescents who do not have MS. Neuropsychological performance will also be compared with brain MRI measures that have already been collected to better understand the neural correlates related to cognitive functioning in children and adolescents with MS. An additional MRI will be performed one year later, and again be compared to a neuropsychological assessment to assess changes in the individual over time. Description of the Research: If you agree to participate, your child will be asked to carry out some tests of cognition (e.g. attention, perception, memory, problem-solving) and academic skills. These are paper and pencil tests and computer tasks. The assessment usually takes approximately 5 to 6 hours (with breaks for rest and lunch) and could be finished in one day or divided over two days. The length also depends on the age, attentional level, and working speed of your child. Parents will be interviewed and asked to complete several questionnaires that will take about 30- to 40-minutes to complete. We would also like to obtain school academic and attendance records and the results of any previously conducted educational or psychological testing (if available).
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It is essential that you arrange for someone to be available to be with your child during breaks and lunch. We cannot provide supervision of your child at those times. You may wish to bring some work or reading with you to help pass the waiting time. A comprehensive history and neurological examination of your child will also be completed by Dr. Banwell (staff neurologist). We will make every effort to coordinate this examination with your child’s visit for neuropsychological testing. After the evaluation is completed, you will be given the opportunity to discuss the results and their implications with the Psychologist. We are usually not able to do this on the same day the evaluation is conducted because we need time to score all the tests and consider what they mean in light of your child’s medical history. A written summary will be prepared and given to you for your records. At the one-year follow-up, we will contact you to co-ordinate a time that you will be returning to Sick Kids with your child in order to complete the neuropsychological assessment again. At this point, we also intend to perform a follow-up MRI. This will be performed on the same scanner at The Hospital for Sick Children as all previous clinic visits. Collecting this follow-up MRI scan will allow us to compare any changes in your neuropsychological performance over time with a time-matched MR image. Potential Harms: We know of no harm that taking part in this study could cause you. Potential Discomforts or Inconvenience: Although we expect no harm, your child may experience some discomfort being in a new situation and an unfamiliar setting. Also, it may be an inconvenience to get to the Hospital for Sick Children or to find time in your schedule to participate in the study.
Potential Benefits: You will receive clinical feedback describing your child’s cognitive strengths and weaknesses from a Psychologist and this information will be shared with you in the form of a report. This information may be useful for educational planning. Results may also be used by your clinical team in order to improve your child’s therapeutic treatment by increased understanding of his or her psychological functioning. Society in general also benefits from your participation in this study. Doctors will be better able to advise parents and children with MS about the impact of this disease on cognitive functioning. In some cases, the results of this study may result in specific recommendations for educational planning.
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Confidentiality: We will respect your privacy. No information about who you are will be given to anyone or be published without your permission, unless the law makes us do this. For example, the law could make us give information about you • If a child has been abused • If you have an illness that could spread to others • If you or someone else talks about suicide (killing themselves), or • If the court orders us to give them the study papers SickKids Clinical Research Office Monitor, or the regulator of the study may see your health record to check on the study. For example, people from Health Canada Health Products and Food Branch, U.S. National Institutes of Health, and U.S. Food and Drug Administration, if necessary, may look at your records”. By signing this consent form, you agree to let these people look at your records. We will put a copy of this research consent form in your patient health records. The data produced from this study will be stored in a secure, locked location. Only members of the research team (and maybe those individuals described above) will have access to the data. Following completion of the research study the data will be kept as long as required by the SickKids “Records Retention and Destruction” policy. The data will then be destroyed according to this same policy. The results of the tests we describe in this form will be used only for this study. If another doctor caring for you needs to see these results, you will have to give us your permission. We will ask you to sign a form saying that you agree that this person can see your results. We recommend that only a registered psychologist or doctor tell you what the results of these tests mean. Reimbursement: We will pay for all your transportation and parking expenses for being in this study. If you stop taking part in the study, we will pay you for your expenses for taking part in the study so far. Sponsorship: Funding for this research is the Multiple Sclerosis Scientific Research Foundation.
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Participation: If you choose to let your child take part in this study you can take your child out of the study at any time. The care your child gets at SickKids will not be affected in any way by whether your child takes part in this study. We will give you a copy of this consent form for your records. Consent : “By signing this form, I agree that: 1) You have explained this study to me. You have answered all my questions. 2) You have explained the possible harms and benefits (if any) of this study. 3) I know what I could do instead of having my child take part in this study. I understand that I have the right to refuse to let my child take part in the study. I also have the right to take my child out of the study at any time. My decision about my child taking part in the study will not affect my child’s health care at SickKids. 4) I am free now, and in the future, to ask questions about the study. 5) I have been told that my child’s medical records will be kept private. You will give no one information about my child, unless the law requires you to. 6) I understand that no information about my child will be given to anyone or be published without first asking my permission. ” 7) I have read and understood pages 1 to 4 of this consent form. I agree, or consent, that my child___________________ may take part in this study. ____________________________________________________________________________ Printed Name of Parent/Legal Guardian Parent/Legal Guardian’s signature & date ____________________________________________________________________________ Printed Name of person who explained consent Signature & date ________________________________________________________________________ Printed Witness’ name (if the parent/legal guardian Witness’ signature & date does not read English) If you have any questions about this study, please call Dr. Brenda Banwell at (416) 813-6660 If you have questions about your rights as a subject in a study or injuries during a study, please call the Research Ethics Manager at (416) 813-5718.
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ASSENT FORM
Title of Study: A longitudinal study of the neuropsychological and MRI characteristics in children and adolescents with multiple sclerosis (MS) Investigators: Principal Investigator: Dr. Brenda Banwell, Staff Neurologist, (416) 813-6660 Co-investigators: Dr. Christine Till, Clinical Neuropsychologist, (416) 597-3422 Ext. 7870 Dr. Donald Mabbott, Psychologist, (416) 813-8875 Allison Bethune, Graduate student (UofT), (416) 813-1500 ext. 1738 Dr. Manohar Shroff, Staff Neurophysicist Why are we doing this study? We would like to know more about children and teenagers, like you, who have Multiple Sclerosis (MS). We want to know if thinking and learning at school are difficult for you. What will happen during the study? We would like to work with you over one or two days at the Hospital for Sick Children (HSC) to complete some tests that look at your thinking abilities. We will ask you to answer questions, copy pictures, and do some school-related activities, such as arithmetic, reading, and spelling. Are there good things and bad things about the study? We think that learning about children and teenagers with MS is important. We think the information from the tests will help you learn better at school. We do not think there are any bad things about this study, but you will have to come to HSC to do the testing and this does take up some of your time.
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ASSENT FORM - continued
Who will know about what I did in the study? If you are part of this study, your name and address will not be given to anyone. If we feel your health may be in danger, we may have to report your results to your doctor. Can I decide if I want to be in the study? If you do not want to be part of this study, that is okay. No one will be upset or disappointed. If you say yes now, but change your mind, you can say no to the doctors and that will be okay. Your mother or father is also reading some information about this study. They will talk to you about it. Please ask your parents or the doctors any questions that you may have. Assent: "I was present when ____________________________ read this form and said that he or she agreed, or assented, to take part in this study”. _____________________________________________________________ Printed Name of person who obtained assent Signature & Date
